[{"id":"bd811034-6faf-445c-8a11-de0c10e6aa0e","slug":"f10qbic0","title":"Insuring AI: Incentivizing Safe and Secure Deployment","dek":null,"authors":[{"name":"Lukasz Szpruch","slug":"lukasz-szpruch"},{"name":"Agni Orfanoudaki","slug":"agni-orfanoudaki"},{"name":"Carsten Maple","slug":"carsten-maple"},{"name":"Matthew Wicker","slug":"matthew-wicker"},{"name":"Yoshua Bengio","slug":"yoshua-bengio"},{"name":"Kwok-Yan Lam","slug":"kwok-yan-lam"},{"name":"Marcin Detyniecki","slug":"marcin-detyniecki"}],"date":"2026-09-02","section":"cornucopia","issue":"volume8issue3","doi":"10.1162/99608f92.bd811034","abstract":"Artificial intelligence (AI) is permeating every sector, delivering efficiency gains and innovation, but also creating new classes of risk that require explicit measurement, monitoring, and management. This article focuses on developing a conceptual blueprint for AI insurance that addresses unintended outcomes resulting directly from an AI system’s normal operation, where outputs fall within the declared scope but di","keywords":["artificial intelligence","risk management","insurance","AI ethics","governance","AI liability."],"image":"/media/remote/cdc7a8f97a3f4b33-720.webp","collections":[],"label":"Cornucopia"},{"id":"91b8834f-ee19-466c-a774-4827a37a2df7","slug":"tarzjxgu","title":"Unsafe at any AUC: Unlearned Lessons From Sociotechnical Disasters for Responsible AI","dek":null,"authors":[{"name":"Joshua A. Kroll","slug":"joshua-a-kroll"},{"name":"Andrew Smart","slug":"andrew-smart"},{"name":"R. Stuart Geiger","slug":"r-stuart-geiger"},{"name":"Abigail Z. Jacobs","slug":"abigail-z-jacobs"}],"date":"2026-09-01","section":"panorama","issue":"volume8issue3","doi":"10.1162/99608f92.91b8834f","abstract":"As automated decision-making and data-driven technologies pervade society and are used to manage consequential outcomes, understanding the technology’s capabilities, limitations, and attendant risks in context requires analysis of full sociotechnical systems. Sociotechnical analysis of risks in highly complex systems provides clear lessons for the design and evaluation of AI systems, transcending a technical focus on","keywords":["accidents","responsible AI","safety","organizations","sociotechnical","disasters"],"image":"/media/remote/30370d8262edc866-720.webp","collections":[],"label":"Panorama"},{"id":"4672c2da-4f1a-4ca6-a886-ad6fb8ed63b5","slug":"jude8rkl","title":"From Knowledge to Value: Rethinking Knowledge Management Principles for an AI-Powered World","dek":null,"authors":[{"name":"Faisal Hoque","slug":"faisal-hoque"},{"name":"Thomas H. Davenport","slug":"thomas-h-davenport"},{"name":"Paul Scade","slug":"paul-scade"}],"date":"2026-08-13","section":"active-industrial-learning","issue":"volume8issue3","doi":"10.1162/99608f92.4672c2da","abstract":null,"keywords":[],"image":"/media/remote/d33c702e975cbd90-720.webp","collections":[],"label":"Active Industrial Learning"},{"id":"a0170b2a-d45c-4f38-9eda-3ef82fc71806","slug":"jbqroy8g","title":"Of Shapes and Curves—Why AI Should Use More Chernoff Faces, and Data Science More Manifold Fitting","dek":null,"authors":[{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2026-07-31","section":"editorscolumn","issue":"volume8issue3","doi":"10.1162/99608f92.a0170b2a","abstract":null,"keywords":[],"image":"/media/remote/51e33813d7def670-720.webp","collections":[],"label":"Editor’s Column"},{"id":"867d3e52-f503-4816-9337-bc60f20117ce","slug":"fi0cphkz","title":"Non-Euclidean Data Analysis With Metric Statistics","dek":null,"authors":[{"name":"Wookyeong Song","slug":"wookyeong-song"},{"name":"Hang Zhou","slug":"hang-zhou"},{"name":"Yidong Zhou","slug":"yidong-zhou"},{"name":"Hans-Georg Müller","slug":"hans-georg-muller"}],"date":"2026-07-30","section":"milestones-and-millstones","issue":"volume8issue3","doi":"10.1162/99608f92.867d3e52","abstract":"In classical data analysis, the atoms of data are scalars, vectors, or functions, where the fact that these are elements of a vector space is fundamental for statistical methodology. The classical approach falls short for data where the data atoms are random objects located in metric spaces for which linear operations are not available. Examples of practical interest are samples of distributions, networks, covariance","keywords":["centrality","Fréchet regression","networks","random objects","spread","quantiles"],"image":"/media/remote/aae1c2117f491e11-720.webp","collections":["shaping-data-science-a-geometric-perspective"],"label":"Milestones and Millstones"},{"id":"63b65052-759a-473f-917b-b9dee8e5c99b","slug":"op7v0ofq","title":"Shaping Data Science: Statistics and Geometry","dek":null,"authors":[{"name":"Zhigang Yao","slug":"zhigang-yao"},{"name":"Jun Zhang","slug":"jun-zhang"}],"date":"2026-07-30","section":"milestones-and-millstones","issue":"volume8issue3","doi":"10.1162/99608f92.63b65052","abstract":null,"keywords":[],"image":"/media/remote/284f574973f67845-720.webp","collections":["shaping-data-science-a-geometric-perspective"],"label":"Milestones and Millstones"},{"id":"995aeed6-6aff-40ae-a6e0-41ad83709c2b","slug":"32hss7nc","title":"Stochastics of Shapes and Kunita Flows","dek":null,"authors":[{"name":"Stefan Sommer","slug":"stefan-sommer"},{"name":"Gefan Yang","slug":"gefan-yang"},{"name":"Elizabeth Louise Baker","slug":"elizabeth-louise-baker"}],"date":"2026-07-30","section":"milestones-and-millstones","issue":"volume8issue3","doi":"10.1162/99608f92.995aeed6","abstract":"Stochastic processes of evolving shapes are used in applications including evolutionary biology, where morphology changes stochastically as a function of evolutionary processes. Due to the nonlinear and often infinite-dimensional nature of shape spaces, the mathematical construction of suitable stochastic shape processes is far from immediate. We define and formalize properties that stochastic shape processes should ","keywords":["shape stochastics","Kunita flows","bridge sampling","outer shape models","inner shape models"],"image":"/media/remote/08b4fdfb1d62d00b-720.webp","collections":["shaping-data-science-a-geometric-perspective"],"label":"Milestones and Millstones"},{"id":"a8d73ea6-1eb3-45a6-99ff-d0dc53f56563","slug":"kmhbxkmt","title":"Building a Data Science Department in a Research Institution","dek":null,"authors":[{"name":"Rafael A. Irizarry","slug":"rafael-a-irizarry"}],"date":"2026-07-30","section":"panorama","issue":"volume8issue3","doi":"10.1162/99608f92.a8d73ea6","abstract":"In this era of data-intensive science, research institutions face a critical question: How should they structure themselves to support the full range of data-driven scientific inquiry, from experimental design and data generation to analysis, modeling, and reproducible reporting, especially as advances in machine learning and artificial intelligence (AI) methodology continue to transform how we do research?Responses ","keywords":["data science organizations","institutional strategy","research infrastructure","collaborative research","scientific workforce."],"image":"/media/remote/2172ab402fa0ade0-720.webp","collections":[],"label":"Panorama"},{"id":"f437c1b2-41fa-4a43-8291-a99da18e7a86","slug":"1gtyqdn5","title":"Inference for Dimension Reduction on Manifolds","dek":null,"authors":[{"name":"Stephan F. Huckemann","slug":"stephan-f-huckemann"}],"date":"2026-07-30","section":"milestones-and-millstones","issue":"volume8issue3","doi":"10.1162/99608f92.f437c1b2","abstract":"Beginning with classical principal component analysis (PCA), followed by principal nested spheres, ending at torus PCA, we formulate the dimension reduction problem on manifolds in terms of generalized Fréchet means. For their asymptotic theory, we review the current state of the art. While we show that a special case of PCA asymptotics can be assessed with the now nearly 20-year-old asymptotic theory, for the genera","keywords":["generalized Fréchet means","principal component analysis","smeary central limit theorem","two-sample tests"],"image":"/media/remote/688a7d5a11f2110e-720.webp","collections":["shaping-data-science-a-geometric-perspective"],"label":"Milestones and Millstones"},{"id":"29efa129-d28a-4d10-af40-8fb2ae1b6566","slug":"tke5kviu","title":"Broadening Access to Data Science Education: Open Source Tools and Training in High School and Higher Education","dek":null,"authors":[{"name":"Teon L. Brooks","slug":"teon-l-brooks"},{"name":"Steven Azeka","slug":"steven-azeka"}],"date":"2026-07-30","section":"stepping-stones","issue":"volume8issue3","doi":"10.1162/99608f92.29efa129","abstract":"As data play a more integral part to our daily lives, there is a growing need for data science education. However, access to curriculum, tooling, and infrastructure is not readily available to many students in the United States. We review the current state of data science education tools and implementations as well as highlight a set of emerging tools and technologies. We conclude that broadening data science educati","keywords":["data science education","open source software","Python","computing","equity and representation","educator professional development"],"image":"/media/remote/6033642bc0bd7a7b-720.webp","collections":[],"label":"Stepping Stones"},{"id":"7a3d1487-3812-4b05-9f1e-ca76e0c5dbbe","slug":"jc22wpxa","title":"Book Review of Rewiring Democracy: How AI Will Transform Our Politics, Government, and Citizenship&nbsp;by Bruce Schneier and Nathan E. Sanders (MIT Press, 2025)","dek":null,"authors":[{"name":"Paulo Carvão","slug":"paulo-carvao"}],"date":"2026-07-30","section":"bits-and-bites","issue":"volume8issue3","doi":"10.1162/99608f92.7a3d1487","abstract":"This review situates Rewiring Democracy within debates on artificial intelligence, democratic governance, and institutional design. In contrast to work that emphasizes technological determinism or narrowly defined risk mitigation, the review engages Bruce Schneier and Nathan E. Sanders’s argument that AI is becoming embedded in democratic institutions and everyday civic practices, shaping how governments process info","keywords":["AI and democracy","democratic governance","institutional design","trustworthy AI","civic participation","AI governance"],"image":"/media/remote/41d525aa871753b3-720.webp","collections":[],"label":"Bits and Bites"},{"id":"06b73ece-4d69-4e77-ba38-e7435fb55235","slug":"cyt6sb1z","title":"Impacts of Differences Between Secondary Market and Release Prices for Cult and Non-Cult Wines","dek":null,"authors":[{"name":"Jill J. McCluskey","slug":"jill-j-mccluskey"},{"name":"Ron Mittelhammer","slug":"ron-mittelhammer"},{"name":"Botir Okhunjanov","slug":"botir-okhunjanov"}],"date":"2026-07-30","section":"cornucopia","issue":"volume8issue3","doi":"10.1162/99608f92.06b73ece","abstract":"For U.S. cult wines, Okhunjanov et al. (2025) provide evidence that secondary market prices are higher than corresponding release prices. They also present a theory in which a firm may choose to price its product below the market equilibrium level in order to create a perception of scarcity, which increases demand in subsequent periods. In this article, we extend this work by examining whether the price gap, that is,","keywords":["cult wines","scarcity pricing","secondary market"],"image":"/media/remote/ea6622f88badb7e4-720.webp","collections":[],"label":"Cornucopia"},{"id":"686fa68c-9bd0-4a90-a771-adc5be5519f6","slug":"lbucoqwm","title":"Manifold Fitting: A Review of Methods and Applications","dek":null,"authors":[{"name":"Zhigang Yao","slug":"zhigang-yao"},{"name":"Jiaji Su","slug":"jiaji-su"}],"date":"2026-07-30","section":"milestones-and-millstones","issue":"volume8issue3","doi":"10.1162/99608f92.686fa68c","abstract":"With data growing in scale and complexity, traditional linear dimension-reduction techniques are becoming inadequate in some settings. Manifold fitting offers an important alternative by capturing low-dimensional latent geometric structures within high-dimensional spaces. This capability allows it to support downstream analysis in complex data settings.In this review, we explore the development and applications of ma","keywords":["manifold fitting","nonlinear dimension reduction","reach","geometric analysis"],"image":"/media/remote/6149c881d513549f-720.webp","collections":["shaping-data-science-a-geometric-perspective"],"label":"Milestones and Millstones"},{"id":"efb39de6-f846-4673-a916-7f2e20fd51ec","slug":"crrc0h60","title":"Data Science at a Fork in the Age of AI: Recognizing Divergent Missions in Research and Education","dek":null,"authors":[{"name":"Zhiwei Zhu","slug":"zhiwei-zhu"}],"date":"2026-07-30","section":"panorama","issue":"volume8issue3","doi":"10.1162/99608f92.efb39de6","abstract":"As artificial intelligence (AI) continues to reshape how we live and work, this article argues that the most consequential force behind contemporary AI is not technological acceleration alone, but a gradual redefinition of what counts as data. Because computational systems ultimately operate on data, advances in AI are inseparable from the expanding scope of data they can represent and process. Beneath the rapid prog","keywords":["data science","AI","data science education","analytical foundations","disciplinary identity","human–AI collaboration","intelligent systems"],"image":"/media/remote/6ae4421f8392c5e4-720.webp","collections":[],"label":"Panorama"},{"id":"86d5f928-b8c8-4d2a-ab19-0abd57b83186","slug":"xxb041qb","title":"The Importance of the Scientific Method in Machine Learning Education","dek":null,"authors":[{"name":"Brian Wright","slug":"brian-wright"},{"name":"Loreto Alonzi","slug":"loreto-alonzi"},{"name":"Ajay Anand","slug":"ajay-anand"},{"name":"Galin Jones","slug":"galin-jones"},{"name":"Stella Min","slug":"stella-min"},{"name":"Micaela Parker","slug":"micaela-parker"},{"name":"Ali Rivera","slug":"ali-rivera"},{"name":"Anthony Suen","slug":"anthony-suen"}],"date":"2026-07-30","section":"stepping-stones","issue":"volume8issue3","doi":"10.1162/99608f92.86d5f928","abstract":"Data science is maturing as an academic field, but many machine learning (ML) courses omit a core best practice from established STEM disciplines: the scientific method. This paper argues that centering rigorous, hypothesis-driven experimentation within ML education is essential for developing students’ critical thinking and analytical skills, especially in the age of AI. Drawing on the data science project life cycl","keywords":["data science education","scientific method","machine learning","experimental design"],"image":"/media/remote/9eb8ecce5eb21267-720.webp","collections":[],"label":"Stepping Stones"},{"id":"8bed4b20-14dd-4952-be99-fa3d7b197484","slug":"u1bjbc93","title":"What Is Data Science? A Comparative View of High School Students and Experts","dek":null,"authors":[{"name":"Maureen Min","slug":"maureen-min"}],"date":"2026-07-30","section":"minding-the-future","issue":"volume8issue3","doi":"10.1162/99608f92.8bed4b20","abstract":null,"keywords":[],"image":"/media/remote/dfcab3009ac98d08-720.webp","collections":[],"label":"Minding the Future"},{"id":"d87b0e8d-79ce-4992-854a-894a043c3f71","slug":"jbrq4l9u","title":"A Personal Journey and the Future of Work AI—A Fireside Chat With Arvind Jain, CEO of Glean","dek":"An interview with Arvind Jain by Francesca Dominici and Harry Archerman.","authors":[{"name":"Arvind Jain","slug":"arvind-jain"},{"name":"Francesca Dominici","slug":"francesca-dominici"},{"name":"Harry Archerman","slug":"harry-archerman"}],"date":"2026-07-09","section":"panorama","issue":"volume8issue2","doi":"10.1162/99608f92.d87b0e8d","abstract":"This fireside chat between Arvind Jain, Francesca Dominici, and Harry Archerman took place at the Harvard AI Summit, cohosted by the Harvard Data Science Initiative, Harvard Data Science Review, and Archerman Capital, on October 21, 2025, at the American Academy of Arts and Sciences in Cambridge, Massachusetts. Arvind Jain is the founder and CEO of the enterprise AI company Glean and cofounder of data security compan","keywords":["enterprise AI","enterprise search","horizontal AI platforms","organizational productivity"],"image":"/media/remote/c99228378f201978-720.webp","collections":[],"label":"Panorama"},{"id":"1d54a704-6e23-469d-9938-f7158d03821f","slug":"n7la10vt","title":"From Silicon to Intelligence: The Future of AI Compute—A Fireside Chat With Jim Keller, CEO of Tenstorrent","dek":"An interview with Jim Keller by Francesca Dominici and Harry Archerman.","authors":[{"name":"Jim Keller","slug":"jim-keller"},{"name":"Francesca Dominici","slug":"francesca-dominici"},{"name":"Harry Archerman","slug":"harry-archerman"}],"date":"2026-07-09","section":"panorama","issue":"volume8issue2","doi":"10.1162/99608f92.1d54a704","abstract":"This fireside chat between Jim Keller, Francesca Dominici, and Harry Archerman took place at the Harvard AI Summit, cohosted by the Harvard Data Science Initiative, Harvard Data Science Review, and Archerman Capital, on October 21, 2025, at the American Academy of Arts and Sciences in Cambridge, Massachusetts. Jim Keller is a pioneering chip architect and CEO of the semiconductor company Tenstorrent. Francesca Domini","keywords":["artificial intelligence","computer architecture","semiconductor design","RISC-V","tensor processors","Moore’s law"],"image":"/media/remote/0d64d1172ac9f59c-720.webp","collections":[],"label":"Panorama"},{"id":"373b4512-eda2-4981-bb71-79197b4c4e37","slug":"l9iaw8m3","title":"Uncovering Hidden Geometry in Transformers via Disentangling Position and Context","dek":null,"authors":[{"name":"Jiajun Song","slug":"jiajun-song"},{"name":"Yiqiao Zhong","slug":"yiqiao-zhong"}],"date":"2026-06-04","section":"milestones-and-millstones","issue":"volume8issue2","doi":"10.1162/99608f92.373b4512","abstract":"Transformers are widely used to extract semantic meanings from input tokens, yet they usually operate as black-box models. In this article, we present a simple yet informative decomposition of hidden states (or embeddings) of trained transformers into interpretable components. For any layer, embedding vectors of input sequence samples are represented by a tensor h∈RC×T×d{\\bm{h}}\\in \\mathbb{R}^{C \\times T \\times d}h∈R","keywords":["large language models","mechanistic interpretability","visualization","ANOVA","Fourier analysis","incoherence"],"image":"/media/remote/2a174476a67a08da-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"7d74e33e-413c-4807-859c-cabf36f99edc","slug":"ak16gxi2","title":"Toward Causal Field Evaluations of AI Systems","dek":null,"authors":[{"name":"David Arbour","slug":"david-arbour"},{"name":"Iavor Bojinov","slug":"iavor-bojinov"},{"name":"Avi Feller","slug":"avi-feller"},{"name":"Tu Ni","slug":"tu-ni"}],"date":"2026-05-11","section":"catalytic-causal-conversations","issue":"volume8issue2","doi":"10.1162/99608f92.7d74e33e","abstract":null,"keywords":[],"image":"/media/remote/870101ad3d04c1d1-720.webp","collections":[],"label":"Catalytic Causal Conversations"},{"id":"969f93dc-7c8c-42e3-b60d-113bb31b9e83","slug":"ivfh1rx2","title":"From Awaiting AGI to Better Aligning AGI: Academia, Government, and Industry","dek":null,"authors":[{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2026-04-30","section":"editorscolumn","issue":"volume8issue2","doi":"10.1162/99608f92.969f93dc","abstract":null,"keywords":[],"image":"/media/remote/3da2ab9780f10d8e-720.webp","collections":[],"label":"Editor’s Column"},{"id":"fdb2338d-90bf-490a-857a-1a10580b112e","slug":"2f0veaek","title":"Toward Differentially Private Finite Population Estimation: An Approach Based on Survey Weight Regularization","dek":null,"authors":[{"name":"Jeremy Seeman","slug":"jeremy-seeman"},{"name":"Yajuan Si","slug":"yajuan-si"},{"name":"Jerome Reiter","slug":"jerome-reiter"}],"date":"2026-04-30","section":"milestones-and-millstones","issue":"volume8issue2","doi":"10.1162/99608f92.fdb2338d","abstract":"In general, it is challenging to release differentially private versions of survey-weighted statistics with low error and acceptable privacy loss. In part, this is because weighted statistics from complex sample survey data can be highly sensitive to the values of the survey weights, resulting in differentially private mechanisms that can add substantial noise to unbiased estimates of finite population quantities. On","keywords":["disclosure","differential privacy","sampling design","survey weighting"],"image":"/media/remote/c3671ae2b8defc3f-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"c0e75932-766b-4edf-83c9-cee22f43280b","slug":"w0l9k3gr","title":"Explainability in the Wild, or Wild Explanations? Evidence From Predicting Tax Evasion","dek":null,"authors":[{"name":"Isabel O. Gallegos","slug":"isabel-o-gallegos"},{"name":"Benjamin B. Seiler","slug":"benjamin-b-seiler"},{"name":"Jacob Goldin","slug":"jacob-goldin"},{"name":"Carlos Guestrin","slug":"carlos-guestrin"},{"name":"Daniel E. Ho","slug":"daniel-e-ho"}],"date":"2026-04-30","section":"cornucopia","issue":"volume8issue2","doi":"10.1162/99608f92.c0e75932","abstract":"As artificial intelligence (AI) algorithms become more prevalent in high-stakes risk assessment, policymakers have increasingly relied on explainability tools for interpretability. Despite growing mandates that AI-based decisions include explanations, there remains little empirical evidence demonstrating the effectiveness of these techniques in real-world applications. This gap often stems from the absence of a clear","keywords":["artificial intelligence","explainability","risk assessment"],"image":"/media/remote/5346bafb2faa99c5-720.webp","collections":[],"label":"Cornucopia"},{"id":"53f091e6-2bf7-473b-a3e8-546a1c6e2bdc","slug":"2zvzqo4k","title":"Attention Is Not All You Need: How AI Is Reshaping Consumer Attention and the Future of Brand Strategy","dek":null,"authors":[{"name":"Massimo Chiriatti","slug":"massimo-chiriatti"},{"name":"Marianna Bergamaschi Ganapini","slug":"marianna-bergamaschi-ganapini"},{"name":"Enrico Panai","slug":"enrico-panai"},{"name":"Giuseppe Riva","slug":"giuseppe-riva"}],"date":"2026-04-30","section":"panorama","issue":"volume8issue2","doi":"10.1162/99608f92.53f091e6","abstract":"The emergence of artificial intelligence (AI) as a cognitive extension represents a fundamental shift requiring businesses to optimize content for both human and AI consumption. As AI systems evolve from passive tools to active mediators of consumer attention, brands must master a new paradigm: AI optimization (AIO). We propose a four-step framework for this dual optimization challenge: (1) Structured Data for AI Par","keywords":["System 0","attention economy","AI optimization (AIO)","extended mind theory","cognitive architecture","human–AI interaction"],"image":"/media/remote/f348247aadca3dff-720.webp","collections":[],"label":"Panorama"},{"id":"7ce82aaf-3f8f-4e93-a740-882258f848dd","slug":"fdzqkh85","title":"AI Agents Are Transforming Decision Making: What Leaders Should Know","dek":null,"authors":[{"name":"Monisha Athi Kesavan Premalatha","slug":"monisha-athi-kesavan-premalatha"}],"date":"2026-04-30","section":"active-industrial-learning","issue":"volume8issue2","doi":"10.1162/99608f92.7ce82aaf","abstract":null,"keywords":[],"image":"/media/remote/e58f4512034829bc-720.webp","collections":[],"label":"Active Industrial Learning"},{"id":"0e98898d-236e-46ee-8a0d-ee0685c47542","slug":"nrcn4h7d","title":"Bias in the Loop: How Humans Evaluate AI-Generated Suggestions","dek":null,"authors":[{"name":"Jacob Beck","slug":"jacob-beck"},{"name":"Stephanie Eckman","slug":"stephanie-eckman"},{"name":"Christoph Kern","slug":"christoph-kern"},{"name":"Frauke Kreuter","slug":"frauke-kreuter"}],"date":"2026-04-30","section":"cornucopia","issue":"volume8issue2","doi":"10.1162/99608f92.0e98898d","abstract":"Human–AI collaboration increasingly drives decision-making across industries. While AI systems promise efficiency gains by providing automated suggestions for human review, these workflows can trigger cognitive biases that degrade performance. This article reveals the psychological factors that determine when these collaborations succeed or fail.We conducted an experiment with 2,784 participants. A subset of particip","keywords":["data quality","human–AI collaboration","pre-annotation","cognitive bias"],"image":"/media/remote/6d436fb5bae6d5d2-720.webp","collections":[],"label":"Cornucopia"},{"id":"c384cd63-19ec-4a9d-b2d9-571cecf96401","slug":"nz1swl83","title":"Tracing Wine’s Globalization Empirically: New Updatable Annual Data Sets","dek":null,"authors":[{"name":"Kym Anderson","slug":"kym-anderson"},{"name":"Signe Nelgen","slug":"signe-nelgen"},{"name":"Vicente Pinilla","slug":"vicente-pinilla"},{"name":"German Puga","slug":"german-puga"}],"date":"2026-04-30","section":"cornucopia","issue":"volume8issue2","doi":"10.1162/99608f92.c384cd63","abstract":"Over the past 4 decades, wine has moved from a primarily European consumer good to something far more global, with the share of global wine production that is exported more than trebling. Production of winegrapes (wine’s key input) also has spread from southern Europe to several New World countries, as well as to more northern parts of Europe thanks to warming temperatures. Explaining these developments requires reli","keywords":["wine globalization","climate change","beverage consumption","bilateral beverage trade","evolving winegrape choices","composite market indicators"],"image":"/media/remote/7f04b8b93f83c0d4-720.webp","collections":[],"label":"Cornucopia"},{"id":"59ca6018-7725-43a3-8dc6-991479dc6bf6","slug":"3n0jq4i3","title":"Understanding and Mitigating Unintended Bias in Medical AI Systems","dek":null,"authors":[{"name":"Sam Tyner-Monroe","slug":"sam-tyner-monroe"},{"name":"Bogdana Rakova","slug":"bogdana-rakova"},{"name":"Jee Young Kim","slug":"jee-young-kim"},{"name":"Mark Sendak","slug":"mark-sendak"},{"name":"Suresh Balu","slug":"suresh-balu"},{"name":"Danny Tobey","slug":"danny-tobey"},{"name":"Coran Darling","slug":"coran-darling"},{"name":"Zev Eigen","slug":"zev-eigen"},{"name":"Kyle Kloeppel","slug":"kyle-kloeppel"},{"name":"Divya Sharma","slug":"divya-sharma"}],"date":"2026-04-30","section":"panorama","issue":"volume8issue2","doi":"10.1162/99608f92.59ca6018","abstract":"Complex artificial intelligence (AI) technologies, including generative AI models, are being increasingly introduced in clinical settings without corresponding tools or methods to systematically evaluate them for the unintended biases they may exhibit. Current literature extensively documents the risks of explicit and implicit biases by health care providers, but actionable guidance for identifying and mitigating bia","keywords":["responsible AI","health care","fairness","ethics","AI governance","equity"],"image":"/media/remote/97660b1a0f42f350-720.webp","collections":[],"label":"Panorama"},{"id":"7e2b41e5-3c67-431a-8407-aedc55babdcc","slug":"4g90o7wz","title":"Useful, Not Just Usable: Design Considerations for Instructionally Meaningful K–12 Data Sets","dek":null,"authors":[{"name":"Leticia Perez","slug":"leticia-perez"},{"name":"Sara Salisbury","slug":"sara-salisbury"},{"name":"Frieda Reichsman","slug":"frieda-reichsman"},{"name":"Kirsten Daehler","slug":"kirsten-daehler"},{"name":"Nicole Wong","slug":"nicole-wong"},{"name":"Rasha Eslayed","slug":"rasha-eslayed"}],"date":"2026-04-30","section":"stepping-stones","issue":"volume8issue2","doi":"10.1162/99608f92.7e2b41e5","abstract":"The rapid expansion of open data has created new possibilities for engaging K–12 students with authentic real-world data sets. Yet for many educators, abundant data does not translate into usable classroom resources. In this article, we describe what we call a K–12 ‘data supply chain crisis’: while high-quality secondary data sets from government and public agencies are available, they are rarely prepared in ways tha","keywords":["data literacy","data science education","open data","data biography","data sets","STEM"],"image":"/media/remote/f11a8b59449267c8-720.webp","collections":[],"label":"Stepping Stones"},{"id":"cdf80349-7217-4170-bc19-246e692a9b42","slug":"w1tfg5lx","title":"What Does It Mean to Understand AI?","dek":null,"authors":[{"name":"Jun Otsuka","slug":"jun-otsuka"}],"date":"2026-04-30","section":"meta-data-science","issue":"volume8issue2","doi":"10.1162/99608f92.cdf80349","abstract":null,"keywords":[],"image":"/media/remote/dc00a761dea3fcd8-720.webp","collections":[],"label":"Meta Data Science"},{"id":"fc7e5689-fd3f-4491-b27a-efc20597a73b","slug":"4gxerzte","title":"The Unbearable Weight of Possible Futures: AI and Uncertainty","dek":null,"authors":[{"name":"Jake Sun","slug":"jake-sun"}],"date":"2026-04-30","section":"bits-and-bites","issue":"volume8issue2","doi":"10.1162/99608f92.fc7e5689","abstract":"This article uses the lens of Mary Shelley’s Frankenstein and Kierkegaard’s Either/Or to explore the issue of how to make decisions about the future of AI. Through the works of these Romantic-era authors, it argues that prediction should not be a totalizing force, and supposing its infallibility causes a number of different errors in decision-making. Furthermore, it shows that fear and anxiety caused by a lack of cer","keywords":["artificial intelligence","prediction","choice"],"image":"/media/remote/1127465ea859ad86-720.webp","collections":[],"label":"Bits and Bites"},{"id":"37c1e28d-3c32-4976-abd0-8ab482b90bfc","slug":"q9sagp8n","title":"Learning Models and Modalities to Build Data Equity Competencies","dek":null,"authors":[{"name":"Dan Ferris","slug":"dan-ferris"},{"name":"Amy Hawn Nelson","slug":"amy-hawn-nelson"}],"date":"2026-04-30","section":"stepping-stones","issue":"volume8issue2","doi":"10.1162/99608f92.37c1e28d","abstract":"Data science in the social sector is often framed with a deficit lens and a technical focus, sidelining critical ethical, legal, and equity considerations. A more comprehensive approach can enhance effectiveness and strengthen both internal data cultures and public trust. Centering ethics, equity, and collaboration is essential for building social license and leveraging data for social impact and public good, especia","keywords":["impact data science","capacity building","community-based organizations","data equity","data for public good","social sector"],"image":"/media/remote/07366540161d62dc-720.webp","collections":[],"label":"Stepping Stones"},{"id":"38e378b4-0aa4-4ddb-88cf-ae8ca219b133","slug":"1wnr2do4","title":"Catalyst, Curiosity, and the Crazy Ones: Reflections on the Inaugural AI Summit at Harvard University","dek":null,"authors":[{"name":"Harry Archerman","slug":"harry-archerman"}],"date":"2026-04-30","section":"panorama","issue":"volume8issue2","doi":"10.1162/99608f92.38e378b4","abstract":"This reflective article, written by the founder and CEO of Archerman Capital, recounts the conception, organization, and experience of the Inaugural AI Summit, cohosted by the Harvard Data Science Initiative, Harvard Data Science Review, and Archerman Capital at the American Academy of Arts and Sciences in Cambridge, Massachusetts, on October 21, 2025. Framed as both a personal journey and a community effort, this ar","keywords":["artificial intelligence","AI Summit","Harvard University","Silicon Valley–academia collaboration","data science ecosystem","AI ethics and governance"],"image":"/media/remote/1cea372497a0d319-720.webp","collections":[],"label":"Panorama"},{"id":"a51c61d7-3daa-4682-90a1-e42e98843a94","slug":"0t7z2ndw","title":"The Impact of AI: From Financial Investments to Scientific Investigations—A Fireside Chat Between Peter Galison and Andrew W. Lo","dek":"An interview between Peter Galison and Andrew W. Lo.","authors":[{"name":"Peter Galison","slug":"peter-galison"},{"name":"Andrew W. Lo","slug":"andrew-w-lo"}],"date":"2026-03-05","section":"panorama","issue":"volume8issue1","doi":"10.1162/99608f92.a51c61d7","abstract":"In this wide-ranging conversation conducted at Harvard University’s Inaugural AI Summit in October 2025, Harvard’s Peter Galison and MIT’s Andrew W. Lo examine how AI is transforming scientific research, economic decision-making, and collaborative problem-solving. They trace AI’s evolution from automation toward systems that increasingly resemble partners in thought, enabling new forms of understanding across fields ","keywords":["artificial intelligence","scientific research","interdisciplinary collaboration","data science","black holes"],"image":"/media/remote/3d3ed383c062a5ee-720.webp","collections":[],"label":"Panorama"},{"id":"dab78690-b0f7-4b01-b46a-e8fdfa682fce","slug":"a1bpffpz","title":"A Refreshment Stirred, Not Shaken: Invariant-Preserving Deployments of Differential Privacy for the U.S. Decennial Census","dek":null,"authors":[{"name":"James Bailie","slug":"james-bailie"},{"name":"Ruobin Gong","slug":"ruobin-gong"},{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2026-02-24","section":null,"issue":"specialissue6","doi":"10.1162/99608f92.dab78690","abstract":"Protecting an individual’s privacy when releasing their data is inherently an exercise in relativity, regardless of how privacy is qualified or quantified. This is because we can only limit the gain in information about an individual relative to what could be derived from other sources. This framing is the essence of differential privacy (DP), through which this article examines two statistical disclosure control (SD","keywords":["confidentiality","data swapping","TopDown Algorithm","invariant statistics","statistical disclosure control"],"image":"/media/remote/a8952a0077e2caa6-720.webp","collections":[],"label":"Research on Privacy Protection"},{"id":"072aebb0-f217-4f9e-8781-46e281687893","slug":"ifxlo12v","title":"Watermark in the Classroom: A Conformal Framework for Adaptive AI Usage Detection","dek":null,"authors":[{"name":"Yangxinyu Xie","slug":"yangxinyu-xie"},{"name":"Xuyang Chen","slug":"xuyang-chen"},{"name":"Zhimei Ren","slug":"zhimei-ren"},{"name":"Weijie J. Su","slug":"weijie-j-su"}],"date":"2026-02-18","section":"stepping-stones","issue":"volume8issue1","doi":"10.1162/99608f92.072aebb0","abstract":"As artificial intelligence (AI) tools become ubiquitous in education, maintaining academic integrity while accommodating pedagogically beneficial AI assistance presents unprecedented challenges. Current AI detection systems fail to control false positive rates (FPR) and suffer from bias against minority student groups, prompting institutional suspensions of these technologies. Watermarking techniques embed verifiable","keywords":["AI in education","academic integrity","large language model watermarking","conformal prediction"],"image":"/media/remote/f54826a898590dc5-720.webp","collections":[],"label":"Stepping Stones"},{"id":"1af82b35-8a46-496b-b7ed-6a8e3a248840","slug":"j9p0axa5","title":"On the Formal Privacy Guarantees of Synthetic Data (Generated Without Formal Privacy Guarantees)","dek":null,"authors":[{"name":"Marcel Neunhoeffer","slug":"marcel-neunhoeffer"},{"name":"Jeremy Seeman","slug":"jeremy-seeman"},{"name":"Jörg Drechsler","slug":"jorg-drechsler"}],"date":"2026-02-17","section":null,"issue":"specialissue6","doi":"10.1162/99608f92.1af82b35","abstract":"What privacy guarantees can synthetic data satisfy, even without formal guarantees, during the training of the synthesizer? In this article, we explore this question using a synthesizer under simplified settings to show that the privacy guarantees offered by this synthesizer and potential others can be directly translated into a ρ\\rhoρ-zCDP guarantee. We further explore the conditions under which this equivalence hol","keywords":["synthetic data","differential privacy"],"image":"/media/remote/17fad76edb7492b0-720.webp","collections":[],"label":"Research on Privacy Protection"},{"id":"e3a744e1-ff05-4629-b247-e466843af3ea","slug":"m4mz70zp","title":"Navigating the AI Safari: Wonder, Worry, and Winnow","dek":null,"authors":[{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2026-01-30","section":"editorscolumn","issue":"volume8issue1","doi":"10.1162/99608f92.e3a744e1","abstract":null,"keywords":[],"image":"/media/remote/15d1c131e9c72a27-720.webp","collections":[],"label":"Editor’s Column"},{"id":"c45545cb-44a1-4346-854d-23ae0b2b50c7","slug":"epjyc9qb","title":"Measuring the Impact of Missingness in Traffic Stop Data","dek":null,"authors":[{"name":"Saatvik Kher","slug":"saatvik-kher"},{"name":"Johanna Hardin","slug":"johanna-hardin"}],"date":"2026-01-30","section":"cornucopia","issue":"volume8issue1","doi":"10.1162/99608f92.c45545cb","abstract":"In this article we explore the data available through the Stanford Open Policing Project. The data consist of information on millions of traffic stops across close to 100 different cities and highway patrols. Using a variety of metrics, we identify that the data is not missing completely at random. Furthermore, we develop ways of quantifying and visualizing missingness trends for different variables across the data s","keywords":["fairness","racial profiling","discriminatory policing","MCAR","outcomes test","average treatment effect"],"image":"/media/remote/74c010216bd9ccf1-720.webp","collections":[],"label":"Cornucopia"},{"id":"16636d7e-5c27-4dda-8a90-71dc90b70025","slug":"5to0suwj","title":"Teaching Philosophy of Science to Data Scientists","dek":null,"authors":[{"name":"Carlos Vega","slug":"carlos-vega"}],"date":"2026-01-30","section":"stepping-stones","issue":"volume8issue1","doi":"10.1162/99608f92.16636d7e","abstract":"This article advocates for the integration of a Philosophy of Science course directed to future data scientists. Following similar courses that were often found in physics or biology study programs, the course focuses on the critical examination of the foundational principles underlying data science. This article highlights the need for data scientists to question the often-undisputed assumptions guiding their decisi","keywords":["Philosophy of Science","data science","education","data ethics"],"image":"/media/remote/bb29ee7ef7ed3a9b-720.webp","collections":[],"label":"Stepping Stones"},{"id":"8c9d8a4d-4abd-450d-9ae5-1d69d0f2b667","slug":"raxdtomz","title":"A Collaborative Approach to Building a Human Trafficking Data Ecosystem: A Blueprint for Shared Governance, Technology, and Terminology","dek":null,"authors":[{"name":"Vanessa Bouché","slug":"vanessa-bouche"},{"name":"Eva Garrido","slug":"eva-garrido"},{"name":"Madeleine Moffett","slug":"madeleine-moffett"},{"name":"Deniz Cansin Rodoplu","slug":"deniz-cansin-rodoplu"}],"date":"2026-01-30","section":"milestones-and-millstones","issue":"volume8issue1","doi":"10.1162/99608f92.8c9d8a4d","abstract":"Human trafficking (HT) remains one of the most complex and underdocumented forms of transnational crime, in part due to persistent gaps in data quality, governance, standardization, and scope. This article presents a comprehensive framework for building a Human Trafficking Data Ecosystem that addresses these limitations through three interconnected components: a data trust, a data hub, and a data taxonomy. Drawing fr","keywords":["human trafficking","data ecosystem","data trust","data hub","data taxonomy","technology for good"],"image":"/media/remote/73192ef24ceb589e-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"b56a4380-a7ef-4e5e-a14d-a460b25bd766","slug":"gp2772c2","title":"Distinguishing Calabi-Yau Topology Using Machine Learning","dek":null,"authors":[{"name":"Yang-Hui He","slug":"yang-hui-he"},{"name":"Zhi-Gang Yao","slug":"zhi-gang-yao"},{"name":"Shing-Tung Yau","slug":"shing-tung-yau"}],"date":"2026-01-30","section":"milestones-and-millstones","issue":"volume8issue1","doi":"10.1162/99608f92.b56a4380","abstract":"While the earliest applications of AI methodologies to pure mathematics and theoretical physics began with the study of Hodge numbers of Calabi-Yau manifolds, the topology type of such manifolds also crucially depends on their intersection theory. Continuing the paradigm of machine learning algebraic geometry, we here investigate the triple intersection numbers, focusing on certain divisibility invariants constructed","keywords":["AI-assisted mathematics","machine learning","Calabi-Yau manifolds","string compactification","topology","statistics and geometry"],"image":"/media/remote/0b58b541c201c9ef-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"e968b0be-937c-45b1-9155-925b3798dea1","slug":"ov1ilnnf","title":"The Interplay Between Design and Analysis of Experiments in Complex Environments: Interference and Randomization Tests","dek":null,"authors":[{"name":"Tu Ni","slug":"tu-ni"}],"date":"2026-01-30","section":"catalytic-causal-conversations","issue":"volume8issue1","doi":"10.1162/99608f92.e968b0be","abstract":null,"keywords":[],"image":"/media/remote/cd2f3cdf4409aba2-720.webp","collections":[],"label":"Catalytic Causal Conversations"},{"id":"d41fe89a-ec4e-4d8a-8fec-4ad07d384f6c","slug":"0mrfxamu","title":"The Agent-Centric Enterprise: Why 2–10x Productivity Gains Demand Radical Workflow Redesign","dek":null,"authors":[{"name":"Ulla Kruhse-Lehtonen","slug":"ulla-kruhse-lehtonen"},{"name":"Dirk Hofmann","slug":"dirk-hofmann"}],"date":"2026-01-30","section":"panorama","issue":"volume8issue1","doi":"10.1162/99608f92.d41fe89a","abstract":"Autonomous AI agents are set to radically transform how organizations operate, but most companies today capture only marginal benefits by layering AI onto current, human-centric workflows. Drawing on firsthand transformation cases and benchmark data, we introduce the Agent OS framework and the A.G.E.N.T. playbook as practical tools for building agent-centric enterprises. Through real-world examples—including a global","keywords":["AI","agents","agentic AI","AI","AI strategy","AI productivity","AI workflows"],"image":"/media/remote/43691e2c7603b15f-720.webp","collections":[],"label":"Panorama"},{"id":"c9429f07-088f-4ebd-a5ca-6fe1f4406b92","slug":"u6wp4cy3","title":"Using ChatGPT for Data Science Analyses","dek":null,"authors":[{"name":"Ozan Evkaya","slug":"ozan-evkaya"},{"name":"Miguel de Carvalho","slug":"miguel-de-carvalho"}],"date":"2026-01-30","section":"cornucopia","issue":"volume8issue1","doi":"10.1162/99608f92.c9429f07","abstract":"As a result of recent advancements in generative AI, the field of data science is prone to various changes. The way practitioners construct their data science workflows is now irreversibly shaped by recent advancements, particularly by tools like OpenAI’s data analysis plugin. While it offers powerful support as a quantitative copilot, its limitations demand careful consideration in empirical analyses.This article as","keywords":["large language models","statistical models","data analysis","data science"],"image":"/media/remote/50b5c257cf564bb1-720.webp","collections":[],"label":"Cornucopia"},{"id":"c8780a45-772a-49fe-b382-e1b5600e9dfc","slug":"xv4hxs7u","title":"Prompting the Professoriate: A Qualitative Study of Instructor Perspectives on LLMs in Data Science Education","dek":null,"authors":[{"name":"Ana Elisa Lopez-Miranda","slug":"ana-elisa-lopez-miranda"},{"name":"Tiffany Timbers","slug":"tiffany-timbers"},{"name":"Rohan Alexander","slug":"rohan-alexander"}],"date":"2026-01-30","section":"stepping-stones","issue":"volume8issue1","doi":"10.1162/99608f92.c8780a45","abstract":"Large language models (LLMs) have shifted in just a few years from novelty to ubiquity, raising fundamental questions for data science education. Tasks once used to teach coding, writing, and problem-solving can now be completed by LLMs, forcing educators to reconsider both pedagogy and assessment. To understand how instructors are responding to this new development, we conducted semistructured interviews with 42 ins","keywords":["large language models (LLMs)","generative AI","data science education","curriculum","qualitative interviews","educational innovation","artificial intelligence"],"image":"/media/remote/068f3d24199a939f-720.webp","collections":[],"label":"Stepping Stones"},{"id":"38fc6837-1b51-4066-8a41-7063070c112f","slug":"xxn09ssc","title":"Replicability Within One Study: Harnessing Multiplicity for Observational Causal Inference","dek":null,"authors":[{"name":"Ying Jin","slug":"ying-jin"}],"date":"2026-01-30","section":"catalytic-causal-conversations","issue":"volume8issue1","doi":"10.1162/99608f92.38fc6837","abstract":null,"keywords":[],"image":"/media/remote/5ef4af2983fdd4c8-720.webp","collections":[],"label":"Catalytic Causal Conversations"},{"id":"ac407990-8131-431c-918c-62b468e43d9a","slug":"r6qcizhi","title":"Tracking the Most Intoxicating Data: A Conversation With Eric LeVine","dek":"An interview with Eric LeVine by Xiao-Li Meng and Liberty Vittert Capito.","authors":[{"name":"Eric LeVine","slug":"eric-levine"},{"name":"Xiao-Li Meng","slug":"xiao-li-meng"},{"name":"Liberty Vittert Capito","slug":"liberty-vittert-capito"}],"date":"2026-01-30","section":"panorama","issue":"volume8issue1","doi":"10.1162/99608f92.ac407990","abstract":"Harvard Data Science Review’s Founding Editor-in-Chief, Xiao-Li Meng, and Media Feature Editor, Liberty Vittert Capito, interviewed Eric LeVine, founder of CellarTracker, the world’s earliest and largest crowd-sourced wine cellar and review platform, about what wine can teach us about data, AI, and human behavior. The conversation starts with how LeVine, a former Microsoft program manager, turned a personal spreadshe","keywords":[],"image":"/media/remote/788b872295e68206-720.webp","collections":["conversations-with-leaders"],"label":"Panorama"},{"id":"67925ccf-bff6-4dd9-b07a-7ec3b3898d53","slug":"mkd4x6x3","title":"Comparing uniLasso and Lasso: New Data Sets and New Metrics","dek":null,"authors":[{"name":"Anqi Wang","slug":"anqi-wang"},{"name":"Bin Yu","slug":"bin-yu"}],"date":"2025-12-11","section":null,"issue":"volume7issue3","doi":"10.1162/99608f92.67925ccf","abstract":null,"keywords":[],"image":"/media/remote/11a63372dac971cf-720.webp","collections":[],"label":"Featured Discussion"},{"id":"28e9f828-f5ef-48c8-a835-bc570cb43946","slug":"uynpjlow","title":"An Undergraduate Course in Causality","dek":null,"authors":[{"name":"Lea Bottmer","slug":"lea-bottmer"},{"name":"Guido Imbens","slug":"guido-imbens"},{"name":"Jason Weitze","slug":"jason-weitze"},{"name":"Mary Wootters","slug":"mary-wootters"}],"date":"2025-12-11","section":"stepping-stones","issue":"volume7issue4","doi":"10.1162/99608f92.28e9f828","abstract":"In the fall quarter of 2024 we (a computer scientist and an economist as the faculty in charge of the course, with two economics graduate students as course assistants) taught “Causality, Decision Making, and Data Science,” an undergraduate course cross-listed in Stanford University’s Economics Department, Data Science Major, Computer Science Department, and Graduate School of Business undergraduate program. The cour","keywords":["causality","decision science","correlation","regression"],"image":"/media/remote/fff7823d730cf256-720.webp","collections":[],"label":"Stepping Stones"},{"id":"8975ddd1-3f08-4b9f-a402-201c4b8a14a1","slug":"ppjz2dg9","title":"Can We Predict What Jobs AI Will Take?","dek":null,"authors":[{"name":"Thomas Davenport","slug":"thomas-davenport"},{"name":"Miguel Paredes","slug":"miguel-paredes"}],"date":"2025-12-09","section":"active-industrial-learning","issue":"volume7issue4","doi":"10.1162/99608f92.8975ddd1","abstract":null,"keywords":[],"image":"/media/remote/06e215e7598ee413-720.webp","collections":[],"label":"Active Industrial Learning"},{"id":"fb935f61-88c7-40b4-bb90-dca58d817ab6","slug":"t4txmd81","title":"Modeling Artist Influence for Music Selection and Recommendation: A Purely Network-Based Approach","dek":null,"authors":[{"name":"Elena Badillo-Goicoechea","slug":"elena-badillo-goicoechea"}],"date":"2025-12-03","section":"cornucopia","issue":"volume7issue4","doi":"10.1162/99608f92.fb935f61","abstract":"Given the central role of online recommendation systems amid rapidly growing volumes of digital content, and the fundamental limitations of traditional music recommendation systems (MRS), this work proposes a novel approach for generating music recommendations based on the construction and traversal of a knowledge graph built on the detection of review-based connections between musical artists. The core idea behind o","keywords":["music recommendation systems","content-based recommendations","network analysis"],"image":"/media/remote/5388e0cab14aa8e3-720.webp","collections":[],"label":"Cornucopia"},{"id":"1168daac-8dc4-4701-ad58-02ab9f3986e7","slug":"rsco7d1m","title":"After Intelligence","dek":null,"authors":[{"name":"Stephanie Dick","slug":"stephanie-dick"}],"date":"2025-11-25","section":null,"issue":"volume7issue4","doi":"10.1162/99608f92.1168daac","abstract":null,"keywords":[],"image":"/media/remote/dd7525a0c2feea9f-720.webp","collections":[],"label":"Featured Discussion"},{"id":"7d27980c-a0aa-4d9e-97f4-2e9cf1816d68","slug":"b2mni4xm","title":"Integrating Environmental Health in the AI Era: An Urgent Imperative","dek":null,"authors":[{"name":"Francesca Dominici","slug":"francesca-dominici"},{"name":"Tina Vartziotis","slug":"tina-vartziotis"}],"date":"2025-11-21","section":null,"issue":"volume7issue4","doi":"10.1162/99608f92.7d27980c","abstract":null,"keywords":[],"image":"/media/remote/2d5fd850b1af5b21-720.webp","collections":[],"label":"Featured Discussion"},{"id":"f75b00c0-085a-47f4-b215-96844285938f","slug":"giv2d07s","title":"Epidemiology, Public Health, and Health Inequalities: Random Reflections as the 'Artificial Intelligence Era' Unfolds","dek":null,"authors":[{"name":"Mauricio L. Barreto","slug":"mauricio-l-barreto"}],"date":"2025-11-18","section":null,"issue":"volume7issue4","doi":"10.1162/99608f92.f75b00c0","abstract":null,"keywords":[],"image":"/media/remote/5e320bf9163160bc-720.webp","collections":[],"label":"Featured Discussion"},{"id":"9f41fc96-69bb-48fa-8ede-17aae889f991","slug":"mmunexaf","title":"AI as Cultural Mediators: Rethinking Environmental Intelligence and Human-Machine Interdependence","dek":null,"authors":[{"name":"Veronica Barassi","slug":"veronica-barassi"}],"date":"2025-11-17","section":null,"issue":"volume7issue4","doi":"10.1162/99608f92.9f41fc96","abstract":null,"keywords":[],"image":"/media/remote/07546c6ba4c374c4-720.webp","collections":[],"label":"Featured Discussion"},{"id":"7e0d6442-1624-46ef-aaef-c32f565e7264","slug":"x2nv4pyg","title":"Trust, Openness, and Transparency: Ingredients for the Environmental Intelligence Framework Cake","dek":null,"authors":[{"name":"Hilary Hanahoe","slug":"hilary-hanahoe"}],"date":"2025-11-17","section":null,"issue":"volume7issue4","doi":"10.1162/99608f92.7e0d6442","abstract":null,"keywords":[],"image":"/media/remote/7ddd98febe1ac3e5-720.webp","collections":[],"label":"Featured Discussion"},{"id":"2235cde7-deb5-4767-ad52-33b342019da6","slug":"ja8iock0","title":"Artifactual Epistemology and Environmental Intelligence","dek":null,"authors":[{"name":"Ramón Alvarado","slug":"ramon-alvarado"}],"date":"2025-11-06","section":null,"issue":"volume7issue4","doi":"10.1162/99608f92.2235cde7","abstract":null,"keywords":[],"image":"/media/remote/8f680950b99e983b-720.webp","collections":[],"label":"Featured Discussion"},{"id":"7339d0de-101c-446e-b78b-7fecf286c63a","slug":"9egfxxqj","title":"Writing With AI, About AI, and By AI","dek":null,"authors":[{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2025-10-31","section":"editorscolumn","issue":"volume7issue4","doi":"10.1162/99608f92.7339d0de","abstract":null,"keywords":[],"image":"/media/remote/5dd55f3956235abf-720.webp","collections":[],"label":"Editor’s Column"},{"id":"30a2934f-94ca-4447-bef6-a24c60c8dd07","slug":"lrqoe1ny","title":"Lessons From the Margins: Contextualizing, Reimagining, and Hacking Generative AI in the Global South","dek":null,"authors":[{"name":"Andrés Domínguez Hernández","slug":"andres-dominguez-hernandez"},{"name":"Diana Mosquera","slug":"diana-mosquera-2"},{"name":"Francisco Gallegos","slug":"francisco-gallegos"}],"date":"2025-10-31","section":"panorama","issue":"volume7issue4","doi":"10.1162/99608f92.30a2934f","abstract":"The impacts of generative AI (Gen AI) in regions of Africa, Asia, and Latin America—with their diverse historical and sociopolitical contexts—are often overshadowed by the hyped and universalizing promises of the technology. Global South regions are strategic sites within the global AI value chain grappling with issues of resource exploitation, digital labor outsourcing, and environmental impact. In seeking to overco","keywords":["generative AI","Global South","value chain of AI","responsible AI","political economy"],"image":"/media/remote/350dd4558f55f36a-720.webp","collections":[],"label":"Panorama"},{"id":"ac7c1504-7406-45ba-b3e2-fc1bb2e4989a","slug":"mx9vpa0b","title":"Environmental Intelligence: Redefining the Philosophical Premises of AI","dek":null,"authors":[{"name":"Sabina Leonelli","slug":"sabina-leonelli"}],"date":"2025-10-31","section":"panorama","issue":"volume7issue4","doi":"10.1162/99608f92.ac7c1504","abstract":"As an alternative to the long history of interpreting artificial intelligence (AI) as the attempt to rationalize and mechanize human ingenuity, thereby transcending nature and its perceived limits, this article proposes an interpretation of the conceptual foundations of environmental intelligence (EI) as the effort to develop digital technology and data-intensive algorithmic systems to sustain and enhance life on thi","keywords":["artificial intelligence","environment","planetary health","data science education","transdisciplinary research"],"image":"/media/remote/be1579573d27456f-720.webp","collections":[],"label":"Panorama"},{"id":"7703c9aa-caae-4e7f-9d4d-bc30a43f2bdb","slug":"qdt8pdm7","title":"Environmental Intelligence?! On the Consequences of Sidelining the Materiality of AI","dek":null,"authors":[{"name":"Ulrike Felt","slug":"ulrike-felt"}],"date":"2025-10-31","section":null,"issue":"volume7issue4","doi":"10.1162/99608f92.7703c9aa","abstract":null,"keywords":[],"image":"/media/remote/558f4581343a6706-720.webp","collections":[],"label":"Featured Discussion"},{"id":"4a868f98-885f-4fd2-a905-a1bac33cfb79","slug":"c5hq3by1","title":"Rejoinder: Let Us Rescue Intelligence from Anti-Human AI","dek":null,"authors":[{"name":"Sabina Leonelli","slug":"sabina-leonelli"}],"date":"2025-10-31","section":null,"issue":"volume7issue4","doi":"10.1162/99608f92.4a868f98","abstract":null,"keywords":[],"image":"/media/remote/147f58c289ab2c8f-720.webp","collections":[],"label":"Featured Discussion"},{"id":"15126a15-ddcb-4fb4-9d02-fb45704d6761","slug":"bkei8j1i","title":"A Thoughtful Narrative Emerges","dek":null,"authors":[{"name":"Benedikt Höltgen","slug":"benedikt-holtgen"}],"date":"2025-10-31","section":null,"issue":"volume7issue4","doi":"10.1162/99608f92.15126a15","abstract":null,"keywords":[],"image":"/media/remote/099d8c25c5327b88-720.webp","collections":[],"label":"Featured Discussion"},{"id":"10cce22b-aae7-4cdc-9029-8ac43559c330","slug":"osu8e7pw","title":"“A View on Life Itself”: The Philosophical Ambition of Sabina Leonelli’s Environmental Intelligence","dek":null,"authors":[{"name":"Edward Humpherson","slug":"edward-humpherson"}],"date":"2025-10-31","section":null,"issue":"volume7issue4","doi":"10.1162/99608f92.10cce22b","abstract":null,"keywords":[],"image":"/media/remote/9a47b4827a2a4335-720.webp","collections":[],"label":"Featured Discussion"},{"id":"f67d819c-e582-49ef-96c9-4b8f5d13363d","slug":"qajxfjb6","title":"Reflections on Redefining Intelligence. And Also Humans and AI","dek":null,"authors":[{"name":"Christopher M. Kelty","slug":"christopher-m-kelty"}],"date":"2025-10-31","section":null,"issue":"volume7issue4","doi":"10.1162/99608f92.f67d819c","abstract":null,"keywords":[],"image":"/media/remote/f10f06f64c22eebe-720.webp","collections":[],"label":"Featured Discussion"},{"id":"2eb6655b-2bec-47e7-8c2a-fd09a5a45a93","slug":"r7kk81lc","title":"Expanding the Data Science Toolkit: Mixed Methods for Improving Causal Inference","dek":null,"authors":[{"name":"Noor Qaragholi","slug":"noor-qaragholi"},{"name":"Elizabeth A. Stuart","slug":"elizabeth-a-stuart"}],"date":"2025-10-31","section":"catalytic-causal-conversations","issue":"volume7issue4","doi":"10.1162/99608f92.2eb6655b","abstract":null,"keywords":[],"image":"/media/remote/0a46e85d1688736b-720.webp","collections":[],"label":"Catalytic Causal Conversations"},{"id":"dbc60044-7f77-484f-ac4a-d8ffe1f8b278","slug":"h4puxge7","title":"AI and Writing in the Straddle Generation","dek":null,"authors":[{"name":"Benjamin Outar","slug":"benjamin-outar"}],"date":"2025-10-31","section":"minding-the-future","issue":"volume7issue4","doi":"10.1162/99608f92.dbc60044","abstract":null,"keywords":[],"image":"/media/remote/b23c0db8ae2d9c06-720.webp","collections":[],"label":"Minding the Future"},{"id":"3f78ed8e-2b10-40cc-9198-f39f63c8d88d","slug":"ehs9dr1l","title":"Justice at the Core of a New AI Paradigm","dek":null,"authors":[{"name":"Giuseppe Primiero","slug":"giuseppe-primiero"}],"date":"2025-10-31","section":null,"issue":"volume7issue4","doi":"10.1162/99608f92.3f78ed8e","abstract":null,"keywords":[],"image":"/media/remote/87a54d666e1eccf4-720.webp","collections":[],"label":"Featured Discussion"},{"id":"b7ff6b8f-a7cf-4311-b517-674bcd354aab","slug":"b1ysps0g","title":"Get the G out of AGI: Making Room for Environmental Intelligence","dek":null,"authors":[{"name":"Shannon Vallor","slug":"shannon-vallor"}],"date":"2025-10-31","section":null,"issue":"volume7issue4","doi":"10.1162/99608f92.b7ff6b8f","abstract":null,"keywords":[],"image":"/media/remote/756be27dab2a6370-720.webp","collections":[],"label":"Featured Discussion"},{"id":"73ccb788-f330-411b-8124-0e1218ca6469","slug":"5estwqmh","title":"Environmental Intelligence: Context and Rhetoric","dek":null,"authors":[{"name":"Robert C. Williamson","slug":"robert-c-williamson"}],"date":"2025-10-31","section":null,"issue":"volume7issue4","doi":"10.1162/99608f92.73ccb788","abstract":null,"keywords":[],"image":"/media/remote/cd5d4c96249fad2f-720.webp","collections":[],"label":"Featured Discussion"},{"id":"686a03f5-d14f-4345-8a1e-931434601b43","slug":"mivh5k7u","title":"Tying Together Loose Ends With uniLasso","dek":null,"authors":[{"name":"Chris Harbron","slug":"chris-harbron"}],"date":"2025-10-16","section":null,"issue":"volume7issue3","doi":"10.1162/99608f92.686a03f5","abstract":null,"keywords":[],"image":"/media/remote/8e265dc3fef65cd3-720.webp","collections":[],"label":"Featured Discussion"},{"id":"7501d0e1-9197-404b-a662-3a23d6217f91","slug":"dc3cx5gn","title":"The Accuracy and Interpretability of Univariate Guidance","dek":null,"authors":[{"name":"Lucas Janson","slug":"lucas-janson"}],"date":"2025-10-16","section":null,"issue":"volume7issue3","doi":"10.1162/99608f92.7501d0e1","abstract":null,"keywords":[],"image":"/media/remote/47c0c3329b30cd74-720.webp","collections":[],"label":"Featured Discussion"},{"id":"c7c36a11-e4ea-4338-b27f-38839da66f07","slug":"0sxxeaw2","title":"uniLasso: Uniquely Suited to Translational Research","dek":null,"authors":[{"name":"Daniel H. Katz","slug":"daniel-h-katz"}],"date":"2025-10-16","section":null,"issue":"volume7issue3","doi":"10.1162/99608f92.c7c36a11","abstract":null,"keywords":[],"image":"/media/remote/ba4f848cedb21404-720.webp","collections":[],"label":"Featured Discussion"},{"id":"d01b179f-ba78-40ad-ae5f-d069b4835094","slug":"pt1y94x8","title":"Looking Ahead With uniLasso: New Avenues for Understanding and Development","dek":null,"authors":[{"name":"Arian Maleki","slug":"arian-maleki"},{"name":"Haolei Weng","slug":"haolei-weng"}],"date":"2025-10-16","section":null,"issue":"volume7issue3","doi":"10.1162/99608f92.d01b179f","abstract":null,"keywords":[],"image":"/media/remote/534e7bba2cbddca8-720.webp","collections":[],"label":"Featured Discussion"},{"id":"63f1d9fc-fbfe-41a2-a3cb-33a3df202e5e","slug":"0e3ywguh","title":"From Univariate Analysis to Global Inference","dek":null,"authors":[{"name":"Rajarshi Mukherjee","slug":"rajarshi-mukherjee"}],"date":"2025-10-16","section":null,"issue":"volume7issue3","doi":"10.1162/99608f92.63f1d9fc","abstract":null,"keywords":[],"image":"/media/remote/c024915249d1bb61-720.webp","collections":[],"label":"Featured Discussion"},{"id":"106bb670-8e7d-485e-84a3-23b66f81977f","slug":"m3fppgon","title":"User-Guided Interpretable Models: Rashomon Effect, Interaction, and Computation","dek":null,"authors":[{"name":"Jiachang Liu","slug":"jiachang-liu"},{"name":"Cynthia Rudin","slug":"cynthia-rudin"}],"date":"2025-10-16","section":null,"issue":"volume7issue3","doi":"10.1162/99608f92.106bb670","abstract":null,"keywords":[],"image":"/media/remote/bfa6ff9739cdebf4-720.webp","collections":[],"label":"Featured Discussion"},{"id":"ae0b6544-4b9d-4443-87f4-b37366968701","slug":"0mjg81fk","title":"Interpretable Constraints on Sparse Regression?","dek":null,"authors":[{"name":"Larry Wasserman","slug":"larry-wasserman"}],"date":"2025-10-16","section":null,"issue":"volume7issue3","doi":"10.1162/99608f92.ae0b6544","abstract":null,"keywords":[],"image":"/media/remote/d1e0b2d170d52707-720.webp","collections":[],"label":"Featured Discussion"},{"id":"5d45fb39-8c09-4765-9c4b-800be2637c4c","slug":"89onustq","title":"Imposing the 'Right' Structural Constraints in High-Dimensional Regression","dek":null,"authors":[{"name":"Han Tong","slug":"han-tong"},{"name":"Ming Yuan","slug":"ming-yuan"}],"date":"2025-10-16","section":null,"issue":"volume7issue3","doi":"10.1162/99608f92.5d45fb39","abstract":null,"keywords":[],"image":"/media/remote/93938cce20c5b519-720.webp","collections":[],"label":"Featured Discussion"},{"id":"d8826219-c620-4dff-9942-31d79d25aec3","slug":"w0bh0kzg","title":"\"Univariate-Guided Sparse Regression\": Rejoinder","dek":null,"authors":[{"name":"Sourav Chatterjee","slug":"sourav-chatterjee"},{"name":"Trevor Hastie","slug":"trevor-hastie"},{"name":"Robert Tibshirani","slug":"robert-tibshirani"}],"date":"2025-10-16","section":null,"issue":"volume7issue3","doi":"10.1162/99608f92.d8826219","abstract":null,"keywords":[],"image":"/media/remote/708f9d8d96699b2f-720.webp","collections":[],"label":"Featured Discussion"},{"id":"47359565-e744-432e-a0f0-15b9dac914aa","slug":"bywbvnfy","title":"Extreme Sign Reversals in Regression and Simpson’s Paradox","dek":null,"authors":[{"name":"Yuhao Zhou","slug":"yuhao-zhou"},{"name":"Kai Zhang","slug":"kai-zhang"}],"date":"2025-10-16","section":null,"issue":"volume7issue3","doi":"10.1162/99608f92.47359565","abstract":null,"keywords":[],"image":"/media/remote/bf81957c8bcdb9a3-720.webp","collections":[],"label":"Featured Discussion"},{"id":"11ee195c-b6aa-40f9-99f2-6c542a1eba79","slug":"5ow6i8xi","title":"Data Science in the Kitchen","dek":null,"authors":[{"name":"Mariangela Guidolin","slug":"mariangela-guidolin"},{"name":"Bruno Scarpa","slug":"bruno-scarpa"}],"date":"2025-10-16","section":"stepping-stones","issue":"volume7issue4","doi":"10.1162/99608f92.11ee195c","abstract":"In teaching data science, using images, examples from other disciplines, analogies, and metaphors to convey concepts and ideas that may seem quite abstract, particularly to students who lack a quantitative background, is often useful. During one of our lectures, we realized that it is possible to describe the various phases of data science analysis, from data collection to the communication of results, by paralleling","keywords":["data science","cooking","teaching experience","data literacy"],"image":null,"collections":[],"label":"Stepping Stones"},{"id":"a1c2b7cb-06a9-4159-b300-3e46333c0456","slug":"6fwktup9","title":"But Can You Use It? Design Recommendations for Practical Alternatives to Differentially Private Interactive Systems","dek":null,"authors":[{"name":"Liudas Panavas","slug":"liudas-panavas"},{"name":"Joshua Snoke","slug":"joshua-snoke"},{"name":"Erika Tyagi","slug":"erika-tyagi"},{"name":"Claire McKay Bowen","slug":"claire-bowen"},{"name":"Aaron R. Williams","slug":"aaron-williams"}],"date":"2025-09-26","section":"milestones-and-millstones","issue":"volume7issue4","doi":"10.1162/99608f92.a1c2b7cb","abstract":"Accessing data collected by federal statistical agencies is essential for public policy research and improving evidence-based decision-making, such as evaluating the effectiveness of social programs, understanding demographic shifts, or addressing public health challenges. Differentially private interactive systems have been proposed as a crucial part of the data-sharing infrastructure. They could allow researchers t","keywords":["interactive query systems","validation servers","differential privacy","synthetic data","human–computer interaction","federal statistical system","usability"],"image":"/media/remote/8973740d5e2bc72c-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"4a1ebf70-ab5b-47e7-bfff-571d8fd329f5","slug":"ntchx9im","title":"A Simulated Reconstruction and Reidentification Attack on the 2010 U.S. Census","dek":null,"authors":[{"name":"John M. Abowd","slug":"john-abowd"},{"name":"Tamara Adams","slug":"tamara-adams"},{"name":"Robert Ashmead","slug":"robert-ashmead"},{"name":"David Darais","slug":"david-darais"},{"name":"Sourya Dey","slug":"sourya-dey"},{"name":"Simson Garfinkel","slug":"simson-garfinkel"},{"name":"Nathan Goldschlag","slug":"nathan-goldschlag"},{"name":"Michael B. Hawes","slug":"michael-hawes"},{"name":"Daniel Kifer","slug":"daniel-kifer"},{"name":"Philip Leclerc","slug":"philip-leclerc"},{"name":"Ethan Lew","slug":"ethan-lew"},{"name":"Scott Moore","slug":"scott-moore"},{"name":"Rolando A. Rodríguez","slug":"rolando-a-rodriguez"},{"name":"Ramy N. Tadros","slug":"ramy-n-tadros"},{"name":"Lars Vilhuber","slug":"lars-vilhuber"}],"date":"2025-09-18","section":"cornucopia","issue":"volume7issue3","doi":"10.1162/99608f92.4a1ebf70","abstract":"For the last half-century, it has been a common and accepted practice for statistical agencies, including the United States Census Bureau, to adopt different strategies to protect the confidentiality of aggregate tabular data products from those used to protect the individual records contained in publicly released microdata products. This strategy was premised on the assumption that the aggregation used to generate t","keywords":["statistical disclosure limitation","reconstruction attack","record-linkage attack","differential privacy","swapping","suppression"],"image":"/media/remote/50e9467774780274-720.webp","collections":[],"label":"Cornucopia"},{"id":"4d1853ce-fa44-4951-8559-b767b51d417f","slug":"8r9mdkpl","title":"Using Containers to Validate Research on Confidential Data at Scale","dek":null,"authors":[{"name":"Lars Vilhuber","slug":"lars-vilhuber"}],"date":"2025-09-04","section":null,"issue":"specialissue6","doi":"10.1162/99608f92.4d1853ce","abstract":"I describe past experience with the validation server process over 10 years and several hundred users, as a means to provide proxy access to confidential data. As a modern replacement, I propose the use of containers: simulated computers that encapsulate entire software and file structures. The use of containers ensures reproducibility and reliable portability and enables scalability. Infrastructure can be outsourced","keywords":["synthetic data","verification server","confidential data","reproducibility","containers"],"image":"/media/remote/6b8465e86cdf3b24-720.webp","collections":[],"label":"Research on Privacy Protection"},{"id":"c79ff6db-4774-4caa-8ddc-ead407ecc8cc","slug":"3i97j340","title":"Univariate-Guided Sparse Regression","dek":null,"authors":[{"name":"Sourav Chatterjee","slug":"sourav-chatterjee"},{"name":"Trevor Hastie","slug":"trevor-hastie"},{"name":"Robert Tibshirani","slug":"robert-tibshirani"}],"date":"2025-08-21","section":"milestones-and-millstones","issue":"volume7issue3","doi":"10.1162/99608f92.c79ff6db","abstract":"In this article, we introduce “uniLasso,” a novel statistical method for regression. This two-stage approach preserves the signs of the univariate coefficients and leverages their magnitude. Both of these properties are attractive for stability and interpretation of the model. Through comprehensive simulations and applications to real-world data sets, we demonstrate that uniLasso outperforms lasso in various settings","keywords":["supervised learning","lasso","modeling"],"image":"/media/remote/d3c5e729d3f76b78-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"4a6236fd-9513-41f0-b40b-75d722035d84","slug":"wtanlmn8","title":"Data Privacy Protection: A More Sustainable Future for Applied Social Science Research","dek":null,"authors":[{"name":"Ruobin Gong","slug":"ruobin-gong"},{"name":"Jörg Drechsler","slug":"jorg-drechsler"},{"name":"V. Joseph Hotz","slug":"v-joseph-hotz"},{"name":"Ian Schmutte","slug":"ian-schmutte"}],"date":"2025-08-20","section":null,"issue":"specialissue6","doi":"10.1162/99608f92.4a6236fd","abstract":null,"keywords":[],"image":"/media/remote/a1afb8033c88da1d-720.webp","collections":[],"label":"From the Editors"},{"id":"c168891c-c301-4bab-8e54-84f95345fbaa","slug":"13thltj3","title":"A Penny Synthesized Is a Penny Earned? An Exploratory Analysis of Accuracy in the SIPP Synthetic Beta","dek":null,"authors":[{"name":"Jordan Stanley","slug":"jordan-stanley"},{"name":"Evan Totty","slug":"evan-totty"}],"date":"2025-08-20","section":null,"issue":"specialissue6","doi":"10.1162/99608f92.c168891c","abstract":"The U.S. Census Bureau has expressed interest in using modern synthetic data modeling techniques for privacy and confidentiality protection in future microdata releases. To aid understanding of the accuracy and usability of synthetic microdata going forward, we perform an exploratory analysis comparing results generated using an early synthetic microdata release known as the SIPP (Survey of Income and Program Partici","keywords":["synthetic data","data privacy","data accuracy","statistical disclosure limitation","labor economics","applied microeconomics"],"image":"/media/remote/84952a6d4c335f3e-720.webp","collections":[],"label":"Research With Privacy Protection"},{"id":"a8fb0371-6657-49e6-b26d-024f87a108ea","slug":"n4db7079","title":"Disclosing Economists’ Privacy Perspectives: A Survey of American Economic Association Members’ Views on Differential Privacy and the Usability of Noise-Infused Data","dek":null,"authors":[{"name":"Aaron R. Williams","slug":"aaron-williams"},{"name":"Joshua Snoke","slug":"joshua-snoke"},{"name":"Claire McKay Bowen","slug":"claire-bowen"},{"name":"Andres Felipe Barrientos","slug":"andres-felipe-barrientos"}],"date":"2025-08-20","section":null,"issue":"specialissue6","doi":"10.1162/99608f92.a8fb0371","abstract":"Policymakers often rely on official statistics and administrative data to make essential public policy decisions, such as using administrative tax data to broaden our understanding of individuals’ and firms’ responses to economic incentives through quantitative research. However, direct access to federal confidential data is limited to a select few researchers. Recently, privacy researchers and policymakers proposed ","keywords":["administrative data","data confidentiality","data privacy","differential privacy","social science","tax policy analysis"],"image":"/media/remote/a5697bf8b5994354-720.webp","collections":[],"label":"Perspectives"},{"id":"b3447fbe-6fa0-4d11-9aa1-8312443f629a","slug":"sxzmhss7","title":"Using Synthetic Data to Estimate Earnings Dynamics: Evidence From the Survey of Income and Program Participation Gold Standard File and Synthetic Beta","dek":null,"authors":[{"name":"Michael D. Carr","slug":"michael-d-carr"},{"name":"Emily E. Wiemers","slug":"emily-wiemers"},{"name":"Robert A. Moffitt","slug":"robert-a-moffitt"}],"date":"2025-08-20","section":null,"issue":"specialissue6","doi":"10.1162/99608f92.b3447fbe","abstract":"One of the methods of increased privacy protection is the creation of synthetic data sets. In this article we consider the differences that emerge between synthetic and nonsynthetic data in one of the very few attempts to create a synthetic data file for a major household survey data set, the Survey of Income and Program Participation (SIPP). The data we use are nonsynthetic and synthetic versions of the SIPP linked ","keywords":["synthetic data","SIPP SSB","SIPP GSF"],"image":"/media/remote/cf615b6a7abb6d78-720.webp","collections":[],"label":"Research With Privacy Protection"},{"id":"db29c137-2666-46ae-8526-57158314c7d1","slug":"bux70n5u","title":"Toward a Principled Framework for Disclosure Avoidance","dek":null,"authors":[{"name":"Michael B. Hawes","slug":"michael-hawes"},{"name":"Evan M. Brassell","slug":"evan-m-brassell"},{"name":"Anthony Caruso","slug":"anthony-caruso"},{"name":"Ryan Cumings-Menon","slug":"ryan-cumings-menon"},{"name":"Jason Devine","slug":"jason-devine"},{"name":"Cassandra Dorius","slug":"cassandra-dorius"},{"name":"David Evans","slug":"david-evans"},{"name":"Kenneth Haase","slug":"kenneth-haase"},{"name":"Michele C. Hedrick","slug":"michele-c-hedrick"},{"name":"Scott H. Holan","slug":"scott-h-holan"},{"name":"Cynthia D. Hollingsworth","slug":"cynthia-d-hollingsworth"},{"name":"Eric B. Jensen","slug":"eric-b-jensen"},{"name":"Daniel Kifer","slug":"daniel-kifer"},{"name":"Alexandra Krause","slug":"alexandra-krause"},{"name":"Philip Leclerc","slug":"philip-leclerc"},{"name":"James Livsey","slug":"james-livsey"},{"name":"Rolando A. Rodríguez","slug":"rolando-a-rodriguez"},{"name":"Luke T. Rogers","slug":"luke-t-rogers"},{"name":"Matthew Spence","slug":"matthew-spence"},{"name":"Victoria Velkoff","slug":"victoria-velkoff"},{"name":"Michael Walsh","slug":"michael-walsh"},{"name":"James Whitehorne","slug":"james-whitehorne"},{"name":"Sallie Ann Keller","slug":"sallie-ann-keller"}],"date":"2025-08-20","section":null,"issue":"specialissue6","doi":"10.1162/99608f92.db29c137","abstract":"Responsible disclosure limitation is an iterative exercise in risk assessment and mitigation. From time to time, as disclosure risks grow and evolve and as data users’ needs change, agencies must consider redesigning the disclosure avoidance system(s) they use. Discussions about candidate systems often conflate inherent features of those systems with implementation decisions independent of those systems. For example,","keywords":["official statistics","confidentiality","disclosure avoidance","statistical disclosure limitation"],"image":"/media/remote/f201cf62bde9467b-720.webp","collections":[],"label":"Perspectives"},{"id":"19a0e1c7-6a51-45c5-9274-b1d2d962fcfd","slug":"212v4buw","title":"Restricted-Access Data in Economics: Adoption, Diffusion, and Impact of U.S. Census Bureau's Microdata","dek":null,"authors":[{"name":"Abhishek Nagaraj","slug":"abhishek-nagaraj"},{"name":"Fernando Stipanicic","slug":"fernando-stipanicic"},{"name":"Matteo Tranchero","slug":"matteo-tranchero"}],"date":"2025-08-20","section":null,"issue":"specialissue6","doi":"10.1162/99608f92.19a0e1c7","abstract":"Microdata from government agencies is believed to be valuable for economics research, and yet access to this data is highly restricted due to concerns about privacy and security. We provide an empirical assessment of the use and impact of restricted-access data that researchers can analyze at the U.S. Census Bureau’s secure facilities. Our findings show that the use of Census Bureau’s confidential data is growing and","keywords":["confidential microdata","U.S. Census Bureau","FSRDCs","data access","economics research","inequality in science"],"image":"/media/remote/1299e03789c3f0d6-720.webp","collections":[],"label":"Perspectives"},{"id":"dc58eb7a-6915-428f-b5f4-dc36d0aa79cb","slug":"wd8vfdjm","title":"Navigating the Privacy Landscape: Harmonizing Legislative and Public Sector Approaches in the Canadian Context","dek":null,"authors":[{"name":"Pierre Desrochers","slug":"pierre-desrochers"},{"name":"Eric Rancourt","slug":"eric-rancourt"}],"date":"2025-08-20","section":null,"issue":"specialissue6","doi":"10.1162/99608f92.dc58eb7a","abstract":"The conceptualization and operationalization of privacy protection are continuously evolving in response to advances in technology and shifts in societal values. This article addresses a tripartite set of concerns linked to the Canadian context from the perspective of Statistics Canada: essential criteria for privacy protection models from a methodological standpoint, prevailing societal attitudes toward privacy, and","keywords":["data access","privacy protection","economic statistics","data ethics","public trust","citizen engagement"],"image":"/media/remote/39561e5505d69a3d-720.webp","collections":[],"label":"Perspectives"},{"id":"39d8bfa4-5941-4b26-8515-48f3ac74f7a7","slug":"eb9hacc7","title":"Quantifying Privacy Risks of Public Statistics to Residents of Subsidized Housing","dek":null,"authors":[{"name":"Ryan Steed","slug":"ryan-steed"},{"name":"Diana Qing","slug":"diana-qing"},{"name":"Steven Wu","slug":"steven-wu"}],"date":"2025-08-20","section":null,"issue":"specialissue6","doi":"10.1162/99608f92.39d8bfa4","abstract":"As the U.S. Census Bureau implements its controversial new disclosure avoidance system (DAS), researchers and policymakers debate the necessity of new privacy protections for public statistics. With experiments on both public statistics and synthetic microdata, we explore a particular privacy concern: respondents in subsidized housing may deliberately not mention unauthorized children and other household members for ","keywords":["reconstruction attack","reidentification attack","differential privacy","swapping","census","subsidized housing"],"image":"/media/remote/a98fee644306eeb6-720.webp","collections":[],"label":"Research With Privacy Protection"},{"id":"171157d2-f460-4634-98cf-7bd22cee7094","slug":"19cwd6qx","title":"The Future of Credit Underwriting and Insurance Under the EU AI Act: Implications for Europe and Beyond","dek":null,"authors":[{"name":"Philipp Hacker","slug":"philipp-hacker"},{"name":"Maximilian Eber","slug":"maximilian-eber"}],"date":"2025-08-06","section":"panorama","issue":"volume7issue3","doi":"10.1162/99608f92.171157d2","abstract":"In modern underwriting, lenders and insurers routinely use external credit scores as a baseline, then apply proprietary data sources, custom scoring models, or business-specific rules to more accurately assess borrower risk. Similarly, machine learning is used to enhance decision-making in the insurance market. However, this approach is now on the brink of tighter regulation as the European Union’s (EU) first Artific","keywords":["Artificial Intelligence (AI) Act","artificial intelligence (AI) regulation","artificial intelligence","credit underwriting","finance","AI policy"],"image":"/media/remote/3986a5f22e78ac03-720.webp","collections":[],"label":"Panorama"},{"id":"6d5fb5ef-2e22-4984-8029-a06a8af27808","slug":"pyo0xs3k","title":"‘Written in the Style of’: ChatGPT and the Literary Canon","dek":null,"authors":[{"name":"Gabi Kirilloff","slug":"gabi-kirilloff"},{"name":"Claudia Carroll","slug":"claudia-carroll-2"},{"name":"Zeina Daboul","slug":"zeina-daboul"},{"name":"Arianwyn Frank","slug":"arianwyn-frank"},{"name":"Razi Khan","slug":"razi-khan-2"},{"name":"Marielle Hinrichs-Morrow","slug":"marielle-hinrichs-morrow"},{"name":"Rebecca Weingart","slug":"rebecca-weingart"}],"date":"2025-08-05","section":"cornucopia","issue":"volume7issue4","doi":"10.1162/99608f92.6d5fb5ef","abstract":"We compare a corpus of 6,000 paragraph–length synthetic texts generated by GPT4 ‘in the style of’ 10 canonical, 19th-century, English language authors to authentic texts written by the same authors. We selected these authors based on existing humanities scholarship and metrics of cultural significance, including number of Goodreads reviews. The contemporary popularity of 19th-century writers has been largely determin","keywords":["large language models","machine learning","cultural bias","artificial intelligence","literary history","stylometry"],"image":"/media/remote/95bb8e8fdf61af20-720.webp","collections":[],"label":"Cornucopia"},{"id":"0318e889-ffc2-4dd3-a3bb-e55f3495346e","slug":"8uui4gv2","title":"Deep Learning, Deeper Probing, and Deepest Understanding—Unless We’re Courting Deep Trouble","dek":null,"authors":[{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2025-07-31","section":"editorscolumn","issue":"volume7issue3","doi":"10.1162/99608f92.0318e889","abstract":null,"keywords":[],"image":"/media/remote/3886a5e9e8f25e09-720.webp","collections":[],"label":"Editor’s Column"},{"id":"462f48a2-9d96-41c1-b48a-481a04fc8dd8","slug":"7fcc3jhv","title":"Multitask Learning 1997–2024: Part I Fundamentals","dek":null,"authors":[{"name":"Jun Yu","slug":"jun-yu"},{"name":"Xiaokang Liu","slug":"xiaokang-liu"},{"name":"Chongliang Luo","slug":"chongliang-luo"},{"name":"Jin Huang","slug":"jin-huang"},{"name":"Rong Zhou","slug":"rong-zhou"},{"name":"Yixin Liu","slug":"yixin-liu"},{"name":"Jie Hu","slug":"jie-hu"},{"name":"Jianmin Chen","slug":"jianmin-chen"},{"name":"Ke Zhang","slug":"ke-zhang"},{"name":"Dazheng Zhang","slug":"dazheng-zhang"},{"name":"Yishan Shen","slug":"yishan-shen"},{"name":"Eashan Adhikarla","slug":"eashan-adhikarla"},{"name":"Yutong Dai","slug":"yutong-dai"},{"name":"Kai Zhang","slug":"kai-zhang"},{"name":"Zhaoming Kong","slug":"zhaoming-kong"},{"name":"Wenxuan Ye","slug":"wenxuan-ye"},{"name":"Yilong Yin","slug":"yilong-yin"},{"name":"Vinod Namboodiri","slug":"vinod-namboodiri"},{"name":"Brian Davison","slug":"brian-davison"},{"name":"Jason Moore","slug":"jason-moore-2"},{"name":"Yong Chen","slug":"yong-chen"}],"date":"2025-07-31","section":"panorama","issue":"volume7issue3","doi":"10.1162/99608f92.462f48a2","abstract":"In this three-part survey, we review the literature on multitask learning (MTL) from its inception in the 1990s to the present in 2024. Unlike single-task learning (STL), MTL is a learning paradigm that simultaneously learns multiple related tasks by leveraging both task-specific and shared information. MTL offers a range of benefits, including streamlined model architectures, improved performance, and enhanced gener","keywords":["deep learning","generative pretrained transformers","multiobjective optimization","multitask learning","pretrained foundation models","prompt learning"],"image":"/media/remote/d7aa9b512b45cc4c-720.webp","collections":[],"label":"Panorama"},{"id":"f1065159-45d7-48f6-905e-31f68c8bdced","slug":"qatts4z9","title":"Disclosure Avoidance for the 2020 Census Demographic and Housing Characteristics File","dek":null,"authors":[{"name":"Ryan Cumings-Menon","slug":"ryan-cumings-menon"},{"name":"Robert Ashmead","slug":"robert-ashmead"},{"name":"Daniel Kifer","slug":"daniel-kifer"},{"name":"Philip Leclerc","slug":"philip-leclerc"},{"name":"Matthew Spence","slug":"matthew-spence"},{"name":"Pavel Zhuravlev","slug":"pavel-zhuravlev"},{"name":"John M. Abowd","slug":"john-abowd"}],"date":"2025-07-31","section":"cornucopia","issue":"volume7issue3","doi":"10.1162/99608f92.f1065159","abstract":"Abowd et al. (2022) describe the concepts and methods used by the Disclosure Avoidance System (DAS) to produce formally private output in support of the 2020 Census statistical data product releases, with a particular focus on the DAS implementation that was used to create the 2020 Census Redistricting Data (P.L. 94-171) Summary File. In this article we describe the updates to the DAS that were required to release th","keywords":["differential privacy","2020 Census","TopDown Algorithm","Demographic and Housing Characteristics File"],"image":"/media/remote/ca996feee37760f1-720.webp","collections":[],"label":"Cornucopia"},{"id":"08da1513-3e9a-4ce9-9988-841243aec72a","slug":"ulfgjwy1","title":"Research Lifecycle Management: Using Analysis Reproducibility Research Software to Define Contextual Data Governance Policies","dek":null,"authors":[{"name":"Paul Meijer","slug":"paul-meijer"},{"name":"Yousef Aggoune","slug":"yousef-aggoune"},{"name":"Madeline Ambrose","slug":"madeline-ambrose"},{"name":"Aldan Beaubien","slug":"aldan-beaubien"},{"name":"James Harvey","slug":"james-harvey"},{"name":"Nicole Howard","slug":"nicole-howard"},{"name":"Neelima Inala","slug":"neelima-inala"},{"name":"Edward Johnson","slug":"edward-johnson"},{"name":"Autumn Kelsey","slug":"autumn-kelsey"},{"name":"Melissa Kinsey","slug":"melissa-kinsey"},{"name":"Jessica Liang","slug":"jessica-liang"},{"name":"Paul Mariz","slug":"paul-mariz"},{"name":"Stark Pister","slug":"stark-pister"},{"name":"Sathya Subramanian","slug":"sathya-subramanian"},{"name":"Vitalii Tereshchenko","slug":"vitalii-tereshchenko"},{"name":"Anne Vetto","slug":"anne-vetto"}],"date":"2025-07-31","section":"cornucopia","issue":"volume7issue3","doi":"10.1162/99608f92.08da1513","abstract":"Traditional scientific data governance relies on static IT-centered metrics to determine data relevance. In this article, we advocate for contextual data governance enabled by analysis reproducibility research software. By relying on the reproducibility principles of this platform, scientists can proactively create a complete, transparent, and reproducible record of their analysis as their work progresses, and share ","keywords":["research data management","data governance","provenance","transparency","analysis reproducibility","open science"],"image":"/media/remote/35adc3abc82adf5b-720.webp","collections":[],"label":"Cornucopia"},{"id":"a6370111-d841-4133-9b29-5d60b970a5b6","slug":"9ir6e1j6","title":"Data Science and Decision Science Skills: Are They Different and Does It Matter?","dek":null,"authors":[{"name":"Jeffrey D. Camm","slug":"jeffrey-camm"},{"name":"Michael J. Fry","slug":"michael-fry"},{"name":"Scott M. Shafer","slug":"scott-shafer"}],"date":"2025-07-31","section":"stepping-stones","issue":"volume7issue3","doi":"10.1162/99608f92.a6370111","abstract":"In this article, we examine the skill sets of data science and decision science. Using empirical analyses of job postings and academic curricula, we identify systematic differences in the skills emphasized in these two areas. We find that data science tends to emphasize skills in coding, machine learning, and data management, while decision science tends to focus on skills in problem framing, decision modeling, and b","keywords":["data science","decision science","analytical skills"],"image":"/media/remote/628f82dbc6ba0ffd-720.webp","collections":[],"label":"Stepping Stones"},{"id":"1476e26a-36be-4508-9504-48e1e0ae9c09","slug":"9zgh9isd","title":"Navigating Progress: Enhancing Public Transit for More Equitable Communities via Interpretable Causal Inference","dek":null,"authors":[{"name":"Gaurav Rajesh Parikh","slug":"gaurav-rajesh-parikh"},{"name":"Jenny Huang","slug":"jenny-huang"},{"name":"Albert Sun","slug":"albert-sun"},{"name":"Lesia Semenova","slug":"lesia-semenova"},{"name":"Cynthia Rudin","slug":"cynthia-rudin"}],"date":"2025-07-31","section":"cornucopia","issue":"volume7issue3","doi":"10.1162/99608f92.1476e26a","abstract":"Public transit can be vital in providing mobility for low-income communities. King County Metro, which serves the greater Seattle area, is one of the largest transit providers focused on equitable public transit. They have rolled out innovative subsidy-based incentive programs for low-income riders with the goal of increasing equitable access to public transit, and in turn improving access to jobs, housing, education","keywords":["interpretability","causal inference","transportation","mobility","machine learning"],"image":"/media/remote/cbcb732e3d06f866-720.webp","collections":[],"label":"Cornucopia"},{"id":"da63c0cb-f75c-430c-bfb4-061c284dbd18","slug":"42vjucmq","title":"Large Language Models Are Zero-Shot Problem Solvers—Just Like Modern Computers","dek":null,"authors":[{"name":"Tim Z. Xiao","slug":"tim-xiao-2"},{"name":"Weiyang Liu","slug":"weiyang-liu"},{"name":"Robert Bamler","slug":"robert-bamler"}],"date":"2025-07-31","section":"bits-and-bites","issue":"volume7issue3","doi":"10.1162/99608f92.da63c0cb","abstract":"Language models (LMs) are trained specifically for one out of many natural language processing (NLP) tasks. By simply scaling them up to large LMs (LLMs), they emerge the ability to solve many other NLP tasks that they have not been trained for. This emergence of zero-shot problem-solving ability of LLMs surprised the community. In this survey, we draw an overlooked connection between LLMs and modern computers that a","keywords":["large language models","universal problem solver","emergence","computer"],"image":"/media/remote/bf9a74a010fd91ac-720.webp","collections":[],"label":"Bits and Bites"},{"id":"f9d748d4-df43-42f7-85d3-7596fa9d76fa","slug":"suy0nbh5","title":"Code Review, Reproducibility, and Improving the Scholarly Record","dek":null,"authors":[{"name":"Nicholas Ottone","slug":"nicholas-ottone"},{"name":"Limor Peer","slug":"limor-peer"}],"date":"2025-07-31","section":"reinforcing-rr","issue":"volume7issue3","doi":"10.1162/99608f92.f9d748d4","abstract":null,"keywords":[],"image":"/media/remote/a403e4b624ee67f1-720.webp","collections":[],"label":"Reinforcing Reproducibility and Replicability"},{"id":"63a523ec-eace-4fec-af5c-58b1805e209a","slug":"nl7ahle1","title":"Spreadsheet Data Science in K–12: An On-Ramp for the Future Data Literate Citizen","dek":null,"authors":[{"name":"Kelsey Badger","slug":"kelsey-badger"},{"name":"Emily Nutwell","slug":"emily-nutwell"},{"name":"Jessica T. Kulp","slug":"jessica-kulp-2"}],"date":"2025-07-31","section":"minding-the-future","issue":"volume7issue3","doi":"10.1162/99608f92.63a523ec","abstract":null,"keywords":[],"image":"/media/remote/7687cfb4e22549c9-720.webp","collections":[],"label":"Minding the Future"},{"id":"03dd064a-456a-463a-b51f-20f2ae10c157","slug":"kuqgze5o","title":"Multitask Learning 1997–2024: Part II Regularization and Optimization","dek":null,"authors":[{"name":"Xiaokang Liu","slug":"xiaokang-liu"},{"name":"Jun Yu","slug":"jun-yu"},{"name":"Yutong Dai","slug":"yutong-dai"},{"name":"Yishan Shen","slug":"yishan-shen"},{"name":"Jianmin Chen","slug":"jianmin-chen"},{"name":"Jie Hu","slug":"jie-hu"},{"name":"Jin Huang","slug":"jin-huang"},{"name":"Yixin Liu","slug":"yixin-liu"},{"name":"Yilong Yin","slug":"yilong-yin"},{"name":"Vinod Namboodiri","slug":"vinod-namboodiri"},{"name":"Brian Davison","slug":"brian-davison"},{"name":"Jason Moore","slug":"jason-moore-2"},{"name":"Yong Chen","slug":"yong-chen"}],"date":"2025-07-30","section":"panorama","issue":"volume7issue3","doi":"10.1162/99608f92.03dd064a","abstract":"In Part I of this survey, we introduced the fundamentals of multitask learning (MTL): its definition, significance, and core operational mechanisms. This Part II builds on that foundation by summarizing existing knowledge and reviewing key methodologies that underpin MTL, with a focus on regularization and optimization techniques that facilitate effective information sharing across tasks. We begin by exploring a vari","keywords":["multitask learning","feature selection","low-rank methods","matrix decomposition","multiobjective optimization","adversarial training","neural architecture search"],"image":"/media/remote/b28a67c22c155b2f-720.webp","collections":[],"label":"Panorama"},{"id":"ca91645f-77aa-4ae6-8e0c-8a8edb6b22a4","slug":"lgmkutcd","title":"Multitask Learning 1997–2024: Part III Applications","dek":null,"authors":[{"name":"Jun Yu","slug":"jun-yu"},{"name":"Jin Huang","slug":"jin-huang"},{"name":"Kai Zhang","slug":"kai-zhang"},{"name":"Yixin Liu","slug":"yixin-liu"},{"name":"Ke Zhang","slug":"ke-zhang"},{"name":"Yishan Shen","slug":"yishan-shen"},{"name":"Dazheng Zhang","slug":"dazheng-zhang"},{"name":"Rong Zhou","slug":"rong-zhou"},{"name":"Xiaokang Liu","slug":"xiaokang-liu"},{"name":"Yilong Yin","slug":"yilong-yin"},{"name":"Vinod Namboodiri","slug":"vinod-namboodiri"},{"name":"Brian Davison","slug":"brian-davison"},{"name":"Jason Moore","slug":"jason-moore-2"},{"name":"Yong Chen","slug":"yong-chen"}],"date":"2025-07-30","section":"panorama","issue":"volume7issue3","doi":"10.1162/99608f92.ca91645f","abstract":"In Parts I and II of this article, we introduced the fundamentals of multitask learning (MTL) and the technical foundations of regularization and optimization methods that facilitate information sharing across tasks. This Part III focuses on the practical application of MTL theory in real-world industrial contexts, offering actionable guidance and demonstrating how theoretical concepts are effectively implemented in ","keywords":["deep learning","generative pretrained transformers","prompt learning","pretrained foundation models","large language models"],"image":"/media/remote/46c8e9310aabe8a8-720.webp","collections":[],"label":"Panorama"},{"id":"9ac68105-7d8f-4825-a426-fbc660ab3170","slug":"1vd80i9t","title":"Mapping the Conceptual Foundation(s) of ‘Data Science Education’","dek":null,"authors":[{"name":"Michelle Hoda Wilkerson","slug":"michelle-hoda-wilkerson"}],"date":"2025-06-25","section":"stepping-stones","issue":"volume7issue3","doi":"10.1162/99608f92.9ac68105","abstract":"The emerging field of data science education is broadly interdisciplinary, and related literature is distributed across a variety of outlets. Navigating this landscape can be challenging, especially for those who are just entering the field or who seek insights from multiple disciplinary communities. This report contributes to a growing body of research that aims to characterize the emerging field of ‘data science ed","keywords":["data science education","bibliometric analysis","literature review","K–12 data science","data science major"],"image":"/media/remote/a83e1d651e3fe77d-720.webp","collections":[],"label":"Stepping Stones"},{"id":"b5fc31f3-2392-44c7-9a31-cf4c32ec6e71","slug":"egbu01o3","title":"A Conversation on Fundamental Data Literacy Concepts for Undergraduate Education","dek":"An interview with Anna Bargagliotti, David Homa, Arshia Mathur, Keith McCormick, Paula Payton, and Brian Wright by Wendy Pothier","authors":[{"name":"Wendy Pothier","slug":"wendy-pothier"},{"name":"Anna Bargagliotti","slug":"anna-bargagliotti"},{"name":"David Homa","slug":"david-homa"},{"name":"Arshia Mathur","slug":"arshia-mathur"},{"name":"Keith McCormick","slug":"keith-mccormick"},{"name":"Paula Payton","slug":"paula-payton"},{"name":"Brian Wright","slug":"brian-wright"}],"date":"2025-06-06","section":"stepping-stones","issue":"volume7issue2","doi":"10.1162/99608f92.b5fc31f3","abstract":"As the demand for data literacy grows, integrating foundational data literacy skills into undergraduate education becomes increasingly essential. This panel presents six core themes for cultivating data literacy among undergraduate students, addressing its interdisciplinary nature. Drawing upon the collective expertise of the group, literature research, case studies, and interviews, the panel explores the need for da","keywords":["data literacy","data science","undergraduate curriculum","data citizenship","data literacy education"],"image":"/media/remote/b1fbc167df037bd6-720.webp","collections":["data-literacy-at-scale"],"label":"Stepping Stones"},{"id":"e00f6919-fb88-44f1-97bc-7be4b13eda1b","slug":"6g3q4shv","title":"Implications of Climate Change for Prices of Ultra-Premium Cabernet Sauvignon Wines From California","dek":null,"authors":[{"name":"Sarah C. Whitnall","slug":"sarah-whitnall"},{"name":"Julian M. Alston","slug":"julian-alston"}],"date":"2025-06-02","section":"cornucopia","issue":"volume7issue2","doi":"10.1162/99608f92.e00f6919","abstract":"In 1980, the U.S. government created American Viticultural Areas (AVAs) as a mechanism for wine producers to signal product quality and better capture the benefits from collective reputation associated with the location of production. The objective of this study is to analyze the effects of climate and vintage weather on wine prices and, hence, the potential for climate change to disrupt the role of AVAs in providing","keywords":["wine prices","climate and weather","fine varietal wine","California AVAs"],"image":"/media/remote/4bf6944544415404-720.webp","collections":["vine-to-mind"],"label":"Cornucopia"},{"id":"cad06401-d667-4c60-af1c-17f631955f7a","slug":"3836flyd","title":"Large Language Models and Machine Learning: The Way Ahead","dek":null,"authors":[{"name":"Ralf Herbrich","slug":"ralf-herbrich"}],"date":"2025-05-31","section":null,"issue":"specialissue5","doi":"10.1162/99608f92.cad06401","abstract":null,"keywords":[],"image":"/media/remote/cf8425c23cc6a03a-720.webp","collections":[],"label":"From the Editors"},{"id":"fda99122-5b9f-4256-aff6-e9a946c5a09e","slug":"yn7p8jik","title":"Is AI Good for Society?","dek":null,"authors":[{"name":"Francine Berman","slug":"francine-berman"}],"date":"2025-05-31","section":null,"issue":"specialissue5","doi":"10.1162/99608f92.fda99122","abstract":null,"keywords":[],"image":"/media/remote/23813d0f4fe08ad4-720.webp","collections":[],"label":"From the Editors"},{"id":"e6f531e6-2a15-4579-9662-c2256f74acd9","slug":"bcp7n3bs","title":"From Future Shock to the Vico Effect: Generative AI and the Return of History","dek":null,"authors":[{"name":"David Leslie","slug":"david-leslie"}],"date":"2025-05-31","section":null,"issue":"specialissue5","doi":"10.1162/99608f92.e6f531e6","abstract":null,"keywords":[],"image":"/media/remote/b3c64de96920e99e-720.webp","collections":[],"label":"From the Editors"},{"id":"abf14c9d-e5c9-48af-91c7-7f76d6c58975","slug":"ufaltur6","title":"Challenges and Opportunities for Statistics in the Era of Data Science","dek":null,"authors":[{"name":"Claudia Kirch","slug":"claudia-kirch"},{"name":"Soumendra Lahiri","slug":"soumendra-lahiri"},{"name":"Harald Binder","slug":"harald-binder"},{"name":"Werner Brannath","slug":"werner-brannath"},{"name":"Ivor Cribben","slug":"ivor-cribben"},{"name":"Holger Dette","slug":"holger-dette"},{"name":"Philipp Doebler","slug":"philipp-doebler"},{"name":"Oliver Feng","slug":"oliver-feng"},{"name":"Axel Gandy","slug":"axel-gandy"},{"name":"Sonja Greven","slug":"sonja-greven"},{"name":"Barbara Hammer","slug":"barbara-hammer"},{"name":"Stefan Harmeling","slug":"stefan-harmeling"},{"name":"Thomas Hotz","slug":"thomas-hotz"},{"name":"Göran Kauermann","slug":"goran-kauermann"},{"name":"Joscha Krause","slug":"joscha-krause"},{"name":"Georg Krempl","slug":"georg-krempl"},{"name":"Alicia Nieto-Reyes","slug":"alicia-nieto-reyes"},{"name":"Ostap Okhrin","slug":"ostap-okhrin"},{"name":"Hernando Ombao","slug":"hernando-ombao"},{"name":"Florian Pein","slug":"florian-pein"},{"name":"Michal Pešta","slug":"michal-pesta"},{"name":"Dimitris Politis","slug":"dimitris-politis"},{"name":"Li-Xuan Qin","slug":"li-xuan-qin"},{"name":"Tom Rainforth","slug":"tom-rainforth"},{"name":"Holger Rauhut","slug":"holger-rauhut"},{"name":"Henry Reeve","slug":"henry-reeve"},{"name":"David Salinas","slug":"david-salinas"},{"name":"Johannes Schmidt-Hieber","slug":"johannes-schmidt-hieber"},{"name":"Clayton Scott","slug":"clayton-scott"},{"name":"Johan Segers","slug":"johan-segers"},{"name":"Myra Spiliopoulou","slug":"myra-spiliopoulou"},{"name":"Adalbert Wilhelm","slug":"adalbert-wilhelm"},{"name":"Ines Wilms","slug":"ines-wilms"},{"name":"Yi Yu","slug":"yi-yu"},{"name":"Johannes Lederer","slug":"johannes-lederer"}],"date":"2025-05-28","section":"panorama","issue":"volume7issue2","doi":"10.1162/99608f92.abf14c9d","abstract":"Statistics as a scientific discipline is currently facing the great challenge of finding its place in data science once more. At the beginning of the last century, the development of the discipline of statistics was initiated by data-related research questions. Nowadays, it is often viewed to have not kept up with the current developments in data science, which are largely focused on algorithmic, exploratory, and com","keywords":["generalization","identifiability","machine learning","overparametrization","reproducibility","uncertainty quantification"],"image":"/media/remote/fb8046b07bd5d9b7-720.webp","collections":[],"label":"Panorama"},{"id":"01674a29-fc5d-4d1e-8a29-8ab69790c3bf","slug":"e1t7mbw6","title":"One Person Dialogues: Concerns About AI-Human Interactions","dek":null,"authors":[{"name":"Darren Frey","slug":"darren-frey-2"},{"name":"Daniel H. Weiss","slug":"daniel-weiss"}],"date":"2025-05-28","section":"panorama","issue":"volume7issue2","doi":"10.1162/99608f92.01674a29","abstract":"Potential disruptions to economic, educational, and political affairs have remained at the fore of conversations about the implications of large language models (LLMs); however, remarkably little attention has been paid to the potentially more immediate ethical, psychological, and sociological repercussions of these and similar technologies. In the following, the authors develop a number of concerns about sustained L","keywords":["artificial intelligence","ethics","human-computer interaction","cognitive science","behavioral science","dialogue"],"image":"/media/remote/78be9d9c0eab1ac2-720.webp","collections":[],"label":"Panorama"},{"id":"24d90bdc-4a5d-4b88-98ad-1a50af6eb797","slug":"o1nj6f46","title":"Integrating Data Literacy Into K–12 Education","dek":null,"authors":[{"name":"Chad Dorsey","slug":"chad-dorsey"},{"name":"Jacob Sagrans","slug":"jacob-sagrans"},{"name":"Konstantina Yaneva","slug":"konstantina-yaneva"},{"name":"Daniel O'Brien","slug":"daniel-obrien"},{"name":"Ian Collins","slug":"ian-collins"},{"name":"Nora Gannon-Slater","slug":"nora-ngannon-slaterairorg"},{"name":"Alicia Jalbert","slug":"alicia-jalbert"},{"name":"Kate Kastelein","slug":"kate-kastelein"},{"name":"Pete Laver","slug":"pete-laver"},{"name":"Joseph Reilly","slug":"joseph-reilly"},{"name":"Paul Schwein","slug":"paul-schwein"}],"date":"2025-05-22","section":"stepping-stones","issue":"volume7issue2","doi":"10.1162/99608f92.24d90bdc","abstract":"In a world increasingly driven by data, the ability to understand, interpret, and make use of data has assumed a fundamental role. Yet despite its importance, data literacy remains conspicuously absent from the U.S. K–12 educational landscape. This article explores the obstacles in integrating data literacy into the educational system and proposes a multipronged approach to address these challenges.We contend that su","keywords":["data literacy","K–12","education","teacher preparation","data tools","educator professional development"],"image":"/media/remote/51562aa2cf0b5b30-720.webp","collections":["data-literacy-at-scale"],"label":"Stepping Stones"},{"id":"27d2dc76-e500-4cf1-87de-a880d11b4d79","slug":"cho9ytma","title":"Participatory Engineering of Algorithmic Social Technologies: An Extended Book Review of David G. Robinson’s Voices in the Code","dek":null,"authors":[{"name":"Rajesh Venkatachalapathy","slug":"rajesh-venkatachalapathy"}],"date":"2025-05-20","section":"panorama","issue":"volume7issue2","doi":"10.1162/99608f92.27d2dc76","abstract":"David G. Robinson’s Voices in the Code, reviewed here, approaches the issue of governance and regulation of algorithmic social technologies with a case study orientation that offers a grounding in the details, illuminating the fundamentally sociological and processual nature of modern governance issues that are mostly missed or left implicit in academic, regulatory, and legislative discussions. This important book ur","keywords":["book review","algorithmic fairness","transparency in machine learning","bureaucracy","trust","collaborative governance and regulation"],"image":"/media/remote/ce5190e519bc75c6-720.webp","collections":[],"label":"Panorama"},{"id":"35d32cfc-eded-4772-8406-999034371e46","slug":"2mvqwgmf","title":"From Data Creator to Data Reuser: Distance Matters","dek":null,"authors":[{"name":"Christine L. Borgman","slug":"christine-l-borgman"},{"name":"Paul Groth","slug":"paul-groth"}],"date":"2025-04-30","section":"milestones-and-millstones","issue":"volume7issue2","doi":"10.1162/99608f92.35d32cfc","abstract":"Sharing research data is necessary, but not sufficient, for data reuse. Open science policies focus more heavily on data sharing than on reuse, yet both are complex, labor-intensive, expensive, and require infrastructure investments by multiple stakeholders. The value of data reuse lies in relationships between creators and reusers. By addressing knowledge exchange rather than mere transactions between stakeholders, ","keywords":["data sharing","data reuse","science policy","knowledge infrastructures","data archives","economics of open science"],"image":"/media/remote/2fc81eaa3e2eaf45-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"e2153cfe-5448-40a9-9238-f27eb61e694f","slug":"shn1mqmm","title":"What’s a Healthy Distance Between “Yes, We Can” and “No, You Can’t—Or Shouldn’t”","dek":null,"authors":[{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2025-04-30","section":"editorscolumn","issue":"volume7issue2","doi":"10.1162/99608f92.e2153cfe","abstract":null,"keywords":[],"image":"/media/remote/524302f1667e0a38-720.webp","collections":[],"label":"Editor’s Column"},{"id":"c066fe9c-29f1-4989-b26e-4899163cb08a","slug":"a7kmqk35","title":"Statistics and AI: A Fireside Conversation","dek":"A series of interviews.","authors":[{"name":"Xihong Lin","slug":"xihong-lin"},{"name":"Tianxi Cai","slug":"tianxi-cai"},{"name":"David Donoho","slug":"david-donoho"},{"name":"Haoda Fu","slug":"haoda-fu"},{"name":"Tracy Ke","slug":"tracy-ke"},{"name":"Jiashun Jin","slug":"jiashun-jin"},{"name":"Xiao-Li Meng","slug":"xiao-li-meng"},{"name":"Annie Qu","slug":"annie-qu"},{"name":"Chengchun Shi","slug":"chengchun-shi"},{"name":"Peter Song","slug":"peter-song"},{"name":"Qiang Sun","slug":"qiang-sun"},{"name":"Wenyi Wang","slug":"wenyi-wang"},{"name":"Hulin Wu","slug":"hulin-wu"},{"name":"Bin Yu","slug":"bin-yu"},{"name":"Heping Zhang","slug":"heping-zhang"},{"name":"Tian Zheng","slug":"tian-zheng"},{"name":"Harrison Zhou","slug":"harrison-zhou"},{"name":"Jin Zhou","slug":"jin-zhou"},{"name":"Hongtu Zhu","slug":"hongtu-zhu"},{"name":"Ji Zhu","slug":"ji-zhu"}],"date":"2025-04-30","section":"panorama","issue":"volume7issue2","doi":"10.1162/99608f92.c066fe9c","abstract":"A 3-hour webinar titled “Statistics and AI – A Fireside Conversation” was held on Sunday, March 17, 2024, attracting an online audience of approximately 1,000. The event featured three sessions aimed at engaging the statistical community on key topics in the AI era: addressing statistical challenges and opportunities (Panel I), evolving the publication process (Panel II), and advancing next-generation statistical pip","keywords":["artificial intelligence","statistical research","publication culture","statistics education","reproducibility","team science"],"image":null,"collections":[],"label":"Panorama"},{"id":"caab9466-cb43-4730-bd22-4cd0221de701","slug":"pemdo9gl","title":"Interpretable Sentiment Analysis Using the Attention-Based Multiple Instance Classification Model: An Application to Wine Reviews","dek":null,"authors":[{"name":"Chenyu Yang","slug":"chenyu-yang"},{"name":"Jing Cao","slug":"jing-cao"}],"date":"2025-04-30","section":"cornucopia","issue":"volume7issue2","doi":"10.1162/99608f92.caab9466","abstract":"Sentiment analysis (SA) is widely used for analyzing text data to identify the underlying opinion or emotion expressed in a document. Neural network methods in natural language processing have produced state-of-the-art results in SA. However, many of those methods are ‘black-box’ algorithms that do not provide interpretable results. In this article, we develop an interpretable word-level context-based SA tool, which ","keywords":["interpretability","sentiment analysis","attention mechanism","multiple instance classification","wine reviews"],"image":"/media/remote/3b097a3906c33b6e-720.webp","collections":["vine-to-mind"],"label":"Cornucopia"},{"id":"d605e50f-c9d7-4b5f-8804-05fb57b36fee","slug":"dab3xd93","title":"The Problem of Terroir in the Anthropocene","dek":null,"authors":[{"name":"E. M. Wolkovich","slug":"e-m-wolkovich"}],"date":"2025-04-30","section":"cornucopia","issue":"volume7issue2","doi":"10.1162/99608f92.d605e50f","abstract":"Climate is integral to the concept of terroir. With anthropogenic climate change, the terroir of the world's winegrowing regions is changing, and will continue to change for decades or centuries. The clearest signal of this shift comes from the earlier harvests of wine grapes over the last several decades with harvests 2–3 weeks earlier in France and other regions. These earlier harvests have reshaped the climatic pr","keywords":["wine grape varieties","phenology","flowering","harvest timing","human adaptation to climate change","dynamic terroir"],"image":null,"collections":["vine-to-mind"],"label":"Cornucopia"},{"id":"9fc78e4c-43a8-4389-b62a-72ff48461e7c","slug":"6j8p2sl1","title":"How Can Reliability of Artificial Intelligence Be Ensured?","dek":null,"authors":[{"name":"Gitta Kutyniok","slug":"gitta-kutyniok"}],"date":"2025-04-30","section":"diving-into-data","issue":"volume7issue2","doi":"10.1162/99608f92.9fc78e4c","abstract":null,"keywords":[],"image":"/media/remote/f83908532395c989-720.webp","collections":[],"label":"Diving into Data"},{"id":"81f98145-0723-4232-89cb-02714c8cc7b8","slug":"lu3jcolh","title":"How Can We Use Econometrics to Assess Climate Impacts on the Wine Industry?","dek":null,"authors":[{"name":"German Puga","slug":"german-puga"},{"name":"Kym Anderson","slug":"kym-anderson"}],"date":"2025-04-30","section":"cornucopia","issue":"volume7issue2","doi":"10.1162/99608f92.81f98145","abstract":"Wine grape production is very susceptible to weather conditions, with climate change presenting significant risks to the industry. Econometric analyses can offer valuable insights into the effects of past weather and climates, and of potential future climate changes. While econometrics is not the only method for deriving such insights, there is considerable potential for its application, especially given the rich (of","keywords":["grape growing","wine production","weather","climate change","physical risks","adaptation"],"image":"/media/remote/6442841ce6825950-720.webp","collections":["vine-to-mind"],"label":"Cornucopia"},{"id":"6939122d-9193-48a6-b0d1-d77ce4899893","slug":"ex6vbavk","title":"An Investigation Into Probabilities of Streaks in Online Chess","dek":null,"authors":[{"name":"Jeffrey S. Rosenthal","slug":"jeffrey-s-rosenthal"}],"date":"2025-04-30","section":"cornucopia","issue":"volume7issue2","doi":"10.1162/99608f92.6939122d","abstract":"We investigate the probabilities of long chess winning streaks by the player Hikaru on the online site Chess.com, in response to recent high-level allegations of cheating. We find that, under a reasonable model of chess win and draw probabilities, the observed streaks are comfortably within the range of statistical expectation. We conclude that these streaks do not provide evidence of cheating or suspicious behavior.","keywords":["chess","rating","winning streak","hot hand","cheating"],"image":"/media/remote/cfd43da52b3bfc8b-720.webp","collections":[],"label":"Cornucopia"},{"id":"c2e4090c-bcb0-4237-b97e-64089ec97fff","slug":"9gvoe60e","title":"Blending Retail and Innovation: A Conversation With Troy Rice, CEO of Total Wine & More","dek":"An interview with Troy Rice by Xiao-Li Meng and Liberty Vittert","authors":[{"name":"Troy Rice","slug":"troy-rice"},{"name":"Xiao-Li Meng","slug":"xiao-li-meng"},{"name":"Liberty Vittert","slug":"liberty-vittert"}],"date":"2025-04-30","section":"cornucopia","issue":"volume7issue2","doi":"10.1162/99608f92.c2e4090c","abstract":"In this insightful interview hosted by Harvard Data Science Review’s Founding Editor-in-Chief, Xiao-Li Meng, and Media Feature Editor, Liberty Vittert, Troy Rice, CEO of Total Wine & More, discusses his career trajectory from an assistant manager at a retail store to leading a major retail beverage chain. He highlights the influence of early frontline retail experiences on his customer-centric leadership style and ap","keywords":[],"image":"/media/remote/39a73b25f35f3d84-720.webp","collections":["conversations-with-leaders","vine-to-mind"],"label":"Cornucopia"},{"id":"c0d03e91-23c9-4b31-a35d-a6151b2273cb","slug":"jt90t3y2","title":"How Platforms Constrain Matters of Distance and Data Reuse","dek":null,"authors":[{"name":"Amelia Acker","slug":"amelia-acker"}],"date":"2025-04-30","section":null,"issue":"volume7issue2","doi":"10.1162/99608f92.c0d03e91","abstract":null,"keywords":[],"image":"/media/remote/56431e04e712cc2a-720.webp","collections":[],"label":"Featured Discussion"},{"id":"c17c3adb-622e-4f03-9f40-a0c90dbd1260","slug":"zgx7oyq6","title":"Infrastructure, Intermediaries, and Artificial Intelligence: A Rejoinder to Commentaries on “From Data Creator to Data Reuser: Distance Matters\"","dek":null,"authors":[{"name":"Christine L. Borgman","slug":"christine-l-borgman"},{"name":"Paul Groth","slug":"paul-groth"}],"date":"2025-04-30","section":null,"issue":"volume7issue2","doi":"10.1162/99608f92.c17c3adb","abstract":null,"keywords":[],"image":"/media/remote/f3f65eb45ef9ae8e-720.webp","collections":[],"label":"Featured Discussion"},{"id":"56200055-f510-4113-966c-a3fbb67d01a7","slug":"yen5ysj5","title":"Styles of Statistics","dek":null,"authors":[{"name":"Christopher J. Phillips","slug":"chris-phillips"}],"date":"2025-04-30","section":"mining-the-past","issue":"volume7issue2","doi":"10.1162/99608f92.56200055","abstract":null,"keywords":[],"image":"/media/remote/da09c0d39ee82f87-720.webp","collections":[],"label":"Mining the Past"},{"id":"c35ca0f3-b972-4fb9-ae92-7bdb75679a97","slug":"iygea4du","title":"Strengthening Human Infrastructure Through Data Matchmakers","dek":null,"authors":[{"name":"danah boyd","slug":"danah-boyd"},{"name":"Jayshree Sarathy","slug":"jayshree-sarathy"}],"date":"2025-04-30","section":null,"issue":"volume7issue2","doi":"10.1162/99608f92.c35ca0f3","abstract":null,"keywords":[],"image":"/media/remote/840d03f20b48d564-720.webp","collections":[],"label":"Featured Discussion"},{"id":"598a17b4-723b-4e51-bf11-4e98488eee95","slug":"m0dqvamt","title":"Humanities Data Reuse: Humanity First","dek":null,"authors":[{"name":"Gregory Crane","slug":"gregory-crane"},{"name":"Alison Babeu","slug":"alison-babeu"},{"name":"James Tauber","slug":"james-tauber"}],"date":"2025-04-30","section":null,"issue":"volume7issue2","doi":"10.1162/99608f92.598a17b4","abstract":null,"keywords":[],"image":"/media/remote/8f50d726b9dca4ec-720.webp","collections":[],"label":"Featured Discussion"},{"id":"e488fbd9-8a74-4767-bb34-87272d4294a6","slug":"3h5ypls7","title":"Beyond Distance: Why Elevation Matters Too","dek":null,"authors":[{"name":"Alyssa Goodman","slug":"alyssa-goodman"}],"date":"2025-04-30","section":null,"issue":"volume7issue2","doi":"10.1162/99608f92.e488fbd9","abstract":null,"keywords":[],"image":"/media/remote/f41444970e4d0d1e-720.webp","collections":[],"label":"Featured Discussion"},{"id":"e95b5c26-69de-4207-b334-8176e27f508f","slug":"c4t7vqmm","title":"What to Do About Data Distance? Responsible Alternatives to Data Sharing","dek":null,"authors":[{"name":"Sabina Leonelli","slug":"sabina-leonelli"}],"date":"2025-04-30","section":null,"issue":"volume7issue2","doi":"10.1162/99608f92.e95b5c26","abstract":null,"keywords":[],"image":"/media/remote/a768ffa6583b00e7-720.webp","collections":[],"label":"Featured Discussion"},{"id":"5cf1ad26-db5c-4fa3-93eb-02268cc5ddeb","slug":"yw80rzmu","title":"Distance Matters and Data in the Evolution of Knowledge","dek":null,"authors":[{"name":"Andrea Scharnhorst","slug":"andrea-scharnhorst"}],"date":"2025-04-30","section":null,"issue":"volume7issue2","doi":"10.1162/99608f92.5cf1ad26","abstract":null,"keywords":[],"image":"/media/remote/363f16bb66652016-720.webp","collections":[],"label":"Featured Discussion"},{"id":"71c3e176-dcc4-4871-bdc9-5ca836360bbc","slug":"kcbrpn3x","title":"Dimensions of Distance: Descriptive or Distracting?","dek":null,"authors":[{"name":"Andrew Treloar","slug":"andrew-treloar"}],"date":"2025-04-30","section":null,"issue":"volume7issue2","doi":"10.1162/99608f92.71c3e176","abstract":null,"keywords":[],"image":"/media/remote/9ead80853290fd46-720.webp","collections":[],"label":"Featured Discussion"},{"id":"1d109ab1-1c8c-4508-b8d0-09f08a7a2a5b","slug":"nthfaf2i","title":"Distance and Other Metaphors About Reuse of Research Data","dek":null,"authors":[{"name":"Sally Wyatt","slug":"sally-wyatt"}],"date":"2025-04-30","section":null,"issue":"volume7issue2","doi":"10.1162/99608f92.1d109ab1","abstract":null,"keywords":[],"image":"/media/remote/14cb16b93ee1e541-720.webp","collections":[],"label":"Featured Discussion"},{"id":"3a5ebf2b-173f-47e1-a966-55e79ba697d7","slug":"pp1wobyp","title":"Going the Distance: Achieving Sufficiency in Data Reuse","dek":null,"authors":[{"name":"Elizabeth Yakel","slug":"elizabeth-yakel"}],"date":"2025-04-30","section":null,"issue":"volume7issue2","doi":"10.1162/99608f92.3a5ebf2b","abstract":null,"keywords":[],"image":"/media/remote/7f73dad1fa0a872e-720.webp","collections":[],"label":"Featured Discussion"},{"id":"c94174e8-3624-4404-87ef-2623401f37f2","slug":"vhs829uf","title":"Wine Economics: Emergence, Topics, and Outlook","dek":null,"authors":[{"name":"Karl Storchmann","slug":"karl-storchmann"},{"name":"Jing Cao","slug":"jing-cao"}],"date":"2025-03-19","section":"panorama","issue":"volume7issue1","doi":"10.1162/99608f92.c94174e8","abstract":"Fine wine has several characteristics that differentiate it from other agricultural commodities and beverages that render it a potentially interesting topic for economists. First, the best wines can fetch bottle prices that exceed several thousand dollars. Second, if properly stored, fine wine can increase in value with age. Third, the quality and prices of fine wines are extraordinarily sensitive to fluctuations in ","keywords":["wine economics","finance","climate change","expert opinion","machine learning","econometric models"],"image":"/media/remote/631d3bcc158fad46-720.webp","collections":["vine-to-mind"],"label":"Panorama"},{"id":"a17caeae-d7cf-4281-bc5b-acce20f54e15","slug":"pbyurwkv","title":"Global Climate Change and Wine Production: Industry and Academic Perspectives","dek":"An interview with Laura Catena, Elisabeth Forrestel, Jean-Baptiste Rivail, Mark Sahn, and Daniel A. Sumner, by Robert N. Stavins. Abstract only. Full article forthcoming.","authors":[{"name":"Robert N. Stavins","slug":"robert-n-stavins"},{"name":"Laura Catena","slug":"laura-catena"},{"name":"Elisabeth Forrestel","slug":"elisabeth-forrestel"},{"name":"Jean-Baptiste Rivail","slug":"jean-baptiste-rivail"},{"name":"Mark Sahn","slug":"mark-sahn"},{"name":"Daniel A. Sumner","slug":"daniel-a-sumner"}],"date":"2025-02-13","section":"panorama","issue":"volume7issue1","doi":"10.1162/99608f92.a17caeae","abstract":"For more than a decade, industry practitioners have been confronting—and academic researchers have been studying—the implications of global climate change for the wine industry. This panel brought together leaders from industry and knowledgeable researchers from academia to engage in a discussion about the effects of global climate change on wine production, and what can be done about it. The panelists brought their ","keywords":["global climate change","greenhouse gas emissions","extreme weather events"],"image":"/media/remote/fb74180eec64a6e7-720.webp","collections":["vine-to-mind"],"label":"Panorama"},{"id":"7fcd521d-b495-44f7-81b9-ecb2e4e0a0ae","slug":"qasl4fza","title":"What Are Statistical Assumptions About? An Answer From Perspectivism","dek":null,"authors":[{"name":"Kino Zhao","slug":"kino-zhao"}],"date":"2025-02-10","section":"milestones-and-millstones","issue":"volume7issue1","doi":"10.1162/99608f92.7fcd521d","abstract":"This article presents a perspectivist framework for understanding and evaluating statistical assumptions. Drawing on the thesis of perspectivism from the philosophy of science, this framework treats statistical assumptions not as empirical hypotheses that are descriptively accurate or inaccurate about the world but as prescribing a particular perspective from which statistical knowledge is generated. What this means ","keywords":["modeling assumptions","philosophy of science","perspectivism"],"image":"/media/remote/f6d098b2476dcd61-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"90142312-5191-4fdd-92fc-544f2c298f29","slug":"5ppk3igy","title":"Navigating the Rapid Evolution of AI With Todd James","dek":"An interview with Todd James by Hamit Hamutcu.","authors":[{"name":"Todd James","slug":"todd-james"},{"name":"Hamit Hamutcu","slug":"hamit-hamutcu"}],"date":"2025-02-06","section":"active-industrial-learning","issue":"volume7issue1","doi":"10.1162/99608f92.90142312","abstract":null,"keywords":[],"image":"/media/remote/5b18f9e3110f1bc1-720.webp","collections":[],"label":"Active Industrial Learning"},{"id":"aab45e43-9085-443d-8c4e-f4af177584a9","slug":"vun3lbag","title":"From Bottle Shock to Future Shock: Everything Data Science and Data Science for Everyone","dek":null,"authors":[{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2025-01-30","section":"panorama","issue":"volume7issue1","doi":"10.1162/99608f92.aab45e43","abstract":"Originally written as the opening editorial for HDSR’s Vine to Mind symposium brochure, this slightly updated version highlights the intertwined nature of wine, statistics, and the broader realms of data science and AI. Revisiting the transformative 1976 Judgment of Paris, a landmark blind tasting event in the history of wine, this article illustrates both the power and complexity of statistical evidence. Transitioni","keywords":["Judgment of Paris","variations","Vine to Mind","wine data","wine industry","wine tasting"],"image":"/media/remote/f8b6edbeb722707e-720.webp","collections":["vine-to-mind"],"label":"Panorama"},{"id":"84acc1c1-f7a3-412c-ab93-04cc31e5c887","slug":"1zrbtdac","title":"The Biggest Ideas for the Future of Wine: Recap of the Vine to Mind Symposium","dek":null,"authors":[{"name":"Christian Kittery","slug":"christian-kittery"}],"date":"2025-01-30","section":"bits-and-bites","issue":"volume7issue1","doi":"10.1162/99608f92.84acc1c1","abstract":null,"keywords":[],"image":"/media/remote/29cd4d76fe743254-720.webp","collections":["vine-to-mind"],"label":"Bits and Bites"},{"id":"56d4c11d-6459-499c-8d36-6d144ff356e7","slug":"k8v8kkrp","title":"An Optimist’s View on the Future of Wine","dek":null,"authors":[{"name":"Donald St. Pierre","slug":"donald-st-pierre"}],"date":"2025-01-30","section":"panorama","issue":"volume7issue1","doi":"10.1162/99608f92.56d4c11d","abstract":"This article, updated from its original appearance in HDSR’s Vine to Mind symposium brochure, recounts personal experiences establishing companies in the wine industry, detailing challenges such as cultural and financial obstacles, as well as the resilience and adaptability required in the dynamic wine industry. The narrative explores significant industry changes over almost three decades, including technological adv","keywords":["wine industry","business","consumer engagement","generational shifts","Vine to Mind"],"image":"/media/remote/c831f959ca12ad44-720.webp","collections":["vine-to-mind"],"label":"Panorama"},{"id":"0aecbcb3-b016-477e-8efc-b2e78e02b9f1","slug":"nfb4i1zk","title":"Data Science and AI: Everything Everywhere All at Once","dek":null,"authors":[{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2025-01-30","section":"editorscolumn","issue":"volume7issue1","doi":"10.1162/99608f92.0aecbcb3","abstract":null,"keywords":[],"image":"/media/remote/fd5de6dbf43109c0-720.webp","collections":[],"label":"Editor’s Column"},{"id":"4d41c5a4-aeac-4601-8886-b375ee678a92","slug":"yj9bmui7","title":"Data Science for Central Banks and Supervisors: How to Make It Work, Actually","dek":null,"authors":[{"name":"Patty Duijm","slug":"patty-duijm"},{"name":"Iman van Lelyveld","slug":"iman-van-lelyveld"}],"date":"2025-01-30","section":"panorama","issue":"volume7issue1","doi":"10.1162/99608f92.4d41c5a4","abstract":"Public authorities, such as central banks and supervisory authorities, are not known for their ability to quickly adopt new techniques in a rapidly changing world. However, these authorities play a central role in society, such as safeguarding the financial system. The challenge of keeping the financial system safe is formidable and data science could potentially help. We discuss how to leverage the potential of data","keywords":["data science","central banking","supervision","project implementation"],"image":"/media/remote/6e29509153afe35d-720.webp","collections":[],"label":"Panorama"},{"id":"ca01ab52-c0d1-4b0f-923a-602032f8f5f8","slug":"m3fk4fah","title":"The Nation’s Data at Risk: The First Annual Report on the Federal Statistical System","dek":null,"authors":[{"name":"Jonathan Auerbach","slug":"jonathan-auerbach"},{"name":"Claire McKay Bowen","slug":"claire-bowen"},{"name":"Constance F. Citro","slug":"constance-f-citro"},{"name":"Steve Pierson","slug":"steve-pierson"},{"name":"Zachary H. Seeskin","slug":"zachary-h-seeskin"}],"date":"2025-01-30","section":"panorama","issue":"volume7issue1","doi":"10.1162/99608f92.ca01ab52","abstract":"We present the first annual report evaluating the health of the U.S. federal statistical system. The report is a collaborative effort between the American Statistical Association and George Mason University. It highlights the crucial role of federal statistics as a foundational U.S. infrastructure and describes the methodologies and frameworks employed in the analysis. The recommendations (endorsed by the American St","keywords":["official statistics","federal statistics","government statistics","public policy and statistics","trust","independence and autonomy"],"image":"/media/remote/5dbfd2a446dd69d3-720.webp","collections":[],"label":"Panorama"},{"id":"b033a087-00d1-448b-bc8b-ad7fed53cd3a","slug":"jaqt0vpb","title":"Confidence in the Reasoning of Large Language Models","dek":null,"authors":[{"name":"Yudi Pawitan","slug":"yudi-pawitan"},{"name":"Chris Holmes","slug":"chris-holmes"}],"date":"2025-01-30","section":"cornucopia","issue":"volume7issue1","doi":"10.1162/99608f92.b033a087","abstract":"There is a growing literature on reasoning by large language models (LLMs), but the discussion on the uncertainty in their responses is still lacking. Our aim is to assess the extent of confidence that LLMs have in their answers and how it correlates with accuracy. Confidence is measured (i) qualitatively in terms of persistence in keeping their answer when prompted to reconsider, and (ii) quantitatively in terms of ","keywords":["artificial intelligence","BIG-Bench AI tests","chatbots","generative AI","statistical inference","statistical puzzles and paradoxes"],"image":"/media/remote/d9f58551135937b0-720.webp","collections":[],"label":"Cornucopia"},{"id":"9e63a630-d330-4c34-b399-3e58b48085bb","slug":"vifgdvlv","title":"Improving Atrial Fibrillation Detection Using a Shared Latent Space for ECG and PPG Signals","dek":null,"authors":[{"name":"Zhicheng Guo","slug":"zhicheng-guo"},{"name":"Cheng Ding","slug":"cheng-ding"},{"name":"Duc Do","slug":"duc-do"},{"name":"Amit Shah","slug":"amit-shah-2"},{"name":"Randall J. Lee","slug":"randall-j-lee"},{"name":"Xiao Hu","slug":"xiao-hu"},{"name":"Cynthia Rudin","slug":"cynthia-rudin"}],"date":"2025-01-30","section":"cornucopia","issue":"volume7issue1","doi":"10.1162/99608f92.9e63a630","abstract":"Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. It is associated with an increased risk of stroke, heart failure, and other cardiovascular complications, but can be clinically silent. The wide availability of consumer wearables has made continuous AF monitoring possible thanks to built-in electrocardiogram (ECG) and photoplethysmography (PPG) sensors. ECG is the gold standard for AF diagnosis,","keywords":["atrial fibrillation","PPG","ECG","Siamese network","contrastive learning","joint training"],"image":"/media/remote/bf01196a199134b7-720.webp","collections":[],"label":"Cornucopia"},{"id":"6f5dfc6f-b251-4a1c-ab52-354761fa638c","slug":"quig0v1l","title":"Data Literacy in Industry: High Time to Focus on Operationalization Through Middle Managers","dek":null,"authors":[{"name":"Dan Koloski","slug":"dan-koloski"},{"name":"Caitlin Porter","slug":"caitlin-porter"},{"name":"Berkeley Almand-Hunter","slug":"berkeley-almand-hunter"},{"name":"Stephen Gatchell","slug":"stephen-gatchell"},{"name":"Valerie Logan","slug":"valerie-logan"}],"date":"2025-01-30","section":"stepping-stones","issue":"volume7issue1","doi":"10.1162/99608f92.6f5dfc6f","abstract":"The recent global business mania around adoption of generative artificial intelligence (GenAI) has amplified interest in organizational data literacy efforts in industry. Data literacy, when scoped clearly and comprehensively, represents a spectrum inclusive of business, data and analytical acumen (inclusive of AI), as well as degrees of capability (literacy, fluency, and mastery). In the face of the well-known chang","keywords":["data literacy","change management","people management","data-driven decision-making"],"image":"/media/remote/a436a64d8333e85c-720.webp","collections":["data-literacy-at-scale"],"label":"Stepping Stones"},{"id":"d622eaff-d7f0-4150-81de-5b028ddbfc0a","slug":"mioxv9zf","title":"Building an Inclusive Data Literacy Community","dek":null,"authors":[{"name":"Emily Cross Hilty","slug":"emily-hilty"},{"name":"Veronica Vilski","slug":"veronica-vilski"},{"name":"Sanjay Mishra","slug":"sanjay-sanjaymishrabrownedu"},{"name":"Patricia Condon","slug":"patricia-condon"},{"name":"Susan Metzger Gracia","slug":"susan-metzger-gracia"}],"date":"2025-01-30","section":"stepping-stones","issue":"volume7issue1","doi":"10.1162/99608f92.d622eaff","abstract":"This article outlines the essential elements of an inclusive data literacy community to address the data literacy gap in organizations. After delving into the opportunity presented by improving data literacy, it elaborates on the foundational concept of community in relation to culture and concludes by offering practical insights into establishing an inclusive and sustainable data literacy community.","keywords":["data literacy","community","culture","role definition"],"image":"/media/remote/016e29a547a67d6c-720.webp","collections":["data-literacy-at-scale"],"label":"Stepping Stones"},{"id":"09ad573b-48e0-4877-847a-6e27678837a9","slug":"jh9dh04m","title":"Introduction to Special Theme on Data Literacy at Scale","dek":null,"authors":[{"name":"Hamit Hamutcu","slug":"hamit-hamutcu"}],"date":"2025-01-30","section":"stepping-stones","issue":"volume7issue1","doi":"10.1162/99608f92.09ad573b","abstract":null,"keywords":[],"image":"/media/remote/310418de30312d37-720.webp","collections":["data-literacy-at-scale"],"label":"Stepping Stones"},{"id":"be44bdad-a79a-4334-b728-c40bc8f99ce0","slug":"axrcdnax","title":"AI: A New and Impactful Player in the Quality Evaluation of Wine","dek":null,"authors":[{"name":"Jing Cao","slug":"jing-cao"}],"date":"2025-01-30","section":"recreations-in-randomness","issue":"volume7issue1","doi":"10.1162/99608f92.be44bdad","abstract":null,"keywords":[],"image":"/media/remote/a566fcffab4c268e-720.webp","collections":[],"label":"Recreations in Randomness"},{"id":"f250e959-8e62-473b-a750-dbd04fbd8c6b","slug":"5bupaly7","title":"In Conversation With Intuit Chief Data Officer Ashok Srivastava","dek":"An interview with Ashok Srivastava by Hamit Hamutcu.","authors":[{"name":"Ashok Srivastava","slug":"ashok-srivastava"},{"name":"Hamit Hamutcu","slug":"hamit-hamutcu"}],"date":"2025-01-30","section":"active-industrial-learning","issue":"volume7issue1","doi":"10.1162/99608f92.f250e959","abstract":null,"keywords":[],"image":"/media/remote/9304570734c7dc8c-720.webp","collections":[],"label":"Active Industrial Learning"},{"id":"21e6bbaa-4004-45b5-94ab-196b9de01fcc","slug":"ujvharkk","title":"Beware the Intention Economy: Collection and Commodification of Intent via Large Language Models","dek":null,"authors":[{"name":"Yaqub Chaudhary","slug":"yaqub-chaudhary"},{"name":"Jonnie Penn","slug":"jonnie-penn"}],"date":"2024-12-30","section":null,"issue":"specialissue5","doi":"10.1162/99608f92.21e6bbaa","abstract":"The rapid proliferation of large language models (LLMs) invites the possibility of a new marketplace for behavioral and psychological data that signals intent. This brief article introduces some initial features of that emerging marketplace. We survey recent efforts by tech executives to position the capture, manipulation, and commodification of human intentionality as a lucrative parallel to—and viable extension of—","keywords":["intention economy","attention economy","large language models","generative AI","persuasive technology"],"image":"/media/remote/9219879c3b085dae-720.webp","collections":[],"label":"Article"},{"id":"0705d8bd-320a-4a6b-afaa-1b786ea94d75","slug":"hiii8fyn","title":"How Can Large Language Models Help Humans in Design And Manufacturing? Part 2: Synthesizing an End-to-End LLM-Enabled Design and Manufacturing Workflow","dek":null,"authors":[{"name":"Liane Makatura","slug":"liane-makatura"},{"name":"Michael Foshey","slug":"michael-foshey"},{"name":"Bohan Wang","slug":"bohan-wang"},{"name":"Felix Hähnlein","slug":"felix-hahnlein"},{"name":"Pingchuan Ma","slug":"pingchuan-ma"},{"name":"Bolei Deng","slug":"bolei-deng"},{"name":"Megan Tjandrasuwita","slug":"megan-tjandrasuwita"},{"name":"Andrew Spielberg","slug":"andrew-spielberg"},{"name":"Crystal Owens","slug":"crystal-owens"},{"name":"Peter Yichen Chen","slug":"peter-yichen-chen"},{"name":"Allan Zhao","slug":"allan-zhao"},{"name":"Amy Zhu","slug":"amy-zhu"},{"name":"Wil Norton","slug":"wil-norton"},{"name":"Edward Gu","slug":"edward-gu-2"},{"name":"Joshua Jacob","slug":"joshua-jacob"},{"name":"Yifei Li","slug":"yifei-li"},{"name":"Adriana Schulz","slug":"adriana-schulz"},{"name":"Wojciech Matusik","slug":"wojciech-matusik"}],"date":"2024-12-23","section":null,"issue":"specialissue5","doi":"10.1162/99608f92.0705d8bd","abstract":"Generative AI models provide foundational knowledge and sufficient reasoning to aid in individual aspects of a computational design and modeling workflow (see Part 1). In this work, we analyze the ability of state-of-the-art LLMs (in particular GPT-4) to reason about the entire end-to-end workflow, from conceptualizing to realization, for two target domains: static physical objects (here, furniture), and dynamical cy","keywords":["large language models","GPT-4","computational design","computational fabrication","CAD","CAM","design for manufacturing"],"image":"/media/remote/a2b6166bcaf72e68-720.webp","collections":[],"label":"Article"},{"id":"cc80fe30-d568-41e1-ae8f-80b3ea312067","slug":"15nqmdzl","title":"How Can Large Language Models Help Humans in Design and Manufacturing? Part 1: Elements of the LLM-Enabled Computational Design and Manufacturing Pipeline","dek":null,"authors":[{"name":"Liane Makatura","slug":"liane-makatura"},{"name":"Michael Foshey","slug":"michael-foshey"},{"name":"Bohan Wang","slug":"bohan-wang"},{"name":"Felix Hähnlein","slug":"felix-hahnlein"},{"name":"Pingchuan Ma","slug":"pingchuan-ma"},{"name":"Bolei Deng","slug":"bolei-deng"},{"name":"Megan Tjandrasuwita","slug":"megan-tjandrasuwita"},{"name":"Andrew Spielberg","slug":"andrew-spielberg"},{"name":"Crystal Owens","slug":"crystal-owens"},{"name":"Peter Yichen Chen","slug":"peter-yichen-chen"},{"name":"Allan Zhao","slug":"allan-zhao"},{"name":"Amy Zhu","slug":"amy-zhu"},{"name":"Wil Norton","slug":"wil-norton"},{"name":"Edward Gu","slug":"edward-gu-2"},{"name":"Joshua Jacob","slug":"joshua-jacob"},{"name":"Yifei Li","slug":"yifei-li"},{"name":"Adriana Schulz","slug":"adriana-schulz"},{"name":"Wojciech Matusik","slug":"wojciech-matusik"}],"date":"2024-12-23","section":null,"issue":"specialissue5","doi":"10.1162/99608f92.cc80fe30","abstract":"The advancement of large language models (LLMs), including GPT-4, provides exciting new opportunities for generative design. We investigate the application of this tool across the entire design and manufacturing workflow. Specifically, we scrutinize the utility of LLMs in tasks such as: converting a text-based prompt into a design specification, transforming a design into manufacturing instructions, producing a desig","keywords":["large language models","GPT-4","computational design","computational fabrication","CAD","CAM","design for manufacturing"],"image":"/media/remote/eb506abcfcdde9e1-720.webp","collections":[],"label":"Article"},{"id":"19d78478-7b33-499b-9e2b-055a63c49f56","slug":"3rvlzjtw","title":"Effective Generative AI: The Human-Algorithm Centaur","dek":null,"authors":[{"name":"Soroush Saghafian","slug":"soroush-saghafian"},{"name":"Lihi Idan","slug":"lihi-idan"}],"date":"2024-12-19","section":"milestones-and-millstones","issue":"volume6issue4","doi":"10.1162/99608f92.19d78478","abstract":"Advanced analytics science methods have enabled combining the power of artificial and human intelligence, creating centaurs that allow superior decision-making. Centaurs are hybrid human-algorithm models that combine both formal analytics and human intuition in a symbiotic manner within their learning and reasoning process. We argue that the future of AI development and use in many domains needs to focus more on cent","keywords":["centaurs","generative AI","large language models","human-AI algorithms"],"image":"/media/remote/be1ea816c74996ab-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"91162b2e-7dd1-4cdb-917c-e562ee92eadd","slug":"150vm8ft","title":"Data Protection and Generative AI: Inconclusive Answers to Unresolved Questions","dek":null,"authors":[{"name":"Romina Garrido","slug":"romina-garrido"}],"date":"2024-12-13","section":null,"issue":"specialissue5","doi":"10.1162/99608f92.91162b2e","abstract":"The article briefly discusses the challenges of data protection and the possible responses that this discipline can provide to generative AI regulations.Generative AI refers to those AI tools capable of creating text, images, music, audio, and videos. These systems use various machine learning techniques that draw inferences from patterns and relationships contained in the large data sets on which they are trained. S","keywords":["generative AI","data protection","algorithms","privacy"],"image":"/media/remote/afa9b6a8b887644c-720.webp","collections":[],"label":"Policy Forum"},{"id":"be4e28f0-3ecd-40fa-931a-ac749f30061c","slug":"vn2v6ety","title":"The Double-Edged Sword of AI: How Generative Language Models Like Google Bard and ChatGPT Pose a Threat to Countering Hate and Misinformation Online","dek":null,"authors":[{"name":"Center for Countering Digital Hate","slug":"center-for-countering-digital-hate"}],"date":"2024-12-11","section":null,"issue":"specialissue5","doi":"10.1162/99608f92.be4e28f0","abstract":"The year 2023 saw a huge leap forward in the capabilities of generative AI models for widespread use by the public. This article by the Center for Countering Digital Hate, originally published in April 2023, explores the potential for generative AI to be misused by malicious actors, and puts forward a set of guiding principles to assess efforts to implement guardrails for public safety.","keywords":["AI","generative","regulation","online","hate","misinformation"],"image":"/media/remote/b648b42bac3e78c6-720.webp","collections":[],"label":"Policy Forum"},{"id":"ad8ebbd4-d909-4f1a-9153-26543b089343","slug":"1yo82mqa","title":"Toward a Theory of AI Errors: Making Sense of Hallucinations, Catastrophic Failures, and the Fallacy of Generative AI","dek":null,"authors":[{"name":"Veronica Barassi","slug":"veronica-barassi"}],"date":"2024-11-25","section":null,"issue":"specialissue5","doi":"10.1162/99608f92.ad8ebbd4","abstract":"The rise of generative AI confronts us with new and key questions about AI failure, and how we make sense of and learn how to coexist with it. While computer scientists understand AI failure as something that we can learn from and predict, in this article I argue that we need to understand AI failure as a complex social reality that is defined by the interconnection between our data, technological design, and structu","keywords":["AI failure","AI hallucination","theory of errors","foundational models","anthropology"],"image":"/media/remote/aba8bc5d90723d45-720.webp","collections":[],"label":"Article"},{"id":"5dbf3265-17ca-4a2a-a199-6a18da40023d","slug":"f90vid3h","title":"An Information-Theoretic Approach for Detecting Edits in AI-Generated Text","dek":null,"authors":[{"name":"Idan Kashtan","slug":"idan-kashtan"},{"name":"Alon Kipnis","slug":"alon-kipnis"}],"date":"2024-11-08","section":null,"issue":"specialissue5","doi":"10.1162/99608f92.5dbf3265","abstract":"We propose a method to determine whether a given article was written entirely by a generative language model or perhaps contains edits by a different author, possibly a human. Our process involves multiple tests for the origin of individual sentences or other pieces of text and combining these tests using a method sensitive to alternatives in which non-null effects are few and scattered across the text in unknown loc","keywords":["generative language model","authorship analysis","information theory","multiple comparisons","sparsity","higher criticism"],"image":"/media/remote/cae8772d47e5c01e-720.webp","collections":[],"label":"Article"},{"id":"71926d9f-8424-4f91-b1a9-764511ef86b5","slug":"gwwvqf6y","title":"AI Has Won Nobel Prizes in Hard Science: Can Humans Be Smarter—and Softer on Each Other?","dek":null,"authors":[{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2024-10-30","section":"editorscolumn","issue":"volume6issue4","doi":"10.1162/99608f92.71926d9f","abstract":null,"keywords":[],"image":"/media/remote/abfa8eeca8e86305-720.webp","collections":[],"label":"Editor’s Column"},{"id":"a10bcaeb-b052-402b-b888-14300fa1535b","slug":"v54ekwyy","title":"AI and Elections: A Conversation With Secretary Steve Simon of Minnesota","dek":"An interview with Steve Simon by Xiao-Li Meng and Liberty Vittert.","authors":[{"name":"Steve Simon","slug":"steve-simon"},{"name":"Xiao-Li Meng","slug":"xiao-li-meng"},{"name":"Liberty Vittert","slug":"liberty-vittert"}],"date":"2024-10-30","section":null,"issue":"volume6issue4","doi":"10.1162/99608f92.a10bcaeb","abstract":"Harvard Data Science Review’s Founding Editor-in-Chief, Xiao-Li Meng, and Media Feature Editor, Liberty Vittert, interviewed Minnesota’s 22nd Secretary of State, Steve Simon, on the topic of elections and the role of secretary of state offices. As secretary of state, Secretary Simon works alongside township, city, and county officials to organize election efforts and ensure the election system is fair.The conversatio","keywords":[],"image":"/media/remote/792d2b5115001459-720.webp","collections":["2024-election","conversations-with-leaders"],"label":"2024 Election"},{"id":"91566bf7-7875-49ef-b7fa-4f34974c525f","slug":"u8mghtzb","title":"Predicting the 2024 Presidential Election","dek":null,"authors":[{"name":"Liberty Vittert","slug":"liberty-vittert"},{"name":"Ryan D. Enos","slug":"ryan-d-enos"},{"name":"Stephen Ansolabehere","slug":"stephen-ansolabehere"}],"date":"2024-10-30","section":null,"issue":"volume6issue4","doi":"10.1162/99608f92.91566bf7","abstract":null,"keywords":[],"image":"/media/remote/ac94060f4a5183d0-720.webp","collections":["2024-election"],"label":"2024 Election"},{"id":"a919e3fa-6374-41a0-9f5d-7ecdde8c4497","slug":"yoa73r1m","title":"Grappling With Uncertainty in Forecasting the 2024 U.S. Presidential Election","dek":null,"authors":[{"name":"Andrew Gelman","slug":"andrew-gelman"},{"name":"Ben Goodrich","slug":"ben-goodrich"},{"name":"Geonhee Han","slug":"geonhee-han"}],"date":"2024-10-30","section":"panorama","issue":"volume6issue4","doi":"10.1162/99608f92.a919e3fa","abstract":"We discuss acknowledged and unacknowledged sources of uncertainty in The Economist magazine’s state-by-state election forecast.Four years ago we worked with The Economist magazine to produce a state-by-state election forecast, combining national polls, state polls, economic and political “fundamentals,” and a hierarchical Bayesian model allowing for correlation among states, variation over time, and sampling and nons","keywords":["Bayesian inference","election forecasting","political science","poll aggregation","statistical communication"],"image":"/media/remote/0e7daeaf6b68a313-720.webp","collections":["2024-election"],"label":"Panorama"},{"id":"a390dc78-097d-41ee-81e5-12b5a9ab9874","slug":"kcnosyqm","title":"The Keys to the White House: Predicting the 2024 Winner","dek":null,"authors":[{"name":"Allan Lichtman","slug":"allan-lichtman"}],"date":"2024-10-30","section":"panorama","issue":"volume6issue4","doi":"10.1162/99608f92.a390dc78","abstract":"This article revisits my predictive model, the Keys to the White House, which I presented to HDSR readers 4 years ago. In 2020, the model predicted that Joe Biden would defeat Donald Trump, primarily due to Trump’s failed response to the COVID-19 pandemic. This article demonstrates that the Keys model remains valid despite the turmoil of Trump’s felony convictions, Biden’s withdrawal from the election, and the nomina","keywords":["prediction","keys","White House","election","Trump","Biden","Harris"],"image":"/media/remote/7881718464e5c75f-720.webp","collections":["2024-election"],"label":"Panorama"},{"id":"717c3fe5-a8cd-43ef-9c7c-ff26874e756f","slug":"p3jtey5d","title":"Some Early History of Data Security","dek":null,"authors":[{"name":"Stephen M. Stigler","slug":"steven-stigler"}],"date":"2024-10-30","section":"mining-the-past","issue":"volume6issue4","doi":"10.1162/99608f92.717c3fe5","abstract":null,"keywords":[],"image":"/media/remote/597e3a867ce1c1f8-720.webp","collections":[],"label":"Mining the Past"},{"id":"4a1751ea-8e50-48d4-9dd1-cf3f5e8ad57d","slug":"kmfrhgcx","title":"How Data Science Can Be Started From Your School’s Yearbook","dek":null,"authors":[{"name":"Anthony Shen","slug":"anthony-shen"}],"date":"2024-10-30","section":"minding-the-future","issue":"volume6issue4","doi":"10.1162/99608f92.4a1751ea","abstract":null,"keywords":[],"image":"/media/remote/51cb00c2752c325b-720.webp","collections":[],"label":"Minding the Future"},{"id":"f5dcab1a-f963-482b-beff-2d0a366c0390","slug":"x5yq8vmk","title":"The Art of Randomness: Sampling and Chance in the Age of Algorithmic Reproduction","dek":null,"authors":[{"name":"Johan Ugander","slug":"johan-ugander"},{"name":"Ziv Epstein","slug":"ziv-epstein"}],"date":"2024-10-30","section":"recreations-in-randomness","issue":"volume6issue4","doi":"10.1162/99608f92.f5dcab1a","abstract":null,"keywords":[],"image":"/media/remote/b004cc33b68a0642-720.webp","collections":[],"label":"Recreations in Randomness"},{"id":"21328ce3-c08b-47a6-91dd-c7922ffb8a1b","slug":"jn90uuur","title":"Introducing Reproducible Research Standards at the World Bank","dek":null,"authors":[{"name":"Maria Jones","slug":"maria-jones"}],"date":"2024-10-30","section":"reinforcing-rr","issue":"volume6issue4","doi":"10.1162/99608f92.21328ce3","abstract":null,"keywords":[],"image":"/media/remote/0b57dca6aac23055-720.webp","collections":[],"label":"Reinforcing Reproducibility and Replicability"},{"id":"19b0a307-b262-4702-9b07-76b756487882","slug":"ixq2bf38","title":"Murder at Polling Manor","dek":null,"authors":[{"name":"Michael A. Bailey","slug":"michael-bailey-2"}],"date":"2024-10-30","section":"bits-and-bites","issue":"volume6issue4","doi":"10.1162/99608f92.19b0a307","abstract":null,"keywords":[],"image":"/media/remote/ac91aaef8710cc3e-720.webp","collections":["2024-election"],"label":"Bits and Bites"},{"id":"ccb395f0-59bc-426c-a87d-10aa2a03864b","slug":"zr6hjsfl","title":"Election Night Forecasting With DDHQ: A Real-Time Predictive Framework","dek":null,"authors":[{"name":"Zachary Donnini","slug":"zachary-donnini"},{"name":"Sydney Louit","slug":"sydney-louit"},{"name":"Shelby Wilcox","slug":"shelby-wilcox"},{"name":"Mukul Ram","slug":"mukul-ram"},{"name":"Patrick McCaul","slug":"patrick-mccaul"},{"name":"Arianwyn Frank","slug":"arianwyn-frank"},{"name":"Matt Rigby","slug":"matt-rigby"},{"name":"Max Gowins","slug":"max-gowins"},{"name":"Scott Tranter","slug":"scott-tranter"}],"date":"2024-10-18","section":"cornucopia","issue":"volume6issue4","doi":"10.1162/99608f92.ccb395f0","abstract":"The Decision Desk HQ (DDHQ) team has built an innovative Live Primary Model for real-time prediction of primary election outcomes. This article provides a comprehensive overview of the model’s methodology, which combines live vote reporting, geospatial data, and demographic information to estimate candidates’ winning probabilities and expected vote shares. The model focuses on completed and nearly completed voting co","keywords":["elections","forecasting","modeling","politics"],"image":"/media/remote/e845727b67ee6403-720.webp","collections":["2024-election"],"label":"Cornucopia"},{"id":"62881547-fb32-4399-8bf0-6009d42fd40d","slug":"0c5ylgo1","title":"Forecasting Turnout","dek":null,"authors":[{"name":"Stephen Ansolabehere","slug":"stephen-ansolabehere"},{"name":"Jacob Brown","slug":"jacob-brown"},{"name":"Kabir Khanna","slug":"kabir-khanna"},{"name":"Connor Phillips","slug":"connor-phillips"},{"name":"Charles Stewart III","slug":"charles-stewart-iii"}],"date":"2024-10-17","section":"milestones-and-millstones","issue":"volume6issue4","doi":"10.1162/99608f92.62881547","abstract":"We evaluate the predictive power of the leading explanatory models of turnout in the academic literature. We compare the power of using registration, lagged vote, demographics, electoral competition, and early vote data to predict turnout. We specify models to capture each of these approaches to understanding turnout, fit those models to the relevant data from prior elections, and use the estimated parameters from pr","keywords":["Elections","turnout","prediction","registration","modeling"],"image":"/media/remote/819de9a9cfa94bf9-720.webp","collections":["2024-election"],"label":"Milestones and Millstones"},{"id":"4a42495c-5a70-486c-af01-b639595ce882","slug":"xdukxlpp","title":"‘Frontier AI,’ Power, and the Public Interest: Who Benefits, Who Decides?","dek":null,"authors":[{"name":"David Leslie","slug":"david-leslie"},{"name":"Carolyn Ashurst","slug":"carolyn-ashurst"},{"name":"Natalia Menéndez González","slug":"natalia-menendez-gonzalez"},{"name":"Frances Griffiths","slug":"frances-griffiths"},{"name":"Smera Jayadeva","slug":"smera-jayadeva"},{"name":"Mackenzie Jorgensen","slug":"mackenzie-jorgensen"},{"name":"Michael Katell","slug":"michael-katell"},{"name":"Shyam Krishna","slug":"shyam-krishna"},{"name":"Doschmund Kwiatkowski","slug":"doschmund-kwiatkowski"},{"name":"Carolina Iglésias Martins","slug":"carolina-iglesias-martins"},{"name":"Sabeehah Mahomed","slug":"sabeehah-mahomed"},{"name":"Carlos Mougan","slug":"carlos-mougan"},{"name":"Shachi Pandit","slug":"shachi-pandit"},{"name":"Mark Richey","slug":"mark-richey"},{"name":"Joseph W. Sakshaug","slug":"joseph-w-sakshaug"},{"name":"Shannon Vallor","slug":"shannon-vallor"},{"name":"Luke Vilain","slug":"luke-vilain"}],"date":"2024-09-09","section":null,"issue":"specialissue5","doi":"10.1162/99608f92.4a42495c","abstract":"As the rapid industrialization of generative AI (GenAI) reached a crescendo in the fall of 2023, a series of international AI policy initiatives, like the UK AI Safety Summit and G7’s Hiroshima Process, cropped up as a response to corresponding global AI governance challenges. The policymakers and government officials who drove these initiatives emphasized that the rise of ‘frontier AI’ technologies was bringing huma","keywords":["generative AI","frontier models","AI ethics and governance","public interest","power","public utility"],"image":"/media/remote/ba9236d06caa5862-720.webp","collections":[],"label":"Policy Forum"},{"id":"2be2c754-3807-42e2-9cfb-9fbf27ea609d","slug":"wwelf587","title":"Amid Advancement, Apprehension, and Ambivalence: AI in the Human Ecosystem","dek":"An interview with David Banks, Michael Jordan, Sabina Leonelli, and Martha Minow by Francine Berman.","authors":[{"name":"Francine Berman","slug":"francine-berman"},{"name":"David Banks","slug":"david-banks"},{"name":"Michael I. Jordan","slug":"michael-i-jordan"},{"name":"Sabina Leonelli","slug":"sabina-leonelli"},{"name":"Martha Minow","slug":"martha-minow"}],"date":"2024-09-04","section":"panorama","issue":"volume6issue3","doi":"10.1162/99608f92.2be2c754","abstract":"This panel was part of the 5th Anniversary Symposium of Harvard Data Science Review—AI and Data Science: Integrating Artificial and Human Ecosystems. The panel explored AI’s impact on society, the intriguing and the scary, as well as the intersection of AI and the public interest. At this moment in history, it’s important for us to understand not just the power and potential of AI but the peril as well. In the face o","keywords":["AI","tech and society","social impact","ethics","markets","inequity"],"image":"/media/remote/222609a7fc1c9f40-720.webp","collections":[],"label":"Panorama"},{"id":"f81142cc-ad9e-41dd-9633-fb15304eade9","slug":"cdubc6qo","title":"Where Data Science and the Disciplines Meet: Innovations in Linking Doctoral Students With Masters-Level Data Science Education","dek":null,"authors":[{"name":"Doreet Preiss","slug":"doreet-preiss"},{"name":"Jessica Sperling","slug":"jessica-sperling"},{"name":"Ryan M. Huang","slug":"ryan-huang"},{"name":"Kyle Bradbury","slug":"kyle-bradbury"},{"name":"Thomas Nechyba","slug":"thomas-nechyba"},{"name":"Robert Calderbank","slug":"robert-calderbank"},{"name":"Gregory Herschlag","slug":"gregory-herschlag"},{"name":"Jana Schaich Borg","slug":"jana-schaich-borg"}],"date":"2024-08-21","section":"stepping-stones","issue":"volume6issue4","doi":"10.1162/99608f92.f81142cc","abstract":"Although the need for data science methodological training is widely recognized across many disciplines, data science training is often absent from PhD programs. At the same time, master’s-level data science educational programs have seen incredible growth and investment. In 2018, Duke University initiated a National Science Foundation (NSF)-funded program to determine whether master’s-level data science programs tha","keywords":["data science","doctoral education","master’s education","interdisciplinary","capstone","collaborative research"],"image":"/media/remote/186a5d8fd48d4beb-720.webp","collections":[],"label":"Stepping Stones"},{"id":"fbdf6128-6d29-4db5-ba78-9646be529f0a","slug":"fscsqwx4","title":"Carbon Emissions in the Tailpipe of Generative AI","dek":null,"authors":[{"name":"Tamara Kneese","slug":"tamara-kneese"},{"name":"Meg Young","slug":"meg-young"}],"date":"2024-08-20","section":null,"issue":"specialissue5","doi":"10.1162/99608f92.fbdf6128","abstract":"This article responds to the call for exploring the wider societal risks and impacts of generative AI, particularly its environmental costs. Through a review of the available evidence on large language model’s (LLM) carbon and water costs, we point out that generative AI technologies are distinctly resource intensive. We argue that the field must reframe the scope of machine learning research and development to inclu","keywords":["artificial intelligence","carbon emissions","climate change","environmental justice"],"image":"/media/remote/2dc47bac6b595337-720.webp","collections":[],"label":"Policy Forum"},{"id":"68c7973b-8e1e-4531-9ba4-de0588bb8eb2","slug":"pzkwyen2","title":"Toward Generalizing Inferences From Trials to Target Populations","dek":null,"authors":[{"name":"Melody Huang","slug":"melody-huang"},{"name":"Harsh Parikh","slug":"harsh-parikh"}],"date":"2024-08-16","section":"panorama","issue":"volume6issue4","doi":"10.1162/99608f92.68c7973b","abstract":"Randomized controlled trials (RCTs) are pivotal in generating internally valid estimates with minimal assumptions, serving as a cornerstone for researchers dedicated to advancing causal inference methods. However, extending these findings beyond the experimental cohort to achieve externally valid estimates is crucial for broader scientific inquiry. This article delves into the forefront of addressing these external v","keywords":["causal inference","generalizability","external validity","machine learning"],"image":"/media/remote/72848a3ecd32c250-720.webp","collections":[],"label":"Panorama"},{"id":"ec74a002-afbd-4d5e-8f2f-0a688463df2c","slug":"jnml28pl","title":"Can ChatGPT Plan Your Retirement?: Generative AI and Financial Advice","dek":null,"authors":[{"name":"Andrew W. Lo","slug":"andrew-lo"},{"name":"Jillian Ross","slug":"jillian-ross"}],"date":"2024-08-06","section":null,"issue":"specialissue5","doi":"10.1162/99608f92.ec74a002","abstract":"We identify some of the most pressing issues facing the adoption of large language models (LLMs) in practical settings, and propose a research agenda to reach the next technological inflection point in generative AI. We focus on three challenges facing most LLM applications: domain-specific expertise and the ability to tailor that expertise to a user’s unique situation, trustworthiness and adherence to the user’s mor","keywords":["financial advisors","robo-advisors","financial technology","generative AI","large language models","ChatGPT"],"image":"/media/remote/d7badc08a81c1e96-720.webp","collections":[],"label":"Article"},{"id":"6d2aec03-9abf-40cf-9567-1fffe8cd1c04","slug":"erlwgmgx","title":"Bringing Students’ Lives Into Data Science Classrooms","dek":null,"authors":[{"name":"David Weintrop","slug":"david-weintrop"},{"name":"Rotem Israel-Fishelson","slug":"rotem-israel-fishelson"}],"date":"2024-08-06","section":"minding-the-future","issue":"volume6issue3","doi":"10.1162/99608f92.6d2aec03","abstract":null,"keywords":[],"image":"/media/remote/60b8099cea86f47d-720.webp","collections":[],"label":"Minding the Future"},{"id":"4eccc443-f682-46fb-aa80-22fa07daa05b","slug":"4brnqfcf","title":"We Should Do More Direct Replications in Science","dek":null,"authors":[{"name":"Stuart Buck","slug":"stuart-buck"}],"date":"2024-08-01","section":"reinforcing-rr","issue":"volume6issue3","doi":"10.1162/99608f92.4eccc443","abstract":null,"keywords":[],"image":"/media/remote/100cb48325026ace-720.webp","collections":[],"label":"Reinforcing Reproducibility and Replicability"},{"id":"9a53b923-6879-4704-a091-7f05a1a933b4","slug":"axvcupj4","title":"To Be a Frequentist or Bayesian? Five Positions in a Spectrum","dek":null,"authors":[{"name":"Hanti Lin","slug":"hanti-lin"}],"date":"2024-08-01","section":"meta-data-science","issue":"volume6issue3","doi":"10.1162/99608f92.9a53b923","abstract":null,"keywords":[],"image":"/media/remote/83d2eb0fc88bf1ea-720.webp","collections":[],"label":"Meta Data Science"},{"id":"cfd61a5a-7f8f-41fd-8315-c9256e941a45","slug":"eilrje2j","title":"How To Foster a Civic Research Community: Lessons From Greater Boston's Annual Insight-to-Impact Summit","dek":null,"authors":[{"name":"Daniel T. O’Brien","slug":"daniel-t-o-brien"},{"name":"Kimberly D. Lucas","slug":"kimberly-d-lucas"}],"date":"2024-08-01","section":"effective-policy-learning","issue":"volume6issue3","doi":"10.1162/99608f92.cfd61a5a","abstract":null,"keywords":[],"image":"/media/remote/a1047ea7292051f4-720.webp","collections":[],"label":"Effective Policy Learning"},{"id":"f9720d1f-f61e-4234-b891-f1fef7a122b9","slug":"4df1pl7f","title":"In-Class Data Analysis Replications: Teaching Students While Testing Science","dek":null,"authors":[{"name":"Kristina Gligorić","slug":"kristina-gligoric"},{"name":"Tiziano Piccardi","slug":"tiziano-piccardi"},{"name":"Jake M. Hofman","slug":"jake-hofman"},{"name":"Robert West","slug":"robert-west"}],"date":"2024-07-31","section":"stepping-stones","issue":"volume6issue3","doi":"10.1162/99608f92.f9720d1f","abstract":"Science is facing a reproducibility crisis. Overcoming it would require concerted efforts to replicate prior studies, but the incentives for researchers are currently weak, as replicating prior studies requires considerable time and effort without providing the same level of recognition as de novo research. Previous work has proposed incorporating data analysis replications into classrooms as a potential solution. Ho","keywords":["reproducibility","data analysis","education","open science","citizen science"],"image":"/media/remote/08b738df9f5405f2-720.webp","collections":[],"label":"Stepping Stones"},{"id":"6a5d7a87-32a8-425b-b7d1-8238e1772a5a","slug":"4if53bjq","title":"Differentially Private Linear Regression With Linked Data","dek":null,"authors":[{"name":"Shurong Lin","slug":"shurong-lin"},{"name":"Elliot Paquette","slug":"elliot-paquette"},{"name":"Eric D. Kolaczyk","slug":"eric-kolaczyk"}],"date":"2024-07-31","section":"milestones-and-millstones","issue":"volume6issue3","doi":"10.1162/99608f92.6a5d7a87","abstract":"There has been increasing demand for establishing privacy-preserving methodologies for modern statistics and machine learning. Differential privacy, a mathematical notion from computer science, is a rising tool offering robust privacy guarantees. Recent work focuses primarily on developing differentially private versions of individual statistical and machine learning tasks, with nontrivial upstream preprocessing typi","keywords":["differential privacy","record linkage","data integration","privacy-preserving record linkage","gradient descent"],"image":"/media/remote/381ac51da90b9852-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"f3abeae2-f3ee-44ee-af45-ca82488e42fd","slug":"uzk6esyk","title":"From COVID-19 to GPT-4o: The Groundbreaking Quinquennium for Harvard Data Science Review (and Humanity)","dek":null,"authors":[{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2024-07-31","section":"editorscolumn","issue":"volume6issue3","doi":"10.1162/99608f92.f3abeae2","abstract":null,"keywords":[],"image":"/media/remote/99c31db4f29e0bd4-720.webp","collections":[],"label":"Editor’s Column"},{"id":"2781452d-49a4-4a99-ba64-ad2b314544fe","slug":"qh3dbdm9","title":"Is ChatGPT More Biased Than You?","dek":null,"authors":[{"name":"Golnoosh Babaei","slug":"golnoosh-babaei"},{"name":"David Banks","slug":"david-banks"},{"name":"Costanza Bosone","slug":"costanza-bosone"},{"name":"Paolo Giudici","slug":"paolo-giudici"},{"name":"Yunhong Shan","slug":"yunhong-shan"}],"date":"2024-07-31","section":"bits-and-bites","issue":"volume6issue3","doi":"10.1162/99608f92.2781452d","abstract":"AI is changing the world in ways that are difficult to forecast, but the impact will surely be enormous. Large language models (LLMs) are the most recent AI system that has captured the public eye. The rise of AI and LLMs offers efficiency and assistance, but raises questions of job loss, fairness, and societal norms. Bias is a significant challenge. Educational reforms are needed, and legal frameworks must adapt to ","keywords":["economics","education","ethics","large language models"],"image":"/media/remote/5b6da50435a3fffb-720.webp","collections":[],"label":"Bits and Bites"},{"id":"b1233596-cdbf-4eea-8cd3-3681249bf62d","slug":"srq23h80","title":"Data Science Learning in Grades K–12: Synthesizing Research Across Divides","dek":null,"authors":[{"name":"Joshua Rosenberg","slug":"joshua-rosenberg"},{"name":"Ryan Seth Jones","slug":"ryan-seth-jones"}],"date":"2024-07-31","section":"stepping-stones","issue":"volume6issue3","doi":"10.1162/99608f92.b1233596","abstract":"What do we know about data science learning at the grades K–12 (precollegiate) level? This article answers this question by using the notion of agency to provide a framework to review the diverse research agendas and learning environments relevant to data science education. Examining research on data science education published in three recent special issues, we highlight key findings from scholars working in differe","keywords":["data science education","grades K–12","literature review","co-citation coupling"],"image":"/media/remote/9b12bc1d7b8c6424-720.webp","collections":[],"label":"Stepping Stones"},{"id":"cdebd845-71e0-4724-b984-d1cb3796334e","slug":"xyeriy3y","title":"Data Science and AI in Context: Summary and Insights","dek":null,"authors":[{"name":"Alfred Spector","slug":"alfred-spector"}],"date":"2024-07-31","section":"panorama","issue":"volume6issue3","doi":"10.1162/99608f92.cdebd845","abstract":"This article explores how to deploy data science and data-driven AI, focusing on the broad collection of considerations beyond those of statistics and machine learning. Building on an analysis rubric introduced in a 2022 textbook, Data Science in Context: Foundations, Challenges, and Opportunities (Spector et al., 2022), this article summarizes some of the book’s key points and adds reflections on AI’s extraordinary ","keywords":["data science","artificial intelligence","context","data science education","societal concerns","regulation"],"image":"/media/remote/0cafc67977958847-720.webp","collections":[],"label":"Panorama"},{"id":"740dcedb-a56d-4d76-b3a3-a76ece4c54ed","slug":"yhlm3wly","title":"Data Scientists Discuss AI Risks and Opportunities","dek":null,"authors":[{"name":"David Banks","slug":"david-banks"},{"name":"Gerard de Melo","slug":"gerard-de-melo"},{"name":"Sam (Xinwei) Gong","slug":"sam-xinwei-gong"},{"name":"Yongchan Kwon","slug":"yongchan-kwon"},{"name":"Cynthia Rudin","slug":"cynthia-rudin"}],"date":"2024-07-31","section":"panorama","issue":"volume6issue3","doi":"10.1162/99608f92.740dcedb","abstract":"This is an edited summary of a virtual panel conversation that took place on December 19, 2023, concerning the risks and benefits of AI systems . The many topics that are covered include AI impacts on education, the economy, cybercrime and warfare, autonomous vehicles, bias and fairness, and regulation. In addition, the role for data scientists is discussed. But the field is moving quickly, and some of the issues and","keywords":["autonomous vehicles","cybercrime","ethics","regulation"],"image":"/media/remote/447a499303cceb98-720.webp","collections":[],"label":"Panorama"},{"id":"8636cb81-70e2-46ce-acf4-9c9f886c71af","slug":"rzxsdufo","title":"A Self-Supervised Algorithm for Denoising Photoplethysmography Signals for Heart Rate Estimation From Wearables","dek":null,"authors":[{"name":"Pranay Jain","slug":"pranay-jain"},{"name":"Cheng Ding","slug":"cheng-ding"},{"name":"Cynthia Rudin","slug":"cynthia-rudin"},{"name":"Xiao Hu","slug":"xiao-hu"}],"date":"2024-07-31","section":"cornucopia","issue":"volume6issue3","doi":"10.1162/99608f92.8636cb81","abstract":"Smartwatches and other wearable devices are equipped with photoplethysmography (PPG) sensors for monitoring heart rate and other aspects of cardiovascular health. However, PPG signals collected from such devices are susceptible to corruption from noise and motion artifacts, resulting in inaccuracies during heart rate estimation. Conventional denoising methods filter or reconstruct signals in ways that eliminate morph","keywords":["unsupervised learning","denoising algorithm","heart rate detection","wearable medical devices","physiological signals"],"image":null,"collections":[],"label":"Cornucopia"},{"id":"1be2ab6e-392c-4c0d-bcbc-27c5886f5986","slug":"la3vitqm","title":"Large Language Models: Trust and Regulation","dek":"An interview with Costanza Bosone, Bob Carpenter, Tarak Shah, and Claudia Shi&nbsp;by David Banks","authors":[{"name":"David Banks","slug":"david-banks"},{"name":"Costanza Bosone","slug":"costanza-bosone"},{"name":"Bob Carpenter","slug":"bob-carpenter"},{"name":"Tarak Shah","slug":"tarak-shah"},{"name":"Claudia Shi","slug":"claudia-shi"}],"date":"2024-07-31","section":"panorama","issue":"volume6issue3","doi":"10.1162/99608f92.1be2ab6e","abstract":"Large language models have burst onto the scene, and may do much to change the way the world operates. They can write, can illustrate, and are rapidly adding new capabilities. This panel discussion brings together a group of experts to discuss ways in which these tools might evolve, particularly in the context of how trustworthy they are and the issues surrounding their regulation.","keywords":["disinformation","GPT","human rights","job loss","OpenAI","trust"],"image":"/media/remote/2c9b680eebfae5ac-720.webp","collections":[],"label":"Panorama"},{"id":"e81732b3-7ab6-449a-b1b9-2758cd83b26e","slug":"iyl87vz5","title":"Rejoinder to Discussion of \"Data Science at the Singularity\"","dek":null,"authors":[{"name":"David Donoho","slug":"david-donoho"}],"date":"2024-07-11","section":null,"issue":"volume6issue1","doi":"10.1162/99608f92.e81732b3","abstract":null,"keywords":[],"image":"/media/remote/b9727aae6694e4e5-720.webp","collections":[],"label":"Featured Discussion"},{"id":"3515c52d-1847-4612-a21e-2d2e83332d2a","slug":"5322mn1e","title":"A Review of Veridical Data Science by Bin Yu and Rebecca L. Barter","dek":null,"authors":[{"name":"Yuval Benjamini","slug":"yuval-benjamini"},{"name":"Yoav Benjamini","slug":"yoav-benjamini"}],"date":"2024-06-26","section":"bits-and-bites","issue":"volume6issue2","doi":"10.1162/99608f92.3515c52d","abstract":null,"keywords":[],"image":"/media/remote/d2a30f2339d3e8fa-720.webp","collections":[],"label":"Bits and Bites"},{"id":"562ff0f5-9447-4496-88bc-c17a52546d94","slug":"wz35dvpo","title":"AI Safety Is a Narrative Problem","dek":null,"authors":[{"name":"Rachel Coldicutt","slug":"rachel-coldicutt"}],"date":"2024-06-05","section":null,"issue":"specialissue5","doi":"10.1162/99608f92.562ff0f5","abstract":"This op-ed explores power and narrative dynamics around AI. Drawing on pop-culture references, the professional experiences of the author and examples from 2023’s “Great AI Safety Hype Roadshow,” this piece draws on the literary criticism technique of practical criticism to consider how speeches and announcements from both Silicon Valley executives and research scientists to interrogate the media-friendly nature of p","keywords":["technology","power","AI","narrative","media literacy"],"image":"/media/remote/71e39ca548899ffb-720.webp","collections":[],"label":"Policy Forum"},{"id":"d949f941-5200-4f54-a110-20abefd13974","slug":"w974bwb0","title":"Government Interventions to Avert Future Catastrophic AI Risks","dek":null,"authors":[{"name":"Yoshua Bengio","slug":"yoshua-bengio"}],"date":"2024-06-04","section":null,"issue":"specialissue5","doi":"10.1162/99608f92.d949f941","abstract":"This essay is a revised transcription of Yoshua Bengio’s July 2023 testimony in front of the U.S. Senate Subcommittee on Privacy, Technology, and the Law meeting on the topic of oversight of AI. It argues for caution and government interventions in regulation and research investments to mitigate the potentially catastrophic outcomes from future advances in AI as the technology approaches human-level cognitive abiliti","keywords":["artificial intelligence","AI safety","AI alignment","AI regulation","AI public policy","AI countermeasures"],"image":"/media/remote/ef0556f34c518ac0-720.webp","collections":[],"label":"Policy Forum"},{"id":"fad6d25c-2665-4713-936e-82c282d49b16","slug":"fblrvqes","title":"Future Shock: Grappling With the Generative AI Revolution","dek":null,"authors":[{"name":"David Leslie","slug":"david-leslie"},{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2024-05-31","section":null,"issue":"specialissue5","doi":"10.1162/99608f92.fad6d25c","abstract":null,"keywords":[],"image":"/media/remote/e6a825cc92ac6f3b-720.webp","collections":[],"label":"From the Editors"},{"id":"a50ec6e6-de81-4e95-8f46-d2f3bbc6f1bf","slug":"xau9dza3","title":"We Must Fix the Lack of Transparency Around the Data Used to Train Foundation Models","dek":null,"authors":[{"name":"Jack Hardinges","slug":"jack-hardinges"},{"name":"Elena Simperl","slug":"elena-simperl"},{"name":"Nigel Shadbolt","slug":"nigel-shadbolt"}],"date":"2024-05-31","section":null,"issue":"specialissue5","doi":"10.1162/99608f92.a50ec6e6","abstract":"Access to information about the data that used to train foundation AI models is vital for many tasks. Despite progress made by sections of the AI community, there remains a general lack of transparency about the content and sources of training data sets. Whether the result of voluntary initiative by firms or regulatory intervention, this has to change.Foundation models are trained on large collections of data, much o","keywords":["artificial intelligence","machine learning","foundation models","training data","transparency","trust"],"image":"/media/remote/6141c13f4ff570ac-720.webp","collections":[],"label":"Policy Forum"},{"id":"ff6335af-d847-4258-845a-8fabef97036e","slug":"ded4vcwl","title":"Scaling Up Mischief: Red-Teaming AI and Distributing Governance","dek":null,"authors":[{"name":"Jacob Metcalf","slug":"jacob-metcalf"},{"name":"Ranjit Singh","slug":"ranjit-singh-2"}],"date":"2024-05-31","section":null,"issue":"specialissue5","doi":"10.1162/99608f92.ff6335af","abstract":"Red-teaming is an emergent strategy for governing large language models (LLMs), which borrows heavily from cybersecurity methods. Policymakers and developers alike have leaned heavily into this promising, yet largely unvalidated approach for regulating generative AI. We argue that AI red-teaming efforts address a particular and unique moderation need of LLM developers: scaling up human mischievousness by inviting a w","keywords":["artificial intelligence","AI governance","red-teaming","large language models","generative AI","AI policy"],"image":"/media/remote/772890ab6b9ef570-720.webp","collections":[],"label":"Policy Forum"},{"id":"cdfc3092-b67c-48df-a119-d9937d4a3bbe","slug":"jqch53w5","title":"Castles in the Sand?: How the Public Sector and Academia Can Partner in Regulatory Sandboxes to Help Leverage Generative AI for Public Good","dek":null,"authors":[{"name":"Alex Moltzau","slug":"alex-moltzau-2"},{"name":"Robindra Prabhu","slug":"robindra-prabhu"}],"date":"2024-05-31","section":null,"issue":"specialissue5","doi":"10.1162/99608f92.cdfc3092","abstract":"The Norwegian public sector has been quick to explore generative AI. Early adoption and experimentation in the public sector can act as unique probes into both strengths and shortcomings of this technology, provided adequate mechanisms are in place to relate instructive feedback to public debates on how societies should govern the challenges that arise. We call for increased collaboration through public–academic cros","keywords":["generative AI","regulatory sandbox","Norway","public good"],"image":"/media/remote/dc751020a6e9f0b9-720.webp","collections":[],"label":"Policy Forum"},{"id":"e6245c19-5fa4-4569-9477-e7fc0959a7eb","slug":"3g71f91d","title":"Unpacking AI Governance From the Margins","dek":null,"authors":[{"name":"Shmyla Khan","slug":"shmyla-khan"}],"date":"2024-05-31","section":null,"issue":"specialissue5","doi":"10.1162/99608f92.e6245c19","abstract":"This position paper pushes against policy panics engendered by generative AI technologies, arguing that regulation that centers experiences from the Global South requires a more intersectional and inclusive approach.The proliferation of generative AI has been seen through the lens of a policy, societal, and ethical emergency, verging on panic in some circles—presented either in apocalyptic terms or as an unmitigated ","keywords":["Global South","AI regulation","tech policy"],"image":"/media/remote/7e1cb3df192770c9-720.webp","collections":[],"label":"Policy Forum"},{"id":"88b4cc98-9c6c-4104-b428-fac8b171f8e9","slug":"yixt9mqu","title":"Future Shock: Generative AI and the International AI Policy and Governance Crisis","dek":null,"authors":[{"name":"David Leslie","slug":"david-leslie"},{"name":"Antonella Maia Perini","slug":"antonella-maia-perini"}],"date":"2024-05-31","section":null,"issue":"specialissue5","doi":"10.1162/99608f92.88b4cc98","abstract":null,"keywords":[],"image":"/media/remote/fcff2720cc15c7da-720.webp","collections":[],"label":"Policy Forum"},{"id":"e360e42d-0e54-4d98-85a4-0a679afedb80","slug":"jo4xfs0s","title":"From Left Behind to Left Out: Generative AI or the Next Pain of the Unconnected","dek":null,"authors":[{"name":"Jean Louis Kedieng Ebongue Fendji","slug":"jean-louis-kedieng-ebongue-fendji"}],"date":"2024-05-31","section":null,"issue":"specialissue5","doi":"10.1162/99608f92.e360e42d","abstract":"There is no doubt that Generative AI (GAI) will affect the unconnected more than what is stressed in current AI debates. The fact that they are left behind in terms of internet access prevents them not only from accessing online information, but also from using GAI. GAI generates new material from online data, material that will be used again in the development of new GAI systems. Thus, the philosophy and reality con","keywords":["generative AI","unconnected","Internet access","discrimination","AI ethics","data"],"image":"/media/remote/d006c6cc6aabc0b9-720.webp","collections":[],"label":"Policy Forum"},{"id":"35a2f3ff-ff36-4c62-a1cc-d16cadc8f671","slug":"wy5a8llx","title":"The Turing Transformation: Artificial Intelligence, Intelligence Augmentation, and Skill Premiums","dek":null,"authors":[{"name":"Ajay Agrawal","slug":"ajay-agrawal"},{"name":"Joshua Gans","slug":"joshua-gans"},{"name":"Avi Goldfarb","slug":"avi-goldfarb"}],"date":"2024-05-31","section":null,"issue":"specialissue5","doi":"10.1162/99608f92.35a2f3ff","abstract":"We ask whether a technical objective of using human performance of tasks as a benchmark for AI performance will result in the negative outcomes highlighted in prior work in terms of jobs and inequality. Instead, we argue that task automation, especially when driven by AI advances, can enhance job prospects and potentially widen the scope for employment of many workers. The neglected mechanism we highlight is the pote","keywords":["artificial intelligence","automation","economics of technology","income inequality","jobs"],"image":"/media/remote/e0dcc0f091ad6b96-720.webp","collections":[],"label":"Article"},{"id":"a377379b-1b7a-4af3-87f9-fc10b331c670","slug":"14unjde2","title":"Toward International Cooperation on Foundational AI Models: An Expanded Role for Trade Agreements and International Economic Policy","dek":null,"authors":[{"name":"Joshua P. Meltzer","slug":"joshua-p-meltzer"}],"date":"2024-05-31","section":null,"issue":"specialissue5","doi":"10.1162/99608f92.a377379b","abstract":"Foundational AI models such as ChatGPT4 will have wide ranging and potentially large scale impacts on economic growth, social flourishing and national security. At the same time these models present risks. International AI governance is needed to support domestic regulation of foundational AI models, to address the spillovers and to develop the standards and compliance mechanisms that can build trust in the global di","keywords":["AI trade","LLMs","regulation","international risk"],"image":"/media/remote/d801bf1dda1dfa3f-720.webp","collections":[],"label":"Policy Forum"},{"id":"2c8e7e81-bd35-4a7d-99cf-284063b785af","slug":"1klv4t3q","title":"Institutional Efforts to Help Academic Researchers Implement Generative AI in Research","dek":null,"authors":[{"name":"Jing Liu","slug":"jing-liu"},{"name":"H. V. Jagadish","slug":"h-v-jagadish"}],"date":"2024-05-31","section":null,"issue":"specialissue5","doi":"10.1162/99608f92.2c8e7e81","abstract":"The scale and speed of the generative AI (artificial intelligence) revolution, while offering unprecedented opportunities to advance science, is also challenging the traditional academic research model in fundamental ways. The academic research model and academic institutions are not set up to be nimble in the face of rapidly advancing technologies, and the task of adopting such new technologies usually falls on indi","keywords":["institutional transformation","best practices","training","academic researcher","rigor and reproducibility","institutional support"],"image":"/media/remote/1e26bf78a34a237d-720.webp","collections":[],"label":"Article"},{"id":"8036d03b-47f2-4be4-b5e3-daae9d0ef1d1","slug":"aelql9qy","title":"AI Transparency in the Age of LLMs: A Human-Centered Research Roadmap","dek":null,"authors":[{"name":"Q. Vera Liao","slug":"q-vera-liao"},{"name":"Jennifer Wortman Vaughan","slug":"jennifer-wortman-vaughan"}],"date":"2024-05-31","section":null,"issue":"specialissue5","doi":"10.1162/99608f92.8036d03b","abstract":"The rise of powerful large language models (LLMs) brings about tremendous opportunities for innovation but also looming risks for individuals and society at large. We have reached a pivotal moment for ensuring that LLMs and LLM-infused applications are developed and deployed responsibly. It is paramount to pursue new approaches to provide transparency—a central pillar of responsible artificial intelligence (AI)—for L","keywords":["LLMs","generative AI","transparency","explainability","human-centered AI"],"image":"/media/remote/8cffbc0e85dcddb8-720.webp","collections":[],"label":"Article"},{"id":"5cd4a32e-af15-46ff-8428-5e630da2358a","slug":"p6q84rww","title":"AI and Creative Work","dek":null,"authors":[{"name":"Seamus McGibbon","slug":"seamus-mcgibbon"},{"name":"Nicola Solomon","slug":"nicola-solomon"}],"date":"2024-05-31","section":null,"issue":"specialissue5","doi":"10.1162/99608f92.5cd4a32e","abstract":"The Creators’ Rights Alliance (CRA) is a coalition of 23 major creative organizations, trade associations, and unions, between them representing over 500,000 creator members and several million individuals working as creators in the United Kingdom’s creative industries.We believe that the sector is at serious risk of losing creative individuals, who will no longer choose to train and invest their own time and money i","keywords":["creators' rights","intellectual property","moral rights","performing rights","consent"],"image":"/media/remote/c4afb1de4dee899f-720.webp","collections":[],"label":"Policy Forum"},{"id":"fe8a9269-9a89-47d5-bfb7-efa4412ab260","slug":"d64xweyz","title":"An Alternate History for Machine Learning?","dek":null,"authors":[{"name":"Peter Rousseeuw","slug":"peter-rousseeuw"}],"date":"2024-05-24","section":null,"issue":"volume6issue1","doi":"10.1162/99608f92.fe8a9269","abstract":null,"keywords":[],"image":"/media/remote/481d524e66870515-720.webp","collections":[],"label":"Featured Discussion"},{"id":"bf3d6b1d-0bf9-4e33-994d-b9a07f8efe14","slug":"1sphh9gu","title":"Collective Intelligence and Collaborative Data Science","dek":null,"authors":[{"name":"Joseph Salmon","slug":"joseph-salmon"}],"date":"2024-05-24","section":null,"issue":"volume6issue1","doi":"10.1162/99608f92.bf3d6b1d","abstract":null,"keywords":[],"image":"/media/remote/05aa7e2aabc204e1-720.webp","collections":[],"label":"Featured Discussion"},{"id":"a8d932cc-d97b-44d2-8386-c931bebe89b3","slug":"k9gp9fzh","title":"Data Science at the Precipice","dek":null,"authors":[{"name":"Peyman Milanfar","slug":"peyman-milanfar"}],"date":"2024-05-24","section":null,"issue":"volume6issue1","doi":"10.1162/99608f92.a8d932cc","abstract":null,"keywords":[],"image":"/media/remote/2fa47b1ace9954f0-720.webp","collections":[],"label":"Featured Discussion"},{"id":"f0f013d4-ff1c-4f6d-b304-c73014a43d2e","slug":"8dqgwqiu","title":"The Mechanics of Frictionless Reproducibility","dek":null,"authors":[{"name":"Benjamin Recht","slug":"benjamin-recht"}],"date":"2024-05-24","section":null,"issue":"volume6issue1","doi":"10.1162/99608f92.f0f013d4","abstract":null,"keywords":[],"image":"/media/remote/e79ef8bc675edad2-720.webp","collections":[],"label":"Featured Discussion"},{"id":"0bd1cfba-87a4-4b1a-96a9-b2b95337202e","slug":"76epokt5","title":"Overcoming Potential Obstacles as We Strive for Frictionless Reproducibility","dek":null,"authors":[{"name":"Adam D. Schuyler","slug":"adam-d-schuyler"}],"date":"2024-05-24","section":null,"issue":"volume6issue1","doi":"10.1162/99608f92.0bd1cfba","abstract":null,"keywords":[],"image":"/media/remote/084d54beca1e2ff2-720.webp","collections":[],"label":"Featured Discussion"},{"id":"040d5523-8a21-4659-8b32-8a86296b2420","slug":"iu5dt359","title":"Hopes and Limitations of Reproducible Statistics and Machine Learning","dek":null,"authors":[{"name":"Andrew Gelman","slug":"andrew-gelman"}],"date":"2024-05-24","section":null,"issue":"volume6issue1","doi":"10.1162/99608f92.040d5523","abstract":null,"keywords":[],"image":"/media/remote/c5f5241a58da87b4-720.webp","collections":[],"label":"Featured Discussion"},{"id":"c03fd9da-f882-4451-9bc9-7dc08076d619","slug":"gjog8dd2","title":"Analytics Challenges and Their Challenges","dek":null,"authors":[{"name":"Stephen Ruberg","slug":"stephen-ruberg"}],"date":"2024-05-24","section":null,"issue":"volume6issue1","doi":"10.1162/99608f92.c03fd9da","abstract":null,"keywords":[],"image":"/media/remote/dca1e5822409dce9-720.webp","collections":[],"label":"Featured Discussion"},{"id":"5782e6b6-e4a5-48af-bacb-afc55b4f1e72","slug":"1wmpyju3","title":"Is Causal Inference Compatible With Frictionless Reproducibility?","dek":null,"authors":[{"name":"Keyon Vafa","slug":"keyon-vafa"}],"date":"2024-05-24","section":null,"issue":"volume6issue1","doi":"10.1162/99608f92.5782e6b6","abstract":null,"keywords":[],"image":"/media/remote/ff60e63b4ec29d42-720.webp","collections":[],"label":"Featured Discussion"},{"id":"dcaa63bc-d1ce-4d2b-8b6a-5b0363852b34","slug":"g6ngcphm","title":"On Emergent Limits to Knowledge—Or, How to Trust the Robot Researchers: A Pocket Guide","dek":null,"authors":[{"name":"Victoria Stodden","slug":"victoria-stodden"}],"date":"2024-05-24","section":null,"issue":"volume6issue1","doi":"10.1162/99608f92.dcaa63bc","abstract":null,"keywords":[],"image":"/media/remote/ff9c10f79f1aa20d-720.webp","collections":[],"label":"Featured Discussion"},{"id":"ea5e6f9a-ee54-4584-9a54-de4f7267219e","slug":"8qexde24","title":"After Computational Reproducibility: Scientific Reproducibility and Trustworthy AI","dek":null,"authors":[{"name":"Bin Yu","slug":"bin-yu"}],"date":"2024-05-24","section":null,"issue":"volume6issue1","doi":"10.1162/99608f92.ea5e6f9a","abstract":null,"keywords":[],"image":"/media/remote/d26eb993e35e2458-720.webp","collections":[],"label":"Featured Discussion"},{"id":"f86474d0-1bdd-4cb4-b271-cb79c7a862c6","slug":"cf8ogynt","title":"A Familiar, Invisible Engine Is Driving the AI Revolution","dek":null,"authors":[{"name":"Matan Gavish","slug":"matan-gavish"}],"date":"2024-05-24","section":null,"issue":"volume6issue1","doi":"10.1162/99608f92.f86474d0","abstract":null,"keywords":[],"image":"/media/remote/ec9743d84e489bd3-720.webp","collections":[],"label":"Featured Discussion"},{"id":"d73c0559-e9c8-46a0-964d-e5be0f7d2cac","slug":"7ncz09ji","title":"The Path to Frictionless Reproducibility Is Still Under Construction","dek":null,"authors":[{"name":"Lorena Barba","slug":"lorena-barba"}],"date":"2024-05-24","section":null,"issue":"volume6issue1","doi":"10.1162/99608f92.d73c0559","abstract":null,"keywords":[],"image":"/media/remote/a7cb76c357adff13-720.webp","collections":[],"label":"Featured Discussion"},{"id":"28953700-be0f-45dc-a161-40f999074518","slug":"4wk8b8ix","title":"The Singularity in Data and Computation-Driven Science: Can It Scale Beyond Machine Learning?","dek":null,"authors":[{"name":"Juliana Freire","slug":"juliana-freire"}],"date":"2024-05-24","section":null,"issue":"volume6issue1","doi":"10.1162/99608f92.28953700","abstract":null,"keywords":[],"image":"/media/remote/57562b4c459125f3-720.webp","collections":[],"label":"Featured Discussion"},{"id":"b7e4d3fa-086a-47fe-84b8-afc49f020f83","slug":"n52725sy","title":"Data Science Is an Ecosystem","dek":null,"authors":[{"name":"Terrence J. Sejnowski","slug":"terrence-j-sejnowski"}],"date":"2024-05-24","section":null,"issue":"volume6issue1","doi":"10.1162/99608f92.b7e4d3fa","abstract":null,"keywords":[],"image":"/media/remote/567faab33c9088cf-720.webp","collections":[],"label":"Featured Discussion"},{"id":"4d0c37ad-d4a7-48bc-b0e7-5e479d50a604","slug":"8zspsm1h","title":"Rethinking the Data Science Singularity","dek":null,"authors":[{"name":"Zhiwei Zhu","slug":"zhiwei-zhu"}],"date":"2024-05-24","section":null,"issue":"volume6issue1","doi":"10.1162/99608f92.4d0c37ad","abstract":null,"keywords":[],"image":"/media/remote/ee4710ed64d99dcf-720.webp","collections":[],"label":"Featured Discussion"},{"id":"07d15e5e-5a65-4689-9ee3-93f489057430","slug":"iqqginpo","title":"Metadata, Instrumentation, and Netsplaining","dek":null,"authors":[{"name":"Brian Wandell","slug":"brian-wandell"}],"date":"2024-05-24","section":null,"issue":"volume6issue1","doi":"10.1162/99608f92.07d15e5e","abstract":null,"keywords":[],"image":"/media/remote/6d1a54303a32bb41-720.webp","collections":[],"label":"Featured Discussion"},{"id":"f9f4b9a4-92da-48a0-bf0d-6bbf7e12af9c","slug":"ags1yyxb","title":"Rejoinder: We Can Improve the Usability of the Census Noisy Measurements File","dek":null,"authors":[{"name":"Cory McCartan","slug":"cory-mccartan"},{"name":"Tyler Simko","slug":"tyler-simko"},{"name":"Kosuke Imai","slug":"kosuke-imai"}],"date":"2024-05-07","section":null,"issue":"volume6issue2","doi":"10.1162/99608f92.f9f4b9a4","abstract":null,"keywords":[],"image":"/media/remote/1b2757efc2159dd9-720.webp","collections":[],"label":"Commentary"},{"id":"16558941-ed81-4515-8617-6d071d8e0db4","slug":"h9zf0lim","title":"Data Science Experiences and Emotions: From Lonesome to Awesome","dek":null,"authors":[{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2024-05-01","section":"editorscolumn","issue":"volume6issue2","doi":"10.1162/99608f92.16558941","abstract":null,"keywords":[],"image":"/media/remote/aad1bc81ac9e156a-720.webp","collections":[],"label":"Editor’s Column"},{"id":"6d4bf9eb-0411-45a3-9d92-34304fe5cf4b","slug":"nqwl2udq","title":"The Role of Third-Party Verification in Research Reproducibility","dek":null,"authors":[{"name":"Christophe Pérignon","slug":"christophe-perignon"}],"date":"2024-04-30","section":"reinforcing-rr","issue":"volume6issue2","doi":"10.1162/99608f92.6d4bf9eb","abstract":null,"keywords":[],"image":"/media/remote/ac664da8891a14b3-720.webp","collections":[],"label":"Reinforcing Reproducibility and Replicability"},{"id":"44fb5f2e-b2b6-4d57-b03c-8f572be4f760","slug":"ozztai78","title":"Needs and Opportunities for Data in Wine: Boots-on-the-Ground Solutions From Napa to Maryland","dek":null,"authors":[{"name":"Cathy Huyghe","slug":"cathy-huyghe"}],"date":"2024-04-30","section":"bits-and-bites","issue":"volume6issue2","doi":"10.1162/99608f92.44fb5f2e","abstract":null,"keywords":[],"image":"/media/remote/1234f246c499bdd7-720.webp","collections":[],"label":"Bits and Bites"},{"id":"7ca04767-39ca-4d43-80c1-288380746a42","slug":"7v1ii597","title":"In the Academy, Data Science Is Lonely: Barriers to Adopting Data Science Methods for Scientific Research","dek":null,"authors":[{"name":"Elle O'Brien","slug":"elle-obrien"},{"name":"Jordan Mick","slug":"jordan-mick"}],"date":"2024-04-30","section":"panorama","issue":"volume6issue2","doi":"10.1162/99608f92.7ca04767","abstract":"Data science has been heralded as a transformative family of methods for scientific discovery. Despite this excitement, putting these methods into practice in scientific research has proved challenging. We conducted a qualitative interview study of 25 researchers at the University of Michigan, all scientists who currently work outside of data science (in fields such as astronomy, education, chemistry, and political s","keywords":["scientific research","computing education","collaboration","mentorship","career paths","academic"],"image":"/media/remote/7421b829a4debffc-720.webp","collections":[],"label":"Panorama"},{"id":"4f3ac3da-ee75-433b-8ed1-f582135f473f","slug":"89bpbwa6","title":"Understanding the Data in K-12 Data Science","dek":null,"authors":[{"name":"Rotem Israel-Fishelson","slug":"rotem-israel-fishelson"},{"name":"Peter Moon","slug":"peter-moon"},{"name":"Rachel Tabak","slug":"rachel-tabak"},{"name":"David Weintrop","slug":"david-weintrop"}],"date":"2024-04-30","section":"stepping-stones","issue":"volume6issue2","doi":"10.1162/99608f92.4f3ac3da","abstract":"Our increasingly data-driven world is amplifying the need for everyone to develop foundational data literacy skills. In response, a growing number of K-12 data science curricula are being designed to introduce all students to data. These curricula define what data science is at the high school level and directly shape how students are introduced to and understand the discipline. Ensuring these curricula are effective","keywords":["data science education","curricula analysis","data sets","high school"],"image":"/media/remote/4c66d7ba6da17487-720.webp","collections":[],"label":"Stepping Stones"},{"id":"141c99cf-a024-410f-b33a-f0fbd29b1321","slug":"jme9l45q","title":"Data Journeys in Popular Science: Producing Climate Change and COVID-19 Data Visualizations at Scientific American","dek":null,"authors":[{"name":"Kathleen Gregory","slug":"kathleen-gregory"},{"name":"Laura Koesten","slug":"laura-koesten"},{"name":"Regina Schuster","slug":"regina-schuster"},{"name":"Torsten Möller","slug":"torsten-moeller"},{"name":"Sarah Davies","slug":"sarah-davies"}],"date":"2024-04-30","section":"cornucopia","issue":"volume6issue2","doi":"10.1162/99608f92.141c99cf","abstract":"Vast amounts of (open) data are increasingly used to make arguments about crisis topics such as climate change and global pandemics. Data visualizations are central to bringing these viewpoints to broader publics. However, visualizations often conceal the many contexts involved in their production, ranging from decisions made in research labs about collecting and sharing data to choices made in editorial rooms about ","keywords":["data visualization","science communication","data practices","open data","data reuse","climate change","COVID-19"],"image":"/media/remote/26ce592bb2ff3484-720.webp","collections":[],"label":"Cornucopia"},{"id":"7f9220ff-8850-407d-9e78-f8eaf52dcc18","slug":"xedo5giw","title":"AI and Generative AI for Research Discovery and Summarization","dek":null,"authors":[{"name":"Mark Glickman","slug":"mark-glickman"},{"name":"Yi Zhang","slug":"yi-zhang"}],"date":"2024-04-30","section":"cornucopia","issue":"volume6issue2","doi":"10.1162/99608f92.7f9220ff","abstract":"Artificial intelligence (AI) and generative AI tools, including chatbots like ChatGPT that rely on large language models (LLMs), have burst onto the scene this year, creating incredible opportunities to increase work productivity and improve our lives. Statisticians and data scientists have begun experiencing the benefits from the availability of these tools in numerous ways, such as the generation of programming cod","keywords":["abductive reasoning","hallucination","literature discovery","manuscript abstraction","research discovery"],"image":"/media/remote/e0b1cdae969d9bfb-720.webp","collections":[],"label":"Cornucopia"},{"id":"dc0540dc-7e31-482f-a7f8-01c63af92013","slug":"9wczyq5o","title":"A Conversation With Tim Ritchie, President of the Museum of Science, Boston","dek":"An interview with Tim Ritchie by Xiao-Li Meng","authors":[{"name":"Tim Ritchie","slug":"tim-ritchie"},{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2024-04-30","section":"panorama","issue":"volume6issue2","doi":"10.1162/99608f92.dc0540dc","abstract":"HDSR’s Founding Editor-in-Chief, Xiao-Li Meng, recently met with Tim Ritchie, President of the Museum of Science (Boston, Massachusetts) for a dynamic and insightful conversation that touched on many topics pertaining to the role of museums in society, particularly in the context of data science, AI, and fundraising.A nonprofit leader with broad experience in science, education, law, and advocacy for people with disa","keywords":[],"image":"/media/remote/8c6b9a4bc20045d3-720.webp","collections":["conversations-with-leaders"],"label":"Panorama"},{"id":"49e3c961-d127-4a1c-b717-fba73778d959","slug":"yb9eq8ja","title":"Decoding Randomized Controlled Trials: An Information Science Perspective","dek":null,"authors":[{"name":"Pavlos Msaouel","slug":"pavlos-msaouel"}],"date":"2024-04-30","section":"diving-into-data","issue":"volume6issue2","doi":"10.1162/99608f92.49e3c961","abstract":null,"keywords":[],"image":"/media/remote/8173e7959f5cbcdc-720.webp","collections":[],"label":"Diving into Data"},{"id":"79d4660d-954c-4bc3-9bc8-c849f65e2886","slug":"vctoo3sw","title":"Noisy Measurements Are Important; The Design of Census Products Is Much More Important","dek":null,"authors":[{"name":"John M. Abowd","slug":"john-abowd"}],"date":"2024-04-30","section":"bits-and-bites","issue":"volume6issue2","doi":"10.1162/99608f92.79d4660d","abstract":null,"keywords":[],"image":"/media/remote/b47884b6151bcf35-720.webp","collections":[],"label":"Bits and Bites"},{"id":"b91339ef-4fbd-4138-a71c-f8b349f40bb9","slug":"g9mau4m0","title":"Data Science at the Singularity","dek":null,"authors":[{"name":"David Donoho","slug":"david-donoho"}],"date":"2024-04-17","section":"panorama","issue":"volume6issue1","doi":"10.1162/99608f92.b91339ef","abstract":"Something fundamental to computation-based research has really changed in the last 10 years. In certain fields, progress is simply dramatically faster than ever. Researchers in affected fields are living through a period of profound transformation as the fields undergo a transition to frictionless reproducibility (FR). This transition markedly changes the rate at which ideas and practices spread, affects scientific m","keywords":["reproducible computational research","challenge problems paradigm","frictionless reproducibility","frictionless research exchange","emergent superpower","AI singularity"],"image":"/media/remote/52b5410771fb4a50-720.webp","collections":[],"label":"Panorama"},{"id":"f2a44cc0-8c25-4873-8189-aee8a7e9b399","slug":"o3qm160v","title":"Data Democratization: An Ecosystemic Contemplation and Coordination","dek":null,"authors":[{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2024-04-05","section":null,"issue":"specialissue4","doi":"10.1162/99608f92.f2a44cc0","abstract":null,"keywords":[],"image":"/media/remote/8d8e2406498be3e3-720.webp","collections":[],"label":"From the Editors"},{"id":"106473cf-50b8-4195-bc88-c73086e135d6","slug":"m1o4oblm","title":"Democratizing Data: Our Vision","dek":null,"authors":[{"name":"Julia Lane","slug":"julia-lane"},{"name":"Nancy Potok","slug":"nancy-potok"}],"date":"2024-04-02","section":null,"issue":"specialissue4","doi":"10.1162/99608f92.106473cf","abstract":null,"keywords":[],"image":null,"collections":[],"label":"From the Editors"},{"id":"44746f24-87fe-4ca4-9427-e6645f22c7cc","slug":"2wewdt1b","title":"On Democratizing Data: Diminishing Disparity and Increasing Scientific Productivity","dek":null,"authors":[{"name":"Ophir Frieder","slug":"ophir-frieder"}],"date":"2024-04-02","section":null,"issue":"specialissue4","doi":"10.1162/99608f92.44746f24","abstract":"Data democratization enhances trust through transparency, inclusion through availability, and diversity and growth through productivity. Their democratization, however, must be systematic and cautious to avoid potentially inflicting harm.","keywords":["artificial intelligence (AI)","data democratization","data science","deep learning","large language model (LLM)","pretrained language model (PLM)"],"image":"/media/remote/1e41ce7329fd80ec-720.webp","collections":[],"label":"Building Collaborative Communities"},{"id":"9469c089-459e-4443-8e38-5337d9cbd7f4","slug":"fysjbutp","title":"Enabling Responsible Artificial Intelligence Research and Development Through the Democratization of Advanced Cyberinfrastructure","dek":null,"authors":[{"name":"Manish Parashar","slug":"manish-parashar"}],"date":"2024-04-02","section":null,"issue":"specialissue4","doi":"10.1162/99608f92.9469c089","abstract":"Artificial intelligence (AI) is driving discovery, innovation, and economic growth, and has the potential to transform science and society. However, realizing the positive, transformative potential of AI requires that AI research and development (R&D) progress responsibly; that is, in a way that protects privacy, civil rights, and civil liberties, and promotes principles of fairness, accountability, transparency, and","keywords":["responsible AI","data democratization","FAIR data","open and equitable access","NAIRR","National Data Platform"],"image":"/media/remote/cec98d2fe0764e30-720.webp","collections":[],"label":"Building Collaborative Communities"},{"id":"e1f349c2-5e08-4b52-b161-f0a0666ee97c","slug":"l3g0j3bk","title":"Building Trust: Data Metrics as a Focal Point for Responsible Data Stewardship","dek":null,"authors":[{"name":"Iratxe Puebla","slug":"iratxe-puebla"},{"name":"Daniella Lowenberg","slug":"daniella-lowenberg"}],"date":"2024-04-02","section":null,"issue":"specialissue4","doi":"10.1162/99608f92.e1f349c2","abstract":"The widespread adoption of open data practices has resulted in a wealth of open data sets and increased attention on data as a public asset. In order to fulfill responsible data stewardship, we must move beyond enabling data access and take steps to understand how administrative and research data are found, accessed, and reused. We need standardized evaluations of data usage to assess whether and how open data advanc","keywords":["data metrics","open data","data usage","evaluation","research policy","infrastructure"],"image":"/media/remote/8fcf53164d6de6ec-720.webp","collections":[],"label":"Building Collaborative Communities"},{"id":"b73aae77-0996-4e0a-8abf-3c091827ef06","slug":"imfn7i7f","title":"The Future of Data in Research Publishing: From Nice to Have to Need to Have?","dek":null,"authors":[{"name":"Christine L. Borgman","slug":"christine-l-borgman"},{"name":"Amy Brand","slug":"amy-brand"}],"date":"2024-04-02","section":null,"issue":"specialissue4","doi":"10.1162/99608f92.b73aae77","abstract":"Science policy promotes open access to research data for purposes of transparency and reuse of data in the public interest. We expect demands for open data in scholarly publishing to accelerate, at least partly in response to the opacity of artificial intelligence (AI) algorithms. Open data should be findable, accessible, interoperable, and reusable (FAIR), and also trustworthy and verifiable. The current state of op","keywords":["research data","open access policy","verifiability","data reuse","data stewardship","knowledge infrastructure"],"image":"/media/remote/c82c51ee141a3dda-720.webp","collections":[],"label":"Building Collaborative Communities"},{"id":"4c3e11ca-25f1-49e4-8d4e-c40e0b73f74e","slug":"psnc8zsr","title":"Discovering Datasets on the Web Scale: Challenges and Recommendations for Google Dataset Search","dek":null,"authors":[{"name":"Katrina Sostek","slug":"katrina-sostek"},{"name":"Daniel M. Russell","slug":"daniel-russell"},{"name":"Nitesh Goyal","slug":"nitesh-goyal"},{"name":"Tarfah Alrashed","slug":"tarfah-alrashed"},{"name":"Stella Dugall","slug":"stella-dugall"},{"name":"Natasha Noy","slug":"natasha-noy"}],"date":"2024-04-02","section":null,"issue":"specialissue4","doi":"10.1162/99608f92.4c3e11ca","abstract":"With the rise of open data in the last two decades, more datasets are online and more people are using them for projects and research. But how do people find datasets? We present the first user study of Google Dataset Search, a dataset-discovery tool that uses a web crawl and open ecosystem to find datasets. Google Dataset Search contains a superset of the datasets in other dataset-discovery tools—a total of 45 milli","keywords":["data discovery","data search","data reuse","data sharing","open data policy"],"image":"/media/remote/c31f9405b586d5b0-720.webp","collections":[],"label":"Search and Discovery Methods"},{"id":"03719804-b277-447a-903a-a02b86f75d3d","slug":"6blzwgpg","title":"An Invisible Hand for Creating Public Value From Data","dek":null,"authors":[{"name":"Julia Lane","slug":"julia-lane"},{"name":"Alfred Spector","slug":"alfred-spector"},{"name":"Michael Stebbins","slug":"michael-stebbins"}],"date":"2024-04-02","section":null,"issue":"specialissue4","doi":"10.1162/99608f92.03719804","abstract":"In this article we describe how data usage statistics could be designed to reward the contributions of both researchers and government agencies, functionally creating a carrot that rewards engagement. We provide a concrete use case—the Democratizing Data pilot project—and discuss how the results could be used to develop a community-driven incentive structure, or an ‘invisible hand,’ to generate better evidence for po","keywords":["democratizing data","data value","Evidence Act","community-driven incentive structure","invisible hand"],"image":"/media/remote/2d5edd35f62beb5c-720.webp","collections":[],"label":"The Value of Data"},{"id":"652877ca-0a95-4d3c-8299-9570cff18c4c","slug":"605i9bgg","title":"Data Usage Information and Connecting With Data Users: U.S. Mandates and Guidance for Government Agency Evidence Building","dek":null,"authors":[{"name":"Nancy Potok","slug":"nancy-potok"}],"date":"2024-04-02","section":null,"issue":"specialissue4","doi":"10.1162/99608f92.652877ca","abstract":"This article delves into the multifaceted landscape of evidence-based policymaking within the U.S. federal government. Specifically, it examines the key components of the Foundations of Evidence-Based Policymaking Act of 2018, the subsequent presidential executive orders and U.S. Office of Management and Budget memoranda aimed at promoting evidence-based practices, federal mandates for agencies to engage with data us","keywords":["Evidence Act","data users","government mandates","user feedback","collaboration"],"image":"/media/remote/cff83decffb44588-720.webp","collections":[],"label":"The New Importance of Data for Evidence Building"},{"id":"18df5545-32d1-45ba-abf0-26332666f6ba","slug":"ou89oggk","title":"Discovering Data Sets Through Machine Learning: An Ensemble Approach to Uncovering the Prevalence of Government-Funded Data Sets","dek":null,"authors":[{"name":"Ryan Hausen","slug":"ryan-hausen"},{"name":"Hosein Azarbonyad","slug":"hosein-azarbonyad"}],"date":"2024-04-02","section":null,"issue":"specialissue4","doi":"10.1162/99608f92.18df5545","abstract":"The prevalence of government-funded data set usage has yet to be comprehensively tracked and understood. The lack of a standardized citation methodology has thus far prevented the government from understanding data set usage in a transparent, accessible way. In this work, we seek to build on recent successes in natural language processing techniques and a recent Kaggle competition to develop an extensible framework f","keywords":["data sets","machine learning","artificial intelligence","natural language processing"],"image":"/media/remote/1c5e773586cbd60c-720.webp","collections":[],"label":"Search and Discovery Methods"},{"id":"d8a3742f-3c55-4380-9993-1e5ff5abf91e","slug":"os94ml33","title":"Turning Visions Into Reality: Lessons Learned From Building a Search and Discovery Platform","dek":null,"authors":[{"name":"Attila Emecz","slug":"attila-emecz"},{"name":"Arik Mitschang","slug":"arik-mitschang"},{"name":"Christina Zdawczyk","slug":"christina-zdawczyk"},{"name":"Maytal Dahan","slug":"maytal-dahan"},{"name":"Jeroen Baas","slug":"jeroen-baas"},{"name":"Gerard Lemson","slug":"gerard-lemson"}],"date":"2024-04-02","section":null,"issue":"specialissue4","doi":"10.1162/99608f92.d8a3742f","abstract":"The Democratizing Data Initiative looks to demonstrate the impact of the data assets produced by U.S. federal agencies. In this article we outline the process flows and cyberinfrastructure that support the achievement of that objective. We describe how the focus on data assets evolved from the initial search for data sets to the challenges of finding how the data assets have been used by the research community. In th","keywords":["data assets","impact","full-text search","machine learning","Kaggle"],"image":"/media/remote/53b4359e423b55f7-720.webp","collections":[],"label":"Practical Implementation"},{"id":"7aadd8ff-dab2-4fd5-b21e-201fd2f6e358","slug":"60n4at1d","title":"The View From Four Statistical Agencies","dek":"An interview with Peggy Carr, Hubert Hamer, Emilda Rivers, and Spiro Stefanou by Nancy Potok","authors":[{"name":"Nancy Potok","slug":"nancy-potok"},{"name":"Peggy Carr","slug":"peggy-carr"},{"name":"Hubert Hamer","slug":"hubert-hamer"},{"name":"Emilda Rivers","slug":"emilda-rivers"},{"name":"Spiro Stefanou","slug":"spiro-stefanou"}],"date":"2024-04-02","section":null,"issue":"specialissue4","doi":"10.1162/99608f92.7aadd8ff","abstract":"This special issue fireside chat features a panel discussion among Peggy Carr, Hubert Hamer, Emilda Rivers, Spiro Stefanou, and Nancy Potok, focusing on the utilization of data within federal statistical agencies. The conversation delves into the significance of understanding how data are used by various stakeholders, ranging from researchers to policymakers, and the public. It emphasizes the value of tools and platf","keywords":[],"image":"/media/remote/76f10489c5a31413-720.webp","collections":["conversations-with-leaders"],"label":"The New Importance of Data for Evidence Building"},{"id":"82f0de5a-2fd1-4408-921c-d111b4463867","slug":"c84g7tz9","title":"A Mapping Lens for Estimating Data Value","dek":null,"authors":[{"name":"Abhishek Nagaraj","slug":"abhishek-nagaraj"}],"date":"2024-04-02","section":null,"issue":"specialissue4","doi":"10.1162/99608f92.82f0de5a","abstract":"Public data is a key resource for society. The digital revolution and a renewed focus on evidence-based policymaking have made estimating the value of public data resources more urgent than ever. However, conventional methods to assess their value are still underdeveloped and generally fail to capture their broader impact. We introduce a novel approach to assess the value of data, anchored in its potential to improve","keywords":["public data","data value","quasi-experimental methods","government data"],"image":"/media/remote/79c933892d9a4afd-720.webp","collections":[],"label":"The Value of Data"},{"id":"9e9ae62b-94b8-45ef-8d72-24ca936dcc5b","slug":"k8ci2fwe","title":"Scale, Publishing, and Open Data","dek":null,"authors":[{"name":"Ian Mulvany","slug":"ian-mulvany"}],"date":"2024-04-02","section":null,"issue":"specialissue4","doi":"10.1162/99608f92.9e9ae62b","abstract":"This perspective highlights the potential of enhancing data systems to improve our understanding of the world and scale up the production of knowledge. It shows that despite an increase in data from various publications over time, we still experience limitations in scaling our systems. Shifts toward a more person-centric view of the world and enhanced open access to data can help address this challenge. The democrati","keywords":["scaling knowledge systems","open access publishing","data democratization","trustworthiness of scholarly record","connecting research communities","enhancing research impact"],"image":"/media/remote/ee08c0747b27476e-720.webp","collections":[],"label":"The Value of Data"},{"id":"77bfa1c9-adc7-4c2d-967a-5aa56d13294d","slug":"myudlnzw","title":"Discovering Data Sets in Unstructured Corpora: Discovering Use and Identifying New Opportunities","dek":null,"authors":[{"name":"Nick Pallotta","slug":"nick-pallotta"},{"name":"John M Locklear","slug":"john-m-locklear"},{"name":"Xiangyu Ren","slug":"xiangyu-ren"},{"name":"Victor Robila","slug":"victor-robila"},{"name":"Adel Alaeddini","slug":"adel-alaeddini"}],"date":"2024-04-02","section":null,"issue":"specialissue4","doi":"10.1162/99608f92.77bfa1c9","abstract":"Federal statistical agencies are keenly aware that scientific research is not the only way in which their data assets are used for evidence building. Entities around the world have been building services on top of curated corpuses of scientific research, to help provide insight as to the importance of the works in the collection. This also provides a much-needed framework for statistical agencies and other data set c","keywords":["Cooperative Extension Service","Beautiful Soup","National Agricultural Statistics Service","web scraping","Evidence Act","data set impact"],"image":"/media/remote/9254abb866399933-720.webp","collections":[],"label":"Search and Discovery Methods"},{"id":"f1cbbfbb-ffe3-44a7-8ed6-72c9302f9b6b","slug":"utkb38d2","title":"Searching for How Data Have Been Used: Intuitive Labels for Data Search and Discovery","dek":null,"authors":[{"name":"Christina Zdawczyk","slug":"christina-zdawczyk"},{"name":"Julia Lane","slug":"julia-lane"},{"name":"Emilda Rivers","slug":"emilda-rivers"},{"name":"May Aydin","slug":"may-aydin"}],"date":"2024-04-02","section":null,"issue":"specialissue4","doi":"10.1162/99608f92.f1cbbfbb","abstract":"Data access, use, and reuse are crucial for empirical science and evidence-based policymaking, and rely on metadata to facilitate data discovery and utilization by users and producers alike. Metadata quality is pivotal for the federal government to understand data available for evidence building, enabling agencies to identify data production gaps and redundancies, and enhancing evidence quality through reproducible r","keywords":["data set search and discovery","metadata for data sets","labels for data sets","topic identification","research fields"],"image":"/media/remote/9e1e39fc665a06b0-720.webp","collections":[],"label":"Search and Discovery Methods"},{"id":"8a2f4507-2755-422f-b97e-af67cd2cddb9","slug":"67asjmnx","title":"A Practical Use Case: Lesson Learned From Social Science Research Data Centers","dek":null,"authors":[{"name":"Stefan Bender","slug":"stefan-bender"},{"name":"Jannick Blaschke","slug":"jannick-blaschke"},{"name":"Christian Hirsch","slug":"christian-hirsch"}],"date":"2024-04-02","section":null,"issue":"specialissue4","doi":"10.1162/99608f92.8a2f4507","abstract":"Access to timely and high-quality granular data is increasingly becoming a key factor for research and evidence-based policy-making. For accessing confidential administrative data, the introduction of research data centers (RDCs) has been a success story. RDCs are restricted-access facilities, often at the premises of the data owner, that provide accredited researchers with safe access to sensitive granular data. Alt","keywords":["democratizing data","data sharing","research data center","usage measures","social science"],"image":"/media/remote/dbfab8b8d1f87f17-720.webp","collections":[],"label":"Practical Implementation"},{"id":"24c26aa9-5c91-4958-b2e8-44dc516948f3","slug":"lakmexrl","title":"Creating Engagements: Bringing the User Into Data Democratization","dek":null,"authors":[{"name":"Lauren Chenarides","slug":"lauren-chenarides"}],"date":"2024-04-02","section":null,"issue":"specialissue4","doi":"10.1162/99608f92.24c26aa9","abstract":"The Foundations for Evidence-Based Policymaking Act of 2018 and supporting open science efforts offer a new opportunity for federal agencies to enhance government data ecosystems that improve public access to federal data. Public engagement will be critical to ensure that the efforts are sustainable. This article focuses on learning from other models to provide insights into building successful user engagement, drawi","keywords":["user engagement","technology adoption","data democratization","open data","evidence-based policy"],"image":"/media/remote/de2df91c2e1e8cbf-720.webp","collections":[],"label":"Building Collaborative Communities"},{"id":"3f34436c-44a0-46a1-93af-5acac5dc89b2","slug":"sx2edx7u","title":"The Importance of Philanthropic Foundations in Democratizing Data","dek":"An interview with Stuart Feldman, Joshua Greenberg, Jonathan Sotsky, and Vilas Dhar&nbsp;by Julia Lane","authors":[{"name":"Julia Lane","slug":"julia-lane"},{"name":"Stuart Feldman","slug":"stuart-feldman"},{"name":"Joshua Greenberg","slug":"joshua-greenberg"},{"name":"Jonathan Sotsky","slug":"jonathan-sotsky"},{"name":"Vilas Dhar","slug":"vilas-dhar"}],"date":"2024-04-02","section":null,"issue":"specialissue4","doi":"10.1162/99608f92.3f34436c","abstract":null,"keywords":[],"image":"/media/remote/443dcb3634a216f7-720.webp","collections":[],"label":"The Value of Data"},{"id":"5317da47-795a-4da3-8b04-2c271cf37754","slug":"y95zitmz","title":"How Is ChatGPT’s Behavior Changing Over Time?","dek":null,"authors":[{"name":"Lingjiao Chen","slug":"lingjiao-chen"},{"name":"Matei Zaharia","slug":"matei-zaharia"},{"name":"James Zou","slug":"james-zou"}],"date":"2024-03-12","section":"milestones-and-millstones","issue":"volume6issue2","doi":"10.1162/99608f92.5317da47","abstract":"GPT-3.5 and GPT-4 are the two most widely used large language model (LLM) services. However, when and how these models are updated over time is opaque. Here, we evaluate the March 2023 and June 2023 versions of GPT-3.5 and GPT-4 on several diverse tasks: 1) math problems, 2) sensitive/dangerous questions, 3) opinion surveys, 4) multi-hop knowledge-intensive questions, 5) generating code, 6) U.S. Medical License tests","keywords":["GPT-4","ChatGPT","LLM drifts","temporal evaluation"],"image":"/media/remote/d53d70686af0af47-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"239082d0-eb6c-447e-95d0-cb6ed91b64b9","slug":"i28gft43","title":"2024: A Year of Crises, Change, Contemplation, and Commemoration","dek":null,"authors":[{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2024-01-31","section":"editorscolumn","issue":"volume6issue1","doi":"10.1162/99608f92.239082d0","abstract":null,"keywords":[],"image":"/media/remote/305e04a43845c1d0-720.webp","collections":[],"label":"Editor’s Column"},{"id":"9d86a749-4eba-4ad4-9701-120e8c0cd794","slug":"ju1nvjw6","title":"Assessing the Prognostic Utility of Clinical and Radiomic Features for COVID-19 Patients Admitted to ICU: Challenges and Lessons Learned","dek":null,"authors":[{"name":"Yuming Sun","slug":"yuming-sun"},{"name":"Stephen Salerno","slug":"stephen-salerno"},{"name":"Ziyang Pan","slug":"ziyang-pan"},{"name":"Eileen Yang","slug":"eileen-yang"},{"name":"Chinakorn Sujimongkol","slug":"chinakorn-sujimongkol"},{"name":"Jiyeon Song","slug":"jiyeon-song"},{"name":"Xinan Wang","slug":"xinan-wang"},{"name":"Peisong Han","slug":"peisong-han"},{"name":"Donglin Zeng","slug":"donglin-zeng"},{"name":"Jian Kang","slug":"jian-kang"},{"name":"David C. Christiani","slug":"david-c-christiani"},{"name":"Yi Li","slug":"yi-li"}],"date":"2024-01-31","section":"cornucopia","issue":"volume6issue1","doi":"10.1162/99608f92.9d86a749","abstract":"Severe cases of COVID-19 often necessitate escalation to the intensive care unit (ICU), where patients may face serious outcomes, including mortality. Chest X-rays play a crucial role in the diagnostic process for evaluating COVID-19 patients. Our collaborative efforts with Michigan Medicine in monitoring patient outcomes within the ICU have motivated us to investigate the potential advantages of incorporating clinic","keywords":["COVID-19","machine learning","survival prediction","X-ray imaging"],"image":"/media/remote/83325c1c2de6238f-720.webp","collections":[],"label":"Cornucopia"},{"id":"87c6125f-d638-4be3-92da-b83ef1a60877","slug":"1ybwbmlw","title":"Causation, Comparison, and Regression","dek":null,"authors":[{"name":"Ambarish Chattopadhyay","slug":"ambarish-chattopadhyay"},{"name":"José R. Zubizarreta","slug":"jose-zubizarreta"}],"date":"2024-01-31","section":"milestones-and-millstones","issue":"volume6issue1","doi":"10.1162/99608f92.87c6125f","abstract":"Comparison and contrast are the basic means to unveil causation and learn which treatments work. To build good comparison groups, randomized experimentation is key, yet often infeasible. In such non-experimental settings, we illustrate and discuss diagnostics to assess how well the common linear regression approach to causal inference approximates desirable features of randomized experiments, such as covariate balanc","keywords":["causal inference","randomized experiments","observational studies","regression modeling","weighting adjustments","multivariate matching"],"image":"/media/remote/ac15148ef262cb1d-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"85868121-3933-45cb-aa08-cff602baae44","slug":"laxlndnv","title":"Causal Inference for Everyone","dek":null,"authors":[{"name":"Iavor Bojinov","slug":"iavor-bojinov"},{"name":"Francesca Dominici","slug":"francesca-dominici"}],"date":"2024-01-31","section":"catalytic-causal-conversations","issue":"volume6issue1","doi":"10.1162/99608f92.85868121","abstract":null,"keywords":[],"image":"/media/remote/f9688b5f87a54602-720.webp","collections":[],"label":"Catalytic Causal Conversations"},{"id":"34f8e75b-8b38-4d43-8e9b-f45d5482ceb5","slug":"9zhitrw8","title":"When Data Science Goes Wrong: How Misconceptions About Data Capture and Processing Causes Wrong Conclusions","dek":null,"authors":[{"name":"Peter Christen","slug":"peter-christen-2"},{"name":"Rainer Schnell","slug":"rainer-schnell"}],"date":"2024-01-31","section":"diving-into-data","issue":"volume6issue1","doi":"10.1162/99608f92.34f8e75b","abstract":null,"keywords":[],"image":"/media/remote/29ad78a91210b3e1-720.webp","collections":[],"label":"Diving into Data"},{"id":"e3d09bff-5861-4226-8efb-7c35cd6f7f36","slug":"nf9bhik3","title":"Discerning Audiences Through Like Buttons","dek":null,"authors":[{"name":"Carina Albrecht","slug":"carina-albrecht"}],"date":"2024-01-31","section":"mining-the-past","issue":"volume6issue1","doi":"10.1162/99608f92.e3d09bff","abstract":null,"keywords":[],"image":"/media/remote/2a86fbd9939db5dd-720.webp","collections":[],"label":"Mining the Past"},{"id":"98cec19b-1d62-489a-935a-756635ce4f69","slug":"9f5hyzb3","title":"Teaching Introductory Statistics in a World Beyond “p < .05”","dek":null,"authors":[{"name":"Catherine Case","slug":"catherine-case-2"}],"date":"2024-01-31","section":"minding-the-future","issue":"volume6issue1","doi":"10.1162/99608f92.98cec19b","abstract":null,"keywords":[],"image":"/media/remote/f68a8c9b9169d1cb-720.webp","collections":[],"label":"Minding the Future"},{"id":"dca148ba-d1e8-4959-841e-6f1b6db8c999","slug":"lvp2cuc2","title":"Why and How We Share Reproducible Research at Yale University’s Institution for Social and Policy Studies","dek":null,"authors":[{"name":"Limor Peer","slug":"limor-peer"}],"date":"2024-01-31","section":"reinforcing-rr","issue":"volume6issue1","doi":"10.1162/99608f92.dca148ba","abstract":null,"keywords":[],"image":"/media/remote/d49b95b53c9b71e4-720.webp","collections":[],"label":"Reinforcing Reproducibility and Replicability"},{"id":"67bb0dd2-c575-48f6-a090-36a1e8c64908","slug":"yizg5bq8","title":"The Groundwater Crisis: The Need for New Data to Inform Public Policy","dek":null,"authors":[{"name":"Nick Dudley Ward","slug":"nick-dudley-ward"}],"date":"2024-01-31","section":"effective-policy-learning","issue":"volume6issue1","doi":"10.1162/99608f92.67bb0dd2","abstract":null,"keywords":[],"image":"/media/remote/8986331b782ebcce-720.webp","collections":[],"label":"Effective Policy Learning"},{"id":"bff007ab-eb8a-4e61-8684-93a711e57c69","slug":"pqiufdew","title":"What Should Data Science Education Do With Large Language Models?","dek":null,"authors":[{"name":"Xinming Tu","slug":"xinming-tu"},{"name":"James Zou","slug":"james-zou"},{"name":"Weijie Su","slug":"weijie-su"},{"name":"Linjun Zhang","slug":"linjun-zhang"}],"date":"2024-01-19","section":"stepping-stones","issue":"volume6issue1","doi":"10.1162/99608f92.bff007ab","abstract":"The rapid advances of large language models (LLMs), such as ChatGPT, are revolutionizing data science and statistics. These state-of-the-art tools can streamline complex processes such as data cleaning, model building, interpretation, and report writing. As a result, they reshape the role of data scientists. We argue that LLMs are transforming the responsibilities of data scientists, shifting their focus from hands-o","keywords":["large language models","ChatGPT","data science","education"],"image":"/media/remote/7e3065182c00fc7a-720.webp","collections":[],"label":"Stepping Stones"},{"id":"d3197524-faf6-4b9c-bee2-3de97c4157f3","slug":"sl9we8gh","title":"Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment","dek":null,"authors":[{"name":"Rachel Cummings","slug":"rachel-cummings"},{"name":"Damien Desfontaines","slug":"damien-desfontaines"},{"name":"David Evans","slug":"david-evans"},{"name":"Roxana Geambasu","slug":"roxana-geambasu"},{"name":"Yangsibo Huang","slug":"yangsibo-huang"},{"name":"Matthew Jagielski","slug":"matthew-jagielski"},{"name":"Peter Kairouz","slug":"peter-kairouz"},{"name":"Gautam Kamath","slug":"gautam-kamath"},{"name":"Sewoong Oh","slug":"sewoong-oh"},{"name":"Olga Ohrimenko","slug":"olga-ohrimenko"},{"name":"Nicolas Papernot","slug":"nicolas-papernot"},{"name":"Ryan Rogers","slug":"ryan-rogers"},{"name":"Milan Shen","slug":"milan-shen"},{"name":"Shuang Song","slug":"shuang-song"},{"name":"Weijie Su","slug":"weijie-su"},{"name":"Andreas Terzis","slug":"andreas-terzis"},{"name":"Abhradeep Thakurta","slug":"abhradeep-thakurta"},{"name":"Sergei Vassilvitskii","slug":"sergei-vassilvitskii"},{"name":"Yu-Xiang Wang","slug":"yu-xiang-wang"},{"name":"Li Xiong","slug":"li-xiong"},{"name":"Sergey Yekhanin","slug":"sergey-yekhanin"},{"name":"Da Yu","slug":"da-yu"},{"name":"Huanyu Zhang","slug":"huanyu-zhang"},{"name":"Wanrong Zhang","slug":"wanrong-zhang"}],"date":"2024-01-16","section":"panorama","issue":"volume6issue1","doi":"10.1162/99608f92.d3197524","abstract":"In this article, we present a detailed review of current practices and state-of-the-art methodologies in the field of differential privacy (DP), with a focus of advancing DP’s deployment in real-world applications. Key points and high-level contents of the article were originated from the discussions from “Differential Privacy (DP): Challenges Towards the Next Frontier,” a workshop held in July 2022 with experts from","keywords":["differential privacy","privacy infrastructure","utility trade-offs","privacy protection"],"image":"/media/remote/36c9eedcf533ca5a-720.webp","collections":[],"label":"Panorama"},{"id":"4187b1b4-5ab5-4a9a-b36c-636c5563fbf9","slug":"joyjc5tw","title":"Rejoinder: Building a Paradigm That Allows for the Possibility of Non-Ignorable Nonresponse","dek":null,"authors":[{"name":"Michael A. Bailey","slug":"michael-bailey-2"}],"date":"2023-11-09","section":null,"issue":"volume5issue3","doi":"10.1162/99608f92.4187b1b4","abstract":null,"keywords":[],"image":"/media/remote/57fcdd9d57ab0d15-720.webp","collections":[],"label":"Featured Discussion"},{"id":"c3c87223-68f4-48a0-a62f-18970b99f217","slug":"y6oilhhe","title":"Making Differential Privacy Work for Census Data Users","dek":null,"authors":[{"name":"Cory McCartan","slug":"cory-mccartan"},{"name":"Tyler Simko","slug":"tyler-simko"},{"name":"Kosuke Imai","slug":"kosuke-imai"}],"date":"2023-11-01","section":"cornucopia","issue":"volume5issue4","doi":"10.1162/99608f92.c3c87223","abstract":"The U.S. Census Bureau collects and publishes detailed demographic data about Americans, which are heavily used by researchers and policymakers. The bureau has recently adopted the framework of differential privacy in an effort to improve confidentiality of individual census responses. A key output of this privacy protection system is the Noisy Measurement File (NMF), which is produced by adding random noise to tabul","keywords":["census data","differential privacy","open science"],"image":"/media/remote/c3a5e31c82e34c8d-720.webp","collections":[],"label":"Cornucopia"},{"id":"11d9241f-d94d-4f5b-9752-3390194c7819","slug":"nw5bilq7","title":"Human Intelligence, Artificial Intelligence, and Homo sapiens Intelligence?","dek":null,"authors":[{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2023-10-27","section":"editorscolumn","issue":"volume5issue4","doi":"10.1162/99608f92.11d9241f","abstract":null,"keywords":[],"image":"/media/remote/3a6712a0be30c4f2-720.webp","collections":[],"label":"Editor’s Column"},{"id":"49328257-5bce-4c76-97a7-16a7db95a881","slug":"ffgh22dh","title":"From Financial Markets to ChatGPT: A Conversation With Andrew Lo","dek":"An interview with Andrew Lo by Xiao-Li Meng and Liberty Vittert","authors":[{"name":"Andrew W. Lo","slug":"andrew-lo"},{"name":"Xiao-Li Meng","slug":"xiao-li-meng"},{"name":"Liberty Vittert","slug":"liberty-vittert"}],"date":"2023-10-27","section":"panorama","issue":"volume5issue4","doi":"10.1162/99608f92.49328257","abstract":"Harvard Data Science Review’s Founding Editor-in-Chief, Xiao-Li Meng, and Media Feature Editor, Liberty Vittert, interviewed Andrew W. Lo, the Charles E. and Susan T. Harris Professor at MIT’s Sloan School of Management, Director of the MIT Laboratory for Financial Engineering, and a principal investigator at the MIT Computer Science and Artificial Intelligence Laboratory, on the topic of data science for financial m","keywords":[],"image":"/media/remote/cbd5b47a1ee0a69f-720.webp","collections":[],"label":"Panorama"},{"id":"c37d3572-cf9b-459b-aef7-8a4f0bffff71","slug":"vbbb4ukb","title":"The Intelligence and Rationality of AI and Humans: A Conversation With Steven Pinker","dek":"An interview with Steven Pinker by Xiao-Li Meng and Liberty Vittert","authors":[{"name":"Steven Pinker","slug":"steven-pinker"},{"name":"Xiao-Li Meng","slug":"xiao-li-meng"},{"name":"Liberty Vittert","slug":"liberty-vittert"}],"date":"2023-10-27","section":"panorama","issue":"volume5issue4","doi":"10.1162/99608f92.c37d3572","abstract":"Harvard Data Science Review’s Founding Editor-in-Chief, Xiao-Li Meng, and Media Feature Editor, Liberty Vittert, interviewed Dr. Steven Pinker, the Johnstone Family Professor in the Department of Psychology at Harvard University and expert on the human mind, about how artificial intelligence is viewed within his fields of study. Dr. Pinker has researched and published several articles and books on language, cognition","keywords":[],"image":"/media/remote/cc95fb06bec2e0c0-720.webp","collections":[],"label":"Panorama"},{"id":"c1292c54-28f5-4a91-b281-9c764ceec78c","slug":"fx7g21em","title":"Resolving the Credibility Crisis: Recommendations for Improving Predictive Algorithms for Clinical Utility","dek":null,"authors":[{"name":"Stephen Ruberg","slug":"stephen-ruberg"},{"name":"Sandeep Menon","slug":"sandeep-menon"},{"name":"Charmaine Demanuele","slug":"charmaine-demanuele"}],"date":"2023-10-27","section":"panorama","issue":"volume5issue4","doi":"10.1162/99608f92.c1292c54","abstract":"The promise of artificial intelligence (AI) and machine learning (ML) for improving clinical outcomes has been marked by modest successes, but also many failures. Access to data and to advanced algorithms makes developing AI/ML tools relatively easy—but the real challenge is demonstrating their clinical utility. Clinical predictive algorithms (CPAs)—algorithms for diagnosis or prognosis—are failing to meet their pote","keywords":["clinical predictive algorithms","machine learning","artificial intelligence","clinical outcomes","drug development","biomarker development"],"image":"/media/remote/568404550ca22438-720.webp","collections":[],"label":"Panorama"},{"id":"1d3cf75d-56d8-4fb3-a623-96883c0c0f70","slug":"dm2hrtx0","title":"Demonstrations of the Potential of AI-based Political Issue Polling","dek":null,"authors":[{"name":"Nathan E. Sanders","slug":"nathan-sanders"},{"name":"Alex Ulinich","slug":"alex-ulinich"},{"name":"Bruce Schneier","slug":"bruce-schneier"}],"date":"2023-10-27","section":"milestones-and-millstones","issue":"volume5issue4","doi":"10.1162/99608f92.1d3cf75d","abstract":"Political polling is a multi-billion-dollar industry with outsized influence on the societal trajectory of the United States and nations around the world. However, in recent years it has been severely challenged by rising nonresponse rates and other factors that stress its cost, availability, and accuracy. At the same time, artificial intelligence (AI) chatbots such as ChatGPT have become highly compelling stand-ins ","keywords":["machine learning","artificial intelligence","public polling","large language models"],"image":"/media/remote/cef3271271ac26a0-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"1a58d824-6a03-4f18-b8f4-7659b52a8daa","slug":"0i6hycwy","title":"Close to Refuge: Integrating AI and Human Insights for Intervention and Prevention: A Conversation With Seema Iyer","dek":"An interview with Seema Iyer by Xiao-Li Meng and Liberty Vittert","authors":[{"name":"Seema Iyer","slug":"seema-iyer"},{"name":"Xiao-Li Meng","slug":"xiao-li-meng"},{"name":"Liberty Vittert","slug":"liberty-vittert"}],"date":"2023-10-27","section":"panorama","issue":"volume5issue4","doi":"10.1162/99608f92.1a58d824","abstract":"Harvard Data Science Review’s Founding Editor-in-Chief, Xiao-Li Meng, and Media Feature Editor, Liberty Vittert, engage in a conversation with Dr. Seema Iyer, the Senior Director of the Hive at USA for UNHCR, the UN Refugee Agency. The Hive is the innovation lab responsible for bringing data science, machine learning, and new technologies into the organization’s operations to address the needs of refugees around the ","keywords":[],"image":"/media/remote/cd71bec7a20f2e21-720.webp","collections":[],"label":"Panorama"},{"id":"280afe66-4a68-4c5f-999a-6e66c5c923cb","slug":"by22efhx","title":"The Birth of a New Discipline: Data Science Education","dek":null,"authors":[{"name":"Koby Mike","slug":"koby-mike"},{"name":"Benny Kimelfeld","slug":"benny-kimelfeld"},{"name":"Orit Hazzan","slug":"orit-hazzan"}],"date":"2023-10-27","section":"stepping-stones","issue":"volume5issue4","doi":"10.1162/99608f92.280afe66","abstract":"Data science is emerging from an interdisciplinary integration of mathematics, statistics, computer science, and many other application domains such as business, transportation, biology, and education. Parallel to the emergence of data science, a new field is developing as well—data science education.Based on our expertise in data science education, gained in our extensive research, the main research topics of the em","keywords":["data science education","systematic literature review","SPECTER embeddings"],"image":"/media/remote/b0468f6a62884f5e-720.webp","collections":[],"label":"Stepping Stones"},{"id":"dc2a1d1b-8cc8-4d2e-9885-3ccd86a2bcf8","slug":"imun7hmg","title":"Understanding and Fostering Regional Artificial Intelligence Ecosystems: A Case Study in Maine","dek":null,"authors":[{"name":"Hamit Hamutcu","slug":"hamit-hamutcu"},{"name":"Usama Fayyad","slug":"usama-fayyad"},{"name":"Michael Pollastri","slug":"michael-pollastri"}],"date":"2023-10-27","section":"bits-and-bites","issue":"volume5issue4","doi":"10.1162/99608f92.dc2a1d1b","abstract":null,"keywords":[],"image":"/media/remote/b247f7ac6b8f9dd3-720.webp","collections":[],"label":"Bits and Bites"},{"id":"c0219d0e-23d8-49ec-ad4c-a96f3942361b","slug":"923mgyky","title":"Continuing Implementation of the Foundations for Evidence-Based Policymaking Act of 2018: Who Is Using the Data?","dek":null,"authors":[{"name":"Nancy Potok","slug":"nancy-potok"}],"date":"2023-10-27","section":"effective-policy-learning","issue":"volume5issue4","doi":"10.1162/99608f92.c0219d0e","abstract":null,"keywords":[],"image":"/media/remote/45776b9485adc54a-720.webp","collections":[],"label":"Effective Policy Learning"},{"id":"9f0adc39-5004-498a-a843-79edc59b9018","slug":"1cbv006q","title":"Perils and Opportunities of ChatGPT: A High School Perspective","dek":null,"authors":[{"name":"Nicole Lazar","slug":"nicole-lazar"},{"name":"James Byrns","slug":"james-byrns"},{"name":"Danielle Crowe","slug":"danielle-crowe"},{"name":"Meghan McGinty","slug":"meghan-mcginty"},{"name":"Angela Abraham","slug":"angela-abraham"},{"name":"Mike Guo","slug":"mike-guo"},{"name":"Megan Mann","slug":"megan-mann"},{"name":"Prithvi Narayanan","slug":"prithvi-narayanan"},{"name":"Lydia Roberts","slug":"lydia-roberts"},{"name":"Benjamin Sidore","slug":"benjamin-sidore"},{"name":"Maxwell Wager","slug":"maxwell-wager"}],"date":"2023-10-27","section":"minding-the-future","issue":"volume5issue4","doi":"10.1162/99608f92.9f0adc39","abstract":null,"keywords":[],"image":"/media/remote/7ce35ef9fe4e6747-720.webp","collections":[],"label":"Minding the Future"},{"id":"151c41e3-6a5d-487a-ac97-9ae54a709d6d","slug":"6oi846rx","title":"Crisis? What Crisis? Sociology’s Slow Progress Toward Scientific Transparency","dek":null,"authors":[{"name":"Kim A. Weeden","slug":"kim-weeden"}],"date":"2023-10-27","section":"reinforcing-rr","issue":"volume5issue4","doi":"10.1162/99608f92.151c41e3","abstract":null,"keywords":[],"image":"/media/remote/9ff3c0500f55e56d-720.webp","collections":[],"label":"Reinforcing Reproducibility and Replicability"},{"id":"33fb61ba-d833-49d1-8c78-8ff1d3d6af16","slug":"s1ssx14k","title":"Not Really a New Paradigm for Polling","dek":null,"authors":[{"name":"Murray Edelman","slug":"murray-edelman"}],"date":"2023-10-13","section":null,"issue":"volume5issue3","doi":"10.1162/99608f92.33fb61ba","abstract":null,"keywords":[],"image":"/media/remote/f98cf15095b6c1d5-720.webp","collections":[],"label":"Featured Discussion"},{"id":"4e03a698-228f-4dcd-9121-0ea0ae350a9e","slug":"yujdtjca","title":"Methodological Context and Communication on Data Quality: Discussion of Bailey (2023)","dek":null,"authors":[{"name":"John Eltinge","slug":"john-eltinge"}],"date":"2023-10-06","section":null,"issue":"volume5issue3","doi":"10.1162/99608f92.4e03a698","abstract":null,"keywords":[],"image":"/media/remote/e618c35bcc228e4e-720.webp","collections":[],"label":"Featured Discussion"},{"id":"2b901b7f-bbf7-4a13-88a3-c0bf553458c7","slug":"sqinvh4c","title":"Assuming a Nonresponse Model Does Not Make It True","dek":null,"authors":[{"name":"Sharon L. Lohr","slug":"sharon-l-lohr"}],"date":"2023-10-05","section":null,"issue":"volume5issue3","doi":"10.1162/99608f92.2b901b7f","abstract":null,"keywords":[],"image":"/media/remote/7d4655004d9c404a-720.webp","collections":[],"label":"Featured Discussion"},{"id":"49b9b14a-a55d-4262-86bf-d4c2f7897632","slug":"a1j2cxqg","title":"Paradigm Lost? Paradigm Regained? Comment on “A New Paradigm for Polling” by Michael A. Bailey","dek":null,"authors":[{"name":"Walter Dempsey","slug":"walter-dempsey"}],"date":"2023-10-05","section":null,"issue":"volume5issue3","doi":"10.1162/99608f92.49b9b14a","abstract":null,"keywords":[],"image":"/media/remote/1da969fb412c0f3d-720.webp","collections":[],"label":"Featured Discussion"},{"id":"d2379c69-d45a-4416-ac69-3dd8803fa898","slug":"tdsptiqq","title":"Challenges in Adjusting a Survey That Overrepresents People Interested in Politics","dek":null,"authors":[{"name":"Andrew Gelman","slug":"andrew-gelman"},{"name":"Gustavo Novoa","slug":"gustavo-novoa"}],"date":"2023-10-03","section":null,"issue":"volume5issue3","doi":"10.1162/99608f92.d2379c69","abstract":null,"keywords":[],"image":"/media/remote/4a51a2fac099f5d5-720.webp","collections":[],"label":"Featured Discussion"},{"id":"cfb82819-504e-4c9e-82f8-2be710a80302","slug":"vm1b9zk2","title":"An Instrumental Variable for Non-Ignorable Nonresponse","dek":null,"authors":[{"name":"Shiro Kuriwaki","slug":"shiro-kuriwaki"}],"date":"2023-10-03","section":null,"issue":"volume5issue3","doi":"10.1162/99608f92.cfb82819","abstract":null,"keywords":[],"image":"/media/remote/d1f2b43a5afb8f2e-720.webp","collections":[],"label":"Featured Discussion"},{"id":"edaeee0c-7e9f-4e58-95a3-9ff6a23e35c0","slug":"qp47w8cm","title":"From Data Defects to Response Functions: A View From Particle Physics","dek":null,"authors":[{"name":"Aurore Courtoy","slug":"aurore-courtoy"},{"name":"Pavel Nadolsky","slug":"pavel-nadolsky"}],"date":"2023-09-28","section":null,"issue":"volume5issue3","doi":"10.1162/99608f92.edaeee0c","abstract":null,"keywords":[],"image":"/media/remote/e34d84f58c7088eb-720.webp","collections":[],"label":"Featured Discussion"},{"id":"6b049957-8aad-4454-9dcd-b4680c37e18b","slug":"82q0yhk4","title":"The “Law of Large Populations” Does Not Herald a Paradigm Shift in Survey Sampling","dek":null,"authors":[{"name":"Roderick J. Little","slug":"roderick-j-little"}],"date":"2023-09-27","section":null,"issue":"volume5issue3","doi":"10.1162/99608f92.6b049957","abstract":null,"keywords":[],"image":"/media/remote/86c3fefb317d66f6-720.webp","collections":[],"label":"Featured Discussion"},{"id":"707dff70-e3ee-435b-8511-8cadb3145a9a","slug":"romcjy7d","title":"Is It Time for a New Paradigm in Biodiversity Monitoring? Lessons From Opinion Polling","dek":null,"authors":[{"name":"Robin J. Boyd","slug":"robin-j-boyd"}],"date":"2023-09-22","section":null,"issue":"volume5issue3","doi":"10.1162/99608f92.707dff70","abstract":null,"keywords":[],"image":"/media/remote/e2e0c70e6d2aa172-720.webp","collections":[],"label":"Featured Discussion"},{"id":"a02188d0-23db-4954-9f54-b6238eeabb2c","slug":"ir6swusb","title":"Seek a Paradigm and Distrust It? Statistical Arguments and the Representation of Uncertainty","dek":null,"authors":[{"name":"Oliver L. Pescott","slug":"oliver-pescott"}],"date":"2023-09-19","section":null,"issue":"volume5issue3","doi":"10.1162/99608f92.a02188d0","abstract":null,"keywords":[],"image":"/media/remote/e27a710ccb39e4a4-720.webp","collections":[],"label":"Featured Discussion"},{"id":"bc6818d3-bf5a-4fc9-8a28-a70d875d1238","slug":"s9cynk9u","title":"The Role of Sampling in Medicine","dek":null,"authors":[{"name":"Pavlos Msaouel","slug":"pavlos-msaouel"}],"date":"2023-09-12","section":null,"issue":"volume5issue3","doi":"10.1162/99608f92.bc6818d3","abstract":null,"keywords":[],"image":"/media/remote/bf43fc4d4c73a879-720.webp","collections":[],"label":"Featured Discussion"},{"id":"8a5f2975-0b13-4d71-9a67-b1f38d67a178","slug":"lgxfbdh0","title":"AI (Agnostic and Independent) Data Science: Is It Possible?","dek":null,"authors":[{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2023-07-27","section":"editorscolumn","issue":"volume5issue3","doi":"10.1162/99608f92.8a5f2975","abstract":null,"keywords":[],"image":"/media/remote/e6d0146fa5888c0a-720.webp","collections":[],"label":"Editor’s Column"},{"id":"545db2cf-755e-402f-b1ce-6597c7ae61d2","slug":"9n4uzkg3","title":"Critical Data Challenges in Measuring the Performance of Sustainable Development Goals: Solutions and the Role of Big-Data Analytics","dek":null,"authors":[{"name":"Mehrbakhsh Nilashi","slug":"mehrbakhsh-nilashi"},{"name":"Ooi Keng Boon","slug":"ooi-keng-boon"},{"name":"Garry Tan","slug":"garry-tan"},{"name":"Binshan Lin","slug":"binshan-lin"},{"name":"Rabab Abumalloh","slug":"rabab-abumalloh"}],"date":"2023-07-27","section":"cornucopia","issue":"volume5issue3","doi":"10.1162/99608f92.545db2cf","abstract":"In 2015, 193 member states of the United Nations (UN) adopted a 15-year sustainable plan—the 2030 Agenda—to work toward achieving the Sustainable Development Goals (SDGs) by 2030. The SDGs are a set of specific, measurable, and time-sensitive goals for national development. The international community evaluates the SDGs using indicators based on available data and various methodological developments. Thus, effective ","keywords":["sustainable development goals","big data analytics","data quality","data challenges","SWOT","performance"],"image":"/media/remote/6d9e7ed1e50f5601-720.webp","collections":[],"label":"Cornucopia"},{"id":"712d4124-20de-4aef-91ab-8274c2cd56ca","slug":"vh39oxit","title":"A Conversation With Mercè Crosas","dek":"An interview with Mercè Crosas by Xiao-Li Meng","authors":[{"name":"Merce Crosas","slug":"merce-crosas"},{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2023-07-27","section":"panorama","issue":"volume5issue3","doi":"10.1162/99608f92.712d4124","abstract":"Harvard Data Science Review’s Founding Editor-in-Chief, Xiao-Li Meng, recently met with Dr. Mercè Crosas, Head of the Computational Social Sciences Program at the Barcelona Supercomputing Center. Dr. Crosas has had a substantial and extensive professional life, having worked in leadership roles in academia, industry, and government. Her previous positions include director of software development in the educational so","keywords":[],"image":"/media/remote/eb4c6ecc3784a99c-720.webp","collections":["conversations-with-leaders"],"label":"Panorama"},{"id":"9ba2bd43-f77c-4111-a329-26fa7d937804","slug":"l8dmf3cm","title":"Reinforcing Reproducibility and Replicability: An Introduction","dek":null,"authors":[{"name":"Lars Vilhuber","slug":"lars-vilhuber"},{"name":"Ian Schmutte","slug":"ian-schmutte"},{"name":"Aleksandr Michuda","slug":"aleksandr-michuda"},{"name":"Marie Connolly","slug":"marie-connolly"}],"date":"2023-07-27","section":"reinforcing-rr","issue":"volume5issue3","doi":"10.1162/99608f92.9ba2bd43","abstract":null,"keywords":[],"image":"/media/remote/86e17b01490e121a-720.webp","collections":[],"label":"Reinforcing Reproducibility and Replicability"},{"id":"80a1b88b-f41d-44f0-b636-64cf2276ca95","slug":"lr5qgw5t","title":"Reproducibility in Economics: Status and Update","dek":null,"authors":[{"name":"Hilary Hoynes","slug":"hilary-hoynes"}],"date":"2023-07-27","section":"reinforcing-rr","issue":"volume5issue3","doi":"10.1162/99608f92.80a1b88b","abstract":null,"keywords":[],"image":"/media/remote/67802710f62481be-720.webp","collections":[],"label":"Reinforcing Reproducibility and Replicability"},{"id":"a631dfc5-8fcb-4676-8554-de831aedf0ae","slug":"qhszdvhn","title":"Open Data and Code at the Urban Institute","dek":null,"authors":[{"name":"Graham MacDonald","slug":"graham-macdonald"}],"date":"2023-07-27","section":"reinforcing-rr","issue":"volume5issue3","doi":"10.1162/99608f92.a631dfc5","abstract":null,"keywords":[],"image":"/media/remote/391cdc59677955a3-720.webp","collections":[],"label":"Reinforcing Reproducibility and Replicability"},{"id":"54a00239-5486-41e2-969d-af12e3d15c6a","slug":"rl6maajr","title":"Reproducibility With Confidential Data: The Experience of BPLIM","dek":null,"authors":[{"name":"Paulo Guimarães","slug":"paulo-guimaraes"}],"date":"2023-07-27","section":"reinforcing-rr","issue":"volume5issue3","doi":"10.1162/99608f92.54a00239","abstract":null,"keywords":[],"image":"/media/remote/dd123e2fb77818eb-720.webp","collections":[],"label":"Reinforcing Reproducibility and Replicability"},{"id":"c2835391-abdd-4308-8dfe-a70045568fcc","slug":"ohexlllx","title":"Data Citations and Reproducibility in the Undergraduate Curriculum","dek":null,"authors":[{"name":"Diego Mendez-Carbajo","slug":"diego-mendez-carbajo"},{"name":"Alejandro Dellachiesa","slug":"alejandro-dellachiesa"}],"date":"2023-07-27","section":"reinforcing-rr","issue":"volume5issue3","doi":"10.1162/99608f92.c2835391","abstract":null,"keywords":[],"image":"/media/remote/79c34427285cacac-720.webp","collections":[],"label":"Reinforcing Reproducibility and Replicability"},{"id":"db2a2554-dcea-4521-83a9-172a130466d5","slug":"z9fpx2js","title":"The Case for Data Archives at Journals","dek":null,"authors":[{"name":"Timothy C. Salmon","slug":"tim-salmon"}],"date":"2023-07-27","section":"reinforcing-rr","issue":"volume5issue3","doi":"10.1162/99608f92.db2a2554","abstract":null,"keywords":[],"image":"/media/remote/c771abc6511a724c-720.webp","collections":[],"label":"Reinforcing Reproducibility and Replicability"},{"id":"63de8e58-8983-4e4b-aa4f-ac0041fa90c4","slug":"mnhzk8gq","title":"Costs and Benefits of Reproducibility in Finance and Economics","dek":null,"authors":[{"name":"Toni Whited","slug":"toni-whited"}],"date":"2023-07-27","section":"reinforcing-rr","issue":"volume5issue3","doi":"10.1162/99608f92.63de8e58","abstract":null,"keywords":[],"image":"/media/remote/ce948def5fbbc498-720.webp","collections":[],"label":"Reinforcing Reproducibility and Replicability"},{"id":"9e002f7b-20a9-40a5-a642-10e80a5a639a","slug":"mza9ii75","title":"“Yes We Can!”: A Practical Approach to Teaching Reproducibility to Undergraduates","dek":null,"authors":[{"name":"Richard Ball","slug":"richard-ball"}],"date":"2023-07-27","section":"reinforcing-rr","issue":"volume5issue3","doi":"10.1162/99608f92.9e002f7b","abstract":null,"keywords":[],"image":"/media/remote/1b6012bd94e13fe8-720.webp","collections":[],"label":"Reinforcing Reproducibility and Replicability"},{"id":"aba61304-02c7-476a-950e-6313bb687572","slug":"j0ny4jrg","title":"Publishing Replication Packages: Insights From the Federal Reserve Bank of Kansas City","dek":null,"authors":[{"name":"Courtney R. Butler","slug":"courtney-butler"}],"date":"2023-07-27","section":"reinforcing-rr","issue":"volume5issue3","doi":"10.1162/99608f92.aba61304","abstract":null,"keywords":[],"image":"/media/remote/97b0cc3fdbaf407d-720.webp","collections":[],"label":"Reinforcing Reproducibility and Replicability"},{"id":"dab5e00b-555f-43ed-bfe2-4377e6509259","slug":"a9ztnvn4","title":"Looking at the Weather: The Politics of Meteorological Data","dek":null,"authors":[{"name":"Elaine LaFay","slug":"elaine-lafay"}],"date":"2023-07-27","section":"mining-the-past","issue":"volume5issue3","doi":"10.1162/99608f92.dab5e00b","abstract":null,"keywords":[],"image":"/media/remote/2fb002478e622fb8-720.webp","collections":[],"label":"Mining the Past"},{"id":"97c9f60f-7732-4c1d-9efa-c6b0c8d5134c","slug":"8wsiqh1c","title":"Fostering Better Coding Practices for Data Scientists","dek":null,"authors":[{"name":"Randall Pruim","slug":"randall-pruim"},{"name":"Maria-Cristiana Gîrjău","slug":"maria-cristiana-girjau"},{"name":"Nicholas Jon Horton","slug":"nicholas-horton"}],"date":"2023-07-27","section":"stepping-stones","issue":"volume5issue3","doi":"10.1162/99608f92.97c9f60f","abstract":"Many data science students and practitioners do not see the value in making time to learn and adopt good coding practices as long as the code ‘works.’ However, code standards are an important part of modern data science practice, and they play an essential role in the development of data acumen. Good coding practices lead to more reliable code and save more time than they cost, making them important even for beginner","keywords":["data acumen","data science","data science practice","data science education","code quality","code style"],"image":"/media/remote/601c3305c59717c4-720.webp","collections":[],"label":"Stepping Stones"},{"id":"9898eede-aa62-46c1-8e1c-688b8162dbc8","slug":"ejk5yhgv","title":"A New Paradigm for Polling","dek":null,"authors":[{"name":"Michael A. Bailey","slug":"michael-bailey-2"}],"date":"2023-07-27","section":"milestones-and-millstones","issue":"volume5issue3","doi":"10.1162/99608f92.9898eede","abstract":"Scientific fields operate within paradigms that define problems and solutions for a community of researchers. The dominant paradigm in polling centers on random sampling, which is unfortunate because random sampling is, for all practical purposes, dead. The pollsters who try to produce random samples fail because hardly anyone responds. And more and more pollsters do not even try. The field therefore has folded weigh","keywords":["survey nonresponse","survey design","survey methods"],"image":"/media/remote/a7182eae1cdcb1bd-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"5cc8971c-38ba-4046-83fc-ccdbf78d4cf8","slug":"qgbl0eb5","title":"Safeguarding Facts in an Era of Disinformation: The Case for Independently Monitoring the U.S. Statistical System","dek":null,"authors":[{"name":"Jonathan Auerbach","slug":"jonathan-auerbach"}],"date":"2023-07-27","section":"effective-policy-learning","issue":"volume5issue3","doi":"10.1162/99608f92.5cc8971c","abstract":null,"keywords":[],"image":"/media/remote/6811bcb463682fb4-720.webp","collections":[],"label":"Effective Policy Learning"},{"id":"68a012eb-a28d-4fe7-8e64-9cbf51601656","slug":"812vijgg","title":"Data Science and Engineering With Human in the Loop, Behind the Loop, and Above the Loop","dek":null,"authors":[{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2023-04-27","section":"editorscolumn","issue":"volume5issue2","doi":"10.1162/99608f92.68a012eb","abstract":null,"keywords":[],"image":"/media/remote/36b4ebdc824cee7d-720.webp","collections":["data-science-for-business-and-industry"],"label":"Editor’s Column"},{"id":"1f068331-8537-4d48-a34a-f058b20bbf37","slug":"wfr9k3vq","title":"Managing Embedded Data Science Teams for Success: How Managers Can Navigate the Advantages and Challenges of Distributed Data Science","dek":null,"authors":[{"name":"Marta Stelmaszak","slug":"marta-stelmaszak-rosa"},{"name":"Kelsey Kline","slug":"kelsey-kline"}],"date":"2023-04-27","section":"stepping-stones","issue":"volume5issue2","doi":"10.1162/99608f92.1f068331","abstract":"Organizations that invest in developing data science capabilities can opt for, among other choices, embedding data science teams in functional departments. While this structure promises more success in analytics projects by connecting them closely with specific needs, providing dedicated resources, and focusing expertise, challenges are in the way of securing these benefits. We conducted a case study of a data scienc","keywords":["data science teams","data science projects","managing data science","data science","analytics"],"image":"/media/remote/1d24e9802ddf3aaf-720.webp","collections":["data-science-for-business-and-industry"],"label":"Stepping Stones"},{"id":"35281d57-50df-42aa-afa9-32f6f460a484","slug":"x716gu6b","title":"Remembering David Cox","dek":null,"authors":[{"name":"Sylvia Richardson","slug":"sylvia-richardson-2"},{"name":"Nanny Wermuth","slug":"nanny-wermuth"}],"date":"2023-04-27","section":"panorama","issue":"volume5issue2","doi":"10.1162/99608f92.35281d57","abstract":null,"keywords":[],"image":"/media/remote/893d1cd50197476a-720.webp","collections":["celebrating-sir-david-cox"],"label":"Panorama"},{"id":"1ea13423-8ce1-4d95-a510-545b9cb4411d","slug":"g7fzhuzj","title":"Innovating a Centuries-Old Sport: How Emerging Data Analytics Tools Are Redefining Cricket","dek":null,"authors":[{"name":"Kavya Mehul Shah","slug":"kavya-shah"},{"name":"Ammaar Ahmed Saeed","slug":"ammaar-saeed"}],"date":"2023-04-27","section":"recreations-in-randomness","issue":"volume5issue2","doi":"10.1162/99608f92.1ea13423","abstract":null,"keywords":[],"image":"/media/remote/b6f0849fdf85c776-720.webp","collections":[],"label":"Recreations in Randomness"},{"id":"eea782bf-d202-460d-a223-4a28b40f9566","slug":"59k7dz80","title":"Frontline Data Science: Lessons Learned From a Pandemic","dek":null,"authors":[{"name":"Emily A. Beck","slug":"emily-a-beck"},{"name":"Hannah Tavalire","slug":"hannah-tavalire"},{"name":"Jake Searcy","slug":"jake-searcy"}],"date":"2023-04-27","section":"panorama","issue":"volume5issue2","doi":"10.1162/99608f92.eea782bf","abstract":"Disasters like the COVID-19 pandemic highlight the importance and difficulties of using data science to craft and implement effective disaster responses. While data science solutions are vital to the effectiveness and, importantly, the scaling of any frontline disaster response, lack of accessibility, training, and flexibility of these solutions can be extremely problematic. Here we outline our vision of Frontline Da","keywords":["SARS-CoV-2","COVID-19 pandemic","accessibility","interoperability","public health","disaster recovery"],"image":"/media/remote/0c4fb111cf9dde4c-720.webp","collections":[],"label":"Panorama"},{"id":"7a329be7-ad31-492d-a2f9-d3c63e2a496c","slug":"palmqnaw","title":"Digital Transformation of Financial Regulators and the Emergence of Supervisory Technologies (SupTech): A Case Study of the U.K. Financial Conduct Authority","dek":null,"authors":[{"name":"Pavle Avramović","slug":"pavle-avramovic"}],"date":"2023-04-27","section":"cornucopia","issue":"volume5issue2","doi":"10.1162/99608f92.7a329be7","abstract":"The ability of financial regulators to supervise the fast-evolving and technologically enabled world of financial services is uncertain. Scars from the 2007–2008 crisis remain and, with novel technological approaches being deployed at an unprecedented rate, the question of regulatory ability is often overlooked. How can financial regulators meet the challenges of this growing ecosystem and prevent harmful outcomes fo","keywords":["digital transformation","SupTech","financial regulation","innovation"],"image":"/media/remote/04d7dc2b8733577d-720.webp","collections":[],"label":"Cornucopia"},{"id":"aedb3266-8b51-432f-b30a-1a717ac850af","slug":"yiwo2meq","title":"D. R. Cox: Extracts From a Memorial Lecture","dek":null,"authors":[{"name":"Heather Battey","slug":"heather-battey"}],"date":"2023-04-27","section":null,"issue":"volume5issue2","doi":"10.1162/99608f92.aedb3266","abstract":null,"keywords":[],"image":"/media/remote/aa91be2f46e09fd5-720.webp","collections":["celebrating-sir-david-cox"],"label":"Celebrating Sir David Cox"},{"id":"5d66f6d4-a106-41a8-aeed-3177363eaf3d","slug":"e75o7f79","title":"Reflection on David Cox","dek":null,"authors":[{"name":"Thomas B. Berrett","slug":"thomas-b-berrett"}],"date":"2023-04-27","section":null,"issue":"volume5issue2","doi":"10.1162/99608f92.5d66f6d4","abstract":null,"keywords":[],"image":"/media/remote/a2db5b2ab91793d6-720.webp","collections":["celebrating-sir-david-cox"],"label":"Celebrating Sir David Cox"},{"id":"85038493-d4ba-4936-880f-0005b22e17f5","slug":"23ads6wx","title":"Some Reminiscences of David Cox","dek":null,"authors":[{"name":"A. C. Davison","slug":"a-c-davison"}],"date":"2023-04-27","section":null,"issue":"volume5issue2","doi":"10.1162/99608f92.85038493","abstract":null,"keywords":[],"image":"/media/remote/f93bcb984e798c39-720.webp","collections":["celebrating-sir-david-cox"],"label":"Celebrating Sir David Cox"},{"id":"25212c73-d191-47f4-819f-25c61f1324ff","slug":"ojfi5nnw","title":"Sir David Cox—A Life Well Lived","dek":null,"authors":[{"name":"Christl A. Donnelly","slug":"christl-a-donnelly"}],"date":"2023-04-27","section":null,"issue":"volume5issue2","doi":"10.1162/99608f92.25212c73","abstract":null,"keywords":[],"image":"/media/remote/8f3e0c4e2a6a81a8-720.webp","collections":["celebrating-sir-david-cox"],"label":"Celebrating Sir David Cox"},{"id":"87cef65e-79c5-408a-bee0-ba78a490f558","slug":"0fet3epf","title":"Remembering David Cox","dek":null,"authors":[{"name":"Bradley Efron","slug":"bradley-efron"}],"date":"2023-04-27","section":null,"issue":"volume5issue2","doi":"10.1162/99608f92.87cef65e","abstract":null,"keywords":[],"image":"/media/remote/e251bef4a4fa8e4a-720.webp","collections":["celebrating-sir-david-cox"],"label":"Celebrating Sir David Cox"},{"id":"259559bf-8009-4b8e-a58f-1818f56a5994","slug":"wr3ywa16","title":"A Note on Knowing David Cox","dek":null,"authors":[{"name":"Vernon Farewell","slug":"vernon-farewell"}],"date":"2023-04-27","section":null,"issue":"volume5issue2","doi":"10.1162/99608f92.259559bf","abstract":null,"keywords":[],"image":"/media/remote/99625d276fcea611-720.webp","collections":["celebrating-sir-david-cox"],"label":"Celebrating Sir David Cox"},{"id":"f27cc65f-2256-4b00-806f-b22b900614d8","slug":"sitoe5a3","title":"Sir David Cox: A Few Memories","dek":null,"authors":[{"name":"David Firth","slug":"david-firth"}],"date":"2023-04-27","section":null,"issue":"volume5issue2","doi":"10.1162/99608f92.f27cc65f","abstract":null,"keywords":[],"image":"/media/remote/231ae19867dd4e64-720.webp","collections":["celebrating-sir-david-cox"],"label":"Celebrating Sir David Cox"},{"id":"f1b5b1ba-abf1-4e8a-a493-cfee17960723","slug":"8lge4bz6","title":"Sir David Cox: Some Early Reminiscences","dek":null,"authors":[{"name":"Agnes M. Herzberg","slug":"agnes-m-herzberg"}],"date":"2023-04-27","section":null,"issue":"volume5issue2","doi":"10.1162/99608f92.f1b5b1ba","abstract":null,"keywords":[],"image":"/media/remote/c9676000c271a61b-720.webp","collections":["celebrating-sir-david-cox"],"label":"Celebrating Sir David Cox"},{"id":"0c40a235-4d50-47b5-a7a5-73d25d233384","slug":"l3s3z944","title":"Celebrating the Life and Work of Sir David Cox","dek":null,"authors":[{"name":"Jane L. Hutton","slug":"jane-l-hutton"}],"date":"2023-04-27","section":null,"issue":"volume5issue2","doi":"10.1162/99608f92.0c40a235","abstract":null,"keywords":[],"image":"/media/remote/9156433f3bb9427c-720.webp","collections":["celebrating-sir-david-cox"],"label":"Celebrating Sir David Cox"},{"id":"8d7d48fd-a13d-457d-8594-214bae34b5cd","slug":"iku06my4","title":"Memories of Sir David Cox: Friendship and Collaboration","dek":null,"authors":[{"name":"Valerie Isham","slug":"valerie-isham"}],"date":"2023-04-27","section":null,"issue":"volume5issue2","doi":"10.1162/99608f92.8d7d48fd","abstract":null,"keywords":[],"image":"/media/remote/7156566f9ebe115f-720.webp","collections":["celebrating-sir-david-cox"],"label":"Celebrating Sir David Cox"},{"id":"fe700a61-8157-4d25-babd-7873dea2f456","slug":"g307y344","title":"David R. Cox: Some Reflections","dek":null,"authors":[{"name":"Christiana Kartsonaki","slug":"christiana-kartsonaki"}],"date":"2023-04-27","section":null,"issue":"volume5issue2","doi":"10.1162/99608f92.fe700a61","abstract":null,"keywords":[],"image":"/media/remote/119f52a6a1bd8354-720.webp","collections":["celebrating-sir-david-cox"],"label":"Celebrating Sir David Cox"},{"id":"8e56c54f-85cb-448a-a25e-5cea83c36da1","slug":"5az5w257","title":"D. R. Cox: Approach to Applied Statistics and the Importance of Being Sensible","dek":null,"authors":[{"name":"Ruth H. Keogh","slug":"ruth-keogh"}],"date":"2023-04-27","section":null,"issue":"volume5issue2","doi":"10.1162/99608f92.8e56c54f","abstract":null,"keywords":[],"image":"/media/remote/847e0df89c341701-720.webp","collections":["celebrating-sir-david-cox"],"label":"Celebrating Sir David Cox"},{"id":"d4a3ece7-53e2-4876-91cb-20ea4e604367","slug":"fk3tf6yd","title":"Early Interactions With David Cox","dek":null,"authors":[{"name":"Peter McCullagh","slug":"peter-mccullagh"}],"date":"2023-04-27","section":null,"issue":"volume5issue2","doi":"10.1162/99608f92.d4a3ece7","abstract":null,"keywords":[],"image":"/media/remote/bb7ef6ec6b57da0d-720.webp","collections":["celebrating-sir-david-cox"],"label":"Celebrating Sir David Cox"},{"id":"81172cdd-a9da-436b-b737-8b80283f7158","slug":"mlb5z86w","title":"On “A conversation with Sir David Cox”","dek":null,"authors":[{"name":"Nancy Reid","slug":"nancy-reid"}],"date":"2023-04-27","section":null,"issue":"volume5issue2","doi":"10.1162/99608f92.81172cdd","abstract":null,"keywords":[],"image":"/media/remote/833a685aac8d0f3e-720.webp","collections":["celebrating-sir-david-cox"],"label":"Celebrating Sir David Cox"},{"id":"49571926-e073-47df-8344-ef2a87d3b9db","slug":"cs1jl4k4","title":"Reflections on Sir David Cox","dek":null,"authors":[{"name":"Dominik Rothenhäusler","slug":"dominik-rothenhausler"}],"date":"2023-04-27","section":null,"issue":"volume5issue2","doi":"10.1162/99608f92.49571926","abstract":null,"keywords":[],"image":"/media/remote/da22267c26f31049-720.webp","collections":["celebrating-sir-david-cox"],"label":"Celebrating Sir David Cox"},{"id":"701c8e31-3249-44bd-a137-1ae040ad167d","slug":"4hajv18m","title":"In Honor of David Cox","dek":null,"authors":[{"name":"Andrea Rotnitzky","slug":"andrea-rotnitzky"}],"date":"2023-04-27","section":null,"issue":"volume5issue2","doi":"10.1162/99608f92.701c8e31","abstract":null,"keywords":[],"image":"/media/remote/67708b09af0a3922-720.webp","collections":["celebrating-sir-david-cox"],"label":"Celebrating Sir David Cox"},{"id":"0d5193ff-4053-4eee-bfd0-207798b1b3c3","slug":"qpgr28nf","title":"Interactions With Sir David R. Cox","dek":null,"authors":[{"name":"Neil Shephard","slug":"neil-shephard"}],"date":"2023-04-27","section":null,"issue":"volume5issue2","doi":"10.1162/99608f92.0d5193ff","abstract":null,"keywords":[],"image":"/media/remote/3e3c31df0687bcb4-720.webp","collections":["celebrating-sir-david-cox"],"label":"Celebrating Sir David Cox"},{"id":"6a4a57fc-0dc3-4c56-b05e-b7e8532090a8","slug":"qj4z4dj0","title":"The Impact of David Cox’s Work and Leadership on My Research","dek":null,"authors":[{"name":"Chengchun Shi","slug":"chengchun-shi"}],"date":"2023-04-27","section":null,"issue":"volume5issue2","doi":"10.1162/99608f92.6a4a57fc","abstract":null,"keywords":[],"image":"/media/remote/4c4e8557d9d62768-720.webp","collections":["celebrating-sir-david-cox"],"label":"Celebrating Sir David Cox"},{"id":"971da134-68f0-4ea4-8cbf-794a9469fda3","slug":"texxg1kz","title":"From Inspiration to Impact: Sir David Cox's Influence on My Research","dek":null,"authors":[{"name":"Tengyao Wang","slug":"tengyao-wang"}],"date":"2023-04-27","section":null,"issue":"volume5issue2","doi":"10.1162/99608f92.971da134","abstract":null,"keywords":[],"image":"/media/remote/10003c2d8fe50443-720.webp","collections":["celebrating-sir-david-cox"],"label":"Celebrating Sir David Cox"},{"id":"73fc83a3-b332-4331-9738-a22384d826f5","slug":"2z4dnhds","title":"To Deploy Machine Learning, You Must Manage Operational Change—Here Is How UPS Got It Right","dek":null,"authors":[{"name":"Eric Siegel","slug":"eric-siegel"}],"date":"2023-04-27","section":"cornucopia","issue":"volume5issue2","doi":"10.1162/99608f92.73fc83a3","abstract":"The greatest tool is the hardest to use. Machine learning has earned its title as “the most important general-purpose technology of our era” (Brynjolfsson & McAfee, 2017)—but it is notoriously difficult to launch. Outside a handful of leading companies, machine learning initiatives routinely fail to deploy and improve business operations. On the one hand, the world has become enamored with machine learning as the imp","keywords":["machine learning deployment","machine learning leadership","machine learning management","machine learning project success","business analytics","data science"],"image":"/media/remote/6ed70230e7f63224-720.webp","collections":["data-science-for-business-and-industry"],"label":"Cornucopia"},{"id":"74fb2783-96c4-4dad-a3a6-89ed29298b7c","slug":"hhcrzne1","title":"Sir David R. Cox: A Beautiful Mind With a Beautiful Heart","dek":null,"authors":[{"name":"Joseph K. Blitzstein","slug":"joseph-k-blitzstein"}],"date":"2023-04-27","section":"bits-and-bites","issue":"volume5issue2","doi":"10.1162/99608f92.74fb2783","abstract":null,"keywords":[],"image":"/media/remote/9fffe9557149e474-720.webp","collections":["celebrating-sir-david-cox"],"label":"Bits and Bites"},{"id":"018bf012-b53c-4503-bf44-8892c174639f","slug":"f0f7h5cu","title":"Software Engineering Practices in Academia: Promoting the 3Rs—Readability, Resilience, and Reuse","dek":null,"authors":[{"name":"Andrew Connolly","slug":"andrew-connolly"},{"name":"Joseph Hellerstein","slug":"joseph-hellerstein"},{"name":"Naomi Alterman","slug":"naomi-alterman"},{"name":"David Beck","slug":"david-beck-2"},{"name":"Rob Fatland","slug":"rob-fatland"},{"name":"Ed Lazowska","slug":"ed-lazowska"},{"name":"Vani Mandava","slug":"vani-mandava"},{"name":"Sarah Stone","slug":"sarah-stone"}],"date":"2023-04-27","section":"milestones-and-millstones","issue":"volume5issue2","doi":"10.1162/99608f92.018bf012","abstract":"Over the past decade as data science has become integral to the research workflow, we, like many others, have learned that good data science requires high-quality software engineering. Unfortunately, our experience is that many data science projects can be limited by the absence of software engineering processes. We advocate that data science projects should incorporate what we call the 3Rs of software engineering: r","keywords":["software engineering","reproducibility","reuse","data science education"],"image":"/media/remote/0bba057cc2d4c336-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"2596b714-af4c-4ea8-9699-309e557fb67f","slug":"caxxndai","title":"Rejoinder: The Emerging Role of Data Science in Cybersecurity","dek":null,"authors":[{"name":"Alfred Hero","slug":"alfred-hero"},{"name":"Soummya Kar","slug":"soummya-kar"},{"name":"Jose Moura","slug":"jose-moura"},{"name":"Joshua Neil","slug":"joshua-neil"},{"name":"H. Vincent Poor","slug":"h-vincent-poor"},{"name":"Melissa Turcotte","slug":"melissa-turcotte"},{"name":"Bowei Xi","slug":"bowei-xi"}],"date":"2023-04-20","section":null,"issue":"volume5issue1","doi":"10.1162/99608f92.2596b714","abstract":null,"keywords":[],"image":"/media/remote/641db752a91e9fd1-720.webp","collections":[],"label":"Featured Discussion"},{"id":"7b8b6a89-c637-4acb-a705-a6c10b722a89","slug":"c5ikncc8","title":"Challenges for Anomaly Detection in Large-Scale Cyber-Physical Systems","dek":null,"authors":[{"name":"George Michailidis","slug":"george-michailidis"}],"date":"2023-03-20","section":null,"issue":"volume5issue1","doi":"10.1162/99608f92.7b8b6a89","abstract":null,"keywords":[],"image":"/media/remote/dc6026a152424948-720.webp","collections":[],"label":"Featured Discussion"},{"id":"3a330a0b-6274-4392-a9bf-dae894ea5f20","slug":"t1e5g8jf","title":"Trust and Physical Layer Security for 6G Cyber-Physical Systems","dek":null,"authors":[{"name":"Arsenia Chorti","slug":"arsenia-chorti"}],"date":"2023-03-20","section":null,"issue":"volume5issue1","doi":"10.1162/99608f92.3a330a0b","abstract":null,"keywords":[],"image":"/media/remote/6593ee569cda8797-720.webp","collections":[],"label":"Featured Discussion"},{"id":"75522fed-7334-4c8c-b11c-0820d95a0133","slug":"vso25wv1","title":"Securing Distributed Wireless Edge Networks Via Information-Theoretic Security Approaches","dek":null,"authors":[{"name":"Yingbin Liang","slug":"yingbin-liang"}],"date":"2023-03-20","section":null,"issue":"volume5issue1","doi":"10.1162/99608f92.75522fed","abstract":null,"keywords":[],"image":"/media/remote/1a029ded1f9fe7ee-720.webp","collections":[],"label":"Featured Discussion"},{"id":"9934f8a6-836f-41cf-8ecd-aec91f85d7a8","slug":"113dbn76","title":"Data Quality: Comments on “Statistics and Data Science for Cybersecurity”","dek":null,"authors":[{"name":"Dennis K. J. Lin","slug":"dennis-k-j-lin"}],"date":"2023-03-20","section":null,"issue":"volume5issue1","doi":"10.1162/99608f92.9934f8a6","abstract":null,"keywords":[],"image":"/media/remote/97d3202c3400ed83-720.webp","collections":[],"label":"Featured Discussion"},{"id":"240383c7-139b-4392-9c1a-409df63f72cd","slug":"tz4vvp1h","title":"Statistical Cybersecurity: A Brief Discussion of Challenges, Data Structures, and Future Directions","dek":null,"authors":[{"name":"Francesco Sanna Passino","slug":"francesco-sanna-passino"},{"name":"Niall M. Adams","slug":"niall-m-adams"},{"name":"Edward A.K. Cohen","slug":"edward-a-k-cohen"},{"name":"Marina Evangelou","slug":"marina-evangelou"},{"name":"Nicholas A. Heard","slug":"nicholas-a-heard"}],"date":"2023-03-20","section":null,"issue":"volume5issue1","doi":"10.1162/99608f92.240383c7","abstract":null,"keywords":[],"image":"/media/remote/a62b4e79b3e56aaf-720.webp","collections":[],"label":"Featured Discussion"},{"id":"8e43a2d2-f686-4de1-a5a9-d7b11cd9c101","slug":"5t8n1cnt","title":"Response to Kenny et al.’s Commentary","dek":null,"authors":[{"name":"danah boyd","slug":"danah-boyd"},{"name":"Jayshree Sarathy","slug":"jayshree-sarathy"}],"date":"2023-02-27","section":null,"issue":"specialissue2","doi":"10.1162/99608f92.8e43a2d2","abstract":null,"keywords":[],"image":"/media/remote/3b29a53fd7f019ba-720.webp","collections":["data-science-for-governing-and-policy-making"],"label":"Commentary"},{"id":"abc2c765-e33d-4f83-b7d8-ecb14ff5c96f","slug":"6ffzuq19","title":"Comment: The Essential Role of Policy Evaluation for the 2020 Census DisclosureAvoidance System","dek":null,"authors":[{"name":"Christopher T. Kenny","slug":"christopher-kenny"},{"name":"Shiro Kuriwaki","slug":"shiro-kuriwaki"},{"name":"Cory McCartan","slug":"cory-mccartan"},{"name":"Evan T. R. Rosenman","slug":"evan-rosenman"},{"name":"Tyler Simko","slug":"tyler-simko"},{"name":"Kosuke Imai","slug":"kosuke-imai"}],"date":"2023-01-31","section":null,"issue":"specialissue2","doi":"10.1162/99608f92.abc2c765","abstract":"In “Differential Perspectives: Epistemic Disconnects Surrounding the U.S. Census Bureau’s Use of Differential Privacy,” boyd and Sarathy argue that empirical evaluations of the Census Disclosure Avoidance System (DAS), including our published analysis (Kenny et al., 2021b), failed to recognize that the benchmark data against which the 2020 DAS was evaluated is never a ground truth of population counts. In this commen","keywords":["BISG","data utility","differential privacy","redistricting","swapping"],"image":"/media/remote/ff4994782a94c741-720.webp","collections":[],"label":"Commentary"},{"id":"d3b2eadd-5a18-4f83-9c03-b4e58d1849b2","slug":"rwkctrgm","title":"Motivating Data Science Students to Participate and Learn","dek":null,"authors":[{"name":"Deniz Marti","slug":"deniz-marti"},{"name":"Michael D. Smith","slug":"michael-smith-4"}],"date":"2023-01-26","section":"stepping-stones","issue":"volume5issue1","doi":"10.1162/99608f92.d3b2eadd","abstract":"Data science education increasingly involves human subjects and societal issues such as privacy, ethics, and fairness. Data scientists need to be equipped with skills to tackle the complexities of the societal context surrounding their data science work. In this article, we offer insights into how to structure our data science classes so that they motivate students to deeply engage with material about societal contex","keywords":["education","assessment","data science","critical thinking","motivation","class participation"],"image":"/media/remote/1101f8f66d650807-720.webp","collections":[],"label":"Stepping Stones"},{"id":"c18db966-a6ad-4183-9f89-f8ed002ba352","slug":"1qxkrnig","title":"A Review of Data Valuation Approaches and Building and Scoring a Data Valuation Model","dek":null,"authors":[{"name":"Mike Fleckenstein","slug":"mike-fleckenstein"},{"name":"Ali Obaidi","slug":"ali-obaidi"},{"name":"Nektaria Tryfona","slug":"nektaria-tryfona"}],"date":"2023-01-26","section":"cornucopia","issue":"volume5issue1","doi":"10.1162/99608f92.c18db966","abstract":"Data valuation has been given increasing thought for the past 20 years. The importance of data as an asset in both the private and public sectors has systematically increased, and organizations are striving to treat it as such. However, this remains a challenge, as data is an intangible asset. Today, there is no standard to measure the value of data. Different approaches include market-based valuation, economic model","keywords":["data valuation","intangibles","data as an asset","data monetization","data valuation model","data valuation framework"],"image":"/media/remote/814d0b61d6d88cdb-720.webp","collections":[],"label":"Cornucopia"},{"id":"4708d42a-c7bf-4299-bcd4-ffc701588e93","slug":"i8byg207","title":"Cultures and Value of Data Science, Data Science Education, Cybersecurity, and More!","dek":null,"authors":[{"name":"Francesca Dominici","slug":"francesca-dominici"}],"date":"2023-01-26","section":"editorscolumn","issue":"volume5issue1","doi":"10.1162/99608f92.4708d42a","abstract":null,"keywords":[],"image":"/media/remote/2551e274e15e0ac0-720.webp","collections":[],"label":"Editor’s Column"},{"id":"3e516c04-968c-4485-a840-35451790e1c9","slug":"b2tswdg6","title":"Better Metrics to Guide Public Health Policy: Lessons Learned From COVID-19 for Data Systems Improvement","dek":null,"authors":[{"name":"Michael A. Stoto","slug":"michael-stoto"},{"name":"John D. Kraemer","slug":"john-d-kraemer"},{"name":"Rachael Piltch-Loeb","slug":"rachael-piltch-loeb"}],"date":"2023-01-26","section":"cornucopia","issue":"volume5issue1","doi":"10.1162/99608f92.3e516c04","abstract":"As communities adjust COVID-19 policies, objective ‘metrics’ are needed to guide decisions. But metrics are only as good as their inputs. Most COVID-19 data are based on case surveillance. Individuals’ decisions to be tested depend on concerns about exposure and symptoms, as well as the availability of testing. These factors cause drop-offs at every step in the reporting process, so the numbers of actual cases, as we","keywords":["COVID-19","surveillance","metrics","public health policy","data quality","epidemiology"],"image":"/media/remote/9e89d49b171e4491-720.webp","collections":[],"label":"Cornucopia"},{"id":"89f6fe66-9cae-4e30-a938-fd317dfd8bb5","slug":"uo4hjcx6","title":"Statistical Modeling: The Three Cultures","dek":null,"authors":[{"name":"Adel Daoud","slug":"adel-daoud"},{"name":"Devdatt Dubhashi","slug":"devdatt-dubhashi"}],"date":"2023-01-26","section":"panorama","issue":"volume5issue1","doi":"10.1162/99608f92.89f6fe66","abstract":"Social scientists distinguish between predictive and causal research. While this distinction clarifies the aims of two research traditions, this clarity is blurred by the introduction of machine learning (ML) algorithms. Although ML excels in prediction, scholars are increasingly using ML not only for prediction but also for causation. While using ML for causation appears as a category mistake, this article shows tha","keywords":["causal inference","prediction","social sciences","machine learning","artificial intelligence","data science."],"image":null,"collections":[],"label":"Panorama"},{"id":"e2720e81-eb57-450a-a4b3-af87822f490f","slug":"k4jt0uu0","title":"Data Science Transfer Pathways From Associate’s to Bachelor’s Programs","dek":null,"authors":[{"name":"Benjamin S. Baumer","slug":"benjamin-bbaumersmithedu"},{"name":"Nicholas Jon Horton","slug":"nicholas-horton"}],"date":"2023-01-26","section":"stepping-stones","issue":"volume5issue1","doi":"10.1162/99608f92.e2720e81","abstract":"A substantial fraction of students who complete their college education at a public university in the United States begin their journey at one of the 935 public 2-year colleges. While the number of 4-year colleges offering bachelor’s degrees in data science continues to increase, data science instruction at many 2-year colleges lags behind. A major impediment is the relative paucity of introductory data science cours","keywords":["articulation","associate’s programs","bachelor’s programs","two-year colleges","community colleges"],"image":"/media/remote/a3dac527087b38ac-720.webp","collections":[],"label":"Stepping Stones"},{"id":"a42024d0-2085-408c-b705-82d5c6ed7489","slug":"koyzu1te","title":"Statistics and Data Science for Cybersecurity","dek":null,"authors":[{"name":"Alfred Hero","slug":"alfred-hero"},{"name":"Soummya Kar","slug":"soummya-kar"},{"name":"Jose Moura","slug":"jose-moura"},{"name":"Joshua Neil","slug":"joshua-neil"},{"name":"H. Vincent Poor","slug":"h-vincent-poor"},{"name":"Melissa Turcotte","slug":"melissa-turcotte"},{"name":"Bowei Xi","slug":"bowei-xi"}],"date":"2023-01-26","section":"milestones-and-millstones","issue":"volume5issue1","doi":"10.1162/99608f92.a42024d0","abstract":"Cybersecurity is an ever-important aspect of our interconnected world, but security defenses lag behind the adversaries who with increasing sophistication seek to disrupt cybersystems. The emergence of massively distributed systems such as the Internet of Things (IoT) has opened up new vulnerabilities that go beyond traditional protective measures such as firewalls, password protection, and single point-of-attack res","keywords":["Adversarial machine learning","enterprise cybersecurity","resilient distributed networks","information theoretic security"],"image":"/media/remote/36bc7299cd37a002-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"06ed24c0-99ce-4dac-ac51-6d359c5db04f","slug":"sa5htffr","title":"The Role of Research in Data Science Education","dek":null,"authors":[{"name":"Anne Mykland","slug":"anne-mykland"}],"date":"2023-01-26","section":"minding-the-future","issue":"volume5issue1","doi":"10.1162/99608f92.06ed24c0","abstract":null,"keywords":[],"image":"/media/remote/de0005a099bd4682-720.webp","collections":[],"label":"Minding the Future"},{"id":"6c87bfc0-58b9-44e4-9383-94cc758274c2","slug":"v27yux58","title":"A Conversation With Sylvia Richardson","dek":"An interview with Sylvia Richardson by Francesca Dominici","authors":[{"name":"Sylvia Richardson","slug":"sylvia-richardson-2"},{"name":"Francesca Dominici","slug":"francesca-dominici"}],"date":"2023-01-26","section":"panorama","issue":"volume5issue1","doi":"10.1162/99608f92.6c87bfc0","abstract":"Harvard Data Science Review’s interim Co-Editor-in-Chief Francesca Dominici recently met with Dr. Sylvia Richardson, Emeritus Director of the Medical Research Council Biostatistics Unit at the University of Cambridge, Immediate Past President of the Royal Statistical Society, and HDSR Associate Editor. The pair discussed the definition of data science, Dr. Richardson’s data science journey, her areas of research, and","keywords":[],"image":"/media/remote/ba93090163cdd190-720.webp","collections":["conversations-with-leaders"],"label":"Panorama"},{"id":"c389b26b-3833-42a6-a006-40e054f91c7e","slug":"sbzc5yf1","title":"Data Science From Every Angle","dek":null,"authors":[{"name":"David C. Parkes","slug":"david-parkes"}],"date":"2022-10-27","section":"editorscolumn","issue":"volume4issue4","doi":"10.1162/99608f92.c389b26b","abstract":null,"keywords":[],"image":"/media/remote/1881566509dc27ca-720.webp","collections":[],"label":"Editor’s Column"},{"id":"ad105ec8-5d97-42f8-aedd-87f35af5c36f","slug":"0gbgybxv","title":"Spinning Up a Data Science Initiative at Harvard","dek":null,"authors":[{"name":"Francesca Dominici","slug":"francesca-dominici"},{"name":"Elizabeth Langdon-Gray","slug":"elizabeth-langdon-gray"},{"name":"David C. Parkes","slug":"david-parkes"}],"date":"2022-10-27","section":"panorama","issue":"volume4issue4","doi":"10.1162/99608f92.ad105ec8","abstract":"On the occasion of its fifth anniversary, the leadership of the Harvard Data Science Initiative look back to embrace and celebrate their foundational principles, touch on particular successes, and reflect on how the work has been by shaped by the exigencies of a complex academic environment and the societal tumult of the past 5 years.In October 2014, Harvard University’s Provost, Alan Garber, tasked a committee of fa","keywords":["impact","data","university","interdisciplinary","Harvard"],"image":"/media/remote/23f35dc529634a15-720.webp","collections":[],"label":"Panorama"},{"id":"18da72db-7413-4ae1-8253-2fbe305e9bc4","slug":"pjhqev7u","title":"Data Science in Public Health: Building Next Generation Capacity","dek":null,"authors":[{"name":"Nicholas Mirin","slug":"nicholas-mirin"},{"name":"Heather Mattie","slug":"heather-mattie"},{"name":"Latifa Jackson","slug":"latifa-jackson"},{"name":"Zainab Samad","slug":"zainab-samad"},{"name":"Rumi Chunara","slug":"rumi-chunara"}],"date":"2022-10-27","section":"stepping-stones","issue":"volume4issue4","doi":"10.1162/99608f92.18da72db","abstract":"Rapidly evolving technology, data, and analytic landscapes are permeating many fields and professions. In public health, the need for data science skills, including data literacy, is particularly prominent given both the potential of novel data types and analysis methods to fill gaps in existing public health research and intervention practices, as well as the potential of such data or methods to perpetuate or augmen","keywords":["public health","data","data science","ethics","curriculum"],"image":"/media/remote/9c6d9622920d3d8a-720.webp","collections":[],"label":"Stepping Stones"},{"id":"7fea779a-b8a9-424f-b20a-c9c2c5ce8c84","slug":"p9iemahk","title":"Transforming Curriculum and Building Capacity in K–12 Data Science Education","dek":null,"authors":[{"name":"Travis Weiland","slug":"travis-weiland"},{"name":"Christopher Engledowl","slug":"christopher-engledowl"}],"date":"2022-10-27","section":"stepping-stones","issue":"volume4issue4","doi":"10.1162/99608f92.7fea779a","abstract":"The recently released and updated Pre-K–12 Guidelines for Assessment and Instruction in Statistics Education (GAISE II; Bargagliotti et al., 2020) provides guidance as to how teachers can support the development of data literacy for all students in the pre-K–12 curriculum. However, to truly meet the vision of the GAISE II report and to support all students in developing data literacy for today’s societies, significan","keywords":["data science education","data literacy","statistics education","teacher education","doctoral student education","educational policy","curriculum standards"],"image":"/media/remote/90688b676cf84291-720.webp","collections":[],"label":"Stepping Stones"},{"id":"dec7d780-bb1a-4dd3-bb10-3e90e7fdeaac","slug":"qixx99zn","title":"On Learnability Under General Stochastic Processes","dek":null,"authors":[{"name":"Philip Dawid","slug":"philip-dawid"},{"name":"Ambuj Tewari","slug":"ambuj-tewari"}],"date":"2022-10-27","section":"milestones-and-millstones","issue":"volume4issue4","doi":"10.1162/99608f92.dec7d780","abstract":"Statistical learning theory under independent and identically distributed (iid) sampling and online learning theory for worst case individual sequences are two of the best developed branches of learning theory. Statistical learning under general non-iid stochastic processes is less mature. We provide two natural notions of learnability of a function class under a general stochastic process. We show that both notions ","keywords":["learnability","statistical learning","online learning","prequential principle","uniform martingale law of large numbers"],"image":"/media/remote/a928460d304788e8-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"3e79086d-38b1-4ffd-b50a-ecf5152a9d59","slug":"hujitggn","title":"The Mean-ness of Statistics","dek":null,"authors":[{"name":"Arunabh Ghosh","slug":"arunabh-ghosh"}],"date":"2022-10-27","section":"mining-the-past","issue":"volume4issue4","doi":"10.1162/99608f92.3e79086d","abstract":null,"keywords":[],"image":"/media/remote/30e1ade9e83c4411-720.webp","collections":[],"label":"Mining the Past"},{"id":"3c7b563e-d76c-4907-a649-1051a7f752db","slug":"lpq8o3bl","title":"Can a Computer Really Write Poetry?","dek":null,"authors":[{"name":"Edwin Agnew","slug":"edwin-agnew"},{"name":"Lily Zhu","slug":"lily-zhu"},{"name":"Sam Wiseman","slug":"sam-wiseman"},{"name":"Cynthia Rudin","slug":"cynthia-rudin"}],"date":"2022-10-27","section":"recreations-in-randomness","issue":"volume4issue4","doi":"10.1162/99608f92.3c7b563e","abstract":null,"keywords":[],"image":"/media/remote/cd6f4fceaf1396ba-720.webp","collections":[],"label":"Recreations in Randomness"},{"id":"13e1d60e-7649-4733-97c6-a634c5d2f4b7","slug":"x0l4x099","title":"The Use of Data Science in a National Statistical Office","dek":null,"authors":[{"name":"Sevgui Erman","slug":"sevgui-erman"},{"name":"Eric Rancourt","slug":"eric-rancourt"},{"name":"Yanick Beaucage","slug":"yanick-beaucage"},{"name":"Andre Loranger","slug":"andre-loranger"}],"date":"2022-10-27","section":"cornucopia","issue":"volume4issue4","doi":"10.1162/99608f92.13e1d60e","abstract":"Objective statistical information is vital to an open and democratic society. It provides a solid foundation so that informed decisions can be made by our elected representatives, businesses, unions, and non-profit organizations, as well as individual citizens. There is a great shift towards a more virtual and digital economy and society. The traditional official statistical systems are centered on surveys, and must ","keywords":["National Statistical Office","data science applications","machine learning","statistical information","alternative non-survey data","government organizations","hub and spoke model","responsible data science use"],"image":"/media/remote/50df64700de4cf8f-720.webp","collections":[],"label":"Cornucopia"},{"id":"1d34757b-1895-4154-ba00-675d61bdc62a","slug":"zkib7xth","title":"Toward a 'Standard Model' of Machine Learning","dek":null,"authors":[{"name":"Zhiting Hu","slug":"zhiting-hu"},{"name":"Eric P. Xing","slug":"eric-p-xing"}],"date":"2022-10-27","section":"milestones-and-millstones","issue":"volume4issue4","doi":"10.1162/99608f92.1d34757b","abstract":"Machine learning (ML) is about computational methods that enable machines to learn concepts from experience. In handling a wide variety of experience ranging from data instances, knowledge, and constraints to rewards, adversaries, and lifelong interaction in an ever-growing spectrum of tasks, contemporary ML/AI (artificial intelligence) research has resulted in a multitude of learning paradigms and methodologies. Des","keywords":["standard model","panoramic learning","experience","composable machine learning","unification"],"image":"/media/remote/1e0017e4f7a39673-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"4ba61ca4-13bc-47a5-8421-9c2728ac5c22","slug":"kub8kr96","title":"Moving Forward With the U.S. Census Bureau’s Annual Population Estimates Post-2020","dek":null,"authors":[{"name":"Victoria Velkoff","slug":"victoria-velkoff"},{"name":"Christine Hartley","slug":"christine-hartley"}],"date":"2022-10-27","section":"effective-policy-learning","issue":"volume4issue4","doi":"10.1162/99608f92.4ba61ca4","abstract":null,"keywords":[],"image":"/media/remote/b7b5d9c7fef79216-720.webp","collections":[],"label":"Effective Policy Learning"},{"id":"116c43fe-fb9d-4db6-9f4f-380d32705132","slug":"pv2y77y3","title":"Introducing Data Sciences to N-of-1 Designs, Statistics, Use-Cases, the Future, and the Moniker ‘N-of-1’ Trial","dek":null,"authors":[{"name":"Karina Davidson","slug":"karina-davidson"},{"name":"Ken Cheung","slug":"ken-cheung"},{"name":"Ciaran Friel","slug":"ciaran-friel"},{"name":"Jerry Suls","slug":"jerry-suls"}],"date":"2022-09-08","section":null,"issue":"specialissue3","doi":"10.1162/99608f92.116c43fe","abstract":"This article, an introduction to HDSR’s “Personalized (N-of-1) Trials: Methods, Applications, and Impact” special issue, describes the rationale for a primer of the methods, data types and management, designs, and use cases for personalized (N-of-1) trials. It explains that the design and implementation of personalized (N-of-1) trials is only useful if patients volunteer for research involving them and actively parti","keywords":["between-subject designs","N-of-1 trials","personalized trials","single-patient trials","within-subject trials"],"image":"/media/remote/8606162a02cc2e30-720.webp","collections":[],"label":"From the Editors"},{"id":"ff9300a8-e9d4-4e42-b28d-2da273a7d30c","slug":"nqvadq0w","title":"The Family of Single-Case Experimental Designs","dek":null,"authors":[{"name":"Leonard H. Epstein","slug":"leonard-h-epstein"},{"name":"Jesse Dallery","slug":"jesse-dallery"}],"date":"2022-09-08","section":null,"issue":"specialissue3","doi":"10.1162/99608f92.ff9300a8","abstract":"Single-case experimental designs (SCEDs) represent a family of research designs that use experimental methods to study the effects of treatments on outcomes. The fundamental unit of analysis is the single case—which can be an individual, clinic, or community—ideally with replications of effects within and/or between cases. These designs are flexible and cost-effective and can be used for treatment development, transl","keywords":["single-case experimental designs","reversal designs","multiple baseline designs","personalized medicine"],"image":"/media/remote/b5e207c11adcf71a-720.webp","collections":[],"label":"Article"},{"id":"e11adff0-ef6c-4277-93a3-1ea4826c0466","slug":"g00qkur7","title":"Evaluating Personalized (N-of-1) Trials in Rare Diseases: How Much Experimentation Is Enough?","dek":null,"authors":[{"name":"Ken Cheung","slug":"ken-cheung"},{"name":"Hiroshi Mitsumoto","slug":"hiroshi-mitsumoto"}],"date":"2022-09-08","section":null,"issue":"specialissue3","doi":"10.1162/99608f92.e11adff0","abstract":"For rare diseases, conducting large, randomized trials of new treatments can be infeasible due to limited sample size, and it may answer the wrong scientific questions due to heterogeneity of treatment effects. Personalized (N-of-1) trials are multi-period crossover studies that aim to estimate individual treatment effects, thereby identifying the optimal treatments for individuals. This article examines the statisti","keywords":["ALS","heterogeneity of treatment effects (HTE)","minimally clinically important heterogeneity","patient-centered research","rare diseases","sample size formulae"],"image":"/media/remote/e4436a7c80455a1f-720.webp","collections":[],"label":"Article"},{"id":"f1eef6f4-dc47-408e-a7f6-cfebf54214a7","slug":"e1akaswm","title":"Accommodating Serial Correlation and Sequential Design Elements in Personalized Studies and Aggregated Personalized Studies","dek":null,"authors":[{"name":"Nicholas Schork","slug":"nicholas-schork"}],"date":"2022-09-08","section":null,"issue":"specialissue3","doi":"10.1162/99608f92.f1eef6f4","abstract":"Single subject, or ‘N-of-1,’ studies are receiving a great deal of attention from both theoretical and applied researchers. This is consistent with the growing acceptance of ‘personalized’ approaches to health care and the need to prove that personalized interventions tailored to an individual’s likely unique physiological profile and other characteristics work as they should. In fact, the preferred way of referring ","keywords":[],"image":"/media/remote/db9b32a166f70b06-720.webp","collections":[],"label":"Article"},{"id":"8439a336-5c42-4e67-882f-234217f89d26","slug":"dsu525z7","title":"Personalized Data Science and Personalized (N-of-1) Trials: Promising Paradigms for Individualized Health Care","dek":null,"authors":[{"name":"Naihua Duan","slug":"naihua-duan"},{"name":"Daniel Norman","slug":"daniel-norman"},{"name":"Christopher Schmid","slug":"christopher-schmid"},{"name":"Ida Sim","slug":"ida-sim"},{"name":"Richard L. Kravitz","slug":"richard-kravitz"}],"date":"2022-09-08","section":null,"issue":"specialissue3","doi":"10.1162/99608f92.8439a336","abstract":"The term ‘data science’ usually refers to the process of extracting value from big data obtained from a large group of individuals. An alternative rendition, which we call personalized data science (Per-DS), aims to collect, analyze, and interpret personal data to inform personal decisions. This article describes the main features of Per-DS, and reviews its current state and future outlook. A Per-DS investigation is ","keywords":["case-crossover design","heterogeneity of treatment effects","monitoring","pre-post trials","self-tracking","symptom triggers"],"image":"/media/remote/8f2338d39a6764a5-720.webp","collections":[],"label":"Article"},{"id":"6c21dab7-993e-41f0-9be8-75673c960cca","slug":"sfzyu2c5","title":"An R Shiny App for a Chronic Lower Back Pain Study, Personalized N-of-1 Trial","dek":null,"authors":[{"name":"Thevaa Chandereng","slug":"thevaa-chandereng"}],"date":"2022-09-08","section":null,"issue":"specialissue3","doi":"10.1162/99608f92.6c21dab7","abstract":"The call for personalized medicine highlights the need for personalized (N-of-1) trials to find what treatment works best for individual patients. Conventional (between-subject) randomized controlled trials (RCT) yield effects for the ‘average patient,’ but a personalized trial administers all treatments within-subject, so benefits or harms to the individual patient can be identified. The design and analysis of perso","keywords":["personalized medicine","fitbit data","imputation","computing platforms"],"image":"/media/remote/1bee4d24ef0c3a7c-720.webp","collections":[],"label":"Article"},{"id":"3574f1dc-5a93-4b05-9cb4-9017f4ab4cde","slug":"bosopi07","title":"Quantitative Synthesis of Personalized Trials Studies: Meta-Analysis of Aggregated Data Versus Individual Patient Data","dek":null,"authors":[{"name":"Mariola Moeyaert","slug":"mariola-moeyaert"},{"name":"Joelle Fingerhut","slug":"joelle-fingerhut"}],"date":"2022-09-08","section":null,"issue":"specialissue3","doi":"10.1162/99608f92.3574f1dc","abstract":"We have entered an era in which scientific knowledge and evidence increasingly inform research practice and policy. As there is an exponential increase in the use of personalized trials, there is a remarkable growing interest in the quantitative synthesis of personalized trials. One technique that is developed and can be applied for this purpose is meta-analysis. Meta-analysis involves the quantitative integration of","keywords":["personalized trials","effect size","aggregated data meta-analysis","individual patient data meta-analysis","multilevel modeling","evidence-based decision and inferences"],"image":"/media/remote/a13767746ce2960a-720.webp","collections":[],"label":"Article"},{"id":"a65a257a-c4b8-4a16-957e-c1c43891c099","slug":"x0akqpvz","title":"N-of-1 Trials, Their Reporting Guidelines, and the Advancement of Open Science Principles","dek":null,"authors":[{"name":"Antony Porcino","slug":"antony-porcino"},{"name":"Sunita Vohra","slug":"sunita-vohra"}],"date":"2022-09-08","section":null,"issue":"specialissue3","doi":"10.1162/99608f92.a65a257a","abstract":"N-of-1 trials are multiple crossover trials done over time within a single person; they can also be done with a series of individuals. Their focus on the individual as the unit of analysis maintains statistical power while accommodating greater differences between patients than most standard clinical trials. This makes them particularly useful in rare diseases, while also being applicable across many health condition","keywords":["N-of-1 trials","single case experimental designs","reporting guidelines","open science","scholarly communication","reproducible research"],"image":"/media/remote/98bfc7b3fc50a5e7-720.webp","collections":[],"label":"Article"},{"id":"2ded0fc5-a643-4d37-8b20-f28e4bfb2c5c","slug":"iqj8gt7o","title":"Personalized Trial Ethics and Institutional Review Board Submissions","dek":null,"authors":[{"name":"Joyce P. Samuel","slug":"joyce-samuel"},{"name":"Susan H. Wootton","slug":"susan-h-wootton"}],"date":"2022-09-08","section":null,"issue":"specialissue3","doi":"10.1162/99608f92.2ded0fc5","abstract":"The ethical and regulatory oversight of any clinical activity related to human subjects is commonly determined based on its categorization as either clinical practice or research. Prominent bioethicists have criticized the traditional distinctions used to delineate these categories, calling them counterproductive and outmoded, and arguing that learning and clinical practice should be deliberately and appropriately in","keywords":["ethics","consent","personalized trial","N-of-1 trial"],"image":"/media/remote/b7de76d6fa6853fa-720.webp","collections":[],"label":"Article"},{"id":"901255e7-c4b6-415a-82c6-f6d0efbefa13","slug":"hy7mjtzo","title":"Conduct and Implementation of Personalized Trials in Research and Practice","dek":null,"authors":[{"name":"Richard L. Kravitz","slug":"richard-kravitz"},{"name":"Naihua Duan","slug":"naihua-duan"}],"date":"2022-09-08","section":null,"issue":"specialissue3","doi":"10.1162/99608f92.901255e7","abstract":"The mainstay of evidence development in medicine is the parallel-group randomized controlled trial (RCT), which generates estimates of treatment efficacy or effectiveness for the average person in the trial. In contrast, personalized trials (sometimes referred to as ‘single-person trials’ or ‘N-of-1 trials’) assess the comparative effectiveness of two or more treatments in a single individual. These single-subject, r","keywords":["personalized trial","randomized controlled trial (RCT)","heterogeneity of treatment effects (HTE)","individualized treatment effect (ITE)","blinding","washout"],"image":"/media/remote/87b267e538d9d40c-720.webp","collections":[],"label":"Article"},{"id":"d5b57784-b797-48d9-b829-0eaa2d8a7e99","slug":"n3hbli1d","title":"Personalized Feedback for Personalized Trials: Construction of Summary Reports for Participants in a Series of Personalized Trials for Chronic Lower Back Pain","dek":null,"authors":[{"name":"Stefani D'Angelo","slug":"stefani-dangelo-2"},{"name":"Heejoon Ahn","slug":"heejoon-ahn"},{"name":"Danielle Miller","slug":"danielle-miller"},{"name":"Rachel Monane","slug":"rachel-monane"},{"name":"Mark Butler","slug":"mark-butler"}],"date":"2022-09-08","section":null,"issue":"specialissue3","doi":"10.1162/99608f92.d5b57784","abstract":"Personalized (N-of-1) trials offer a patient-centered research approach that can provide important clinical information for patients when selecting which treatment options best manage their chronic health concern. Researchers utilizing this approach should present trial results to patients in a clear and understandable manner in order for personalized research trials to be useful to participants. The current study pr","keywords":["patient education","lay summary","patient feedback","personalized trial","lower back pain","virtual","patient report"],"image":"/media/remote/1b999774f6d9040e-720.webp","collections":[],"label":"Article"},{"id":"3f1772ce-b9c0-43b1-b189-382d597f6bde","slug":"b6efwlql","title":"Bayesian Models for N-of-1 Trials","dek":null,"authors":[{"name":"Christopher Schmid","slug":"christopher-schmid"},{"name":"Jiabei Yang","slug":"jiabei-yang"}],"date":"2022-09-08","section":null,"issue":"specialissue3","doi":"10.1162/99608f92.3f1772ce","abstract":"We describe Bayesian models for data from N-of-1 trials, reviewing both the basics of Bayesian inference and applications to data from single trials and collections of trials sharing the same research questions and data structures. Bayesian inference is natural for drawing inferences from N-of-1 trials because it can incorporate external and subjective information to supplement trial data as well as give straightforw","keywords":["meta-analysis","multilevel model","inflammatory bowel disease","personalized medicine","Markov chain Monte Carlo","posterior inference"],"image":"/media/remote/9f0391e6034bce88-720.webp","collections":[],"label":"Article"},{"id":"abc57f1b-0d4e-4076-8fdc-8e55ec889161","slug":"cqmjttbh","title":"Why Do I Get Side Effects? Personalized (N-of-1) Trials for Statin Intolerance and the Nocebo Effect","dek":null,"authors":[{"name":"James Philip Howard","slug":"james-philip-howard"},{"name":"Frances A. Wood","slug":"frances-a-wood"},{"name":"Darrel P. Francis","slug":"darrel-p-francis"}],"date":"2022-09-08","section":null,"issue":"specialissue3","doi":"10.1162/99608f92.abc57f1b","abstract":"The ability of statins to reduce the morbidity and mortality of cardiovascular disease has ensured that they are among the most prescribed drugs in modern medicine. Unfortunately, most patients who start taking statins will end up stopping them, most commonly due to side effects. Confusingly, however, in blinded placebo-controlled trials, side effects appear no more common in those taking statins than those taking pl","keywords":["statins","N-of-1 trials","side effects","nocebo","drug intolerance"],"image":"/media/remote/93f354c73d34b44a-720.webp","collections":[],"label":"Use Cases"},{"id":"72cd8432-a7d5-4025-97a3-59e750a8b859","slug":"9c034nf7","title":"A Series of Virtual Interventions for Chronic Lower Back Pain: A Feasibility Pilot Study for a Series of Personalized (N-of-1) Trials","dek":null,"authors":[{"name":"Mark Butler","slug":"mark-butler"},{"name":"Stefani D'Angelo","slug":"stefani-dangelo-2"},{"name":"Melissa Kaplan","slug":"melissa-kaplan"},{"name":"Zarrin Tashnim","slug":"zarrin-tashnim"},{"name":"Danielle Miller","slug":"danielle-miller"},{"name":"Heejoon Ahn","slug":"heejoon-ahn"},{"name":"Louise Falzon","slug":"louise-falzon"},{"name":"Andrew J. Dominello","slug":"andrew-j-dominello"},{"name":"Cirrus Foroughi","slug":"cirrus-foroughi"},{"name":"Thevaa Chandereng","slug":"thevaa-chandereng"},{"name":"Ken Cheung","slug":"ken-cheung"},{"name":"Karina Davidson","slug":"karina-davidson"}],"date":"2022-09-08","section":null,"issue":"specialissue3","doi":"10.1162/99608f92.72cd8432","abstract":"Chronic lower back pain (CLBP) affects 25% of U.S. adults and is associated with high costs due to physician visits and reduced productivity. Research shows that massage and yoga can be effective nonpharmacological treatments for CLBP, but the feasibility, scalability, individual treatment, and adverse-event heterogeneity of these treatments are unknown. The current study evaluated the feasibility and acceptability o","keywords":["N-of-1","yoga","massage","personalized","personalized trial","pain"],"image":"/media/remote/e777bb5ca7cb7801-720.webp","collections":[],"label":"Use Cases"},{"id":"d99e6ff5-3004-47f0-9913-12d655444d75","slug":"isdcalw3","title":"Personalized (N-of-1) Trials for Patient-Centered Treatments of Multimorbidity","dek":null,"authors":[{"name":"Jerry Suls","slug":"jerry-suls"},{"name":"Catherine Alfano","slug":"catherine-alfano"},{"name":"Christina Yap","slug":"christina-yap"}],"date":"2022-09-08","section":null,"issue":"specialissue3","doi":"10.1162/99608f92.d99e6ff5","abstract":"Treatment of patients who suffer from concurrent health conditions is not well served by (1) evidence-based clinical guidelines that mainly specify treatment of single conditions and (2) conventional randomized controlled trials (RCTs) that identify treatments as safe and effective on average. Clinical decision-making based on the average patient effect may be inappropriate for treatment of those with multimorbidity ","keywords":["cancer","coronavirus disease 2019 (COVID-19)","multimorbidity","personalized (N-of-1) trials","randomized controlled trials (RCTs)","comorbidity"],"image":"/media/remote/a73878f6312b18c5-720.webp","collections":[],"label":"Use Cases"},{"id":"e5a9a4b0-ca94-4f7a-8247-c33edcc4f7a7","slug":"ftwzgrij","title":"Psephological Correlated Simulation Techniques With Decision Desk HQ: For the 2022 Midterms and Beyond","dek":null,"authors":[{"name":"Sydney Louit","slug":"sydney-louit"},{"name":"Mukul Ram","slug":"mukul-ram"},{"name":"Kiel Williams","slug":"kiel-williams"},{"name":"Alex Alduncin","slug":"alex-alduncin"},{"name":"Patrick McCaul","slug":"patrick-mccaul"},{"name":"Scott Tranter","slug":"scott-tranter"}],"date":"2022-09-01","section":"cornucopia","issue":"volume4issue4","doi":"10.1162/99608f92.e5a9a4b0","abstract":"In today’s highly polarized political environment, there is a high level of interest in accurate forecasts of presidential and congressional elections, addressing how likely a given party is to win an election or a chamber of Congress. However, electoral outcomes at the federal level in the United States are strongly correlated from one state to another. Correctly capturing these race-level correlations is a major co","keywords":["elections","simulation","midterms","correlation"],"image":"/media/remote/738f4c47ffe7e5f2-720.webp","collections":[],"label":"Cornucopia"},{"id":"44975b62-3ebe-4d08-aeb9-9c7b90f93472","slug":"p1xu0son","title":"Changing the Culture on Data Management and Sharing: Overview and Highlights from a Workshop Held by the National Academies of Sciences, Engineering, and Medicine","dek":null,"authors":[{"name":"Maryann E. Martone","slug":"maryann-martone"},{"name":"Richard Nakamura","slug":"richard-nakamura"}],"date":"2022-07-29","section":"panorama","issue":"volume4issue3","doi":"10.1162/99608f92.44975b62","abstract":"The U.S. National Institutes of Health (NIH) released a broad new policy on data management and sharing on October 29, 2020, mandating that all data from NIH-funded or conducted research must be managed and most must be shared. This policy requires the submission of data management and sharing plans in all grant applications consistent with good data management practices and sets expectations for sharing of scientifi","keywords":["Open Science","Data Sharing","NIH Data Management and Sharing Policy","Data Management Plan"],"image":"/media/remote/e6fb48a30ee9ef6f-720.webp","collections":["changing-the-culture-on-data-management-and-data-sharing-in-biomedicine"],"label":"Panorama"},{"id":"42285dcb-afe6-488a-ace6-7728077a0365","slug":"33gp60fi","title":"Building a More Robust Data Science, Toward a More Robust World","dek":null,"authors":[{"name":"David C. Parkes","slug":"david-parkes"}],"date":"2022-07-28","section":"editorscolumn","issue":"volume4issue3","doi":"10.1162/99608f92.42285dcb","abstract":null,"keywords":[],"image":"/media/remote/d8358b63597c24e1-720.webp","collections":[],"label":"Editor’s Column"},{"id":"9624ea51-343d-4124-a2f8-6f58494a5802","slug":"mlconlea","title":"Promoting and Enabling Reproducible Data Science Through a Reproducibility Challenge","dek":null,"authors":[{"name":"Jing Liu","slug":"jing-liu"},{"name":"Jacob Carlson","slug":"jacob-carlson"},{"name":"Josh Pasek","slug":"josh-pasek"},{"name":"Brian Puchala","slug":"brian-puchala"},{"name":"Arvind Rao","slug":"arvind-rao"},{"name":"H. V. Jagadish","slug":"h-v-jagadish"}],"date":"2022-07-28","section":"panorama","issue":"volume4issue3","doi":"10.1162/99608f92.9624ea51","abstract":"The reproducibility of research results is the basic requirement for the reliability of scientific discovery, yet it is hard to achieve. Whereas funding agencies, scientific journals, and professional societies are developing guidelines, requirements, and incentives, and researchers are developing tools and processes, the role of a university in promoting and enabling reproducible research has been unclear. In this r","keywords":["data science","reproducibility","processes and tools","generalization","the role of universities"],"image":"/media/remote/f440ea9f4eed6a06-720.webp","collections":[],"label":"Panorama"},{"id":"5ff070bf-0402-4e31-9959-33e193e3e0f7","slug":"z79nem4e","title":"Legal Compliance and Good Data Stewardship in Data Sharing Plans","dek":null,"authors":[{"name":"Kristen B. Rosati","slug":"kristen-b-rosati"}],"date":"2022-07-28","section":"panorama","issue":"volume4issue3","doi":"10.1162/99608f92.5ff070bf","abstract":"The sharing of health-related data is governed by myriad federal and state (and sometimes international) requirements. Moreover, these health-related data sharing rules are evolving, which poses a substantial challenge to creating long-term data management and sharing plans for National Institutes of Health (NIH)-funded or conducted research. To comply with inconsistent and evolving legal standards, the author recomm","keywords":["data sharing","de-identification","HIPAA","Common Rule","NIH Certificates of Confidentiality","legal issues"],"image":"/media/remote/cc31eb403924dc9f-720.webp","collections":["changing-the-culture-on-data-management-and-data-sharing-in-biomedicine"],"label":"Panorama"},{"id":"9497f68e-b3e5-4b2f-bfe6-b459ae85894c","slug":"72kcw990","title":"Promoting Open Science Through Research Data Management","dek":null,"authors":[{"name":"John Borghi","slug":"john-borghi"},{"name":"Ana Van Gulick","slug":"ana-van-gulick"}],"date":"2022-07-28","section":"panorama","issue":"volume4issue3","doi":"10.1162/99608f92.9497f68e","abstract":"Data management, which encompasses activities and strategies related to the storage, organization, and description of data and other research materials, helps ensure the usability of data sets—both for the original research team and for others. For librarians and other service providers, describing data management as an integral part of a research process or workflow may help contextualize the importance of related r","keywords":["research data management","open science","reproducibility","data sharing"],"image":"/media/remote/7b726b3182aa3fc4-720.webp","collections":["changing-the-culture-on-data-management-and-data-sharing-in-biomedicine"],"label":"Panorama"},{"id":"42eec111-f8e7-4955-9617-7217a510f11d","slug":"wyxni26q","title":"Why It Takes a Village to Manage and Share Data","dek":null,"authors":[{"name":"Christine L. Borgman","slug":"christine-l-borgman"},{"name":"Philip Bourne","slug":"philip-bourne"}],"date":"2022-07-28","section":"panorama","issue":"volume4issue3","doi":"10.1162/99608f92.42eec111","abstract":"Implementation plans for the National Institutes of Health policy for data management and sharing, which takes effect in 2023, provide an opportunity to reflect on the stakeholders, infrastructures, practice, economics, and sustainability of data sharing. Responsibility for fulfilling data-sharing requirements tends to fall on principal investigators, whereas it takes a village of stakeholders to construct, manage, a","keywords":["data sharing","research policy","data management","biomedicine","incentives","scientific practice"],"image":"/media/remote/6efb802de49bc52b-720.webp","collections":["changing-the-culture-on-data-management-and-data-sharing-in-biomedicine"],"label":"Panorama"},{"id":"c71c3479-2d1e-490d-9959-9a38ecc3e039","slug":"8vfnfvag","title":"Recognizing Our Collective Responsibility in the Prioritization of Open Data Metrics","dek":null,"authors":[{"name":"Daniella Lowenberg","slug":"daniella-lowenberg"}],"date":"2022-07-28","section":"panorama","issue":"volume4issue3","doi":"10.1162/99608f92.c71c3479","abstract":"With the rise in data-sharing policies, development of supportive infrastructure, and the amount of data published over the last decades, evaluation and assessment are increasingly necessary to understand the reach, impact, and return on investment of data-sharing practices. As biomedical research stakeholders prepare for the implementation of the updated National Institutes of Health (NIH) Data Management and Sharin","keywords":["open data","data metrics","research evaluation","research policy"],"image":"/media/remote/a578981766f11ffe-720.webp","collections":["changing-the-culture-on-data-management-and-data-sharing-in-biomedicine"],"label":"Panorama"},{"id":"a9717b34-e1fa-4e83-811c-97c6bcd2f780","slug":"3qj5hynb","title":"Harmonization-Information Trade-Offs for Sharing Individual Participant Data in Biomedicine","dek":null,"authors":[{"name":"Abel Torres-Espín","slug":"abel-torres-espin"}],"date":"2022-07-28","section":"panorama","issue":"volume4issue3","doi":"10.1162/99608f92.a9717b34","abstract":"Biomedical practice is evidence-based. Peer-reviewed papers are the primary medium to present evidence and data-supported results to drive clinical practice. However, it could be argued that scientific literature does not contain data, but rather narratives about and summaries of data. Meta-analyses of published literature may produce biased conclusions due to the lack of transparency in data collection, publication ","keywords":["FAIR data sharing","data harmonization","standardization","harmonization-information trade-off","biomedicine data science"],"image":"/media/remote/6bac250432b3eda9-720.webp","collections":["changing-the-culture-on-data-management-and-data-sharing-in-biomedicine"],"label":"Panorama"},{"id":"44d21b86-cd31-42dc-b1d2-2294298a5973","slug":"qjpg8oik","title":"Sharing Begins at Home: How Continuous and Ubiquitous FAIRness Can Enhance Research Productivity and Data Reuse","dek":null,"authors":[{"name":"William P Dempsey","slug":"william-dempsey"},{"name":"Ian Foster","slug":"ian-foster"},{"name":"Scott Fraser","slug":"scott-fraser"},{"name":"Carl Kesselman","slug":"carl-kesselman"}],"date":"2022-07-28","section":"panorama","issue":"volume4issue3","doi":"10.1162/99608f92.44d21b86","abstract":"The broad sharing of research data is widely viewed as critical for the speed, quality, accessibility, and integrity of science. Despite increasing efforts to encourage data sharing, both the quality of shared data and the frequency of data reuse remain stubbornly low. We argue here that a significant reason for this unfortunate state of affairs is that the organization of research results in the Findable, Accessible","keywords":["data management","FAIRness","reproducibility"],"image":"/media/remote/f16b387f5810ba03-720.webp","collections":["changing-the-culture-on-data-management-and-data-sharing-in-biomedicine"],"label":"Panorama"},{"id":"b9e4ceec-dcf0-4d12-a145-4ca781d63e2f","slug":"kdgve0dl","title":"A Conversation With Dr. Lawrence Tabak and Dr. Lyric Jorgenson on the NIH Perspective on Data Sharing and Management","dek":"An interview with Dr. Lawrence Tabak and Dr. Lyric Jorgenson by Maryann Martone and Richard Nakamura","authors":[{"name":"Lawrence Tabak","slug":"lawrence-tabak"},{"name":"Lyric Jorgenson","slug":"lyric-jorgenson"},{"name":"Maryann E. Martone","slug":"maryann-martone"},{"name":"Richard Nakamura","slug":"richard-nakamura"}],"date":"2022-07-28","section":"panorama","issue":"volume4issue3","doi":"10.1162/99608f92.b9e4ceec","abstract":"In June 2022, Harvard Data Science Review (HDSR) guest editors for the special theme on Changing the Culture on Data Management and Data Sharing in Biomedicine, Maryann Martone and Richard Nakamura, conducted an online interview with Dr. Lawrence Tabak, Acting Director of the National Institutes of Health (NIH) and Dr. Lyric Jorgenson, Acting Associate Director for Science Policy and the Acting Director of the Office","keywords":[],"image":"/media/remote/f6e03c3d2f1f8374-720.webp","collections":["changing-the-culture-on-data-management-and-data-sharing-in-biomedicine","conversations-with-leaders"],"label":"Panorama"},{"id":"a579756e-5272-4650-b34d-110b2e70720d","slug":"aj31wj81","title":"Online Experimentation: Benefits, Operational and Methodological Challenges, and Scaling Guide","dek":null,"authors":[{"name":"Iavor Bojinov","slug":"iavor-bojinov"},{"name":"Somit Gupta","slug":"somit-gupta"}],"date":"2022-07-28","section":"cornucopia","issue":"volume4issue3","doi":"10.1162/99608f92.a579756e","abstract":"In the past decade, online controlled experimentation, or A/B testing, at scale has proved to be a significant driver of business innovation. The practice was first pioneered by the technology sector and, more recently, has been adopted by traditional companies undergoing a digital transformation. This article provides a primer to business leaders, data scientists, and academic researchers on business experimentation","keywords":["experimentation","A/B testing","product development","data-driven culture"],"image":"/media/remote/8ea52e27443b940f-720.webp","collections":[],"label":"Cornucopia"},{"id":"008b5006-3608-4630-8d89-d6a7984bb069","slug":"60qq269p","title":"From Unicorn Data Scientist to Key Roles in Data Science: Standardizing Roles","dek":null,"authors":[{"name":"Usama Fayyad","slug":"usama-fayyad"},{"name":"Hamit Hamutcu","slug":"hamit-hamutcu"}],"date":"2022-07-28","section":"stepping-stones","issue":"volume4issue3","doi":"10.1162/99608f92.008b5006","abstract":"The lack of an agreed-upon classification of job roles related to data science is causing much confusion that is challenging to the industry, educational sector, and practitioners. Prior work in this area has considered different aspects from different fields or points of view and has shown that more detail is needed in subcategorizing data science professionals. However, other prior work has also shown that avoiding","keywords":["data science","job roles","skill requirements in data science","categorization of roles","data science unicorns"],"image":"/media/remote/40b530ca022c8659-720.webp","collections":["data-science-for-business-and-industry"],"label":"Stepping Stones"},{"id":"db1aba8b-3686-451e-b777-9e4ef07bba1d","slug":"wgss79vu","title":"The Importance of Being Ernest, Ekundayo, or Eswari: An Interpretable Machine Learning Approach to Name-Based Ethnicity Classification","dek":null,"authors":[{"name":"Vaishali Jain","slug":"vaishali-jain"},{"name":"Ted Enamorado","slug":"ted-enamorado"},{"name":"Cynthia Rudin","slug":"cynthia-rudin"}],"date":"2022-07-28","section":"milestones-and-millstones","issue":"volume4issue3","doi":"10.1162/99608f92.db1aba8b","abstract":"Name-based ethnicity classification is the task of predicting ethnicity from a name. Ethnicity classification can be a key tool for assessing the fairness of algorithms, demographic studies, and political analysis. While previous state-of-the-art approaches for this task rely on complex neural networks that are difficult to understand, troubleshoot, and tune, we provide an interpretable and intuitive solution that ou","keywords":["name ethnicity classification","interpretable machine learning","campaign contributions"],"image":"/media/remote/091a76091d8128dd-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"4ec90534-0f76-47c4-9bfd-38615a5f1000","slug":"mix9b32x","title":"Bringing Complex Data Into the Classroom","dek":null,"authors":[{"name":"Pip Arnold","slug":"pip-arnold"},{"name":"Anna Bargagliotti","slug":"anna-bargagliotti"},{"name":"Christine Franklin","slug":"christine-franklin"},{"name":"Robert Gould","slug":"robert-gould"}],"date":"2022-07-28","section":"minding-the-future","issue":"volume4issue3","doi":"10.1162/99608f92.4ec90534","abstract":null,"keywords":[],"image":"/media/remote/f37c614c32bd3d45-720.webp","collections":[],"label":"Minding the Future"},{"id":"6650ce2b-4b71-4454-93d0-f9e9c3210227","slug":"yb0lddel","title":"Changing the Culture on Data Management and Sharing: Getting Ready for the New NIH Data Sharing Policy","dek":null,"authors":[{"name":"Maryann E. Martone","slug":"maryann-martone"},{"name":"Richard Nakamura","slug":"richard-nakamura"}],"date":"2022-07-28","section":"panorama","issue":"volume4issue3","doi":"10.1162/99608f92.6650ce2b","abstract":null,"keywords":[],"image":"/media/remote/23225d64957e6bc7-720.webp","collections":["changing-the-culture-on-data-management-and-data-sharing-in-biomedicine"],"label":"Panorama"},{"id":"bcd0b999-c332-4829-9018-8d4859d07682","slug":"z7drh7c8","title":"Perspectives on Data Sharing and the New NIH Policy from the European Union","dek":"An interview with Jan G. Bjaalie, Carole Goble, and Susanna-Assunta Sansone by Richard Nakamura and Maryann Martone","authors":[{"name":"Jan G. Bjaalie","slug":"jan-g-bjaalie"},{"name":"Carole Goble","slug":"carole-goble"},{"name":"Susanna-Assunta Sansone","slug":"susanna-assunta-sansone"},{"name":"Richard Nakamura","slug":"richard-nakamura"},{"name":"Maryann E. Martone","slug":"maryann-martone"}],"date":"2022-07-28","section":"panorama","issue":"volume4issue3","doi":"10.1162/99608f92.bcd0b999","abstract":"The U.S. National Institutes of Health sponsored a meeting in April 2021 to consider how to increase the scientific culture of data sharing. The editors of this special section on the meeting asked three prominent scientists to provide perspectives from Europe on data sharing and management and about the possibilities for global cooperation.","keywords":["Data management and sharing plans","DSM","FAIR","citation","data sharing","privacy","intellectual property","database"],"image":"/media/remote/ef562515cfd88b9b-720.webp","collections":["changing-the-culture-on-data-management-and-data-sharing-in-biomedicine"],"label":"Panorama"},{"id":"cb06b469-5e2d-4f98-9d63-f6c46a5bd899","slug":"fgyf5cne","title":"Harnessing the Known Unknowns: Differential Privacy and the 2020 Census","dek":null,"authors":[{"name":"Ruobin Gong","slug":"ruobin-gong"},{"name":"Erica L. Groshen","slug":"erica-groshen"},{"name":"Salil Vadhan","slug":"salil-vadhan"}],"date":"2022-06-24","section":null,"issue":"specialissue2","doi":"10.1162/99608f92.cb06b469","abstract":null,"keywords":[],"image":"/media/remote/db594ebc2dbfce78-720.webp","collections":[],"label":"From the Editors"},{"id":"a93d96fa-8199-4532-9a73-7f2a0887f45c","slug":"ft9fhmku","title":"The Effect of Differentially Private Noise Injection on Sampling Efficiency and Funding Allocations: Evidence From the 1940 Census","dek":null,"authors":[{"name":"Quentin Brummet","slug":"quentin-brummet"},{"name":"Edward Mulrow","slug":"edward-mulrow"},{"name":"Kirk Wolter","slug":"kirk-wolter"}],"date":"2022-06-24","section":null,"issue":"specialissue2","doi":"10.1162/99608f92.a93d96fa","abstract":"The Census Bureau has recently released a plan to ensure that all statistics released as part of the 2020 Census are protected by a noise injection algorithm that satisfies the definition of differential privacy. Using 1940 Census data that has been treated with a noise injection process similar to that proposed for the 2020 Census, we explore the utility of the 1940 data by analyzing how the additional noise might a","keywords":["differential privacy","decennial census","oversampling","funding allocations","probability sample","hard-to-reach population"],"image":null,"collections":["data-science-for-governing-and-policy-making"],"label":"Empirical Evaluations"},{"id":"1cfad278-f2b1-4e60-9395-fe0e39c91e22","slug":"xjb781mj","title":"Disclosure Protection in the Context of Statistical Agency Operations: Data Quality and Related Constraints","dek":null,"authors":[{"name":"John Eltinge","slug":"john-eltinge"}],"date":"2022-06-24","section":null,"issue":"specialissue2","doi":"10.1162/99608f92.1cfad278","abstract":"This article explores the interface between disclosure protection and multiple dimensions of data quality (accuracy, accessibility, relevance, coherence, granularity, punctuality, and interpretability) in the dissemination of statistical information products and services. Operational constraints and related trade-offs receive special attention. The main ideas are placed in the context of previous literature, and summ","keywords":["constrained optimization","decennial census","differential privacy","formal privacy","intangible capital","invariants","privacy budget","public goods","stakeholder communications","tiered access"],"image":"/media/remote/ac6d1fafb921e9b0-720.webp","collections":["data-science-for-governing-and-policy-making"],"label":"Census: Importance, History, and Technical Changes"},{"id":"b5d3faaa-c953-4826-8ac8-fd5b6ea4a2a8","slug":"ld4smnnf","title":"Transparent Privacy Is Principled Privacy","dek":null,"authors":[{"name":"Ruobin Gong","slug":"ruobin-gong"}],"date":"2022-06-24","section":null,"issue":"specialissue2","doi":"10.1162/99608f92.b5d3faaa","abstract":"In a technical treatment, this article establishes the necessity of transparent privacy for drawing unbiased statistical inference for a wide range of scientific questions. Transparency is a distinct feature enjoyed by differential privacy: the probabilistic mechanism with which the data are privatized can be made public without sabotaging the privacy guarantee. Uncertainty due to transparent privacy may be conceived","keywords":["statistical inference","unbiasedness","uncertainty quantification","total survey error","privacy-utility trade-off","invariants"],"image":"/media/remote/6d79124c1e92cddf-720.webp","collections":["data-science-for-governing-and-policy-making"],"label":"Commentary and Critique"},{"id":"aed7f34f-0c5e-4f96-a4da-5feda5fda292","slug":"tgi3gol2","title":"Disclosure Avoidance and the 2020 Census: What Do Researchers Need to Know?","dek":null,"authors":[{"name":"Erica L. Groshen","slug":"erica-groshen"},{"name":"Daniel Goroff","slug":"daniel-goroff"}],"date":"2022-06-24","section":null,"issue":"specialissue2","doi":"10.1162/99608f92.aed7f34f","abstract":"The U.S. Census Bureau’s plans for release of data from the 2020 Decennial Census of Population and Housing will publicly and transparently address the unavoidable trade-off between data privacy and data accuracy. Statistical analysts can and should, therefore, take into account the planned presence of well-specified, well-justified noise in data releases based on the 2020 Decennial Census.To aid researchers’ prepara","keywords":["social science research","differential privacy","2020 Census","disclosure avoidance","data privacy","Census Bureau","privacy protection","TopDown Algorithm","postprocessing"],"image":"/media/remote/13699763a57c48be-720.webp","collections":["data-science-for-governing-and-policy-making"],"label":"Broader Perspectives"},{"id":"5cd8024e-aa6b-40c0-afaa-de279c1a8120","slug":"1rsg867y","title":"Assessing the Impact of Differential Privacy on Measures of Population and Racial Residential Segregation","dek":null,"authors":[{"name":"Brian Asquith","slug":"brian-asquith"},{"name":"Brad Hershbein","slug":"brad-hershbein"},{"name":"Tracy Kugler","slug":"tracy-kugler"},{"name":"Shane Reed","slug":"shane-reed"},{"name":"Steven Ruggles","slug":"steven-ruggles"},{"name":"Jonathan Schroeder","slug":"jonathan-schroeder"},{"name":"Steve Yesiltepe","slug":"steve-yesiltepe"},{"name":"David Van Riper","slug":"david-van-riper"}],"date":"2022-06-24","section":null,"issue":"specialissue2","doi":"10.1162/99608f92.5cd8024e","abstract":"The U.S. Census Bureau plans to use a new disclosure avoidance technique based on differential privacy to protect respondent confidentiality for the 2020 Decennial Census of Population and Housing. Their new technique injects noise based on a number of parameters into published statistics. While the noise injection does protect respondent confidentiality, it achieves the protection at the cost of less accurate data. ","keywords":["differential privacy","residential segregation","accuracy","census"],"image":"/media/remote/5665ce95d07f6003-720.webp","collections":["data-science-for-governing-and-policy-making"],"label":"Empirical Evaluations"},{"id":"529e3cb9-c4f8-48c9-9aba-15cb477ab081","slug":"7evz361i","title":"The 2020 Census Disclosure Avoidance System TopDown Algorithm","dek":null,"authors":[{"name":"John M. Abowd","slug":"john-abowd"},{"name":"Robert Ashmead","slug":"robert-ashmead"},{"name":"Ryan Cumings-Menon","slug":"ryan-cumings-menon"},{"name":"Simson Garfinkel","slug":"simson-garfinkel"},{"name":"Micah Heineck","slug":"micah-heineck"},{"name":"Christine Heiss","slug":"christine-heiss"},{"name":"Robert Johns","slug":"robert-johns-4"},{"name":"Daniel Kifer","slug":"daniel-kifer"},{"name":"Philip Leclerc","slug":"philip-leclerc"},{"name":"Ashwin Machanavajjhala","slug":"ashwin-machanavajjhala"},{"name":"Brett Moran","slug":"brett-moran"},{"name":"William Sexton","slug":"william-sexton"},{"name":"Matthew Spence","slug":"matthew-spence"},{"name":"Pavel Zhuravlev","slug":"pavel-zhuravlev"}],"date":"2022-06-24","section":null,"issue":"specialissue2","doi":"10.1162/99608f92.529e3cb9","abstract":"The Census TopDown Algorithm (TDA) is a disclosure avoidance system using differential privacy for privacy-loss accounting. The algorithm ingests the final, edited version of the 2020 Census data and the final tabulation geographic definitions. The algorithm then creates noisy versions of key queries on the data, referred to as measurements, using zero-Concentrated Differential Privacy. Another key aspect of the TDA ","keywords":["differential privacy","2020 Census","TopDown Algorithm","redistricting data"],"image":"/media/remote/e3f88cbda1ac6371-720.webp","collections":["data-science-for-governing-and-policy-making"],"label":"Census: Importance, History, and Technical Changes"},{"id":"ff891fe5-834f-4f4e-88e6-de0bd53de52c","slug":"ql9z7ehf","title":"A Chronicle of the Application of Differential Privacy to the 2020 Census","dek":null,"authors":[{"name":"V. Joseph Hotz","slug":"v-joseph-hotz"},{"name":"Joseph Salvo","slug":"joseph-salvo"}],"date":"2022-06-24","section":null,"issue":"specialissue2","doi":"10.1162/99608f92.ff891fe5","abstract":"In this article, we chronicle the U.S. Census Bureau’s development of the Disclosure Avoidance System (DAS) for the publicly released products of the 2020 Census of Population. We provide a brief history of the Census Bureau’s fulfillment of its dual mission of conducting and disseminating the constitutionally mandated decennial information on the U.S. population and its promise of safeguarding the confidentiality of","keywords":[],"image":"/media/remote/619fbc71437b8202-720.webp","collections":["data-science-for-governing-and-policy-making"],"label":"Broader Perspectives"},{"id":"66882f0e-cc08-481b-82e0-f36f4c5292ba","slug":"3vj5j6i0","title":"Differential Perspectives: Epistemic Disconnects Surrounding the U.S. Census Bureau’s Use of Differential Privacy","dek":null,"authors":[{"name":"danah boyd","slug":"danah-boyd"},{"name":"Jayshree Sarathy","slug":"jayshree-sarathy"}],"date":"2022-06-24","section":null,"issue":"specialissue2","doi":"10.1162/99608f92.66882f0e","abstract":"When the U.S. Census Bureau announced its intention to modernize its disclosure avoidance procedures for the 2020 Census, it sparked a controversy that is still underway. The move to differential privacy introduced technical and procedural uncertainties, leaving stakeholders unable to evaluate the quality of the data. More importantly, this transformation exposed the statistical illusions and limitations of census da","keywords":["epistemology","science and technology studies","differential privacy","census"],"image":"/media/remote/9ee394e448b015c9-720.webp","collections":["data-science-for-governing-and-policy-making"],"label":"Broader Perspectives"},{"id":"22fd8a0e-d641-46dd-b0e5-97edebc4ba7b","slug":"954ycugm","title":"Private Numbers in Public Policy: Census, Differential Privacy, and Redistricting","dek":null,"authors":[{"name":"Aloni Cohen","slug":"aloni-cohen"},{"name":"Moon Duchin","slug":"moon-duchin"},{"name":"JN Matthews","slug":"jn-matthews"},{"name":"Bhushan Suwal","slug":"bhushan-suwal"}],"date":"2022-06-24","section":null,"issue":"specialissue2","doi":"10.1162/99608f92.22fd8a0e","abstract":"The 2020 Decennial Census in the United States was released with a new disclosure avoidance system in place, putting differential privacy in the spotlight for a wide range of data users. We consider several key applications of census data in redistricting, developing tools and demonstrations for practitioners who are concerned about the impacts of this new noising algorithm called TopDown. Based on a close look at ni","keywords":["Census","TopDown","differential privacy","redistricting","Voting Rights Act"],"image":"/media/remote/fb3504a414cbc3d8-720.webp","collections":["data-science-for-governing-and-policy-making"],"label":"Empirical Evaluations"},{"id":"5d9b1a8d-657c-4b5a-846f-e811e0d387d3","slug":"lj69n2vc","title":"What Will It Take to Get to Acceptable Privacy-Accuracy Combinations?","dek":"Reflections on Brummet et al. and Asquith et al.","authors":[{"name":"Ori Heffetz","slug":"ori-heffetz"}],"date":"2022-06-24","section":null,"issue":"specialissue2","doi":"10.1162/99608f92.5d9b1a8d","abstract":"What will it take to achieve acceptable privacy-accuracy combinations? I discuss this question in five parts. In Section 1, I review the technicalities of the privacy-accuracy trade-off problem. In Section 2, I introduce the two articles in this symposium that study real-world privacy-accuracy combinations. In Sections 3 and 4, I ask, respectively: What is acceptable accuracy? What is acceptable privacy? I consider t","keywords":["privacy-accuracy trade-off","privacy-accuracy combinations","differential privacy","census","acceptable privacy","acceptable accuracy"],"image":"/media/remote/027512853dd71a5b-720.webp","collections":["data-science-for-governing-and-policy-making"],"label":"Commentary and Critique"},{"id":"681fe3bd-ca6e-4550-b09d-71e4660c75f6","slug":"x1ozqj10","title":"Data Flush","dek":null,"authors":[{"name":"Xiaotong Shen","slug":"xiaotong-shen"},{"name":"Xuan Bi","slug":"xuan-bi"},{"name":"Rex Shen","slug":"rex-shen"}],"date":"2022-05-09","section":"milestones-and-millstones","issue":"volume4issue2","doi":"10.1162/99608f92.681fe3bd","abstract":"Data perturbation is a technique for generating synthetic data by adding ‘noise’ to raw data, which has an array of applications in science and engineering, primarily in data security and privacy. One challenge for data perturbation is that it usually produces synthetic data resulting in information loss at the expense of privacy protection. The information loss, in turn, renders the accuracy loss for any statistical","keywords":["census","differential privacy","distribution preservation","data integration","statistical inference"],"image":"/media/remote/c9ecc43e7b22fb63-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"73a1c910-9d91-43e2-8ee9-c45a639024ba","slug":"5094zlft","title":"World of EdCraft: Challenges and Opportunities in Synchronous Online Teaching","dek":null,"authors":[{"name":"Andrew W. Lo","slug":"andrew-lo"},{"name":"Brian Stevens","slug":"brian-stevens"},{"name":"Sean P. Willems","slug":"sean-p-willems"}],"date":"2022-05-02","section":"stepping-stones","issue":"volume4issue2","doi":"10.1162/99608f92.73a1c910","abstract":"Online teaching at higher educational institutions has become a much higher priority in the face of the COVID-19 pandemic, but most faculty and staff at these institutions are ill-prepared to adapt their teaching methods and content to this new medium. This article guides the reader through three teaching studios developed for online synchronous teaching to very different student populations: a large (90-student) gra","keywords":["online teaching","distance learning","livestreaming","massive open online course (MOOC)","gamification"],"image":"/media/remote/f175fc7ba8ae3c72-720.webp","collections":[],"label":"Stepping Stones"},{"id":"5c45c405-d7ac-4faa-a220-c581ba423885","slug":"izbdzmpi","title":"World of EdCraft: Teaching Healthcare Finance at MIT","dek":null,"authors":[{"name":"Andrew W. Lo","slug":"andrew-lo"}],"date":"2022-05-02","section":"stepping-stones","issue":"volume4issue2","doi":"10.1162/99608f92.73a1c910/5c45c405","abstract":"In this article, I describe my approach to dealing with the challenges and opportunities of synchronous online teaching during the Fall semester of 2020 in the specific context of a 90-student graduate course in Healthcare Finance at the MIT Sloan School of Management.","keywords":["online teaching","distance learning","live streaming","massive online open course (MOOC)","gamification","healthcare finance"],"image":"/media/remote/e54d9ab92cef2c7c-720.webp","collections":[],"label":"Stepping Stones"},{"id":"e303e4d3-2504-4599-b62c-125116e670ff","slug":"8qjgthm3","title":"World of EdCraft: Teaching Stat 201 at the University of Tennessee Knoxville","dek":null,"authors":[{"name":"Brian Stevens","slug":"brian-stevens"}],"date":"2022-05-02","section":"stepping-stones","issue":"volume4issue2","doi":"10.1162/99608f92.73a1c910/e303e4d3","abstract":"Statistics 201 is the introductory statistics class at the University of Tennessee, Knoxville, taught through the Haslam College of Business, that services students from all across the university. Over 1,200 students take it per semester, with backgrounds ranging from nursing to the sciences, as well as business majors. In this article, I describe my approach to teaching this course synchronously in an online format ","keywords":["online teaching","distance learning","livestreaming","massive online open course (MOOC)","gamification"],"image":"/media/remote/090e36c5e137e048-720.webp","collections":[],"label":"Stepping Stones"},{"id":"2b7d0bc7-7237-49bf-abb4-57d6ad4d98e8","slug":"pkdompl2","title":"World of EdCraft: Teaching Introduction to Operations Management at MIT","dek":null,"authors":[{"name":"Sean P. Willems","slug":"sean-p-willems"}],"date":"2022-05-02","section":"stepping-stones","issue":"volume4issue2","doi":"10.1162/99608f92.73a1c910/2b7d0bc7","abstract":"In this article, I describe my experience in teaching a synchronous online course, Introduction to Operations Management, at the MIT Sloan School of Management. The challenges and opportunities of this format are unique, and I summarize my approaches to addressing them in the context of a home studio I built for this purpose. I provide a detailed review of my setup—including a complete listing of my hardware and soft","keywords":["online teaching","distance learning","livestreaming","massive online open course (MOOC)","gamification"],"image":"/media/remote/647327e7e8b611f9-720.webp","collections":[],"label":"Stepping Stones"},{"id":"65cb18b5-7d5d-40b2-99f3-4a4e98cc44bf","slug":"drhcgp91","title":"Online Education in Times of COVID: Adapting and Deploying a Data Science Program in Mexico","dek":null,"authors":[{"name":"Andrea Monserrat Arredondo-Rodriguez","slug":"andrea-monserrat-arredondo-rodriguez"},{"name":"Gabriel Missael Barco","slug":"gabriel-missael-barco"},{"name":"Claudia I. García-Gil","slug":"claudia-garcia-gil"},{"name":"Alicia del Carmen Hernández-Guzman","slug":"alicia-del-carmen-hernandez-guzman"},{"name":"Rogelio Antonio Hernández-López","slug":"rogelio-antonio-hernandez-lopez"},{"name":"Benjamin Manuel Sánchez-Lengeling","slug":"benjamin-manuel-sanchez-lengeling"},{"name":"Carla Márquez-Luna","slug":"carla-marquez-luna"}],"date":"2022-05-02","section":"minding-the-future","issue":"volume4issue2","doi":"10.1162/99608f92.65cb18b5","abstract":null,"keywords":[],"image":"/media/remote/d4ae50a56c200917-720.webp","collections":[],"label":"Minding the Future"},{"id":"5dec149c-263d-451e-953e-5724a24e7432","slug":"oraonikr","title":"What Data Visualization Reveals: Elizabeth Palmer Peabody and the Work of Knowledge Production","dek":null,"authors":[{"name":"Lauren Klein","slug":"lauren-klein"}],"date":"2022-04-28","section":"panorama","issue":"volume4issue2","doi":"10.1162/99608f92.5dec149c","abstract":"This essay offers the chronological charts of Elizabeth Palmer Peabody (1804–1894), the 19th-century educator and intellectual, as early examples of how data visualization can reveal a range of forms of knowledge. It challenges the universality of the goals of clarity and efficiency when designing data visualizations, and argues for the value of visualizations that encourage sustained reflection and imaginative respo","keywords":[],"image":"/media/remote/a57261e6f9038313-720.webp","collections":[],"label":"Panorama"},{"id":"13e4351b-32f8-4c6d-89c2-cd5b449026a9","slug":"kf746cs1","title":"How Can We Make Data Science Even More Valuable?","dek":null,"authors":[{"name":"Francesca Dominici","slug":"francesca-dominici"}],"date":"2022-04-28","section":"editorscolumn","issue":"volume4issue2","doi":"10.1162/99608f92.13e4351b","abstract":null,"keywords":[],"image":"/media/remote/8245eb82e7451945-720.webp","collections":[],"label":"Editor’s Column"},{"id":"cbe67e91-38c0-4053-98fb-ee3d41a63bba","slug":"c68dnpkc","title":"Data Quality in Electronic Health Record Research: An Approach for Validation and Quantitative Bias Analysis for Imperfectly Ascertained Health Outcomes Via Diagnostic Codes","dek":null,"authors":[{"name":"Neal D. Goldstein","slug":"neal-d-goldstein"},{"name":"Deborah Kahal","slug":"deborah-kahal"},{"name":"Karla Testa","slug":"karla-testa"},{"name":"Ed J. Gracely","slug":"ed-j-gracely"},{"name":"Igor Burstyn","slug":"igor-burstyn"}],"date":"2022-04-28","section":"cornucopia","issue":"volume4issue2","doi":"10.1162/99608f92.cbe67e91","abstract":"It is incumbent upon all researchers who use the electronic health record (EHR), including data scientists, to understand the quality of such data. EHR data may be subject to measurement error or misclassification that have the potential to bias results, unless one applies the available computational techniques specifically created for this problem. In this article, we begin with a discussion of data-quality issues i","keywords":["electronic health record","data quality","bias","validation","hepatitis C","International Classification of Diseases"],"image":"/media/remote/059be09f64e026cd-720.webp","collections":[],"label":"Cornucopia"},{"id":"16c71dad-b160-49c3-bdc1-d7d25925aea4","slug":"deziirvg","title":"Private Prediction Sets","dek":null,"authors":[{"name":"Anastasios Nikolas Angelopoulos","slug":"anastasios-angelopoulos"},{"name":"Stephen Bates","slug":"stephen-bates"},{"name":"Tijana Zrnic","slug":"tijana-zrnic"},{"name":"Michael I. Jordan","slug":"michael-i-jordan"}],"date":"2022-04-28","section":"milestones-and-millstones","issue":"volume4issue2","doi":"10.1162/99608f92.16c71dad","abstract":"In real-world settings involving consequential decision-making, the deployment of machine learning systems generally requires both reliable uncertainty quantification and protection of individuals’ privacy. We present a framework that treats these two desiderata jointly. Our framework is based on conformal prediction, a methodology that augments predictive models to return prediction sets that provide uncertainty qua","keywords":["privacy","uncertainty quantification","conformal prediction","prediction sets"],"image":"/media/remote/db3c4e57239ea200-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"9d13ba15-372a-4034-a91d-782661ae3595","slug":"gnf4pmig","title":"Show US the Data","dek":null,"authors":[{"name":"Nancy Potok","slug":"nancy-potok"}],"date":"2022-04-28","section":"diving-into-data","issue":"volume4issue2","doi":"10.1162/99608f92.9d13ba15","abstract":null,"keywords":[],"image":"/media/remote/a3a5e57b2e8ef210-720.webp","collections":[],"label":"Diving into Data"},{"id":"4a01213d-04c6-4df3-b2e6-67049f1d4415","slug":"4zjxu63p","title":"The Value of Science: Special Theme","dek":null,"authors":[{"name":"Julia Lane","slug":"julia-lane"},{"name":"Brian Kim","slug":"brian-kim"},{"name":"Frauke Kreuter","slug":"frauke-kreuter"},{"name":"Allison Nunez","slug":"allison-nunez"}],"date":"2022-04-28","section":"panorama","issue":"volume4issue2","doi":"10.1162/99608f92.4a01213d","abstract":null,"keywords":[],"image":"/media/remote/6f2905888f75708b-720.webp","collections":["value-of-science"],"label":"Panorama"},{"id":"946ef791-899f-4da7-a1ed-8c9c58ea569a","slug":"yadsgbuj","title":"Data Fragmentation and Data Linking: A Threat and an Opportunity","dek":null,"authors":[{"name":"Clemencia Cosentino","slug":"clemencia-cosentino"}],"date":"2022-04-28","section":null,"issue":"volume4issue2","doi":"10.1162/99608f92.946ef791","abstract":null,"keywords":[],"image":"/media/remote/93c70eb5162bec01-720.webp","collections":["value-of-science"],"label":"Commentary"},{"id":"373a3313-f8b9-4e93-92aa-8d693e07c991","slug":"b9avodwx","title":"Unlocking Government Data to Support and Evaluate Scientific Research: An Overview of Selected Papers from the Value of Science Conference","dek":null,"authors":[{"name":"Henry Kautz","slug":"henry-kautz"}],"date":"2022-04-28","section":null,"issue":"volume4issue2","doi":"10.1162/99608f92.373a3313","abstract":null,"keywords":[],"image":"/media/remote/ca88fc755aeaecd8-720.webp","collections":["value-of-science"],"label":"Commentary"},{"id":"cc0da717-4517-4f06-9e57-d8a46e0b81e7","slug":"zgu2u8y6","title":"Interview With Paul Romer","dek":"An interview with Paul Romer by Julia Lane","authors":[{"name":"Paul Romer","slug":"paul-romer"},{"name":"Julia Lane","slug":"julia-lane"}],"date":"2022-04-28","section":"panorama","issue":"volume4issue2","doi":"10.1162/99608f92.cc0da717","abstract":"The 2018 Nobel Laureate in Economic Sciences, Paul Romer of New York University speaks with New York University Professor Julia Lane. His work on endogenous technological change transformed our understanding of economic growth by recognizing that ideas—particularly scientific ideas—are neither a private nor a public good. Although he originally argued that the search for ideas is best fostered through competition, hi","keywords":["value of science","endogenous growth"],"image":"/media/remote/1ccfc13df951dcf2-720.webp","collections":["value-of-science"],"label":"Panorama"},{"id":"8a3f2336-89bc-4ef5-8d77-f747daf95c11","slug":"g6e8noiy","title":"Data Inventories for the Modern Age? Using Data Science to Open Government Data","dek":null,"authors":[{"name":"Julia Lane","slug":"julia-lane"},{"name":"Ernesto Gimeno","slug":"ernesto-gimeno"},{"name":"Ekaterina Levitskaya","slug":"ekaterina-levitskaya"},{"name":"Zheyuan Zhang","slug":"zheyuan-zhang"},{"name":"Alberto Zigoni","slug":"alberto-zigoni"}],"date":"2022-04-28","section":"panorama","issue":"volume4issue2","doi":"10.1162/99608f92.8a3f2336","abstract":"This article describes how data science techniques—machine learning and natural language processing—can be used to open the black box of government data. It then describes how an incentive structure can be established—using human–computer interaction techniques —to create a new and sustainable data ecosystem. The particular focus is on the United States and on scientific researchers, who are major users of government","keywords":["data inventories","democratizing data","Kaggle competition","machine learning","natural language processing"],"image":"/media/remote/4f64df2a84bd737e-720.webp","collections":["value-of-science"],"label":"Panorama"},{"id":"50842b38-a250-4a16-8366-268122d3d581","slug":"dmdofra6","title":"Translating the Promise of the Evidence Act Into a Great Concept","dek":null,"authors":[{"name":"Spiro E. Stefanou","slug":"spiro-e-stefanou"}],"date":"2022-04-28","section":null,"issue":"volume4issue2","doi":"10.1162/99608f92.50842b38","abstract":null,"keywords":[],"image":"/media/remote/673e1719c8c7ac81-720.webp","collections":["value-of-science"],"label":"Commentary"},{"id":"d5684866-c3f3-4048-9317-553357b3c5bf","slug":"9vqo7scb","title":"Data on How Science Is Made Can Make Science Better","dek":null,"authors":[{"name":"Jamshid Sourati","slug":"jamshid-sourati"},{"name":"Alexander Belikov","slug":"alexander-belikov"},{"name":"James Evans","slug":"james-evans"}],"date":"2022-04-28","section":"panorama","issue":"volume4issue2","doi":"10.1162/99608f92.d5684866","abstract":"Science is an engine of innovation and economic growth and a pathway to prosperity for countries around the world. The increasing availability of scientific publications today poses a data-driven opportunity to better understand and improve science. Scientific publications contain data on the content of published research and metadata on the context that gave rise to that research. Here we discuss and demonstrate the","keywords":["science of science","linked data","science policy","artificial intelligence","networks","innovation"],"image":"/media/remote/8beb90247c304bd9-720.webp","collections":["value-of-science"],"label":"Panorama"},{"id":"1e23fb3f-bf3f-453f-8b9c-bc1214cbfa48","slug":"u073rjxs","title":"A Linked Data Mosaic for Policy-Relevant Research on Science and Innovation: Value, Transparency, Rigor, and Community","dek":null,"authors":[{"name":"Wan-Ying Chang","slug":"wan-ying-chang"},{"name":"Maryah Garner","slug":"maryah-garner"},{"name":"Jodi Basner","slug":"jodi-basner"},{"name":"Bruce Weinberg","slug":"bruce-weinberg"},{"name":"Jason Owen-Smith","slug":"jason-owen-smith"}],"date":"2022-04-28","section":"panorama","issue":"volume4issue2","doi":"10.1162/99608f92.1e23fb3f","abstract":"This article presents a new framework for realizing the value of linked data understood as a strategic asset and increasingly necessary form of infrastructure for policy-making and research in many domains. We outline a framework, the ‘data mosaic’ approach, which combines socio-organizational and technical aspects. After demonstrating the value of linked data, we highlight key concepts and dangers for community-deve","keywords":["science","innovation","community","linkage","graduate education"],"image":"/media/remote/a555564338f956cf-720.webp","collections":["value-of-science"],"label":"Panorama"},{"id":"d203ae45-c43d-474c-9dea-ae8cc1fc96c1","slug":"9dkr3v8v","title":"Building on Aotearoa New Zealand’s Integrated Data Infrastructure","dek":null,"authors":[{"name":"Craig Jones","slug":"craig-jones"},{"name":"Anna McDowell","slug":"anna-mcdowell"},{"name":"Vince Galvin","slug":"vince-galvin"},{"name":"Dorothy Adams","slug":"dorothy-adams"}],"date":"2022-04-28","section":"panorama","issue":"volume4issue2","doi":"10.1162/99608f92.d203ae45","abstract":"Stats NZ established the Integrated Data Infrastructure (IDI) to enable longitudinal research into the causes and correlates of social outcomes for New Zealanders. The IDI was established in 2011, building off earlier experiences linking employer and employee data (which itself subsequently formed the basis of another linked database known as the Longitudinal Business Database [LBD]). Data in the IDI form an ‘ever-re","keywords":["integrated data","data science","longitudinal research"],"image":"/media/remote/6ca9ba53718f0eae-720.webp","collections":["value-of-science"],"label":"Panorama"},{"id":"d219b2ce-c0d4-443f-809f-9c9f0163cd0a","slug":"cuh30wk5","title":"Demonstrating the Value of Government Investment in Science: Developing a Data Framework to Improve Science Policy","dek":null,"authors":[{"name":"Tobin L. Smith","slug":"tobin-l-smith"}],"date":"2022-04-28","section":"panorama","issue":"volume4issue2","doi":"10.1162/99608f92.d219b2ce","abstract":"During last year’s Senate debate on the Endless Frontiers Act, a bill that would authorize substantial funding increases for the National Science Foundation (NSF), some lawmakers raised questions concerning the value of federal investments in scientific research. This article suggests that a new framework for studying and understanding the value and impact of federal research investments can help pro-science policyma","keywords":["science policy","Congress","data framework","data use","science funding"],"image":"/media/remote/0dd89c0bf7ef64f2-720.webp","collections":["value-of-science"],"label":"Panorama"},{"id":"49954afb-0289-4124-a80a-6835b554280b","slug":"nqtyaxe9","title":"Linked Data Detail a Gender Gap in STEM That Persists Across Time and Place","dek":null,"authors":[{"name":"Jonathan Auerbach","slug":"jonathan-auerbach"},{"name":"Catherine Elizabeth DeLazzero","slug":"catherine-elizabeth-delazzero"}],"date":"2022-04-28","section":null,"issue":"volume4issue2","doi":"10.1162/99608f92.49954afb","abstract":null,"keywords":[],"image":"/media/remote/ff365c99b9c3de38-720.webp","collections":["value-of-science"],"label":"Commentary"},{"id":"edf0b588-1fea-467f-b147-04ab05066bd6","slug":"vn6ib3o5","title":"Understanding Diversity: Overcoming the Small-n Problem","dek":null,"authors":[{"name":"Kaye Husbands Fealing","slug":"kaye-husbands-fealing"},{"name":"Aubrey DeVeny Incorvaia","slug":"aubrey-deveny-incorvaia"}],"date":"2022-04-28","section":null,"issue":"volume4issue2","doi":"10.1162/99608f92.edf0b588","abstract":null,"keywords":[],"image":"/media/remote/e13dd704448515f3-720.webp","collections":["value-of-science"],"label":"Commentary"},{"id":"dbd5f147-df92-401c-9937-ef269b12bede","slug":"h3tuxk97","title":"Kaleidoscopic Data Science","dek":null,"authors":[{"name":"David C. Parkes","slug":"david-parkes"}],"date":"2022-01-27","section":"editorscolumn","issue":"volume4issue1","doi":"10.1162/99608f92.dbd5f147","abstract":null,"keywords":[],"image":"/media/remote/31ec0caf2ee55224-720.webp","collections":[],"label":"Editor’s Column"},{"id":"57898732-3ce6-4a61-a88b-cd7c95a69ee0","slug":"lualrufk","title":"How Can Data Science Revolutionize Humanitarian Crises?","dek":null,"authors":[{"name":"Liberty Vittert","slug":"liberty-vittert"},{"name":"Rita Ko","slug":"rita-ko"}],"date":"2022-01-27","section":"panorama","issue":"volume4issue1","doi":"10.1162/99608f92.57898732","abstract":null,"keywords":[],"image":"/media/remote/a30fdbcfb2e92f87-720.webp","collections":["world-migration-and-displacement-data-disinformation-and-human-mobility"],"label":"Panorama"},{"id":"c8708890-279e-49d9-be08-ce24ad021bd3","slug":"3msng5db","title":"A Conversation With António Vitorino","dek":"An interview with António Vitorino by Xiao-Li Meng","authors":[{"name":"António Vitorino","slug":"antonio-vitorino"},{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2022-01-27","section":"panorama","issue":"volume4issue1","doi":"10.1162/99608f92.c8708890","abstract":"In May 2021, HDSR founding editor-in-chief, Xiao-Li Meng, met with International Organization for Migration (IOM) Director General António Vitorino for a virtual ‘fireside chat’ at the World Migration and Displacement Symposium: Data, Disinformation, and Human Mobility. The pair discussed IOM’s recently launched Migration Data Strategy, COVID-19’s impact on global migration, and how the United Nations and IOM are con","keywords":[],"image":"/media/remote/a6308c82cdc6ec61-720.webp","collections":["conversations-with-leaders","world-migration-and-displacement-data-disinformation-and-human-mobility"],"label":"Panorama"},{"id":"08b08a97-6f01-41c8-bf75-59c60a5ff7cd","slug":"pt9vgvq9","title":"When Lions Write: An American Immigrant Story","dek":null,"authors":[{"name":"Tolu Olubunmi","slug":"tolu-olubunmi"}],"date":"2022-01-27","section":"panorama","issue":"volume4issue1","doi":"10.1162/99608f92.08b08a97","abstract":"In May 2021, Tolu Olubunmi was invited to deliver the keynote address at the World Migration and Displacement Symposium: Data, Disinformation and Human Mobility. Ms. Olubunmi shared her moving immigration story and described her path toward advocacy and public policy. This piece is an edited and streamlined version of her speech, a video recording of which can be seen below.","keywords":["immigration","migration","advocacy","disinformation","vulnerable populations","human mobility"],"image":"/media/remote/751f060d52206936-720.webp","collections":["world-migration-and-displacement-data-disinformation-and-human-mobility"],"label":"Panorama"},{"id":"a6dbaef3-f36a-4922-81e1-00b302ea60d9","slug":"ktyp6el5","title":"Data for Good, What Is It Good For?: Challenges, Opportunities, and Data Innovation in Service of Refugees","dek":null,"authors":[{"name":"Nicole Smith","slug":"nicole-smith"},{"name":"Muhammed Y. Idris","slug":"muhammed-idris"},{"name":"Friederike Schüür","slug":"friederike-schuur"},{"name":"Rita Ko","slug":"rita-ko"}],"date":"2022-01-27","section":"panorama","issue":"volume4issue1","doi":"10.1162/99608f92.a6dbaef3","abstract":"With 82.4 million forcibly displaced people, we need new approaches to the global refugee crisis. The Hive, the innovation lab at USA for UNHCR, uses data, machine learning (ML), and other emerging technologies to improve lives for refugees in coordination and collaboration with UNHCR (United Nations High Commissioner for Refugees), known as the UN Refugee Agency. We outline five challenges in successfully leveraging","keywords":["data science","innovation","refugees","humanitarian","nonprofit"],"image":"/media/remote/434d3fcc81ef0f7f-720.webp","collections":["world-migration-and-displacement-data-disinformation-and-human-mobility"],"label":"Panorama"},{"id":"b3353b93-3b2f-4143-bda7-fb403b6627d6","slug":"cvx7b2dk","title":"Data, Design, and Deep Domain Knowledge: Science-Policy Collaboration to Combat Misinformation on Migration and Migrants","dek":null,"authors":[{"name":"Marie McAuliffe","slug":"marie-mcauliffe"},{"name":"Guy Abel","slug":"guy-abel"},{"name":"Adrian Kitimbo","slug":"adrian-kitimbo"},{"name":"Jose Ignacio Martin Galan","slug":"jose-ignacio-martin-galan"}],"date":"2022-01-27","section":"panorama","issue":"volume4issue1","doi":"10.1162/99608f92.b3353b93","abstract":"In today’s data-rich societies there is a strong tendency to assess and analyze complex issues through quantitative methods utilizing new, and rapidly evolving and constantly expanding, user-generated data. While new data and new data science present enormous opportunities for innovation and scholarship, we are also witnessing intensification and expansion of digitalized public discourses that are increasingly enabli","keywords":["migration data","migrants","international migration","misinformation","disinformation","science–policy collaboration"],"image":"/media/remote/68d787cfe0ae5be8-720.webp","collections":["world-migration-and-displacement-data-disinformation-and-human-mobility"],"label":"Panorama"},{"id":"a4d9a7c7-82ad-48b9-bc3a-3b05cf7cb8a4","slug":"deut7pug","title":"Misinformation About COVID-19 and Venezuelan Migration: Trends in Twitter Conversation During a Pandemic","dek":null,"authors":[{"name":"Katharine M. Donato","slug":"katharine-m-donato"},{"name":"Lisa Singh","slug":"lisa-singh"},{"name":"Ali Arab","slug":"ali-arab"},{"name":"Elizabeth Jacobs","slug":"elizabeth-jacobs"},{"name":"Douglas Post","slug":"douglas-post"}],"date":"2022-01-27","section":"panorama","issue":"volume4issue1","doi":"10.1162/99608f92.a4d9a7c7","abstract":"This article asks whether and how—during the COVID-19 pandemic at a time of considerable uncertainty—current events and announcements by governments and political leaders are associated with trends in Twitter conversation. Using Spanish-language tweets, we examine the changing dynamics of misinformation conversation about the COVID-19 virus, international border closures, and the sociopolitical reception of Venezuela","keywords":["COVID-19","displacement","migration","misinformation","Venezuela"],"image":"/media/remote/eef1c22bd7aef83f-720.webp","collections":["world-migration-and-displacement-data-disinformation-and-human-mobility"],"label":"Panorama"},{"id":"dfc7409d-dc61-4d35-a140-cd6a6e002f1d","slug":"mz6ufwhl","title":"The Humanitarian Journey: From Root Causes to Recovery","dek":null,"authors":[{"name":"James N. Purcell","slug":"james-n-purcell"}],"date":"2022-01-27","section":"panorama","issue":"volume4issue1","doi":"10.1162/99608f92.dfc7409d","abstract":"This symposium seeks to confirm the reemergence of the Global Humanitarian Cooperation (GHC) model of multilateral action that the teams from the game refugee decade (1979–88) pioneered and that governed international cooperative action until the Arab Spring era of 2011. I concluded then that a new default model, which I later called Nationalism cum Isolationism (NCI), had replaced the GHC. However, recent signs from","keywords":["migration","humanitarian crisis","data","root causes","multilateral action","durable solutions"],"image":"/media/remote/794f2c6f592e0384-720.webp","collections":["world-migration-and-displacement-data-disinformation-and-human-mobility"],"label":"Panorama"},{"id":"762d171a-6ae2-4341-9f6f-065d546f3e50","slug":"0gbwdele","title":"Widening Access to Applied Machine Learning With TinyML","dek":null,"authors":[{"name":"Vijay Janapa Reddi","slug":"vijay-janapa-reddi"},{"name":"Brian Plancher","slug":"brian-plancher"},{"name":"Susan Kennedy","slug":"susan-kennedy"},{"name":"Laurence Moroney","slug":"laurence-moroney"},{"name":"Pete Warden","slug":"pete-warden"},{"name":"Lara Suzuki","slug":"lara-suzuki"},{"name":"Anant Agarwal","slug":"anant-agarwal"},{"name":"Colby Banbury","slug":"colby-banbury"},{"name":"Massimo Banzi","slug":"massimo-banzi"},{"name":"Matthew Bennett","slug":"matthew-bennett"},{"name":"Benjamin Brown","slug":"benjamin-brown"},{"name":"Sharad Chitlangia","slug":"sharad-chitlangia"},{"name":"Radhika Ghosal","slug":"radhika-ghosal"},{"name":"Sarah Grafman","slug":"sarah-grafman"},{"name":"Rupert Jaeger","slug":"rupert-jaeger"},{"name":"Srivatsan Krishnan","slug":"srivatsan-krishnan"},{"name":"Maximilian Lam","slug":"maximilian-lam"},{"name":"Daniel Leiker","slug":"daniel-leiker"},{"name":"Cara Mann","slug":"cara-mann"},{"name":"Mark Mazumder","slug":"mark-mazumder"},{"name":"Dominic Pajak","slug":"dominic-pajak"},{"name":"Dhilan Ramaprasad","slug":"dhilan-ramaprasad"},{"name":"J. Evan Smith","slug":"j-evan-smith"},{"name":"Matthew Stewart","slug":"matthew-stewart"},{"name":"Dustin Tingley","slug":"dustin-tingley"}],"date":"2022-01-27","section":"stepping-stones","issue":"volume4issue1","doi":"10.1162/99608f92.762d171a","abstract":"Broadening access to both computational and educational resources is critical to diffusing machine learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this article, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, applied ","keywords":["applied ML","TinyML","STEM Education","Edge AI","MOOC"],"image":"/media/remote/4dfa89294281f28a-720.webp","collections":[],"label":"Stepping Stones"},{"id":"fd504fc4-9392-4cf6-af61-e1dc0533a57c","slug":"pn3w0aes","title":"Data Science and Energy: Some Lessons From Europe on Higher Education Course Design and Delivery","dek":null,"authors":[{"name":"Hussain Kazmi","slug":"hussain-kazmi"},{"name":"Íngrid Munné-Collado","slug":"ingrid-munne-collado"},{"name":"Khaoula Tidriri","slug":"khaoula-tidriri"},{"name":"Lars Nordström","slug":"lars-nordstrom"},{"name":"Frank Gielen","slug":"frank-gielen"},{"name":"Johan Driesen","slug":"johan-driesen"}],"date":"2022-01-27","section":"stepping-stones","issue":"volume4issue1","doi":"10.1162/99608f92.fd504fc4","abstract":"Data science is seen as a key enabler for technologies that help decarbonize global energy use. However, the energy sector continues to struggle to attract and train enough data scientists. The primary reason for this is the lack of emphasis on data science in most graduate programs in energy engineering, and the high barriers of entry for data scientists from other sectors. In this article, we present a snapshot of ","keywords":["energy","data science","higher education","exploratory data analysis","forecasting","decision making"],"image":"/media/remote/dd95d9013d592a89-720.webp","collections":[],"label":"Stepping Stones"},{"id":"ef5d8928-57c0-4890-9e41-c826c16739ca","slug":"kpn45eyx","title":"When Quantum Computation Meets Data Science: Making Data Science Quantum","dek":null,"authors":[{"name":"Yazhen Wang","slug":"yazhen-wang"}],"date":"2022-01-27","section":"milestones-and-millstones","issue":"volume4issue1","doi":"10.1162/99608f92.ef5d8928","abstract":"Quantum computation and quantum information have attracted considerable attention on multiple frontiers of scientific fields ranging from physics to chemistry and engineering, as well as from computer science to mathematics and statistics. Data science combines statistical methods, computational algorithms, and domain science information to extract knowledge and insights from big data, and to solve complex real-world","keywords":["quantum computation","quantum information","data science","statistics","quantum speedup","quantum supremacy"],"image":"/media/remote/52987f9b71e764ff-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"f9fc894c-a667-48b2-b6b7-bd8c18205a8c","slug":"mfhpwpxq","title":"A Value-Driven Approach to Building Data Infrastructures: The Example of the MidWest Collaborative","dek":null,"authors":[{"name":"Jessica Cunningham","slug":"jessica-cunningham"},{"name":"Anna Hui","slug":"anna-hui"},{"name":"Julia Lane","slug":"julia-lane"},{"name":"George Putnam","slug":"george-putnam"}],"date":"2022-01-27","section":"effective-policy-learning","issue":"volume4issue1","doi":"10.1162/99608f92.f9fc894c","abstract":null,"keywords":[],"image":"/media/remote/069ba0d0b377bbba-720.webp","collections":[],"label":"Effective Policy Learning"},{"id":"1b5c3b7b-b1b1-4ce5-a6cb-5b375e463474","slug":"i4eb4e8b","title":"Finding Love on a First Data: Matching Algorithms in Online Dating","dek":null,"authors":[{"name":"Liesel Sharabi","slug":"liesel-sharabi"}],"date":"2022-01-27","section":"recreations-in-randomness","issue":"volume4issue1","doi":"10.1162/99608f92.1b5c3b7b","abstract":null,"keywords":[],"image":"/media/remote/bb790bec04724d46-720.webp","collections":[],"label":"Recreations in Randomness"},{"id":"9c47e402-9402-4ef1-9b52-05fcce04508a","slug":"3x2sd8nq","title":"Some Recent Progress in Learning Theory: The Quantum Side","dek":null,"authors":[{"name":"Anurag Anshu","slug":"anurag-anshu"}],"date":"2022-01-27","section":null,"issue":"volume4issue1","doi":"10.1162/99608f92.9c47e402","abstract":null,"keywords":[],"image":"/media/remote/0c55709772e485f6-720.webp","collections":[],"label":"Featured Discussion"},{"id":"69c5328d-1d9e-4691-b47b-514ad3a6507e","slug":"23gghb1v","title":"Assume a Quantum Data Set","dek":null,"authors":[{"name":"Mária Kieferová","slug":"maria-kieferova"},{"name":"Yuval Sanders","slug":"yuval-sanders"}],"date":"2022-01-27","section":null,"issue":"volume4issue1","doi":"10.1162/99608f92.69c5328d","abstract":"Data-processing algorithms often require that the data is prepared in appropriate structures that are readily accessible or can be prepared on demand. Quantum computers derive their power from storing and manipulating quantum superpositions and could potentially speed up data science tasks. However, they often require input in the form of a quantum state that encodes a nonquantum data set. Here we describe some of th","keywords":["quantum algorithms","state preparation","quantum machine learning","big data"],"image":"/media/remote/bd4feeb0d78949cc-720.webp","collections":[],"label":"Featured Discussion"},{"id":"0f97ea03-829b-438d-922c-36c2288f0915","slug":"py6vpgeg","title":"Transforming Hiring and Retention: Mr. Edison Meets AI/ML","dek":null,"authors":[{"name":"Maryse Williams","slug":"maryse-williams"}],"date":"2022-01-27","section":"bits-and-bites","issue":"volume4issue1","doi":"10.1162/99608f92.0f97ea03","abstract":null,"keywords":[],"image":"/media/remote/8096f088cd4a1505-720.webp","collections":["ai-and-responsible-data-science"],"label":"Bits and Bites"},{"id":"bb7b9fd3-f4b1-4082-8aaf-11ab48f0bc7b","slug":"647sslvn","title":"Can Digital Construction Save the Planet?","dek":null,"authors":[{"name":"Holger Pietzsch","slug":"holger-pietzsch"}],"date":"2022-01-27","section":"bits-and-bites","issue":"volume4issue1","doi":"10.1162/99608f92.bb7b9fd3","abstract":null,"keywords":[],"image":"/media/remote/bdff9a998fc8284c-720.webp","collections":[],"label":"Bits and Bites"},{"id":"2f71a69e-a6f0-4c8d-9289-3bde67c5d651","slug":"8yr5wptb","title":"The Role of FOMO in Digital Transformation","dek":null,"authors":[{"name":"Ben Dooley","slug":"ben-dooley"}],"date":"2022-01-27","section":"bits-and-bites","issue":"volume4issue1","doi":"10.1162/99608f92.2f71a69e","abstract":null,"keywords":[],"image":"/media/remote/48382bdd9b7fae4b-720.webp","collections":[],"label":"Bits and Bites"},{"id":"162a0e48-bae3-4ebf-a142-cfbb51db97bc","slug":"s9v0snsz","title":"The Future of AI in Business: Chewbacca, Chatbots, and Why I Won’t Win at Ms. Pac Man","dek":null,"authors":[{"name":"Raiford Smith","slug":"raiford-smith"}],"date":"2022-01-27","section":"bits-and-bites","issue":"volume4issue1","doi":"10.1162/99608f92.162a0e48","abstract":null,"keywords":[],"image":"/media/remote/d6e2f220ebea5ab2-720.webp","collections":[],"label":"Bits and Bites"},{"id":"f09b190b-351b-4419-9d05-b501e8e29390","slug":"9lj3cuah","title":"Rethinking Engagement: Challenging the Financial Model of Social Media Platforms","dek":null,"authors":[{"name":"Tom Casey","slug":"tom-casey"}],"date":"2022-01-27","section":"bits-and-bites","issue":"volume4issue1","doi":"10.1162/99608f92.f09b190b","abstract":null,"keywords":[],"image":"/media/remote/549d8c4519bd49c4-720.webp","collections":[],"label":"Bits and Bites"},{"id":"2c5ec0ca-9558-4d4d-9d43-cfe0a28fba31","slug":"1j7onk28","title":"AI and Creativity","dek":null,"authors":[{"name":"Nini Hu","slug":"nini-hu"}],"date":"2022-01-27","section":"bits-and-bites","issue":"volume4issue1","doi":"10.1162/99608f92.2c5ec0ca","abstract":null,"keywords":[],"image":"/media/remote/8080e2e50d9fe203-720.webp","collections":[],"label":"Bits and Bites"},{"id":"5a0c7aaf-0974-4378-9252-0e46294ebfe2","slug":"g9eq8akb","title":"The How Matters: Simulation-Based Assessment of the Potential Contributions of Lateral Flow Device Tests for Keeping Schools Open and COVID-Safe in England","dek":null,"authors":[{"name":"Kevin Kunzmann","slug":"kevin-kunzmann"},{"name":"Camilla Lingjaerde","slug":"camilla-lingjaerde"},{"name":"Sheila Bird","slug":"sheila-bird"},{"name":"Sylvia Richardson","slug":"sylvia-richardson-2"}],"date":"2022-01-27","section":"cornucopia","issue":"volume4issue1","doi":"10.1162/99608f92.5a0c7aaf","abstract":"Environments where exact contact tracing is infeasible pose a challenge to ‘test and trace’ approaches for containing infectious diseases like COVID-19 that rely on following up on the close contacts of an index case. This is particularly problematic in primary schools and some workplaces. The additional benefit of using rapid screening tests over mere isolation restrictions of contacts has repeatedly been put forwar","keywords":["COVID-19","agent-based simulation","public health","policy"],"image":"/media/remote/1e464b81d8d23115-720.webp","collections":[],"label":"Cornucopia"},{"id":"5a9fd72c-212a-4a7d-91ba-07219d24359d","slug":"cgmjzm3c","title":"The Development of Quantum Machine Learning","dek":null,"authors":[{"name":"Khadijeh Najafi","slug":"khadijeh-najafi"},{"name":"Susanne F. Yelin","slug":"susanne-f-yelin"},{"name":"Xun Gao","slug":"xun-gao"}],"date":"2022-01-27","section":null,"issue":"volume4issue1","doi":"10.1162/99608f92.5a9fd72c","abstract":null,"keywords":[],"image":"/media/remote/e033256302098e24-720.webp","collections":[],"label":"Featured Discussion"},{"id":"ea411412-2a9b-40c7-8e2a-c4d7b16a66d4","slug":"4ioy3d9j","title":"Quantum Computation, Data Science, Bell Games","dek":null,"authors":[{"name":"Richard David Gill","slug":"richard-david-gill"}],"date":"2022-01-27","section":null,"issue":"volume4issue1","doi":"10.1162/99608f92.ea411412","abstract":"I draw attention to statistical and probabilistic and computer science aspects of the very, very close by topic of quantum internet.","keywords":["Bell game","loophole-free Bell experiments","Bell inequality","quantum non-locality","classical versus quantum distributed computing"],"image":"/media/remote/0696831d0ef0429b-720.webp","collections":[],"label":"Featured Discussion"},{"id":"68b503ea-1b07-4c59-a729-e3595fc94a9b","slug":"eyywouav","title":"Inspiring Quantum Data Analysts","dek":null,"authors":[{"name":"Yaakov Weinstein","slug":"yaakov-weinstein"}],"date":"2022-01-27","section":null,"issue":"volume4issue1","doi":"10.1162/99608f92.68b503ea","abstract":"The quantum revolution has swept into data analytics with quantum computers promising better algorithms for machine learning, optimization, and other techniques. In this commentary to Prof. Yazhen Wang’s (2021) article on making data science quantum, I note that it may be some time before quantum computers mature enough for data analytics to come online. Nevertheless, there are quantum explorations appropriate for ev","keywords":["quantum-inspired algorithms","quantum machine learning","quantum benchmarks","quantum metrics"],"image":"/media/remote/35e155ce8519fffa-720.webp","collections":[],"label":"Featured Discussion"},{"id":"32fa682f-646f-4136-8a7a-3f605d229994","slug":"mhztdrmm","title":"Data Science on a Future Quantum Internet","dek":null,"authors":[{"name":"Nana Liu","slug":"nana-liu"}],"date":"2022-01-27","section":null,"issue":"volume4issue1","doi":"10.1162/99608f92.32fa682f","abstract":null,"keywords":[],"image":"/media/remote/f0699770b8c1e9f4-720.webp","collections":[],"label":"Featured Discussion"},{"id":"4f5dc421-8372-4eb2-9208-f5a4c1e86f2e","slug":"hbrv65tq","title":"America’s Renewed Commitment to Addressing the Global Refugee Crisis","dek":null,"authors":[{"name":"Anne-Marie Grey","slug":"anne-marie-grey"}],"date":"2022-01-27","section":null,"issue":"volume4issue1","doi":"10.1162/99608f92.4f5dc421","abstract":"A commentary by Anne-Marie Grey, USA for UNHCR’s Executive Director and Chief Executive Officer, on James Purcell’s “The Humanitarian Journey: From Root Causes to Recovery”","keywords":["Purcell","USA for UNHCR","commentary","refugees"],"image":"/media/remote/ffb596542101fe40-720.webp","collections":[],"label":"Commentary"},{"id":"538e463c-0840-41d4-b7bc-ab9d1729570d","slug":"xsrq51th","title":"With, by, and for Refugees","dek":null,"authors":[{"name":"Tyler Radford","slug":"tyler-radford"},{"name":"Maria Camila Garzon-Ruiz","slug":"maria-camila-garzon-ruiz"}],"date":"2022-01-27","section":null,"issue":"volume4issue1","doi":"10.1162/99608f92.538e463c","abstract":null,"keywords":[],"image":"/media/remote/d77a7bbb383649f3-720.webp","collections":[],"label":"Commentary"},{"id":"e0579538-9eb0-4278-b45f-bb531c60de18","slug":"52me9hgi","title":"Tracking and Measuring the Evolution of Humanitarian Efforts","dek":null,"authors":[{"name":"Scott Tranter","slug":"scott-tranter"}],"date":"2022-01-27","section":null,"issue":"volume4issue1","doi":"10.1162/99608f92.e0579538","abstract":null,"keywords":[],"image":"/media/remote/08e41e1907ecadfb-720.webp","collections":[],"label":"Commentary"},{"id":"17a4e925-f4aa-41ab-9ac9-39dc59c75d9b","slug":"4cg8dhgr","title":"Increasing Uptake of Social Distancing During COVID-19: How Machine Learning Strategies Can Lead to Targeted Interventions","dek":null,"authors":[{"name":"Grace Charles","slug":"grace-charles"},{"name":"Mokshada Jain","slug":"mokshada-jain"},{"name":"Yael Caplan","slug":"yael-caplan"},{"name":"Hannah Kemp","slug":"hannah-kemp"},{"name":"Aysha Keisler","slug":"aysha-keisler"},{"name":"Vincent Huang","slug":"vincent-huang"},{"name":"Sema Sgaier","slug":"sema-sgaier"}],"date":"2022-01-06","section":"cornucopia","issue":"volume4issue1","doi":"10.1162/99608f92.17a4e925","abstract":"Social distancing emerged as one of the early critical nonpharmaceutical interventions to fight the spread of COVID-19. However, in the United States, this behavior was not universally adopted. Understanding COVID-relevant behaviors is crucial to helping policymakers develop targeted, actionable interventions that meet the urgent needs of a global pandemic in a precision public health approach—that is, getting the ri","keywords":["social distancing","causal Bayesian network","segmentation","COVID-19","coronavirus","precision public health"],"image":"/media/remote/3a55a6e6010deb59-720.webp","collections":[],"label":"Cornucopia"},{"id":"c1237762-cad0-49d8-96dd-72757971de0b","slug":"1g514ljw","title":"Data Science and Official Statistics: Toward a New Data Culture","dek":null,"authors":[{"name":"Stefan Schweinfest","slug":"stefan-schweinfest"},{"name":"Ronald Jansen","slug":"ronald-jansen"}],"date":"2021-12-01","section":"panorama","issue":"volume3issue4","doi":"10.1162/99608f92.c1237762","abstract":"In the digital age, data are generated continuously by many different devices and are being used by many different actors. National statistical offices (NSOs) should benefit from these opportunities to improve data for decision-making. What could be the expanding role for official statistics in this context and how does this relate to emerging disciplines like data science? This article explores some new ideas. In th","keywords":["data science","official statistics","data steward","data privacy","Fundamental Principles of Official Statistics","data innovation"],"image":"/media/remote/274113a918d605c0-720.webp","collections":["official-statistics-from-the-changing-world-of-data-science"],"label":"Panorama"},{"id":"be565013-1687-4075-baa5-f86a92ee3422","slug":"si7wzxxa","title":"Precisely and Persistently Identifying and Citing Arbitrary Subsets of Dynamic Data","dek":null,"authors":[{"name":"Andreas Rauber","slug":"andreas-rauber"},{"name":"Bernhard Gößwein","slug":"bernhard-go-wein"},{"name":"Carlo Maria Zwölf","slug":"carlo-maria-zwolf"},{"name":"Chris Schubert","slug":"chris-schubert"},{"name":"Florian Wörister","slug":"florian-worister"},{"name":"James Duncan","slug":"james-duncan"},{"name":"Katharina Flicker","slug":"katharina-flicker"},{"name":"Koji Zettsu","slug":"koji-zettsu"},{"name":"Kristof Meixner","slug":"kristof-meixner"},{"name":"Leslie D. McIntosh","slug":"leslie-d-mcintosh"},{"name":"Reyna Jenkyns","slug":"reyna-jenkyns"},{"name":"Stefan Pröll","slug":"stefan-proll"},{"name":"Tomasz Miksa","slug":"tomasz-miksa"},{"name":"Mark A. Parsons","slug":"mark-a-parsons"}],"date":"2021-11-18","section":"milestones-and-millstones","issue":"volume3issue4","doi":"10.1162/99608f92.be565013","abstract":"Precisely identifying arbitrary subsets of data so that these can be reproduced is a daunting challenge in data-driven science, the more so if the underlying data source is dynamically evolving. Yet an increasing number of settings exhibit exactly those characteristics. Larger amounts of data are being continuously ingested from a range of sources (be it sensor values, online questionnaires, documents, etc.), with er","keywords":["data citation","data versioning","dynamic data","persistent identifiers","data stewardship"],"image":"/media/remote/b3f4cf1e35e077f6-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"f0a7df71-1bd7-4d94-9331-cee6b7490c37","slug":"0cxrb76t","title":"Using Photographs as Data Sources to Tell Stories","dek":"GAISE II, Level B","authors":[{"name":"Pip Arnold","slug":"pip-arnold"},{"name":"Leticia Perez","slug":"leticia-perez"},{"name":"Sheri Johnson","slug":"sheri-johnson"}],"date":"2021-11-12","section":"minding-the-future","issue":"volume3issue4","doi":"10.1162/99608f92.f0a7df71","abstract":null,"keywords":[],"image":"/media/remote/f6c8df1f426cf44c-720.webp","collections":[],"label":"Minding the Future"},{"id":"53c4a1b4-cc85-474d-930c-d8e0c75cf925","slug":"j8v2h5hc","title":"Collaboratory at Columbia: An Aspen Grove of Data Science Education","dek":null,"authors":[{"name":"Isabelle Zaugg","slug":"isabelle-zaugg"},{"name":"Patricia Culligan","slug":"patricia-culligan"},{"name":"Richard Witten","slug":"richard-witten"},{"name":"Tian Zheng","slug":"tian-zheng"}],"date":"2021-10-28","section":"stepping-stones","issue":"volume3issue4","doi":"10.1162/99608f92.53c4a1b4","abstract":"Many universities recognize the rapidly growing impact of data science in all fields of study and the professions and seek to embed this expertise widely across their educational offerings. There is often broad interest in developing new data science curricula, with some universities even allocating funds toward this purpose. Yet, it is often unclear what resources are needed for effective data science education, and","keywords":["data science education","transdisciplinary education","data science competency","data science literacy"],"image":"/media/remote/5c4e96997354e9bc-720.webp","collections":[],"label":"Stepping Stones"},{"id":"c28d2a68-7532-40cf-83a3-47b90f087a62","slug":"0svq7n0h","title":"Performance Measurement: Issues, Approaches, and Opportunities","dek":null,"authors":[{"name":"Nicholas Fisher","slug":"nicholas-fisher"}],"date":"2021-10-28","section":"panorama","issue":"volume3issue4","doi":"10.1162/99608f92.c28d2a68","abstract":"Performance measures permeate our lives, whether or not we are aware of them. They can support or frustrate what we are trying to do, help or hinder enterprises going about their business, encourage or distort behaviors, clarify or confuse purpose. We illustrate some of the consequences of poor performance measurement, explore some of the reasons why poor metrics are in use, and describe a systematic way to look for ","keywords":["due diligence","Performance Measurement Framework","quality management","stakeholder value management"],"image":"/media/remote/34ed0e42516ec36c-720.webp","collections":[],"label":"Panorama"},{"id":"52e5fd67-e4a7-4f37-8193-f75a52d33cc3","slug":"4sfs1855","title":"Is “Official” Statistics Ready to Embrace the (Still) “Unofficial” World of Data Science?","dek":null,"authors":[{"name":"Francesca Dominici","slug":"francesca-dominici"}],"date":"2021-10-28","section":"editorscolumn","issue":"volume3issue4","doi":"10.1162/99608f92.52e5fd67","abstract":null,"keywords":[],"image":"/media/remote/e12f296a68744080-720.webp","collections":[],"label":"Editor’s Column"},{"id":"d0871196-79ec-4527-ade6-5a960edaf8a9","slug":"nj6hcy5b","title":"Official Statistics from the Changing World of Data Science","dek":null,"authors":[{"name":"John Bailer","slug":"john-bailer"},{"name":"Wendy Martinez","slug":"wendy-martinez"}],"date":"2021-10-28","section":"panorama","issue":"volume3issue4","doi":"10.1162/99608f92.d0871196","abstract":"Two events were held to answer questions regarding the future of official statistics. The first event took place on World Statistics Day 2020, whose theme was “What Is the Future of Official Statistics?” The second was a special session at the 63rd International Statistical Institute World Statistics Congress held in July 2021. Speakers and panelists who took part in both events were invited to contribute an article ","keywords":["World Statistics Day","official statistics","national statistics offices","modernization"],"image":"/media/remote/91b3e357737cab6d-720.webp","collections":["official-statistics-from-the-changing-world-of-data-science"],"label":"Panorama"},{"id":"ada7ddb1-4b73-4e64-8b73-c8857d5592d7","slug":"rkhifmrl","title":"The Future of Federal Statistics: World Statistics Day Webinar—20 OCTOBER 2020","dek":"An overview and reaction to perspectives voiced in 2020 World Statistics Day Webinar","authors":[{"name":"John Pullinger","slug":"john-pullinger"}],"date":"2021-10-28","section":"panorama","issue":"volume3issue4","doi":"10.1162/99608f92.ada7ddb1","abstract":null,"keywords":[],"image":"/media/remote/3207e580dc0680f3-720.webp","collections":["official-statistics-from-the-changing-world-of-data-science"],"label":"Panorama"},{"id":"7c172aac-87f8-460f-bc26-b62c261d121c","slug":"inhut6fx","title":"Will Official Statistics Become Obsolete?","dek":"An overview of the traditional versus the modern role of National Statistical Offices","authors":[{"name":"Ola Awad","slug":"ola-awad"}],"date":"2021-10-28","section":"panorama","issue":"volume3issue4","doi":"10.1162/99608f92.7c172aac","abstract":"As new data actors arise and data become more accessible, official statistics' role as the primary creator of statistics and distributor of information for policymaking is being tested. A data actor can be any entity involved in production, manipulation, and analysis or dissemination of any type of data. The need for more resources grows. New technologies enable a level of recording, integration, and analysis that ha","keywords":["National Statistical Offices (NSOs)","sustainable development goals (SDGs)","High-level Group for Partnership","Coordination and Capacity-Building for Statistics for the 2030 Agenda for Sustainable Development (HLG-PCCB)","modernization","UN-Fundamental Principles of Official Statistics","data stewardship."],"image":"/media/remote/400c7123070ed5a8-720.webp","collections":["official-statistics-from-the-changing-world-of-data-science"],"label":"Panorama"},{"id":"48466abe-cbc8-45c3-b03e-c8daf4d3ffb9","slug":"w8l955ol","title":"The Evolution of Official Statistics in a Changing World","dek":null,"authors":[{"name":"Wesley Yung","slug":"wesley-yung"}],"date":"2021-10-28","section":"panorama","issue":"volume3issue4","doi":"10.1162/99608f92.48466abe","abstract":"As part of the 2020 World Statistics Day, a virtual discussion on the future of federal statistics was held to share ideas and points of view. During the discussion a common message was that as National Statistical Offices modernize, they will have to accept changing the way that they do business and will have to embrace the use of alternative data, emerging fields such as data science, and solid data stewardship pra","keywords":["official statistics","fundamental principles of official statistics","modern methods","data science"],"image":"/media/remote/9c0676dee4fe299e-720.webp","collections":["official-statistics-from-the-changing-world-of-data-science"],"label":"Panorama"},{"id":"591917c6-aa80-4d35-aee4-6caf9c8fc95f","slug":"vb6hwxez","title":"The Future of Official Statistics","dek":null,"authors":[{"name":"Erica L. Groshen","slug":"erica-groshen"}],"date":"2021-10-28","section":"panorama","issue":"volume3issue4","doi":"10.1162/99608f92.591917c6","abstract":"Official statistics are a pure public good and a key element of our national data infrastructure. They support our well-being by informing a vast set of public and private decisions. Yet, today they stand at a crossroads in the United States. With new technologies, today’s official statistics could be more relevant, interoperable, granular, and timely. Recent experience during the pandemic clarifies the need for impr","keywords":["official statistics","data infrastructure","future","Bureau of Labor Statistics","statistical agency","public goods"],"image":"/media/remote/1b20f4716e994b2d-720.webp","collections":["official-statistics-from-the-changing-world-of-data-science"],"label":"Panorama"},{"id":"2617629d-1094-49dc-8de5-2bd922444053","slug":"7g77sv8m","title":"A Conversation With Ola Awad, President, Palestinian Central Bureau of Statistics","dek":"An interview with Ola Awad by Xiao-Li Meng and Liberty Vittert","authors":[{"name":"Ola Awad","slug":"ola-awad"},{"name":"Xiao-Li Meng","slug":"xiao-li-meng"},{"name":"Liberty Vittert","slug":"liberty-vittert"}],"date":"2021-10-28","section":"panorama","issue":"volume3issue4","doi":"10.1162/99608f92.2617629d","abstract":"Harvard Data Science Review’s Founding Editor-in-Chief, Xiao-Li Meng, and Media Feature Editor, Liberty Vittert, interviewed Ola Award, President of the Palestinian Central Bureau of Statistics (PCBS). They discussed the initiatives Ola leading at the PCBS including efforts to modernize how they collect census data and ways they have encouraged the public to engage and respond to surveys.This interview is part of HDS","keywords":[],"image":"/media/remote/82af62c3d63105cd-720.webp","collections":["conversations-with-leaders","official-statistics-from-the-changing-world-of-data-science"],"label":"Panorama"},{"id":"8b81b005-244d-463b-a952-183f22c5ac67","slug":"myfmx2sk","title":"DeepMiner: Discovering Interpretable Representations for Mammogram Classification and Explanation","dek":null,"authors":[{"name":"Jimmy Wu","slug":"jimmy-wu"},{"name":"Bolei Zhou","slug":"bolei-zhou"},{"name":"Diondra Peck","slug":"diondra-peck"},{"name":"Scott Hsieh","slug":"scott-hsieh"},{"name":"Vandana Dialani","slug":"vandana-dialani"},{"name":"Lester Mackey","slug":"lester-mackey"},{"name":"Geneviève Patterson","slug":"genevieve-patterson"}],"date":"2021-10-28","section":"cornucopia","issue":"volume3issue4","doi":"10.1162/99608f92.8b81b005","abstract":"We propose DeepMiner, a framework to discover interpretable representations in deep neural networks and to build explanations for medical predictions. By probing convolutional neural networks (CNNs) trained to classify cancer in mammograms, we show that many individual units in the final convolutional layer of a CNN respond strongly to diseased tissue concepts specified by the Breast Imaging-Reporting and Data System","keywords":["deep learning","interpretability","human-in-the-loop machine learning","mammography"],"image":"/media/remote/d39557c62b33c5f8-720.webp","collections":[],"label":"Cornucopia"},{"id":"d35b59a0-1f18-4d92-b7bb-8478a55ce3af","slug":"a05b41xa","title":"The Yankee Leviathan Collects Statistics: Federal Education Policy During Reconstruction","dek":null,"authors":[{"name":"Andrew Donnelly","slug":"andrew-donnelly"}],"date":"2021-10-28","section":"mining-the-past","issue":"volume3issue4","doi":"10.1162/99608f92.d35b59a0","abstract":null,"keywords":[],"image":"/media/remote/51351a71119b210f-720.webp","collections":[],"label":"Mining the Past"},{"id":"d07b8d16-dc1c-43f6-aa80-d87584d98ae9","slug":"1h4a86jh","title":"Individualized Decision-Making Under Partial Identification: Three Perspectives, Two Optimality Results, and One Paradox","dek":null,"authors":[{"name":"Yifan Cui","slug":"yifan-cui"}],"date":"2021-10-22","section":"milestones-and-millstones","issue":"volume3issue3","doi":"10.1162/99608f92.d07b8d16","abstract":"Unmeasured confounding is a threat to causal inference and gives rise to biased estimates. In this article, we consider the problem of individualized decision-making under partial identification. Firstly, we argue that when faced with unmeasured confounding, one should pursue individualized decision-making using partial identification in a comprehensive manner. We establish a formal link between individualized decisi","keywords":["causal inference","decision-making strategies","individualized preferences","mixed strategy","optimality","partial identification","sharpness"],"image":"/media/remote/8734624163087655-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"f6805482-046d-436c-8998-facd039ab626","slug":"49opxv6v","title":"Why Do We Plot Data?","dek":"Accompanying text for the “Designing for interactive exploratory data analysis requires theory of graphical inference” Explainer Zine","authors":[{"name":"Kelly Blumenthal","slug":"kelly-blumenthal"},{"name":"Aleksandrina Goeva","slug":"aleksandrina-goeva"},{"name":"Sara Stoudt","slug":"sara-stoudt"},{"name":"Ana Trisovic","slug":"ana-trisovic"},{"name":"Pavle Trisovic","slug":"pavle-trisovic"}],"date":"2021-09-10","section":null,"issue":null,"doi":null,"abstract":null,"keywords":[],"image":null,"collections":[],"label":"Article"},{"id":"8102afed-59b6-455f-b851-8a2a98ced2bd","slug":"hdzphsk6","title":"From Controlled to Undisciplined Data: Estimating Causal Effects in the Era of Data Science Using a Potential Outcome Framework","dek":null,"authors":[{"name":"Francesca Dominici","slug":"francesca-dominici"},{"name":"Falco J. Bargagli Stoffi","slug":"falco-j-bargagli-stoffi"},{"name":"Fabrizia Mealli","slug":"fabrizia-mealli"}],"date":"2021-08-31","section":"panorama","issue":"volume3issue3","doi":"10.1162/99608f92.8102afed","abstract":"This article discusses the fundamental principles of causal inference—the area of statistics that estimates the effect of specific occurrences, treatments, interventions, and exposures on a given outcome from experimental and observational data. We explain the key assumptions required to identify causal effects, and highlight the challenges associated with the use of observational data. We emphasize that experimental","keywords":["causal inference","data science","potential outcomes","randomized control trials","observational studies","Bayesian modeling","machine learning"],"image":"/media/remote/185f2617b6bfa4df-720.webp","collections":[],"label":"Panorama"},{"id":"3c097160-fd9a-4c00-9fa9-68aecc929a3a","slug":"pm67plq9","title":"The Promise of Portfolios: Training Modern Data Scientists","dek":null,"authors":[{"name":"Deborah Nolan","slug":"deborah-nolan"},{"name":"Sara Stoudt","slug":"sara-stoudt"}],"date":"2021-07-30","section":"stepping-stones","issue":"volume3issue3","doi":"10.1162/99608f92.3c097160","abstract":"Data scientists rely on many technical skills and the ability to reason about data to solve problems. As educators grapple with how to prepare students in this new field, they are faced with identifying both what a student must know and what a student should be able to do by the end of their data science education, and also how to collect evidence of those abilities. We present a way to unite and coordinate individua","keywords":["communication","data science assignments","pedagogy","portfolio","visualization"],"image":"/media/remote/bad582efa0207cc2-720.webp","collections":["data-science-education"],"label":"Stepping Stones"},{"id":"eee0b0da-6a49-406a-9ac9-fa8fa58d4e15","slug":"u9s6f22y","title":"Why the Data Revolution Needs Qualitative Thinking","dek":null,"authors":[{"name":"Anissa Tanweer","slug":"anissa-tanweer"},{"name":"Emily Kalah Gade","slug":"emily-kalah-gade"},{"name":"P.M. Krafft","slug":"p-m-krafft"},{"name":"Sarah Dreier","slug":"sarah-dreier"}],"date":"2021-07-30","section":"panorama","issue":"volume3issue3","doi":"10.1162/99608f92.eee0b0da","abstract":"This essay draws on qualitative social science to propose a critical intellectual infrastructure for data science of social phenomena. Qualitative sensibilities—interpretivism, abductive reasoning, and reflexivity in particular—could address methodological problems that have emerged in data science and help extend the frontiers of social knowledge. First, an interpretivist lens—which is concerned with the constructio","keywords":["data science","qualitative methods","data ethics","critical data studies","reproducibility","computational social science"],"image":"/media/remote/a58d250eb1edb077-720.webp","collections":[],"label":"Panorama"},{"id":"3ab8a587-1676-4c38-b089-5fe167fb11f4","slug":"w075glo6","title":"Designing for Interactive Exploratory Data Analysis Requires Theories of Graphical Inference","dek":null,"authors":[{"name":"Jessica Hullman","slug":"jessica-hullman"},{"name":"Andrew Gelman","slug":"andrew-gelman"}],"date":"2021-07-30","section":"panorama","issue":"volume3issue3","doi":"10.1162/99608f92.3ab8a587","abstract":"Research and development in computer science and statistics have produced increasingly sophisticated software interfaces for interactive and exploratory analysis, optimized for easy pattern finding and data exposure. But design philosophies that emphasize exploration over other phases of analysis risk confusing a need for flexibility with a conclusion that exploratory visual analysis is inherently “model free” and ca","keywords":["exploratory data analysis","interactive analysis","graphical inference","Bayesian model check"],"image":"/media/remote/e45533ce0097626a-720.webp","collections":[],"label":"Panorama"},{"id":"f0ad0287-61e8-4753-b88f-8a15fafe25d2","slug":"50vl2b07","title":"ASA President’s Task Force Statement on Statistical Significance and Replicability","dek":null,"authors":[{"name":"Yoav Benjamini","slug":"yoav-benjamini"},{"name":"Richard De Veaux","slug":"richard-de-veaux"},{"name":"Bradley Efron","slug":"bradley-efron"},{"name":"Scott Evans","slug":"scott-evans"},{"name":"Mark Glickman","slug":"mark-glickman"},{"name":"Barry I. Graubard","slug":"barry-i-graubard"},{"name":"Xuming He","slug":"xuming-he"},{"name":"Xiao-Li Meng","slug":"xiao-li-meng"},{"name":"Nancy Reid","slug":"nancy-reid"},{"name":"Stephen M. Stigler","slug":"steven-stigler"},{"name":"Stephen B. Vardeman","slug":"stephen-b-vardeman"},{"name":"Christopher K. Wikle","slug":"christopher-k-wikle"},{"name":"Tommy Wright","slug":"tommy-wright"},{"name":"Linda J. Young","slug":"linda-j-young"},{"name":"Karen Kafadar","slug":"karen-kafadar"}],"date":"2021-07-30","section":"bits-and-bites","issue":"volume3issue3","doi":"10.1162/99608f92.f0ad0287","abstract":null,"keywords":[],"image":"/media/remote/f93b451b044b2824-720.webp","collections":[],"label":"Bits and Bites"},{"id":"2660ce5a-0f8c-4fde-9885-0675a8755cb1","slug":"pvecdoyj","title":"The Evolving Ecosystem of COVID-19 Contact Tracing Applications","dek":null,"authors":[{"name":"Benjamin Levy","slug":"benjamin-levy"},{"name":"Matthew Stewart","slug":"matthew-stewart"}],"date":"2021-07-30","section":"cornucopia","issue":"volume3issue3","doi":"10.1162/99608f92.2660ce5a","abstract":"Since the outbreak of the novel coronavirus, COVID-19, there has been significant interest in the use of digital contact tracing as a means of stopping chains of viral transmission, provoking alarm from privacy advocates. Concerning the ethics of this technology, recent studies have predominantly focused on (1) the formation of guidelines for ethical contact tracing, (2) the analysis of specific implementations, or (","keywords":["COVID-19","contact tracing","exposure notification","ethics","privacy"],"image":"/media/remote/87423b8667ded2e4-720.webp","collections":[],"label":"Cornucopia"},{"id":"72825001-15c4-4be4-9029-1ce0aa0bd47c","slug":"st8rqqj2","title":"Two Regimes of Prison Data Collection","dek":null,"authors":[{"name":"Kaneesha R. Johnson","slug":"kaneesha-r-johnson"}],"date":"2021-07-30","section":"mining-the-past","issue":"volume3issue3","doi":"10.1162/99608f92.72825001","abstract":null,"keywords":[],"image":"/media/remote/babfdc83de24c717-720.webp","collections":[],"label":"Mining the Past"},{"id":"5461cafb-3a90-436b-acf8-3d2d25fee837","slug":"51gp5ktr","title":"Remembering Robert Lue: Giving Students “Not a Data Science Course, but a Data Science Life”","dek":null,"authors":[{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2021-07-30","section":"bits-and-bites","issue":"volume3issue3","doi":"10.1162/99608f92.5461cafb","abstract":null,"keywords":[],"image":null,"collections":[],"label":"Bits and Bites"},{"id":"cee621a9-241f-4688-9dcd-11d021d831ba","slug":"pger71uh","title":"Data Science: A Happy Marriage of Quantitative and Qualitative Thinking?","dek":null,"authors":[{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2021-07-30","section":"editorscolumn","issue":"volume3issue3","doi":"10.1162/99608f92.cee621a9","abstract":null,"keywords":[],"image":"/media/remote/b44ae079bbc671a0-720.webp","collections":[],"label":"Editor’s Column"},{"id":"24ee17a7-a758-4c6f-ae5a-9ba1f7df83aa","slug":"1161y2bt","title":"Introducing the Interim Co-Editors-in-Chief","dek":null,"authors":[{"name":"Francesca Dominici","slug":"francesca-dominici"},{"name":"David C. Parkes","slug":"david-parkes"}],"date":"2021-07-30","section":"editorscolumn","issue":"volume3issue3","doi":"10.1162/99608f92.24ee17a7","abstract":null,"keywords":[],"image":"/media/remote/9adbaa5be7db6bdd-720.webp","collections":[],"label":"Editor’s Column"},{"id":"3b3cf5be-2804-4e9d-8ce8-40671fd216a9","slug":"vszs87oj","title":"Exploratory Analysis and Its Malcontents","dek":null,"authors":[{"name":"Jeffrey Heer","slug":"jeffrey-heer"}],"date":"2021-07-30","section":null,"issue":"volume3issue3","doi":"10.1162/99608f92.3b3cf5be","abstract":null,"keywords":[],"image":"/media/remote/03277e423a1b28b5-720.webp","collections":[],"label":"Featured Discussion"},{"id":"8453435d-a57a-469e-bc35-f8831f882c51","slug":"mpdasaqt","title":"The Foundation Is Available for Thinking About Data Visualization Inferentially","dek":null,"authors":[{"name":"Dianne Cook","slug":"dianne-cook"},{"name":"Nancy Reid","slug":"nancy-reid"},{"name":"Emi Tanaka","slug":"emi-tanaka"}],"date":"2021-07-30","section":null,"issue":"volume3issue3","doi":"10.1162/99608f92.8453435d","abstract":null,"keywords":[],"image":"/media/remote/36f7e0b1899d8d07-720.webp","collections":[],"label":"Featured Discussion"},{"id":"ba00865a-0e16-4dea-916a-cdee6258bcba","slug":"jefx48tr","title":"Exploring the Gap Between Informal Mental and Formal Statistical Models","dek":null,"authors":[{"name":"Hanspeter Pfister","slug":"hanspeter-pfister"},{"name":"Martin Wattenberg","slug":"martin-wattenberg"},{"name":"Johanna Beyer","slug":"johanna-beyer"},{"name":"Carolina Nobre","slug":"carolina-nobre"}],"date":"2021-07-30","section":null,"issue":"volume3issue3","doi":"10.1162/99608f92.ba00865a","abstract":null,"keywords":[],"image":"/media/remote/3092067cb4d9a851-720.webp","collections":[],"label":"Featured Discussion"},{"id":"01df2b32-2c77-4392-8404-b831996572f7","slug":"1kosd14x","title":"'The Art of Graphics Isn't Hard to Master' (pace Elizabeth Bishop)","dek":null,"authors":[{"name":"Antony Unwin","slug":"anthony-unwin"}],"date":"2021-07-30","section":null,"issue":"volume3issue3","doi":"10.1162/99608f92.01df2b32","abstract":null,"keywords":[],"image":"/media/remote/a34a035a27715108-720.webp","collections":[],"label":"Featured Discussion"},{"id":"7d099fd0-470b-4356-80ef-caedb0461769","slug":"m7ur7k3u","title":"Designing Graphics Requires Useful Experimental Testing Frameworks and Graphics Derived From Empirical Results","dek":null,"authors":[{"name":"Susan VanderPlas","slug":"susan-vanderplas"}],"date":"2021-07-30","section":null,"issue":"volume3issue3","doi":"10.1162/99608f92.7d099fd0","abstract":null,"keywords":[],"image":"/media/remote/30ab9ae54116b99c-720.webp","collections":[],"label":"Featured Discussion"},{"id":"049ac5b4-2164-423e-a08f-a3c2ab8d3bcf","slug":"zq7jv1ww","title":"Visualization and Inferences","dek":null,"authors":[{"name":"Jean-Daniel Fekete","slug":"jean-daniel-fekete"}],"date":"2021-07-30","section":null,"issue":"volume3issue3","doi":"10.1162/99608f92.049ac5b4","abstract":null,"keywords":[],"image":"/media/remote/ea7fbd66a8003ccc-720.webp","collections":[],"label":"Featured Discussion"},{"id":"9d108ee6-86ac-4bc0-ae14-5e241b7ae1db","slug":"2ym7zm34","title":"Challenges in Incorporating Exploratory Data Analysis Into Statistical Workflow","dek":null,"authors":[{"name":"Jessica Hullman","slug":"jessica-hullman"},{"name":"Andrew Gelman","slug":"andrew-gelman"}],"date":"2021-07-30","section":null,"issue":"volume3issue3","doi":"10.1162/99608f92.9d108ee6","abstract":null,"keywords":[],"image":"/media/remote/3678da5f6408cd1f-720.webp","collections":[],"label":"Featured Discussion"},{"id":"e5f9a0c7-cb23-485f-bcd3-457e690f4281","slug":"w1gujxim","title":"Self-Organizing Floor Plans","dek":null,"authors":[{"name":"Silvio Carta","slug":"silvio-carta"}],"date":"2021-07-23","section":"cornucopia","issue":"volume3issue3","doi":"10.1162/99608f92.e5f9a0c7","abstract":"This article introduces and comments on some of the techniques currently used by designers to generate automatic building floor plans and spatial configurations in general, with emphasis on machine learning and neural networks models. This is a relatively new tendency in computational design that reflects a growing interest in advanced generative and optimization models by architects and building engineers. The first","keywords":["self-organizing floor plans","computational design","architecture","machine learning","generative adversarial networks","artificial neural networks"],"image":"/media/remote/60c3b166b6466054-720.webp","collections":[],"label":"Cornucopia"},{"id":"4e792052-3df4-4bd1-9c89-49545380e921","slug":"xfgywa6x","title":"From Data Processes to Data Products: Knowledge Infrastructures in Astronomy","dek":null,"authors":[{"name":"Christine L. Borgman","slug":"christine-l-borgman"},{"name":"Morgan F. Wofford","slug":"morgan-wofford"}],"date":"2021-07-12","section":"milestones-and-millstones","issue":"volume3issue3","doi":"10.1162/99608f92.4e792052","abstract":"We explore how astronomers take observational data from telescopes, process them into usable scientific data products, curate them for later use, and reuse data for further inquiry. Astronomers have invested heavily in knowledge infrastructures—robust networks of people, artifacts, and institutions that generate, share, and maintain specific knowledge about the human and natural worlds. Drawing upon a decade of inter","keywords":["astronomy","data reuse","infrastructure","software","maintenance","open science"],"image":"/media/remote/3fbb35552bdd4d12-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"5748b60f-916f-43f8-9386-8833d12a3ea6","slug":"heblv10d","title":"Supra-Disciplinary Data Science","dek":null,"authors":[{"name":"David Madigan","slug":"david-madigan"}],"date":"2021-06-30","section":null,"issue":"volume3issue2","doi":"10.1162/99608f92.5748b60f","abstract":null,"keywords":[],"image":"/media/remote/8ea1f6518af66cab-720.webp","collections":[],"label":"Featured Discussion"},{"id":"5bf277ba-e4be-4dcf-9f9a-864e6dc78ec5","slug":"t7lilqq3","title":"Leadership in Data Science: Lessons Learned From Time Invested in Helping to Build the Field","dek":null,"authors":[{"name":"Patrick J. Wolfe","slug":"patrick-j-wolfe"}],"date":"2021-06-30","section":null,"issue":"volume3issue2","doi":"10.1162/99608f92.5bf277ba","abstract":null,"keywords":[],"image":"/media/remote/23bd24eb5777121e-720.webp","collections":[],"label":"Featured Discussion"},{"id":"1a7d19ab-6d4c-4354-9a53-870c69a88597","slug":"3zxqbzft","title":"Data Science and Computing: The View From a Sister Campus","dek":null,"authors":[{"name":"Hal S. Stern","slug":"hal-s-stern"},{"name":"Debra J. Richardson","slug":"debra-j-richardson"},{"name":"Marios Papaefthymiou","slug":"marios-papaefthymiou"}],"date":"2021-06-30","section":null,"issue":"volume3issue2","doi":"10.1162/99608f92.1a7d19ab","abstract":null,"keywords":[],"image":"/media/remote/19467948631e7474-720.webp","collections":[],"label":"Featured Discussion"},{"id":"b447c07e-e0f4-4953-b9a5-b563eba88974","slug":"0v7qw6jf","title":"A Geometry-Driven Longitudinal Topic Model","dek":null,"authors":[{"name":"Yu Wang","slug":"yu-wang-2"},{"name":"Conrad Hougen","slug":"conrad-hougen"},{"name":"Brandon Oselio","slug":"brandon-oselio"},{"name":"Walter Dempsey","slug":"walter-dempsey"},{"name":"Alfred Hero","slug":"alfred-hero"}],"date":"2021-06-30","section":"milestones-and-millstones","issue":"volume3issue2","doi":"10.1162/99608f92.b447c07e","abstract":"A simple and scalable framework for longitudinal analysis of Twitter data is developed that combines latent topic models with computational geometric methods. Dimensionality reduction tools from computational geometry are applied to learn the intrinsic manifold on which the latent, temporal topics reside. Then shortest path distances on the manifold are used to link together these topics. The proposed framework permi","keywords":["topic models","latent Dirichlet allocation","computational geometry","social media analysis","COVID-19"],"image":"/media/remote/140a221a01c50bfe-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"3e19b17c-73c0-4f89-b48a-73edc77e5065","slug":"nv9xq9yu","title":"Data Science in Domains: Interaction of Physical, Social, and Institutional Systems","dek":null,"authors":[{"name":"Munther A. Dahleh","slug":"munther-a-dahleh"}],"date":"2021-06-30","section":null,"issue":"volume3issue2","doi":"10.1162/99608f92.3e19b17c","abstract":null,"keywords":[],"image":"/media/remote/b4ebbaea4e2dedb5-720.webp","collections":[],"label":"Featured Discussion"},{"id":"6f037267-6cbb-49ab-acb3-8c4fbce50aac","slug":"8si074w9","title":"Data Science Education With Domain Knowledge and System Principles","dek":null,"authors":[{"name":"S. Joe Qin","slug":"s-joe-qin"}],"date":"2021-06-30","section":null,"issue":"volume3issue2","doi":"10.1162/99608f92.6f037267","abstract":null,"keywords":[],"image":"/media/remote/336f94b4066c3b1e-720.webp","collections":[],"label":"Featured Discussion"},{"id":"41803359-4589-426a-bf0b-321b34b3d3b7","slug":"ba3m1fjv","title":"A Conversation With Steve Ballmer","dek":"An interview with Steve Ballmer by Liberty Vittert","authors":[{"name":"Steve Ballmer","slug":"steve-ballmer"},{"name":"Liberty Vittert","slug":"liberty-vittert"}],"date":"2021-06-30","section":null,"issue":null,"doi":"10.1162/99608f92.41803359","abstract":"Harvard Data Science Review got the chance to sit down with Steve Ballmer, the former CEO of Microsoft and current chairman of the Los Angeles Clippers. Steve was Microsoft's 30th employee in 1980, and retired in 2014 after 14 years as CEO. But what you may not know about him is that he and his wife Connie are working to help remove barriers to economic mobility for Americans living in poverty through their philanthr","keywords":[],"image":"/media/remote/60fea22a8b8b4b27-720.webp","collections":["conversations-with-leaders"],"label":"Interview"},{"id":"46f66c22-6e96-4376-9b07-ca7cbd528f92","slug":"91zp0ov6","title":"Building on the Shoulders of Bears: Next Steps in Data Science Education","dek":null,"authors":[{"name":"Dustin Tingley","slug":"dustin-tingley"}],"date":"2021-06-07","section":null,"issue":"volume3issue2","doi":"10.1162/99608f92.46f66c22","abstract":null,"keywords":[],"image":"/media/remote/0690b02c2317f11e-720.webp","collections":[],"label":"Featured Discussion"},{"id":"8bb28793-435d-44ee-a14b-bc586b4aae1a","slug":"4wttgf9j","title":"The Building Blocks of Statistical Education in the Data Science Ecosystem","dek":null,"authors":[{"name":"Alison L. Gibbs","slug":"alison-l-gibbs"},{"name":"Nathan Taback","slug":"nathan-taback"}],"date":"2021-06-07","section":null,"issue":"volume3issue2","doi":"10.1162/99608f92.8bb28793","abstract":null,"keywords":[],"image":"/media/remote/f33a0b964caf1d75-720.webp","collections":[],"label":"Featured Discussion"},{"id":"8d7bff79-4015-401a-89be-ced0a25fb653","slug":"jbpym4zp","title":"A Perspective on Designing Undergraduate Education in the New Discipline of Data Science","dek":null,"authors":[{"name":"Michael J. Franklin","slug":"michael-j-franklin"},{"name":"Dan L. Nicolae","slug":"dan-l-nicolae"}],"date":"2021-06-07","section":null,"issue":"volume3issue2","doi":"10.1162/99608f92.8d7bff79","abstract":null,"keywords":[],"image":"/media/remote/cd5262b5678d0cf1-720.webp","collections":[],"label":"Featured Discussion"},{"id":"94d1d99f-0fae-4f1e-8e48-f0b67ee30811","slug":"bd0tj6ub","title":"Something New Is Afoot in Academia","dek":null,"authors":[{"name":"Ani Adhikari","slug":"ani-adhikari"},{"name":"John DeNero","slug":"john-denero"},{"name":"Michael I. Jordan","slug":"michael-i-jordan"}],"date":"2021-06-07","section":null,"issue":"volume3issue2","doi":"10.1162/99608f92.94d1d99f","abstract":null,"keywords":[],"image":"/media/remote/c6f1adc03f856dee-720.webp","collections":[],"label":"Featured Discussion"},{"id":"811df726-b248-40d4-864f-1fcfbd46e427","slug":"kk63wpuo","title":"A Decision Tree for Introductory Data Science","dek":null,"authors":[{"name":"Allen B. Downey","slug":"allen-b-downey"}],"date":"2021-06-07","section":null,"issue":"volume3issue2","doi":"10.1162/99608f92.811df726","abstract":null,"keywords":[],"image":"/media/remote/74f0a8404b6041b9-720.webp","collections":[],"label":"Featured Discussion"},{"id":"e17f42f3-d132-4568-bafd-ebe3780419a6","slug":"9cdyd5f4","title":"More Computing and Less Programming: A Proposal to Broaden Participation in Data Science","dek":null,"authors":[{"name":"Yolanda Gil","slug":"yolanda-gil"}],"date":"2021-06-07","section":null,"issue":"volume3issue2","doi":"10.1162/99608f92.e17f42f3","abstract":null,"keywords":[],"image":"/media/remote/123be68e9bf3fcac-720.webp","collections":[],"label":"Featured Discussion"},{"id":"f7c60746-33e3-4269-915b-f0c54dd4e392","slug":"4ixq8tjl","title":"Recipes for Connector Courses From the Early-Career Board Kitchen","dek":null,"authors":[{"name":"Aleksandrina Goeva","slug":"aleksandrina-goeva"},{"name":"Peyton Jones","slug":"peyton-jones"},{"name":"Sara Stoudt","slug":"sara-stoudt"},{"name":"Ana Trisovic","slug":"ana-trisovic"}],"date":"2021-06-07","section":null,"issue":"volume3issue2","doi":"10.1162/99608f92.f7c60746","abstract":null,"keywords":[],"image":"/media/remote/501148d187d4cd6f-720.webp","collections":[],"label":"Featured Discussion"},{"id":"85206ff9-021b-440c-9f47-38d8a4ca143d","slug":"8fxt1zop","title":"On Democratizing Data Science: Some iNZights Into Empowering the Many","dek":null,"authors":[{"name":"Chris J. Wild","slug":"chris-j-wild"},{"name":"Tom Elliott","slug":"tom-elliott"},{"name":"Andrew Sporle","slug":"andrew-sporle"}],"date":"2021-06-07","section":null,"issue":"volume3issue2","doi":"10.1162/99608f92.85206ff9","abstract":null,"keywords":[],"image":"/media/remote/0dcd5338b51751d2-720.webp","collections":[],"label":"Featured Discussion"},{"id":"643b1bf8-5f8c-44e5-88c6-a8920452ede5","slug":"i9b9h2ha","title":"Data Science Goals and Pragmatics","dek":null,"authors":[{"name":"Alfred Z. Spector","slug":"alfred-z-spector"}],"date":"2021-06-07","section":null,"issue":"volume3issue2","doi":"10.1162/99608f92.643b1bf8","abstract":null,"keywords":[],"image":"/media/remote/2851b9373dca30cf-720.webp","collections":[],"label":"Featured Discussion"},{"id":"8d7bdf3a-3720-4cf7-9d78-b8f315b63626","slug":"duf2it8q","title":"Variations in Data Science Curriculum: A View From Computing Education","dek":null,"authors":[{"name":"Alan David Fekete","slug":"alan-david-fekete"}],"date":"2021-06-07","section":null,"issue":"volume3issue2","doi":"10.1162/99608f92.8d7bdf3a","abstract":null,"keywords":[],"image":"/media/remote/467669148dd55416-720.webp","collections":[],"label":"Featured Discussion"},{"id":"d3f14ea4-9f8c-4e20-9ac0-6667b18cb098","slug":"ck6gqa7d","title":"Scaling Up Data Science for the Social Sciences","dek":null,"authors":[{"name":"Brian Kim","slug":"brian-kim"}],"date":"2021-06-07","section":null,"issue":"volume3issue2","doi":"10.1162/99608f92.d3f14ea4","abstract":null,"keywords":[],"image":"/media/remote/c0d4f015af49857a-720.webp","collections":[],"label":"Featured Discussion"},{"id":"20580ece-f574-4de5-adee-0e3d2a8dbfae","slug":"5pe5xcvb","title":"An Interview With Bin Yu","dek":"An interview with Bin Yu by Xiao-Li Meng","authors":[{"name":"Bin Yu","slug":"bin-yu"},{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2021-06-07","section":null,"issue":"specialissue1","doi":"10.1162/99608f92.1d4e0dae/20580ece","abstract":null,"keywords":[],"image":"/media/remote/79c1c256eceeb8a5-720.webp","collections":["conversations-with-authors"],"label":"Interview"},{"id":"19de8159-103d-4aa6-b312-d854e6ca901c","slug":"hodep31o","title":"Building Intuition Regarding the Statistical Behavior of Mass Medical Testing Programs","dek":null,"authors":[{"name":"Lance Waller","slug":"lance-waller"},{"name":"Taal Levi","slug":"taal-levi"}],"date":"2021-05-14","section":null,"issue":"specialissue1","doi":"10.1162/99608f92.19de8159","abstract":"The quality of medical decision-making and public health planning alike depends directly upon understanding the accuracy of medical tests, especially during a pandemic. But the statistical concepts and measures used to assess test accuracy can be confusing. Why is there not one single definitive measure of test accuracy? How much should individuals worry about spreading COVID-19 if their test results are negative? Wh","keywords":["medical testing","diagnostic testing","sensitivity","specificity","positive predictive value","false positives"],"image":"/media/remote/836b62abbc55ff4d-720.webp","collections":[],"label":"Article"},{"id":"2025202b-a064-4d3f-b7b2-4605291d5ae9","slug":"zunejoo2","title":"Urban Sustainability Observatories: Leveraging Urban Experimentation for Sustainability Science and Policy","dek":null,"authors":[{"name":"Harvey Miller","slug":"harvey-miller"},{"name":"Kelly Clifton","slug":"kelly-clifton"},{"name":"Gulsah Akar","slug":"gulsah-akar"},{"name":"Kristin Tufte","slug":"kristin-tufte"},{"name":"Sathya Gopalakrishnan","slug":"sathya-gopalakrishnan"},{"name":"John MacArthur","slug":"john-macarthur"},{"name":"Elena Irwin","slug":"elena-irwin"},{"name":"Rajiv Ramnath","slug":"rajiv-ramnath"},{"name":"Jonathan Stiles","slug":"jonathan-stiles"}],"date":"2021-05-14","section":"panorama","issue":"volume3issue2","doi":"10.1162/99608f92.2025202b","abstract":"Humanity is experiencing revolutionary changes in the 21st century, including accelerating urbanization, the introduction of disruptive mobility technology services, and new sources of data generated and consumed by urban and mobility processes. However, the environmental, social, and economic sustainability implications of these new mobility services are unclear given the complex nature of urban systems and the mult","keywords":["data observatory","sustainability","urban experimentation","geospatial data","mobility data"],"image":"/media/remote/e8e472579038a8e8-720.webp","collections":[],"label":"Panorama"},{"id":"beeb1183-a18b-4f13-9781-91d239293b03","slug":"xqle7lat","title":"Modern Machine Learning and Particle Physics","dek":null,"authors":[{"name":"Matthew D. Schwartz","slug":"matthew-d-schwartz"}],"date":"2021-05-13","section":"cornucopia","issue":"volume3issue2","doi":"10.1162/99608f92.beeb1183","abstract":"Over the past five years, modern machine learning has been quietly revolutionizing particle physics. Old methodology is being outdated and entirely new ways of thinking about data are becoming commonplace. This article will review some aspects of the natural synergy between modern machine learning and particle physics, focusing on applications at the Large Hadron Collider (LHC). A sampling of examples is given, from ","keywords":["particle physics","modern machine learning","data-driven methodology"],"image":"/media/remote/47507a5af3010395-720.webp","collections":[],"label":"Cornucopia"},{"id":"abfa0e70-3cd7-4687-9d66-1c590cd009e8","slug":"kdqoo5ax","title":"Building Data Science Infrastructures and Infrastructural Data Science","dek":null,"authors":[{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2021-04-30","section":"editorscolumn","issue":"volume3issue2","doi":"10.1162/99608f92.abfa0e70","abstract":null,"keywords":[],"image":"/media/remote/dbd45549aa20ac88-720.webp","collections":["perspectives-on-data-and-data-science"],"label":"Editor’s Column"},{"id":"11cb886e-3d84-4508-9900-047af3d2ff0e","slug":"863nmirt","title":"A Conversation With Michael V. Drake and Jennifer Chayes","dek":"An interview with Michael V. Drake and Jennifer Chayes by Xiao-Li Meng","authors":[{"name":"Michael V. Drake","slug":"michael-v-drake"},{"name":"Jennifer Chayes","slug":"jennifer-chayes"},{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2021-04-30","section":"panorama","issue":"volume3issue2","doi":"10.1162/99608f92.11cb886e","abstract":"Harvard Data Science Review’s Editor-in-Chief, Xiao-Li Meng, recently met with Dr. Michael Drake, the 21st President of the University of California and Professor Jennifer Chayes, Associate Provost of the Division of Computing, Data Science, and Society and Dean of the School of Information, UC Berkeley. Chayes served a due role as a guest and a co-host, being one of the Co-Editors of Harvard Data Science Review. The","keywords":[],"image":"/media/remote/1cc8f2dfb382b91c-720.webp","collections":["conversations-with-leaders"],"label":"Panorama"},{"id":"12c8533a-f0c0-4077-84e9-d4b2ee169666","slug":"wzhgxmcc","title":"Data Science and Computing at UC Berkeley","dek":null,"authors":[{"name":"Jennifer Chayes","slug":"jennifer-chayes"}],"date":"2021-04-30","section":"panorama","issue":"volume3issue2","doi":"10.1162/99608f92.12c8533a","abstract":"Data science and computing are becoming central to education, scholarship, and societal impact; indeed, they are being woven into the fabric of our society. I discuss how UC Berkeley is organizing its educational, research, and institutional structures to respond to the opportunities and challenges this creates.","keywords":["data science","computing","statistics","information","multi-disciplinarity"],"image":"/media/remote/add6b2bb19e793f3-720.webp","collections":["data-science-education","perspectives-on-data-and-data-science"],"label":"Panorama"},{"id":"df2262f5-76cc-4c1b-85a1-328ef65598e5","slug":"nu90ngsc","title":"Enhancing and Accelerating Social Science via Automation: Challenges and Opportunities","dek":null,"authors":[{"name":"Tal Yarkoni","slug":"tal-yarkoni"},{"name":"Dean Eckles","slug":"dean-eckles"},{"name":"James A. J. Heathers","slug":"james-a-j-heathers"},{"name":"Margaret C. Levenstein","slug":"margaret-c-levenstein"},{"name":"Paul E. Smaldino","slug":"paul-e-smaldino"},{"name":"Julia Lane","slug":"julia-lane"}],"date":"2021-04-30","section":"panorama","issue":"volume3issue2","doi":"10.1162/99608f92.df2262f5","abstract":"Automation plays an increasingly important role in science, but the social sciences have been comparatively slow to take advantage of emerging technologies and methods. In this review, we argue that greater investment in automation would be one of the most effective and cost-effective ways to boost the reliability, validity, and utility of social science research. We identify five core areas ripe for potentially tran","keywords":["social science","automation","standardization","discoverability","data sharing","data access","validation"],"image":"/media/remote/11aff89dd498f2db-720.webp","collections":[],"label":"Panorama"},{"id":"cb0fa8d2-7cd5-42d4-9039-91f53c88c89d","slug":"e69066t4","title":"Interleaving Computational and Inferential Thinking: Data Science for Undergraduates at Berkeley","dek":null,"authors":[{"name":"Ani Adhikari","slug":"ani-adhikari"},{"name":"John DeNero","slug":"john-denero"},{"name":"Michael I. Jordan","slug":"michael-i-jordan"}],"date":"2021-04-30","section":"stepping-stones","issue":"volume3issue2","doi":"10.1162/99608f92.cb0fa8d2","abstract":"The undergraduate data science curriculum at the University of California, Berkeley is anchored in five new courses that emphasize computational thinking, inferential thinking, and the perspective gained by working on real-world problems. We believe that interleaving these elements within our core courses is essential to preparing students to engage in data-driven inquiry at the scale that contemporary scientific and","keywords":["education","curriculum","computation","decision-making","ethics","inference"],"image":"/media/remote/646697f48edc7263-720.webp","collections":["data-science-education","perspectives-on-data-and-data-science"],"label":"Stepping Stones"},{"id":"c7418199-7fe6-454b-a62b-af7f94363702","slug":"ic01qyxv","title":"Lessons From the ‘Data Exhaust’ of Plantation Slavery","dek":null,"authors":[{"name":"Caitlin Rosenthal","slug":"caitlin-rosenthal"}],"date":"2021-04-30","section":"mining-the-past","issue":"volume3issue2","doi":"10.1162/99608f92.c7418199","abstract":null,"keywords":[],"image":"/media/remote/b9b79fb250ea09d6-720.webp","collections":["perspectives-on-data-and-data-science"],"label":"Mining the Past"},{"id":"be3c2ec8-cf04-4154-adf1-0abe6dd8f6e1","slug":"wqoysrrt","title":"Engaging Young Learners With Data: Highlights From GAISE II, Level A","dek":null,"authors":[{"name":"Leticia Perez","slug":"leticia-perez"},{"name":"Denise A. Spangler","slug":"denise-a-spangler"},{"name":"Christine Franklin","slug":"christine-franklin"}],"date":"2021-04-30","section":"minding-the-future","issue":"volume3issue2","doi":"10.1162/99608f92.be3c2ec8","abstract":null,"keywords":[],"image":"/media/remote/2e7fd5565d159aa8-720.webp","collections":["data-science-education"],"label":"Minding the Future"},{"id":"f13a2337-436c-4770-a6eb-8e76d51153dc","slug":"wi9yky5c","title":"Computers Learning Humor Is No Joke","dek":null,"authors":[{"name":"Thomas Winters","slug":"thomas-winters"}],"date":"2021-04-30","section":"recreations-in-randomness","issue":"volume3issue2","doi":"10.1162/99608f92.f13a2337","abstract":null,"keywords":[],"image":"/media/remote/ca9e993b41c77ad3-720.webp","collections":[],"label":"Recreations in Randomness"},{"id":"b9bfc518-6e13-43d8-b409-060f8c0e5e78","slug":"32yz0u49","title":"Comment on \"Reproducibility and Replication of Experimental Particle Physics Results\"","dek":"A Letter to the Editor","authors":[{"name":"Andrew Fowlie","slug":"andrew-fowlie"}],"date":"2021-04-30","section":"bits-and-bites","issue":"volume3issue2","doi":"10.1162/99608f92.b9bfc518","abstract":null,"keywords":[],"image":"/media/remote/5f449d255eeee57a-720.webp","collections":["reproducibility-and-replicability"],"label":"Bits and Bites"},{"id":"ebd73e8c-de60-45a4-b8c5-a0769999721c","slug":"57tywz64","title":"Response to Andrew Fowlie’s Comments","dek":null,"authors":[{"name":"Thomas Junk","slug":"thomas-junk"},{"name":"Louis Lyons","slug":"louis-lyons"}],"date":"2021-04-30","section":null,"issue":"volume3issue2","doi":"10.1162/99608f92.ebd73e8c","abstract":null,"keywords":[],"image":"/media/remote/bc01f2168d4dc0fd-720.webp","collections":[],"label":"Commentary"},{"id":"a11bf693-b0b1-43f7-bc28-77b9ee948797","slug":"2q9jiymv","title":"Modeling and Analysis of the Spread of COVID-19 Under a Multiple-Strain Model With Mutations","dek":null,"authors":[{"name":"Osman Yagan","slug":"osman-yagan"},{"name":"Anirudh Sridhar","slug":"anirudh-sridhar"},{"name":"Rashad Eletreby","slug":"rashad-eletreby"},{"name":"Simon Levin","slug":"simon-levin"},{"name":"Joshua B. Plotkin","slug":"joshua-plot"},{"name":"H. Vincent Poor","slug":"h-vincent-poor"}],"date":"2021-04-23","section":null,"issue":"specialissue1","doi":"10.1162/99608f92.a11bf693","abstract":"Since December 2019, COVID-19 has caused worldwide devastation. To devise effective countermeasures, it is important to develop mathematical models that help us to understand and predict the spreading of COVID-19, as well as to provide guidelines on what can be done to limit its spread. To this end, we leverage recent work of Eletreby et al. (2020), which studies a model where multiple strains of a virus propagate th","keywords":["COVID-19","mutations","mask-wearing"],"image":"/media/remote/40f2a8f80c7fccd1-720.webp","collections":[],"label":"Article"},{"id":"edf2fc97-bdfa-4727-8554-c724cfc50d28","slug":"3uc0p5dq","title":"Individual Acceptance of Using Health Data for Private and Public Benefit: Changes During the COVID-19 Pandemic","dek":null,"authors":[{"name":"Frederic Gerdon","slug":"frederic-gerdon"},{"name":"Helen Nissenbaum","slug":"helen-nissenbaum"},{"name":"Ruben L. Bach","slug":"ruben-l-bach"},{"name":"Frauke Kreuter","slug":"frauke-kreuter"},{"name":"Stefan Zins","slug":"stefan-zins"}],"date":"2021-04-06","section":null,"issue":"specialissue1","doi":"10.1162/99608f92.edf2fc97","abstract":"While the COVID-19 pandemic has been devastating, data collected in this context has unprecedented opportunities for data scientists. The stunning breadth of data obtained through new gathering systems put in place to manage the pandemic offers a richly textured view of a transformed world. Looking forward, privacy researchers worry that these new data-gathering systems risk running afoul of societal norms regarding ","keywords":["privacy attitudes","contextual integrity","COVID-19","data sharing for the public good"],"image":"/media/remote/38592d1c7bd77c6a-720.webp","collections":[],"label":"Article"},{"id":"eb30390f-ade2-45cc-b48d-8e6bb12f585c","slug":"1ds8ptxu","title":"Recombination: A Family of Markov Chains for Redistricting","dek":null,"authors":[{"name":"Daryl DeFord","slug":"daryl-deford"},{"name":"Moon Duchin","slug":"moon-duchin"},{"name":"Justin Solomon","slug":"justin-solomon"}],"date":"2021-03-31","section":"milestones-and-millstones","issue":"volume3issue1","doi":"10.1162/99608f92.eb30390f","abstract":"Redistricting is the problem of partitioning a set of geographic units into a fixed number of subsets called districts, subject to a list of rules and priorities. These districts are used for elections, making their delineation highly consequential. It has been hard for quantitative researchers to orient to an application domain in which rule vagueness can be a feature rather than a bug—but law and policy often prefe","keywords":["redistricting","gerrymandering","Markov chains","sampling"],"image":"/media/remote/9a4b14461a08f6ff-720.webp","collections":["data-science-for-governing-and-policy-making"],"label":"Milestones and Millstones"},{"id":"cc479d52-32e1-4596-95f6-a37ba17f77d0","slug":"xhht1a9s","title":"Convergence in Viral Outbreak Research: Using Natural Language Processing to Define Network Bridges in the Bench-Bedside-Population Paradigm","dek":null,"authors":[{"name":"Margaret V. Powers-Fletcher","slug":"margaret-v-powers-fletcher"},{"name":"Erin E. McCabe","slug":"erin-e-mccabe"},{"name":"Sally Luken","slug":"sally-luken"},{"name":"Danny Wu","slug":"danny-wu"},{"name":"Philip A. Hagedorn","slug":"philip-a-hagedorn"},{"name":"Ezra Edgerton","slug":"ezra-edgerton"},{"name":"Amy Koshoffer","slug":"amy-koshoffer"},{"name":"Dorcas Washington","slug":"dorcas-washington"},{"name":"Suraj Kannayyagari","slug":"suraj-kannayyagari"},{"name":"Jason Lee","slug":"jason-lee"},{"name":"Jennifer Latessa","slug":"jennifer-latessa"},{"name":"Anita Shah","slug":"anita-shah"},{"name":"James Jaehoon Lee","slug":"james-jaehoon-lee"}],"date":"2021-03-22","section":"cornucopia","issue":"volume3issue1","doi":"10.1162/99608f92.cc479d52","abstract":"Research on viral outbreaks at the pandemic scale responds to heightened social urgency and the need to expedite scientific discovery from the ‘bench’ to the ‘bedside’ to the wider population. We sought to better understand translational research within the context of pandemics, both historical and present day, by tracking publication trends in the immediate aftermath of virus outbreaks. We used a blend of natural la","keywords":["interdisciplinary research","team science","natural language processing","social network analysis","COVID-19","biomedical informatics"],"image":"/media/remote/15f247ed7e8364f4-720.webp","collections":[],"label":"Cornucopia"},{"id":"ba6336bc-a90d-4edd-86b6-b592805a122b","slug":"tzrn0vqa","title":"Kaleidoscopic Perspectives on Practicum-Based Data Science Education","dek":"An interview with Shuang Frost, Aleksandrina Goeva, Javin Pombra, Sara Stoudt, Ana Trisovic, and Chris Wang by William Seaton and Catherine Zucker","authors":[{"name":"Shuang Frost","slug":"shuang-frost"},{"name":"Aleksandrina Goeva","slug":"aleksandrina-goeva"},{"name":"Javin Pombra","slug":"javin-pombra"},{"name":"William Seaton","slug":"william-seaton"},{"name":"Sara Stoudt","slug":"sara-stoudt"},{"name":"Ana Trisovic","slug":"ana-trisovic"},{"name":"Chris Wang","slug":"chris-wang"},{"name":"Catherine Zucker","slug":"catherine-zucker"}],"date":"2021-02-25","section":null,"issue":"volume3issue1","doi":"10.1162/99608f92.ba6336bc","abstract":null,"keywords":[],"image":"/media/remote/d3ff2db662c94b8d-720.webp","collections":["data-science-education"],"label":"Featured Discussion"},{"id":"539432b5-cc22-4688-86c7-329c865cbfc0","slug":"85uq7nc4","title":"The Capstone in Everyone’s Delivery Room: Placing ‘Practice’ at the Center of Data Science Education","dek":null,"authors":[{"name":"Antonietta Mira","slug":"antonietta-mira"},{"name":"Ernst Wit","slug":"ernst-wit"}],"date":"2021-02-25","section":null,"issue":"volume3issue1","doi":"10.1162/99608f92.539432b5","abstract":null,"keywords":[],"image":"/media/remote/2fe4a5e08b789467-720.webp","collections":["data-science-education"],"label":"Featured Discussion"},{"id":"d1aaf1d7-00ce-485a-a853-90dcfa2e65f4","slug":"gblp1ru7","title":"Is Data Science Education a Jack of All Trades?","dek":null,"authors":[{"name":"David R. Hardoon","slug":"david-r-hardoon"}],"date":"2021-02-25","section":null,"issue":"volume3issue1","doi":"10.1162/99608f92.d1aaf1d7","abstract":null,"keywords":[],"image":"/media/remote/799622123d3bb225-720.webp","collections":["data-science-education","data-science-for-business-and-industry"],"label":"Featured Discussion"},{"id":"80579436-b75f-4aaf-a51f-0915f2b0fc97","slug":"7n9bnr13","title":"From Outlier to Normalcy: Community Partnering as a Pathway Toward Modularity, Sustainability and Diversity When Centering ‘Practice’ in Data Science Education","dek":null,"authors":[{"name":"David Uminsky","slug":"david-uminsky"}],"date":"2021-02-25","section":null,"issue":"volume3issue1","doi":"10.1162/99608f92.80579436","abstract":null,"keywords":[],"image":"/media/remote/c5cca33aaca07e3d-720.webp","collections":["data-science-education"],"label":"Featured Discussion"},{"id":"0136867f-a8b4-4f25-95f1-2ef2d5ef79d1","slug":"l469mo3u","title":"How Can We Train Data Scientists When We Can’t Agree on Who They Are?","dek":null,"authors":[{"name":"Usama Fayyad","slug":"usama-fayyad"},{"name":"Hamit Hamutcu","slug":"hamit-hamutcu"}],"date":"2021-02-25","section":null,"issue":"volume3issue1","doi":"10.1162/99608f92.0136867f","abstract":null,"keywords":[],"image":"/media/remote/c343cc5306f630c9-720.webp","collections":["data-science-education","data-science-for-business-and-industry"],"label":"Featured Discussion"},{"id":"8233428d-ea5f-4ad6-a4f4-88848d809b05","slug":"nvflcexe","title":"The Data Science Corps Wrangle-Analyze-Visualize Program: Building Data Acumen for Undergraduate Students","dek":null,"authors":[{"name":"Nicholas Jon Horton","slug":"nicholas-horton"},{"name":"Benjamin S. Baumer","slug":"benjamin-bbaumersmithedu"},{"name":"Andrew Zieffler","slug":"andrew-zieffler"},{"name":"Valerie Barr","slug":"valerie-barr"}],"date":"2021-02-25","section":null,"issue":"volume3issue1","doi":"10.1162/99608f92.8233428d","abstract":null,"keywords":[],"image":"/media/remote/69a5dac1028f1b69-720.webp","collections":["data-science-education"],"label":"Featured Discussion"},{"id":"64bbf5af-daea-4a6a-92b6-9e0a0b1d5a77","slug":"udlcdamk","title":"Taking Our Medicine: Standardizing Data Science Education With Practice at the Core","dek":null,"authors":[{"name":"Kit T. Rodolfa","slug":"kit-t-rodolfa"},{"name":"Rayid Ghani","slug":"rayid-ghani"}],"date":"2021-02-25","section":null,"issue":"volume3issue1","doi":"10.1162/99608f92.64bbf5af","abstract":null,"keywords":[],"image":"/media/remote/bae8b42f86d3e183-720.webp","collections":["data-science-education"],"label":"Featured Discussion"},{"id":"44f54f00-e9e3-45c0-9fe9-77700ac32491","slug":"f7mp2chp","title":"Project-Based Learning via Competition for Data Science Students","dek":null,"authors":[{"name":"Philip L. H. Yu","slug":"philip-l-h-yu"},{"name":"Wai Keung Li","slug":"wai-keung-li"}],"date":"2021-02-25","section":null,"issue":"volume3issue1","doi":"10.1162/99608f92.44f54f00","abstract":null,"keywords":[],"image":"/media/remote/5bb6d805d4b5d273-720.webp","collections":["data-science-education"],"label":"Featured Discussion"},{"id":"ff65fd7a-3c77-4323-b8f5-dbda591cffbf","slug":"wsp3noed","title":"Statistical Practice Is Not a Spectator Sport","dek":null,"authors":[{"name":"Jessica Utts","slug":"jessica-utts"}],"date":"2021-02-25","section":null,"issue":"volume3issue1","doi":"10.1162/99608f92.ff65fd7a","abstract":null,"keywords":[],"image":"/media/remote/34eb2fc0a3573c9e-720.webp","collections":["data-science-education"],"label":"Featured Discussion"},{"id":"c5892498-bfd1-44ba-8be5-54ccfd7acdb8","slug":"dqcaqzz8","title":"Rejoinder: Practice-Centric Learning—A Driver for Democratization of Data Science","dek":null,"authors":[{"name":"Eric D. Kolaczyk","slug":"eric-kolaczyk"},{"name":"Haviland Wright","slug":"haviland-wright"},{"name":"Masanao Yajima","slug":"masanao-yajima"}],"date":"2021-02-25","section":null,"issue":"volume3issue1","doi":"10.1162/99608f92.c5892498","abstract":null,"keywords":[],"image":"/media/remote/4e6d875326331cd9-720.webp","collections":["data-science-education"],"label":"Featured Discussion"},{"id":"bc7ba0d2-1416-4dbd-8b49-7b2826053a33","slug":"p6sdle0t","title":"COVID-19 Case Fatality Rate Bias Visual Explainer","dek":null,"authors":[{"name":"Aleksandrina Goeva","slug":"aleksandrina-goeva"},{"name":"Sara Stoudt","slug":"sara-stoudt"}],"date":"2021-02-25","section":null,"issue":"specialissue1","doi":null,"abstract":null,"keywords":[],"image":"/media/remote/94ffd84385327f0b-720.webp","collections":[],"label":"Article"},{"id":"3a25cd24-24b8-4649-b258-79cd83b00630","slug":"fekmqbv4","title":"An Interview With Murray Edelman on the History of the Exit Poll","dek":"An interview with Murray Edelman by Liberty Vittert and Xiao-Li Meng","authors":[{"name":"Murray Edelman","slug":"murray-edelman"},{"name":"Liberty Vittert","slug":"liberty-vittert"},{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2021-02-24","section":"mining-the-past","issue":"volume3issue1","doi":"10.1162/99608f92.3a25cd24","abstract":null,"keywords":[],"image":"/media/remote/715b7037b735ab7d-720.webp","collections":["conversations-with-authors","data-science-for-governing-and-policy-making"],"label":"Mining the Past"},{"id":"9ce979be-6c9a-4b97-a4d4-d2e5631516ec","slug":"f8izbuk2","title":"Understanding the Quality of the 2020 Census Is Essential","dek":null,"authors":[{"name":"John Thompson","slug":"john-thompson"}],"date":"2021-02-22","section":"effective-policy-learning","issue":"volume3issue1","doi":"10.1162/99608f92.9ce979be","abstract":null,"keywords":[],"image":"/media/remote/dd811f2bed267686-720.webp","collections":["data-science-for-governing-and-policy-making"],"label":"Effective Policy Learning"},{"id":"90814c32-a6f1-4fac-8b39-4bfd0c3731bc","slug":"2fu65ujf","title":"Deployment as a Critical Business Data Science Discipline","dek":null,"authors":[{"name":"Thomas Davenport","slug":"thomas-davenport"},{"name":"Katie Malone","slug":"katie-malone"}],"date":"2021-02-10","section":"active-industrial-learning","issue":"volume3issue1","doi":"10.1162/99608f92.90814c32","abstract":null,"keywords":[],"image":"/media/remote/332e76fd9db65e14-720.webp","collections":["data-science-for-business-and-industry"],"label":"Active Industrial Learning"},{"id":"1d4e0dae-3c49-4222-8f7f-595d11b41a04","slug":"p6isyf0g","title":"Curating a COVID-19 Data Repository and Forecasting County-Level Death Counts in the United States","dek":null,"authors":[{"name":"Nicholas Altieri","slug":"nicholas-altieri"},{"name":"Rebecca L Barter","slug":"rebecca-l-barter"},{"name":"James Duncan","slug":"james-duncan"},{"name":"Raaz Dwivedi","slug":"raaz-dwivedi"},{"name":"Karl Kumbier","slug":"karl-kumbier"},{"name":"Xiao Li","slug":"xiao-li"},{"name":"Robert Netzorg","slug":"robert-netzorg"},{"name":"Briton Park","slug":"briton-park"},{"name":"Chandan Singh","slug":"chandan-singh"},{"name":"Yan Shuo Tan","slug":"yan-shuo-tan"},{"name":"Tiffany Tang","slug":"tiffany-tang"},{"name":"Yu Wang","slug":"yu-wang"},{"name":"Chao Zhang","slug":"chao-zhang"},{"name":"Bin Yu","slug":"bin-yu"}],"date":"2021-02-08","section":null,"issue":"specialissue1","doi":"10.1162/99608f92.1d4e0dae","abstract":"As the COVID-19 outbreak evolves, accurate forecasting continues to play an extremely important role in informing policy decisions. In this article we present our continuous curation of a large data repository containing COVID-19 information from a range of sources. We use this data to develop predictions and corresponding prediction intervals for the short-term trajectory of COVID-19 cumulative death counts at the c","keywords":["COVID-19","data-repository","time-series forecasting","ensemble methods","prediction intervals"],"image":"/media/remote/56c1312c0f7d9e2d-720.webp","collections":[],"label":"Article"},{"id":"edcee38f-0c32-40f6-9dd7-ff7ac8123b69","slug":"2xxlfhva","title":"Combining Statistical, Physical, and Historical Evidence to Improve Historical Sea-Surface Temperature Records","dek":null,"authors":[{"name":"Duo Chan","slug":"duo-chan"}],"date":"2021-02-08","section":"cornucopia","issue":"volume3issue1","doi":"10.1162/99608f92.edcee38f","abstract":"Reconstructing past sea-surface temperatures (SSTs) from historical measurements containing more than 100 million ship-based observations taken by over 500,000 ships from more than 150 countries using a variety of methodologies creates a wide range of historical, scientific, and statistical challenges. The reconstruction of historical SSTs for studying climate change is particularly challenging because SST measuremen","keywords":["climate reconstruction","sea-surface temperature","bias correction","data homogenization","linear-mixed-effect model"],"image":"/media/remote/216202b1a6899840-720.webp","collections":[],"label":"Cornucopia"},{"id":"2d65fc70-aa00-425e-ab33-481a24365970","slug":"twyc748y","title":"Statistics Practicum: Placing 'Practice' at the Center of Data Science Education","dek":null,"authors":[{"name":"Eric D. Kolaczyk","slug":"eric-kolaczyk"},{"name":"Haviland Wright","slug":"haviland-wright"},{"name":"Masanao Yajima","slug":"masanao-yajima"}],"date":"2021-01-29","section":"stepping-stones","issue":"volume3issue1","doi":"10.1162/99608f92.2d65fc70","abstract":"Much of the current postsecondary training in core data science fields treats ‘practice’ as something to be relegated to capstone projects or other final preparations before students leave their programs. Here we argue for a paradigm shift, placing a so-called practicum course at the center of a data science program, intentionally organized as a hybrid between an educational classroom and an industry-like environment","keywords":["consulting","education","industry partnership"],"image":"/media/remote/8b3c3ec085f20d2a-720.webp","collections":["data-science-education"],"label":"Stepping Stones"},{"id":"fbb1bdd6-7db1-4702-a41a-a25aa93d5c14","slug":"fi1rol2i","title":"Data Science in Times of Pan(dem)ic","dek":null,"authors":[{"name":"Sabina Leonelli","slug":"sabina-leonelli"}],"date":"2021-01-29","section":"panorama","issue":"volume3issue1","doi":"10.1162/99608f92.fbb1bdd6","abstract":"What are the priorities for data science in tackling COVID-19, and in which ways can big data analysis inform and support responses to the outbreak? It is imperative for data scientists to spend time and resources scoping, scrutinizing, and questioning the possible scenarios of use of their work—particularly given the fast-paced knowledge production required by an emergency situation such as the coronavirus pandemic.","keywords":["COVID-19","predictive modeling","public health","surveillance","engagement","research planning"],"image":"/media/remote/4ec2c5bd594614b1-720.webp","collections":["ai-and-responsible-data-science","perspectives-on-data-and-data-science"],"label":"Panorama"},{"id":"b665c0f4-6342-4117-a69f-fe058d1cb4b9","slug":"uy0zl4i1","title":"Why Tennis Is Still Not Ready to Play Moneyball","dek":null,"authors":[{"name":"Stephanie Kovalchik","slug":"stephanie-kovalchik"}],"date":"2021-01-29","section":"recreations-in-randomness","issue":"volume3issue1","doi":"10.1162/99608f92.b665c0f4","abstract":null,"keywords":[],"image":"/media/remote/6a4967f0eed02a98-720.webp","collections":["data-science-for-business-and-industry"],"label":"Recreations in Randomness"},{"id":"87b0ec26-4b18-4dd8-bc03-27e371807ff2","slug":"3csmghzj","title":"The People Machine: The Earliest Machine Learning?","dek":"An interview with Jill Lepore and Francine Berman","authors":[{"name":"Francine Berman","slug":"francine-berman"},{"name":"Jill Lepore","slug":"jill-lepore"}],"date":"2021-01-29","section":"panorama","issue":"volume3issue1","doi":"10.1162/99608f92.87b0ec26","abstract":"In September 2020, the Harvard Data Science Initiative (HDSI) invited Jill Lepore and Fran Berman to a special event to talk about data science and the Simulmatics Corporation, the focus of Jill’s new book If Then: How the Simulmatics Corporation Invented the Future. Jill’s book tells the compelling story of one of the first corporations to use the power of digital data to both understand and illuminate the world aro","keywords":["data analytics","behavioral science","ethics","simulation","elections","democracy"],"image":"/media/remote/79b773736168f444-720.webp","collections":["ai-and-responsible-data-science"],"label":"Panorama"},{"id":"ee717cf7-8ceb-4c14-ab0b-393d3ca931d6","slug":"bj2dfcwg","title":"What Are the Values of Data, Data Science, or Data Scientists?","dek":null,"authors":[{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2021-01-29","section":"editorscolumn","issue":"volume3issue1","doi":"10.1162/99608f92.ee717cf7","abstract":null,"keywords":[],"image":"/media/remote/e9d28b4867f25324-720.webp","collections":["data-science-for-business-and-industry","perspectives-on-data-and-data-science"],"label":"Editor’s Column"},{"id":"2794e78d-4bac-44e4-a1f5-fb7d3d86f621","slug":"ibjlw7gr","title":"We Need a (Responsible!) Data Science Rapid Response Network","dek":null,"authors":[{"name":"Eric D. Kolaczyk","slug":"eric-kolaczyk"},{"name":"Meredith M. Lee","slug":"meredith-m-lee"},{"name":"Jing Liu","slug":"jing-liu"},{"name":"Micaela S. Parker","slug":"micaela-s-parker"}],"date":"2021-01-29","section":null,"issue":"volume3issue1","doi":"10.1162/99608f92.2794e78d","abstract":null,"keywords":[],"image":"/media/remote/9b123669735e225c-720.webp","collections":[],"label":"Featured Discussion"},{"id":"ea29ee5a-b7cb-4088-84f6-f0b1afca0a33","slug":"2szvobpn","title":"Blue Chips and White Collars: Whose Data Science Is It?","dek":null,"authors":[{"name":"Barbara Prainsack","slug":"barbara-prainsack"}],"date":"2021-01-29","section":null,"issue":"volume3issue1","doi":"10.1162/99608f92.ea29ee5a","abstract":null,"keywords":[],"image":"/media/remote/68b0eaaf95964e6c-720.webp","collections":[],"label":"Featured Discussion"},{"id":"39876cea-a8cb-4ed6-9e19-6ef56d6181ce","slug":"3aw43v33","title":"Data Scientists Should Be Value-Driven, Not Neutral","dek":null,"authors":[{"name":"Timothy Francis Clement-Jones","slug":"timothy-francis-clement-jones"}],"date":"2021-01-29","section":null,"issue":"volume3issue1","doi":"10.1162/99608f92.39876cea","abstract":null,"keywords":[],"image":"/media/remote/d4ab8246c8713c1a-720.webp","collections":[],"label":"Featured Discussion"},{"id":"6d743a66-039f-42b8-899e-870555f8184d","slug":"b1zyu9ji","title":"A Plague of Data","dek":null,"authors":[{"name":"Theodore M. Porter","slug":"theodore-m-porter"}],"date":"2021-01-29","section":null,"issue":"volume3issue1","doi":"10.1162/99608f92.6d743a66","abstract":null,"keywords":[],"image":"/media/remote/71cd3846267bade1-720.webp","collections":[],"label":"Featured Discussion"},{"id":"70f8cb98-8298-4a09-bca2-08f81f1164b3","slug":"90rqficj","title":"Bringing Every Tool to the COVID-19 Fight—What We Need Now","dek":null,"authors":[{"name":"Nicolette Louissaint","slug":"nicolette-louissaint"}],"date":"2021-01-29","section":null,"issue":"volume3issue1","doi":"10.1162/99608f92.70f8cb98","abstract":null,"keywords":[],"image":"/media/remote/a733d7e1d0d9f8d8-720.webp","collections":[],"label":"Featured Discussion"},{"id":"938a18d7-fd65-4eca-bc77-7f27d27a0bf3","slug":"ln5hu6vq","title":"The Arc of the Data Scientific Universe","dek":null,"authors":[{"name":"David Leslie","slug":"david-leslie"}],"date":"2021-01-29","section":null,"issue":"volume3issue1","doi":"10.1162/99608f92.938a18d7","abstract":null,"keywords":[],"image":"/media/remote/5c9a98ab33810b90-720.webp","collections":[],"label":"Featured Discussion"},{"id":"fc216595-9ce5-44d0-8ac5-714c0167ee53","slug":"v8xymvcp","title":"Rejoinder: The Present and Future of Data Science in Society","dek":null,"authors":[{"name":"Sabina Leonelli","slug":"sabina-leonelli"}],"date":"2021-01-29","section":null,"issue":"volume3issue1","doi":"10.1162/99608f92.fc216595","abstract":null,"keywords":[],"image":"/media/remote/5027b266c0de71a0-720.webp","collections":[],"label":"Featured Discussion"},{"id":"46ee6c04-1840-456f-82b6-e54843f3edad","slug":"fmrejqkx","title":"More Efficient and Effective Clinical Decision-Making","dek":null,"authors":[{"name":"Elizabeth L. Ogburn","slug":"elizabeth-ogburn"}],"date":"2021-01-29","section":null,"issue":"volume3issue1","doi":"10.1162/99608f92.46ee6c04","abstract":null,"keywords":[],"image":"/media/remote/1e630ecb553be4c7-720.webp","collections":[],"label":"Featured Discussion"},{"id":"250f995b-aaff-4bc7-b700-84adfedeff2e","slug":"1lhu0zvn","title":"Reproducibility and Replication of Experimental Particle Physics Results","dek":null,"authors":[{"name":"Thomas Junk","slug":"thomas-junk"},{"name":"Louis Lyons","slug":"louis-lyons"}],"date":"2020-12-21","section":"panorama","issue":"volume2issue4","doi":"10.1162/99608f92.250f995b","abstract":"Recently, much attention has been focused on the replicability of scientific results, causing scientists, statisticians, and journal editors to examine closely their methodologies and publishing criteria. Experimental particle physicists have been aware of the precursors of nonreplicable research for many decades and have many safeguards to ensure that the published results are as reliable as possible. The experiment","keywords":["reliability","reproducibility","replication","particle physics"],"image":"/media/remote/85572948d0aa4b6a-720.webp","collections":["reproducibility-and-replicability"],"label":"Panorama"},{"id":"4f6b9e67-3acf-40f8-b456-d042f22f0d8a","slug":"fgpmpj1l","title":"Reproducibility and Replicability in Economics","dek":null,"authors":[{"name":"Lars Vilhuber","slug":"lars-vilhuber"}],"date":"2020-12-21","section":"panorama","issue":"volume2issue4","doi":"10.1162/99608f92.4f6b9e67","abstract":"I provide a summary description of the history and state of reproducibility and replicability in the academic field of economics. I include a discussion of more general replicability and transparency, including the tradition of sharing of research findings and code outside of peer-reviewed publications. I describe the historical context for journals and grey literature in economics, the role of precollected public an","keywords":["reproducibility","transparency","replicability","preprints","open-source","journal policies"],"image":"/media/remote/dc60be9d96b738c5-720.webp","collections":["reproducibility-and-replicability"],"label":"Panorama"},{"id":"25f36a2d-3a8d-478c-868a-021ae0acfa27","slug":"uk1wxi9c","title":"Post-Election Interview With Scott Tranter","dek":"An interview with Scott Tranter by Liberty Vittert and Xiao-Li Meng","authors":[{"name":"Scott Tranter","slug":"scott-tranter"},{"name":"Liberty Vittert","slug":"liberty-vittert"},{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2020-12-21","section":null,"issue":"volume2issue4","doi":"10.1162/99608f92.9663befd/25f36a2d","abstract":null,"keywords":[],"image":"/media/remote/2237bda1082df51b-720.webp","collections":["2020-election","conversations-with-authors"],"label":"Interview"},{"id":"fc62b261-a56b-41b4-9ab6-487b49893fb6","slug":"l39rpgyc","title":"Selective Inference: The Silent Killer of Replicability","dek":null,"authors":[{"name":"Yoav Benjamini","slug":"yoav-benjamini"}],"date":"2020-12-16","section":"panorama","issue":"volume2issue4","doi":"10.1162/99608f92.fc62b261","abstract":"Replicability of results has been a gold standard in science and should remain so, but concerns about lack of it have increased in recent years. Transparency, good design, and reproducible computing and data analysis are prerequisites for replicability. Adopting appropriate statistical methodologies is another identified one, yet which methodologies can be used to enhance replicability of results from a single study ","keywords":["p-value","multiple comparisons","false discovery rate","reproducibility"],"image":"/media/remote/3ee24197ab74bba8-720.webp","collections":["reproducibility-and-replicability"],"label":"Panorama"},{"id":"00cd8f85-436f-45d6-b3f3-3fd9a3bf282e","slug":"dijwtzza","title":"Perspectives on Data Reproducibility and Replicability in Paleoclimate and Climate Science","dek":null,"authors":[{"name":"Rosemary Bush","slug":"rosemary-bush"},{"name":"Andrea Dutton","slug":"andrea-dutton"},{"name":"Michael Evans","slug":"michael-evans"},{"name":"Rich Loft","slug":"rich-loft"},{"name":"Gavin A. Schmidt","slug":"gavin-a-schmidt"}],"date":"2020-12-16","section":"panorama","issue":"volume2issue4","doi":"10.1162/99608f92.00cd8f85","abstract":"This paper summarizes the current state of reproducibility and replicability in the fields of climate and paleoclimate science, including brief histories of their development and applications in climate science, new and recent approaches towards improvement of reproducibility and replicability, and challenges. Recommendations for addressing those challenges include: development of searchable, auto-updated, interlinke","keywords":["data repositories","global climate models","GCMs","proxy archives","replication","reproducibility","metadata"],"image":"/media/remote/622c2a7e118bf9cd-720.webp","collections":["reproducibility-and-replicability"],"label":"Panorama"},{"id":"eb6ddee4-45c1-40e6-9568-82df93ef5852","slug":"0r4v4k4z","title":"Reproducibility in Science: A Metrology Perspective","dek":null,"authors":[{"name":"Anne Plant","slug":"anne-plant"},{"name":"Robert Hanisch","slug":"robert-hanisch"}],"date":"2020-12-16","section":"panorama","issue":"volume2issue4","doi":"10.1162/99608f92.eb6ddee4","abstract":"Scientific progress requires the ability of scientists to build on the results produced by those who preceded them. Because of this, there is concern that irreproducible scientific results are being reported. We suggest that while reproducibility can be an important hallmark of good science, it is not often the most important indicator. The discipline of metrology, or measurement science, describes a measurement resu","keywords":["measurement science","reproducibility","sources of uncertainty","comparability","data","metadata"],"image":"/media/remote/a1b4154ab74d8e4c-720.webp","collections":["reproducibility-and-replicability"],"label":"Panorama"},{"id":"f2823096-cfff-4b62-aeed-0901fc968d38","slug":"3g7u601s","title":"Science Communication in the Context of Reproducibility and Replicability: How Nonscientists Navigate Scientific Uncertainty","dek":null,"authors":[{"name":"Emily L. Howell","slug":"emily-l-howell"}],"date":"2020-12-16","section":"panorama","issue":"volume2issue4","doi":"10.1162/99608f92.f2823096","abstract":"Scientists stand to gain in obvious ways from recent efforts to develop robust standards for and mechanisms of reproducibility and replicability. Demonstrations of reproducibility and replicability may provide clarity with respect to areas of uncertainty in scientific findings and translate into greater impact for the research. But when it comes to public perceptions of science, it is less clear what gains might come","keywords":["public understanding of science","science communication","uncertainty","replicability","reproducibility"],"image":"/media/remote/fecd1381099a8203-720.webp","collections":["reproducibility-and-replicability"],"label":"Panorama"},{"id":"33703976-5fac-402a-9521-343b9b829628","slug":"yosmh9o4","title":"Learning Lessons on Reproducibility and Replicability in Large Scale Genome-Wide Association Studies","dek":null,"authors":[{"name":"Xihong Lin","slug":"xihong-lin"}],"date":"2020-12-16","section":"panorama","issue":"volume2issue4","doi":"10.1162/99608f92.33703976","abstract":"Reproducibility and replicability play a pivotal role in science. The article reflects on reproducibility and replicability as they figure in large scale genome-wide association studies. Overall, we emphasize the importance of enhancing data reproducibility, analysis reproducibility, and result replicability. We make recommendations pertaining to the development of study designs that address 1) batch effects and sele","keywords":["analysis reproducibility and standardization","community culture building and collaborative framework","data reproducibility and standardization and harmonization","data repositories","result replicability","multi-phase study design"],"image":"/media/remote/d659d92c707aab6e-720.webp","collections":["reproducibility-and-replicability"],"label":"Panorama"},{"id":"b69d3134-18c7-4308-ab95-39ba5523f6fb","slug":"bc3hf4cr","title":"Leveraging the National Academies’ Reproducibility and Replication in Science Report to Advance Reproducibility in Publishing","dek":null,"authors":[{"name":"Manish Parashar","slug":"manish-parashar"}],"date":"2020-12-16","section":"panorama","issue":"volume2issue4","doi":"10.1162/99608f92.b69d3134","abstract":"The National Academies of Sciences, Engineering, and Medicine (NASEM) 2019 report, Reproducibility and Replicability in Science, addresses issues related to reproducibility and replicability across science. In this article, we explore the implications of this report and its recommendations in the publishing sphere using our experiences with the ongoing reproducibility pilot for the IEEE Transactions on Parallel and D","keywords":["IEEE TPDS Reproducibility Initiative","code and data review","badging"],"image":"/media/remote/1fddb00441f4e467-720.webp","collections":["reproducibility-and-replicability"],"label":"Panorama"},{"id":"32432837-db4e-4f31-b1e1-daf14d9c1b1d","slug":"ivwnyg4w","title":"Self-Correction by Design","dek":null,"authors":[{"name":"Marcia McNutt","slug":"marcia-mcnutt"}],"date":"2020-12-16","section":"panorama","issue":"volume2issue4","doi":"10.1162/99608f92.32432837","abstract":"Science has always been self-correcting. Through traditional approaches such as establishing benchmarks and standards, and reproducing, replicating, or generalizing prior research results, work that is at the same time important and suspect will be subjected to further testing. With science now becoming simultaneously critically important to society and an expensive enterprise, I argue that we need to adopt an enterp","keywords":["replication","reproducibility","trust","geosciences"],"image":"/media/remote/3070bb3c279b2cc0-720.webp","collections":["reproducibility-and-replicability"],"label":"Panorama"},{"id":"c46a02d4-afc6-4f16-9b07-c61fe0b3a44d","slug":"kincvy67","title":"Theme Editor's Introduction to Reproducibility and Replicability in Science","dek":null,"authors":[{"name":"Victoria Stodden","slug":"victoria-stodden"}],"date":"2020-12-16","section":"panorama","issue":"volume2issue4","doi":"10.1162/99608f92.c46a02d4","abstract":null,"keywords":[],"image":"/media/remote/ef094ffa8344df13-720.webp","collections":["reproducibility-and-replicability"],"label":"Panorama"},{"id":"25982dcf-11b6-4f77-9be4-98658844528b","slug":"f0obb31j","title":"Trust but Verify: How to Leverage Policies, Workflows, and Infrastructure to Ensure Computational Reproducibility in Publication","dek":null,"authors":[{"name":"Craig Willis","slug":"craig-willis"},{"name":"Victoria Stodden","slug":"victoria-stodden"}],"date":"2020-12-16","section":"panorama","issue":"volume2issue4","doi":"10.1162/99608f92.25982dcf","abstract":"This article distills findings from a qualitative study of seven reproducibility initiatives to enumerate nine key decision points for journals seeking to address concerns about the quality and rigor of computational research by expanding the peer review and publication process. We evaluate our guidance in light of the recent National Academies of Science, Engineering, and Medicine (NASEM, 2019) report on Reproducibi","keywords":["reproducibility","reproducibility audits","reproducibility initiative","reproducibility policy","open data and code","peer review"],"image":"/media/remote/9ed184d6f3d9a75c-720.webp","collections":["reproducibility-and-replicability"],"label":"Panorama"},{"id":"1cc3d72a-ad4e-49bc-b2e4-e22cf0773fca","slug":"lm3txy12","title":"Toward Reproducible and Extensible Research: From Values to Action","dek":null,"authors":[{"name":"Aleksandrina Goeva","slug":"aleksandrina-goeva"},{"name":"Sara Stoudt","slug":"sara-stoudt"},{"name":"Ana Trisovic","slug":"ana-trisovic"}],"date":"2020-12-16","section":"panorama","issue":"volume2issue4","doi":"10.1162/99608f92.1cc3d72a","abstract":"The National Academies report Reproducibility and Replicability in Science outlines values and broad goals for reproducible and replicable scientific research, emphasizing the importance of its availability and transparency. Confronted with these goals, we examine what it means for research material to be transparent, available, and reproducible. As early-career researchers, we advocate for the focus to be placed on ","keywords":["reproducibility","replicability","extensible research","transparency","openness"],"image":"/media/remote/958ec781b25155e1-720.webp","collections":["reproducibility-and-replicability"],"label":"Panorama"},{"id":"7e57f4ec-637a-4e45-99cf-98c978e47040","slug":"nas-report-highlights","title":"Reproducibility and Replicability in Science: Report Highlights","dek":null,"authors":[{"name":"National Academies of Sciences, Engineering, and Medicine","slug":"national-academies-of-sciences-engineering-and-medicine"}],"date":"2020-12-16","section":null,"issue":"volume2issue4","doi":null,"abstract":null,"keywords":[],"image":"/media/remote/8c1f3d2dd1d7a99a-720.webp","collections":["reproducibility-and-replicability"],"label":"Article"},{"id":"47452384-b039-4abb-a095-54d0c2d99a7f","slug":"qqms394f","title":"Post-Election Interview With Andrew Gelman and G. Elliott Morris","dek":"An interview with Andrew Gelman and G. Elliott Morris by Liberty Vittert and Xiao-Li Meng","authors":[{"name":"Andrew Gelman","slug":"andrew-gelman"},{"name":"G. Elliott Morris","slug":"g-elliott-morris"},{"name":"Liberty Vittert","slug":"liberty-vittert"},{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2020-12-14","section":null,"issue":"volume2issue4","doi":"10.1162/99608f92.fc62f1e1/47452384","abstract":null,"keywords":[],"image":"/media/remote/d085d0566358b60c-720.webp","collections":["2020-election","conversations-with-authors"],"label":"Interview"},{"id":"6e25c7ee-972a-4dd9-aeb5-b9a2b9f54867","slug":"kxks56er","title":"Recreating the Game: Using Player Tracking Data to Analyze Dynamics in Basketball and Football","dek":null,"authors":[{"name":"Brian Macdonald","slug":"brian-macdonald"}],"date":"2020-12-10","section":"recreations-in-randomness","issue":"volume2issue4","doi":"10.1162/99608f92.6e25c7ee","abstract":null,"keywords":[],"image":"/media/remote/10f8a8a153797392-720.webp","collections":[],"label":"Recreations in Randomness"},{"id":"e45650b8-15ad-4fa5-bcf5-1c7cb01e48ef","slug":"xl0yk6ux","title":"Knowledge Graphs and COVID-19: Opportunities, Challenges, and Implementation","dek":null,"authors":[{"name":"Mayank Kejriwal","slug":"mayank-kejriwal"}],"date":"2020-12-01","section":null,"issue":"specialissue1","doi":"10.1162/99608f92.e45650b8","abstract":"The COVID-19 pandemic has been truly global and multidimensional in scope, with ramifications extending well beyond health. Yet, unlike previous crises, there is hope that timely release of relevant data sets, as well as advents in AI (artificial intelligence) technology, could lead to compressed timescales in finding a vaccine or cure. Despite the huge existing body of academic literature on the coronavirus family, ","keywords":["question answering","domain-specific search","information retrieval"],"image":"/media/remote/492fff1c88b2dc85-720.webp","collections":[],"label":"Article"},{"id":"611350fd-a3a2-4879-9da2-a96852e58125","slug":"yzcakqc7","title":"The Politics of COVID-19: Partisan Polarization About the Pandemic Has Increased, but Support for Health Care Reform Hasn’t Moved at All","dek":null,"authors":[{"name":"John Sides","slug":"john-sides"},{"name":"Chris Tausanovitch","slug":"chris-tausanovitch"},{"name":"Lynn Vavreck","slug":"lynn-vavreck"}],"date":"2020-11-30","section":null,"issue":"specialissue1","doi":"10.1162/99608f92.611350fd","abstract":"We investigate trends in public opinion before and during the COVID-19 pandemic on concern about the virus, efforts to combat its spread, and support for various health care policy reforms. Using data from more than 400,000 interviews covering every state in the country over a 1-year period, we demonstrate initially high levels of concern about the virus and support for restrictions to combat it across geography and ","keywords":["COVID-19","elections","health care","public opinion","polarization","pandemic"],"image":"/media/remote/7a78a86b6d05ccba-720.webp","collections":["data-science-for-governing-and-policy-making"],"label":"Article"},{"id":"7679a1ed-1f7c-4a3e-a019-b7e190c9c0d1","slug":"9421kmzi","title":"Estimating the Number of SARS-CoV-2 Infections and the Impact of Mitigation Policies in the United States","dek":null,"authors":[{"name":"James Johndrow","slug":"james-johndrow"},{"name":"Patrick Ball","slug":"patrick-ball"},{"name":"Maria Gargiulo","slug":"maria-gargiulo"},{"name":"Kristian Lum","slug":"kristian-lum"}],"date":"2020-11-23","section":null,"issue":"specialissue1","doi":"10.1162/99608f92.7679a1ed","abstract":"Knowledge of the number of individuals who have been infected with the novel coronavirus SARS-CoV-2 and the extent to which attempts for mitigation by executive order have been effective at limiting its spread are critical for effective policy going forward. Directly assessing prevalence and policy effects is complicated by the fact that case counts are unreliable. In this article, we present a model for using death-","keywords":["COVID-19","SARS-CoV-2","compartmental model","SIR model","Bayesian"],"image":"/media/remote/570efbddc716f94c-720.webp","collections":[],"label":"Article"},{"id":"989cfb74-98a4-4b26-b2da-8a10aa38b12e","slug":"l8226sh6","title":"Post-Election Interview With Allan Lichtman","dek":"An interview with Allan Lichtman by Liberty Vittert and Xiao-Li Meng","authors":[{"name":"Allan Lichtman","slug":"allan-lichtman"},{"name":"Liberty Vittert","slug":"liberty-vittert"},{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2020-11-19","section":null,"issue":"volume2issue4","doi":"10.1162/99608f92.baaa8f68/989cfb74","abstract":null,"keywords":[],"image":"/media/remote/548a1ebf34b6fa6b-720.webp","collections":["2020-election","conversations-with-authors"],"label":"Interview"},{"id":"6c591bd4-6ae1-4e28-aa5d-3674c624a90c","slug":"l16wrj7d","title":"Reconsidering Lost Votes by Mail","dek":null,"authors":[{"name":"Charles H. Stewart III","slug":"charles-h-stewart-iii"}],"date":"2020-11-03","section":"milestones-and-millstones","issue":"volume2issue4","doi":"10.1162/99608f92.6c591bd4","abstract":"A ‘lost vote’ occurs when a voter does all that is asked of her, and yet her vote is uncounted in the final tally. Estimating the magnitude of lost votes in American presidential elections has followed the work of the Caltech/MIT Voting Technology Project, which initially estimated the magnitude of lost votes in the 2000 presidential election, due to failures of voter registration, polling-place management, and votin","keywords":["elections","voting by mail","absentee ballots","election administration"],"image":"/media/remote/43d6ec1cee2d8884-720.webp","collections":["2020-election"],"label":"Milestones and Millstones"},{"id":"86a46f38-b95f-4068-a973-255ed64bf9ae","slug":"cnxbwum6","title":"Towards Principled Unskewing: Viewing 2020 Election Polls Through a Corrective Lens From 2016","dek":null,"authors":[{"name":"Michael Isakov","slug":"michael-isakov"},{"name":"Shiro Kuriwaki","slug":"shiro-kuriwaki"}],"date":"2020-11-03","section":"cornucopia","issue":"volume2issue4","doi":"10.1162/99608f92.86a46f38","abstract":"We apply the concept of the data defect index (Meng, 2018) to study the potential impact of systematic errors on the 2020 pre-election polls in 12 presidential battleground states. We investigate the impact under the hypothetical scenarios that (1) the magnitude of the underlying nonresponse bias correlated with supporting Donald Trump is similar to that of the 2016 polls, (2) the pollsters’ ability to correct system","keywords":["Bayesian modeling","survey nonresponse bias","election polling","data defect index","data defect correlation","weighting deficiency coefficient"],"image":"/media/remote/b620b88c1f2c8fc3-720.webp","collections":["2020-election"],"label":"Cornucopia"},{"id":"246107bb-7a98-49e5-8264-94ae86ef309e","slug":"cqncbp3l","title":"Introducing GAISE II: A Guideline for Precollege Statistics and Data Science Education","dek":null,"authors":[{"name":"Christine Franklin","slug":"christine-franklin"},{"name":"Anna Bargagliotti","slug":"anna-bargagliotti"}],"date":"2020-10-29","section":"minding-the-future","issue":"volume2issue4","doi":"10.1162/99608f92.246107bb","abstract":null,"keywords":[],"image":"/media/remote/ce9d5c7d3270a391-720.webp","collections":["data-science-education"],"label":"Minding the Future"},{"id":"1e28ce9e-e49a-4678-9c3b-b7ea933907dd","slug":"wtzfot43","title":"High School Data Science Review: Why Data Science Education Should Be Reformed","dek":null,"authors":[{"name":"Angelina Chen","slug":"angelina-chen"}],"date":"2020-10-29","section":"minding-the-future","issue":"volume2issue4","doi":"10.1162/99608f92.1e28ce9e","abstract":null,"keywords":[],"image":"/media/remote/d68c774b1baed4e2-720.webp","collections":["data-science-education"],"label":"Minding the Future"},{"id":"dbfce7f9-0853-4e20-bc9e-70efb5cefe01","slug":"hn51kn68","title":"Reproducibility, Replicability, and Reliability","dek":null,"authors":[{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2020-10-29","section":"editorscolumn","issue":"volume2issue4","doi":"10.1162/99608f92.dbfce7f9","abstract":null,"keywords":[],"image":"/media/remote/3208d9464ca97a4d-720.webp","collections":[],"label":"Editor’s Column"},{"id":"75aed58b-b4f4-4cf7-ba0b-f2c553bb600b","slug":"safie31p","title":"The Opportunities of Two-Year College Data Science","dek":null,"authors":[{"name":"Brian Kotz","slug":"brian-kotz"}],"date":"2020-10-29","section":"stepping-stones","issue":"volume2issue4","doi":"10.1162/99608f92.75aed58b","abstract":"Two-year colleges are poised to play a substantial and possibly transformative role in data science and undergraduate data science education. Current two-year college data science programs provide affordable and rigorous certificate and degree experiences that instill data acumen in students who do not seek or do not fit within the traditional four-year college paradigm. Additional two-year college data science certi","keywords":["data science education","undergraduate education","two-year college","community college","data acumen"],"image":"/media/remote/cbf47c99a107cb01-720.webp","collections":["data-science-education"],"label":"Stepping Stones"},{"id":"9663befd-ee4f-46b9-bd2e-22f8e2b530a6","slug":"gach7e59","title":"Forecasting the 2020 U.S. Elections With Decision Desk HQ: Methodology for Modern American Electoral Dynamics","dek":null,"authors":[{"name":"Mukul Ram","slug":"mukul-ram"},{"name":"Matthew Shor","slug":"matthew-shor"},{"name":"Kiel Williams","slug":"kiel-williams"},{"name":"Sreevani Jarugula","slug":"sreevani-jarugula"},{"name":"Dan DeRemigi","slug":"dan-deremigi"},{"name":"Alex Alduncin","slug":"alex-alduncin"},{"name":"Scott Tranter","slug":"scott-tranter"}],"date":"2020-10-27","section":"cornucopia","issue":"volume2issue4","doi":"10.1162/99608f92.9663befd","abstract":"Øptimus has constructed models to predict the outcomes of the 2020 presidential and congressional general elections in collaboration with Decision Desk HQ. The model is an iteration from its 2018 U.S. Congressional model designed to predict the outcome of the election as if it were held today. The congressional model predicts the probability of a Republican (GOP) victory in individual House and Senate elections, as w","keywords":["elections","political science","government","machine learning"],"image":"/media/remote/90b89d9ee16eb39a-720.webp","collections":["2020-election"],"label":"Cornucopia"},{"id":"baaa8f68-d6f0-4abe-9cec-cf1dec7dd1a7","slug":"xhgpcyoa","title":"The Keys to the White House: Forecast for 2020","dek":null,"authors":[{"name":"Allan Lichtman","slug":"allan-lichtman"}],"date":"2020-10-27","section":"cornucopia","issue":"volume2issue4","doi":"10.1162/99608f92.baaa8f68","abstract":"The Keys to the White House is a historical-based index system for predicting the results of American presidential elections that has been successful since 1984. The Keys system gauges the strength and performance of the party holding the White House. If six or more Keys turn against the White House party, they are predicted losers. With seven negative keys lined up against him, the Keys predict that Donald Trump wil","keywords":["prediction","keys","White House","election","Trump","Biden"],"image":"/media/remote/b7e99ea87f5758fc-720.webp","collections":["2020-election"],"label":"Cornucopia"},{"id":"fc62f1e1-c737-417a-8176-362c96fa9627","slug":"nw1dzd02","title":"An Updated Dynamic Bayesian Forecasting Model for the U.S. Presidential Election","dek":null,"authors":[{"name":"Merlin Heidemanns","slug":"merlin-heidemanns"},{"name":"Andrew Gelman","slug":"andrew-gelman"},{"name":"G. Elliott Morris","slug":"g-elliott-morris"}],"date":"2020-10-27","section":"cornucopia","issue":"volume2issue4","doi":"10.1162/99608f92.fc62f1e1","abstract":"During modern general election cycles, information to forecast the electoral outcome is plentiful. So-called fundamentals like economic growth provide information early in the cycle. Trial-heat polls become informative closer to Election Day. Our model builds on (Linzer, 2013) and is implemented in Stan (Team, 2020). We improve on the estimation of state-level trends, the internal consistency of different predictions","keywords":["poll aggregation","election","forecasting","fundamentals","Bayesian"],"image":"/media/remote/706d9187ab2c9f5b-720.webp","collections":["2020-election"],"label":"Cornucopia"},{"id":"fed3dc89-d22e-44de-af5b-6d854878311a","slug":"yi347huq","title":"Predicting the 2020 Presidential Election","dek":null,"authors":[{"name":"Liberty Vittert","slug":"liberty-vittert"},{"name":"Ryan D. Enos","slug":"ryan-d-enos"},{"name":"Stephen Ansolabehere","slug":"stephen-ansolabehere"}],"date":"2020-10-27","section":"cornucopia","issue":"volume2issue4","doi":"10.1162/99608f92.fed3dc89","abstract":null,"keywords":[],"image":"/media/remote/f26d5bdc58034e31-720.webp","collections":["2020-election"],"label":"Cornucopia"},{"id":"cb310198-29cf-4589-a5f9-31e9a10d887d","slug":"6an6ppum","title":"Highlights of the US National Academies Report on “Reproducibility and Replicability in Science”","dek":"An interview with Harvey Fineberg and Victoria Stodden by Xiao-Li Meng","authors":[{"name":"Harvey Fineberg","slug":"harvey-fineberg"},{"name":"Victoria Stodden","slug":"victoria-stodden"},{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2020-10-27","section":"panorama","issue":"volume2issue4","doi":"10.1162/99608f92.cb310198","abstract":"In 2019, the National Academies of Sciences, Engineering, and Medicine of the United States released a report on reproducibility and replicability in science. This topic is of keen interest to everyone concerned with the reliability of scientific research and the role of computational and statistical analysis in science. In this interview conducted by Xiao-Li Meng, (HDSR’s Editor-in-Chief), report committee chair Har","keywords":["misuse of statistics","National Science Foundation","scientific rigor","scientific reporting","reproducibility","replicability"],"image":"/media/remote/9b91d90abeb3158a-720.webp","collections":["reproducibility-and-replicability"],"label":"Panorama"},{"id":"95388fcb-773a-403e-a716-8f375bb63fa4","slug":"da99kl2q","title":"Challenges and Opportunities in Statistics and Data Science: Ten Research Areas","dek":null,"authors":[{"name":"Xuming He","slug":"xuming-he"},{"name":"Xihong Lin","slug":"xihong-lin"}],"date":"2020-09-30","section":"panorama","issue":"volume2issue3","doi":"10.1162/99608f92.95388fcb","abstract":"As a discipline that deals with many aspects of data, statistics is a critical pillar in the rapidly evolving landscape of data science. The increasingly vital role of data, especially big data, in many applications, presents the field of statistics with unparalleled challenges and exciting opportunities. Statistics plays a pivotal role in data science by assisting with the use of data and decision making in the face","keywords":["biostatistics","causal inference","cloud computing","data visualization","distributed inference and learning","differential privacy","federated learning","integrative analysis","interpretable machine learning","replicability and reproducibility","scalable statistical inference","study design"],"image":"/media/remote/9490f1be3e08c579-720.webp","collections":["perspectives-on-data-and-data-science"],"label":"Panorama"},{"id":"c6577b1f-357b-43b9-81d1-71bda8c7ad0f","slug":"d9j96ne4","title":"Ten Research Challenge Areas in Data Science","dek":null,"authors":[{"name":"Jeannette M. Wing","slug":"jeannette-m-wing"}],"date":"2020-09-30","section":"panorama","issue":"volume2issue3","doi":"10.1162/99608f92.c6577b1f","abstract":"To drive progress in the field of data science, we propose 10 challenge areas for the research community to pursue. Since data science is broad, with methods drawing from computer science, statistics, and other disciplines, and with applications appearing in all sectors, these challenge areas speak to the breadth of issues spanning science, technology, and society. We preface our enumeration with meta-questions about","keywords":["artificial intelligence","causal reasoning","computing systems","data life cycle","deep learning","ethics","machine learning","privacy","trustworthiness"],"image":"/media/remote/02e43e3039eaaaf3-720.webp","collections":["perspectives-on-data-and-data-science"],"label":"Panorama"},{"id":"b68def92-627f-426f-9cc2-2b1f80119b2a","slug":"cbvbcp1i","title":"Toward a Research Agenda for Data Science: The Discussion Begins","dek":null,"authors":[{"name":"Jeannette M. Wing","slug":"jeannette-m-wing"}],"date":"2020-09-30","section":null,"issue":"volume2issue3","doi":"10.1162/99608f92.b68def92","abstract":null,"keywords":[],"image":"/media/remote/47964126bc0c6388-720.webp","collections":[],"label":"Featured Discussion"},{"id":"cfc5dd25-c1e6-408e-aac0-b81b0a1af8b1","slug":"u24wj42y","title":"Deep Learning With Gaussian Differential Privacy","dek":null,"authors":[{"name":"Zhiqi Bu","slug":"zhiqi-bu"},{"name":"Jinshuo Dong","slug":"jinshuo-dong"},{"name":"Qi Long","slug":"qi-long"},{"name":"Weijie Su","slug":"weijie-su"}],"date":"2020-09-30","section":"milestones-and-millstones","issue":"volume2issue3","doi":"10.1162/99608f92.cfc5dd25","abstract":"Deep learning models are often trained on data sets that contain sensitive information such as individuals’ shopping transactions, personal contacts, and medical records. An increasingly important line of work therefore has sought to train neural networks subject to privacy constraints that are specified by differential privacy or its divergence-based relaxations. These privacy definitions, however, have weaknesses i","keywords":["Gaussian differential privacy","deep learning","noisy gradient descent","central limit theorem","privacy accounting"],"image":"/media/remote/96bb06638a2fc757-720.webp","collections":["ai-and-responsible-data-science"],"label":"Milestones and Millstones"},{"id":"f97d1bbc-8be2-4b18-9b0e-315f6e337aab","slug":"r55za7kw","title":"A Call for Broader and Deeper Engagement in Data Science","dek":null,"authors":[{"name":"Xuming He","slug":"xuming-he"},{"name":"Xihong Lin","slug":"xihong-lin"}],"date":"2020-09-30","section":null,"issue":"volume2issue3","doi":"10.1162/99608f92.f97d1bbc","abstract":null,"keywords":[],"image":"/media/remote/16f63b0ce3f6d72d-720.webp","collections":[],"label":"Featured Discussion"},{"id":"6e525663-e095-4292-b5ce-44008b127164","slug":"f2cllynw","title":"Process and Purpose, Not Thing and Technique: How to Pose Data Science Research Challenges","dek":null,"authors":[{"name":"Robert Williamson","slug":"robert-williamson"}],"date":"2020-09-30","section":null,"issue":"volume2issue3","doi":"10.1162/99608f92.6e525663","abstract":null,"keywords":[],"image":"/media/remote/5f6b1bc3bc48c18a-720.webp","collections":[],"label":"Featured Discussion"},{"id":"a134847a-6ea9-4362-afac-308f1b7d5775","slug":"2dqxwu6u","title":"Training and Funding Pipelines for Data Science: The Need for a Common Core of Transdisciplinary Principles","dek":null,"authors":[{"name":"Shivani Agarwal","slug":"shivani-agarwal"}],"date":"2020-09-30","section":null,"issue":"volume2issue3","doi":"10.1162/99608f92.a134847a","abstract":null,"keywords":[],"image":"/media/remote/2af9ee90eca46c76-720.webp","collections":[],"label":"Featured Discussion"},{"id":"34d1d59e-f543-40e2-a9f6-df5e9e0d8a5d","slug":"kl2a5u8y","title":"Insightful Data Science","dek":null,"authors":[{"name":"Hongyu Zhao","slug":"hongyu-zhao"}],"date":"2020-09-30","section":null,"issue":"volume2issue3","doi":"10.1162/99608f92.34d1d59e","abstract":null,"keywords":[],"image":"/media/remote/a1b8ba0e705498dc-720.webp","collections":[],"label":"Featured Discussion"},{"id":"09889b4c-478c-4f0a-8897-d2367b83bf7b","slug":"ekrhsui8","title":"Stability Expanded, in Reality","dek":null,"authors":[{"name":"Bin Yu","slug":"bin-yu"}],"date":"2020-09-30","section":null,"issue":"volume2issue3","doi":"10.1162/99608f92.09889b4c","abstract":null,"keywords":[],"image":"/media/remote/2cb506b95c3cc1cb-720.webp","collections":[],"label":"Featured Discussion"},{"id":"b211611a-773e-4f53-87d3-219f513261da","slug":"mrf00vp9","title":"Early-Career View on Data Science Challenges: Responsibility, Rigor, and Accessibility","dek":null,"authors":[{"name":"Shuang Frost","slug":"shuang-frost"},{"name":"Aleksandrina Goeva","slug":"aleksandrina-goeva"},{"name":"William Seaton","slug":"william-seaton"},{"name":"Sara Stoudt","slug":"sara-stoudt"},{"name":"Ana Trisovic","slug":"ana-trisovic"}],"date":"2020-09-30","section":null,"issue":"volume2issue3","doi":"10.1162/99608f92.b211611a","abstract":null,"keywords":[],"image":"/media/remote/d9ba3bf4e9d50ac2-720.webp","collections":[],"label":"Featured Discussion"},{"id":"f09d7aa3-4714-48d6-a882-97fd91d9a1c6","slug":"r3tfouk7","title":"The Future of Data Science","dek":null,"authors":[{"name":"Sach Mukherjee","slug":"sach-mukherjee"},{"name":"Sylvia Richardson","slug":"sylvia-richardson-2"}],"date":"2020-09-30","section":null,"issue":"volume2issue3","doi":"10.1162/99608f92.f09d7aa3","abstract":null,"keywords":[],"image":"/media/remote/3fd1c74d29d6bc88-720.webp","collections":[],"label":"Featured Discussion"},{"id":"66fb00a2-5c55-462d-840b-6d0e26c81d35","slug":"9fnd629q","title":"Mathematics: The Tao of Data Science","dek":null,"authors":[{"name":"Tamara Kolda","slug":"tamara-kolda"}],"date":"2020-09-30","section":null,"issue":"volume2issue3","doi":"10.1162/99608f92.66fb00a2","abstract":null,"keywords":[],"image":"/media/remote/cc056b74efff74c7-720.webp","collections":[],"label":"Featured Discussion"},{"id":"4ff28438-a4fb-4b98-a5e9-41ba5a3dfb34","slug":"diub13so","title":"Top 10 Essential Data Science Topics to Real-World Application From the Industry Perspectives","dek":null,"authors":[{"name":"Victor S.Y. Lo","slug":"victor-lo"}],"date":"2020-09-30","section":null,"issue":"volume2issue3","doi":"10.1162/99608f92.4ff28438","abstract":null,"keywords":[],"image":"/media/remote/a328e25f2e8288c4-720.webp","collections":[],"label":"Featured Discussion"},{"id":"68bb12e4-21e5-493f-8301-b37cdd39d71a","slug":"zgoxm3ve","title":"Geo-Mapping of COVID-19 Risk Correlates Across Districts and Parliamentary Constituencies in India","dek":null,"authors":[{"name":"S V Subramanian","slug":"s-v-subramanian"},{"name":"Omar Karlsson","slug":"omar-karlsson"},{"name":"Weixing Zhang","slug":"weixing-zhang"},{"name":"Rockli Kim","slug":"rockli-kim"}],"date":"2020-09-21","section":null,"issue":"specialissue1","doi":"10.1162/99608f92.68bb12e4","abstract":"In the current stage of the COVID-19 pandemic, as countries open up after an extended period of lockdown, it is important to assure the population that their health is not being sacrificed. In this article, we develop a geomapping approach to identify high-risk areas by considering four nonclinical risk correlates for COVID-19. These are population density, percentage of the population that is exposed to crowding in ","keywords":["demographic and health Surveys","public policy","risk correlates","districts","parliamentary constituencies"],"image":"/media/remote/b857a897dde1d9aa-720.webp","collections":[],"label":"Article"},{"id":"dfca59ef-8e9d-46fe-916d-d805c81a87fc","slug":"qv5e8q8j","title":"Geo-Mapping of COVID-19 Risk Correlates in India: Supplementary Materials","dek":null,"authors":[{"name":"S V Subramanian","slug":"s-v-subramanian"},{"name":"Omar Karlsson","slug":"omar-karlsson"},{"name":"Weixing Zhang","slug":"weixing-zhang"},{"name":"Rockli Kim","slug":"rockli-kim"}],"date":"2020-09-21","section":null,"issue":"specialissue1","doi":null,"abstract":null,"keywords":[],"image":"/media/remote/5a7eaf160ae0933a-720.webp","collections":[],"label":"Article"},{"id":"b4bb506a-353e-4529-85a1-52982a59d1ec","slug":"gylaxji4","title":"SCRAM: A Platform for Securely Measuring Cyber Risk","dek":null,"authors":[{"name":"Leo de Castro","slug":"leo-de-castro"},{"name":"Andrew W. Lo","slug":"andrew-lo"},{"name":"Taylor Reynolds","slug":"taylor-reynolds"},{"name":"Fransisca Susan","slug":"fransisca-susan"},{"name":"Vinod Vaikuntanathan","slug":"vinod-vaikuntanathan"},{"name":"Daniel Weitzner","slug":"daniel-weitzner"},{"name":"Nicolas Zhang","slug":"nicolas-zhang"}],"date":"2020-09-16","section":"cornucopia","issue":"volume2issue3","doi":"10.1162/99608f92.b4bb506a","abstract":"We develop a new cryptographic platform called SCRAM (Secure Cyber Risk Aggregation and Measurement) that allows multiple entities to compute aggregate cyber-risk measures without requiring any entity to disclose its own sensitive data on cyberattacks, penetrations, and losses. Using the SCRAM platform, we present results from two computations in a pilot study with six large private-sector companies: (1) benchmarks o","keywords":["cyber risk","cybersecurity","cyber losses","cybersecurity policy","cybersecurity metrics","multiparty computation","privacy preserving risk computation"],"image":"/media/remote/96a4ebb9a8e1c9f0-720.webp","collections":["data-science-for-governing-and-policy-making"],"label":"Cornucopia"},{"id":"7a9f2f4d-b6a5-48a0-afb6-4d9122c99d47","slug":"wkhg4f7a","title":"Data Science for Everyone Starts in Kindergarten: Strategies and Initiatives From the American Statistical Association","dek":null,"authors":[{"name":"Wendy Martinez","slug":"wendy-martinez"},{"name":"Donna LaLonde","slug":"donna-lalonde-2"}],"date":"2020-09-15","section":"stepping-stones","issue":"volume2issue3","doi":"10.1162/99608f92.7a9f2f4d","abstract":"The American Statistical Association (ASA) is committed to supporting and enhancing statistics and data science education at all levels, from early grades to the PhD. Attitudes about statistics and mathematics form early, so it is critical to ensure that students in the elementary grades have positive and meaningful statistics and data science education experiences. Provided with the appropriate resources and support","keywords":["K–12 education","Common Core","mathematics","statistics","learning resources"],"image":"/media/remote/77fea86fa8852aad-720.webp","collections":["data-science-education"],"label":"Stepping Stones"},{"id":"828f40de-5a05-421b-94e6-f51732a522ed","slug":"ozgjx0yn","title":"Time Series Models Based on Growth Curves With Applications to Forecasting Coronavirus","dek":null,"authors":[{"name":"Andrew Harvey","slug":"andrew-harvey"},{"name":"Paul Kattuman","slug":"paul-kattuman"}],"date":"2020-08-25","section":null,"issue":"specialissue1","doi":"10.1162/99608f92.828f40de","abstract":"Time series models are developed for predicting future values of a variable that when cumulated is subject to an unknown saturation level. Such models are relevant for many disciplines, but here attention is focused on the spread of epidemics and the applications are for coronavirus. The time series models are relatively simple but are such that their specification can be assessed by standard statistical test procedu","keywords":["generalized logistic","Gompertz curve","Kalman filter","negative binomial distribution","score-driven models","stochastic trend"],"image":"/media/remote/112688961effb2a5-720.webp","collections":[],"label":"Article"},{"id":"ca11a017-a2a7-4118-b661-d47f79b66b2d","slug":"hakf37oi","title":"A Conversation on COVID-19 With Head of Statistics for BBC News Robert Cuffe","dek":"An interview with Robert Cuffe by Liberty Vittert and Xiao-Li Meng","authors":[{"name":"Robert Cuffe","slug":"robert-cuffe"},{"name":"Liberty Vittert","slug":"liberty-vittert"},{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2020-08-21","section":null,"issue":"specialissue1","doi":"10.1162/99608f92.ca11a017","abstract":"Editor-in-Chief Xiao-Li Meng and Media Feature Editor Liberty Vittert conducted a virtual dialogue on May 18, 2020, with Robert Cuffe, Head of Statistics for BBC News for an in-depth conversation about the challenges of communicating accurate statistics concerning COVID-19 to the general public. The three discussed the importance of data accuracy when trying to compare one country’s death rate to another, the regiona","keywords":[],"image":"/media/remote/2da09ad083aa55ff-720.webp","collections":[],"label":"Interview"},{"id":"e889ba8d-8e8e-435b-9bbd-95036fbd3707","slug":"8xzzjvbu","title":"The Question of When: The Oscars, Class Presentations, and Prom Dates","dek":null,"authors":[{"name":"Angelina Chen","slug":"angelina-chen"}],"date":"2020-08-07","section":"minding-the-future","issue":"volume2issue3","doi":"10.1162/99608f92.e889ba8d","abstract":null,"keywords":[],"image":"/media/remote/9b4730d26eedbd6d-720.webp","collections":[],"label":"Minding the Future"},{"id":"79e1f45e-bc08-4a5e-a962-be9287c0559a","slug":"qqg19a0r","title":"A Spatiotemporal Epidemiological Prediction Model to Inform County-Level COVID-19 Risk in the United States","dek":null,"authors":[{"name":"Yiwang Zhou","slug":"yiwang-zhou"},{"name":"Lili Wang","slug":"lili-wang"},{"name":"Leyao Zhang","slug":"leyao-zhang"},{"name":"Lan Shi","slug":"lan-shi"},{"name":"Kangping Yang","slug":"kangping-yang"},{"name":"Jie He","slug":"jie-he"},{"name":"Zhao Bangyao","slug":"zhao-bangyao"},{"name":"William Overton","slug":"william-overton"},{"name":"Soumik Purkayastha","slug":"soumik-purkayastha"},{"name":"Peter Song","slug":"peter-song"}],"date":"2020-08-06","section":null,"issue":"specialissue1","doi":"10.1162/99608f92.79e1f45e","abstract":"As the COVID-19 pandemic continues worsening in the United States, it is of critical importance to develop a health information system that provides timely risk evaluation and prediction of the COVID-19 infection in communities. We propose a spatiotemporal epidemiological forecast model that combines a spatial cellular automata (CA) with a temporal extended susceptible-antibody-infectious-removed (eSAIR) model under ","keywords":["cellular automata","coronavirus infectious disease","risk prediction","SAIR model"],"image":"/media/remote/0cf817a9f92c8e29-720.webp","collections":[],"label":"Article"},{"id":"3b87b6b0-3385-4a41-aeb5-7004bc8ee390","slug":"wjhth9tr","title":"The Importance of Being Causal","dek":null,"authors":[{"name":"Iavor Bojinov","slug":"iavor-bojinov"},{"name":"Albert Chen","slug":"albert-chen"},{"name":"Min Liu","slug":"min-liu"}],"date":"2020-07-30","section":"cornucopia","issue":"volume2issue3","doi":"10.1162/99608f92.3b87b6b0","abstract":"Causal inference is the study of how actions, interventions, or treatments affect outcomes of interest. The methods that have received the lion’s share of attention in the data science literature for establishing causation are variations of randomized experiments. Unfortunately, randomized experiments are not always feasible for a variety of reasons, such as an inability to fully control the treatment assignment, hig","keywords":["causal inference","observational studies","cross-sectional studies","panel studies","interrupted time-series","instrumental variables"],"image":"/media/remote/bfe104c8dcc82a2d-720.webp","collections":[],"label":"Cornucopia"},{"id":"a010feeb-7f56-421d-ad0e-e06db20a27f9","slug":"4vlrf0x2","title":"How to Define and Execute Your Data and AI Strategy","dek":null,"authors":[{"name":"Ulla Kruhse-Lehtonen","slug":"ulla-kruhse-lehtonen"},{"name":"Dirk Hofmann","slug":"dirk-hofmann"}],"date":"2020-07-30","section":"panorama","issue":"volume2issue3","doi":"10.1162/99608f92.a010feeb","abstract":"Over the past decade, many organizations have come to recognize that their future success will depend on data and AI (artificial intelligence) capabilities. Expectations are high and companies are heavily investing in the area. However, our experience advising organizations in diverse industries suggests that many have also become disillusioned in their journey to create companywide, data-driven business transformati","keywords":["data strategy","AI strategy","data-driven business transformation","AI leadership","digital transformation"],"image":"/media/remote/deff4420cb0c94fb-720.webp","collections":["ai-and-responsible-data-science","data-science-for-business-and-industry"],"label":"Panorama"},{"id":"a6699bda-4134-446f-87b9-b5dd2083a67e","slug":"ht5wwmz5","title":"Statistical Science: Some Current Challenges","dek":null,"authors":[{"name":"David R. Cox","slug":"david-r-cox"},{"name":"Christiana Kartsonaki","slug":"christiana-kartsonaki"},{"name":"Ruth H. Keogh","slug":"ruth-keogh"}],"date":"2020-07-30","section":"panorama","issue":"volume2issue3","doi":"10.1162/99608f92.a6699bda","abstract":"A broad review is given of the role of statistical concepts in the design of studies, in various aspects of data collection and definition and especially in the analysis and interpretation of data. Outline examples are given from various fields of application with some emphasis on epidemiology and medical statistics. The role of probability in various stages of investigation is outlined, in particular its place in th","keywords":["interpretation","metrology","probability","statistics","study design","uncertainty"],"image":"/media/remote/796df8972872937b-720.webp","collections":["perspectives-on-data-and-data-science"],"label":"Panorama"},{"id":"a3e88876-9f5e-4377-843d-c0675355af01","slug":"9ptj5iu7","title":"What Is Your List of 10 Challenges in Data Science?","dek":null,"authors":[{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2020-07-30","section":"editorscolumn","issue":"volume2issue3","doi":"10.1162/99608f92.a3e88876","abstract":null,"keywords":[],"image":"/media/remote/89f15d677929707a-720.webp","collections":["perspectives-on-data-and-data-science"],"label":"Editor’s Column"},{"id":"bfb344cc-bc8a-4852-868c-c964caae7bf7","slug":"3omret8r","title":"A Conversation With L. Rafael Reif on College of Computing, COVID-19, and the Future Workforce","dek":"An interview with L. Rafael Reif by Xiao-Li Meng and Liberty Vittert","authors":[{"name":"L. Rafael Reif","slug":"l-rafael-reif"},{"name":"Xiao-Li Meng","slug":"xiao-li-meng"},{"name":"Liberty Vittert","slug":"liberty-vittert"}],"date":"2020-07-30","section":"panorama","issue":"volume2issue3","doi":"10.1162/99608f92.bfb344cc","abstract":"On June 11, 2020, Harvard Data Science Review’s Editor-in-Chief, Xiao-Li Meng, and Media Feature Editor, Liberty Vittert, conducted an online interview with L. Rafael Reif, the 17th President of the Massachusetts Institute of Technology (MIT). President Reif joined the editors to discuss the newly established MIT Stephen A. Schwarzman College of Computing; the use of data behind the difficult decision-making process ","keywords":[],"image":"/media/remote/91ab5a127870a618-720.webp","collections":["conversations-with-leaders"],"label":"Panorama"},{"id":"eecdfa4e-8409-4b3d-8d49-6830720dac7b","slug":"uan1b4m9","title":"Prediction, Machine Learning, and Individual Lives: An Interview With Matthew Salganik","dek":"An interview with Matthew Salganik by Larien Maffeo and Cynthia Rudin","authors":[{"name":"Matthew J. Salganik","slug":"matthew-salganik"},{"name":"Lauren Maffeo","slug":"lauren-maffeo"},{"name":"Cynthia Rudin","slug":"cynthia-rudin"}],"date":"2020-07-30","section":"panorama","issue":"volume2issue3","doi":"10.1162/99608f92.eecdfa4e","abstract":"Machine learning techniques are increasingly used throughout society to predict individual’s life outcomes. However, research published in the Proceedings of the National Academy of Sciences raises questions about the accuracy of these predictions. Led by researchers at Princeton University, this mass collaboration involved 160 teams of data and social scientists building statistical and machine learning models to pr","keywords":["machine learning","data science","social science","predictive modeling","policy","interviews","life course","mass collaboration"],"image":"/media/remote/de0210a31f740a54-720.webp","collections":[],"label":"Panorama"},{"id":"caab8ba0-f3cc-4367-a860-af48ed1da4d4","slug":"sf1fi1p0","title":"Stop Flaunting Those Curves! Time for Stats to Get Down and Dirty With the Public","dek":null,"authors":[{"name":"Timandra Harkness","slug":"timandra-harkness"}],"date":"2020-07-30","section":"diving-into-data","issue":"volume2issue3","doi":"10.1162/99608f92.caab8ba0","abstract":null,"keywords":[],"image":"/media/remote/436764d329e11fe6-720.webp","collections":[],"label":"Diving into Data"},{"id":"05852aa8-89ad-42d8-bba3-d27bbce58d29","slug":"wjrl1qsq","title":"Recipes for Success: Data Science in the Home Kitchen","dek":null,"authors":[{"name":"Shuyang Li","slug":"shuyang-li"},{"name":"Julian McAuley","slug":"julian-mcauley"}],"date":"2020-07-30","section":"recreations-in-randomness","issue":"volume2issue3","doi":"10.1162/99608f92.05852aa8","abstract":null,"keywords":[],"image":"/media/remote/e2584c0d94c27ddc-720.webp","collections":[],"label":"Recreations in Randomness"},{"id":"b3fc5cc8-369a-47ba-bc8e-e4c4aa571cfc","slug":"1um18ajd","title":"Data Science and Cities: A Critical Approach","dek":null,"authors":[{"name":"Fábio Duarte","slug":"fabio-duarte"},{"name":"Priyanka deSouza","slug":"priyanka-desouza"}],"date":"2020-07-30","section":"bits-and-bites","issue":"volume2issue3","doi":"10.1162/99608f92.b3fc5cc8","abstract":"Sensors increasingly permeate our lives and generate a plethora of data, which has transformed the way we live in cities. Planners have been using data-science to improve our understanding of urban issues. While other domains have highlighted concerns with big data collection, aggregation, and analytical methods to understand different phenomena, urban planning has an additional aspiration: not only to understand, bu","keywords":["urban science","urban informatics","sensors","data analytics","data politics"],"image":"/media/remote/cb107d380947b3d5-720.webp","collections":["ai-and-responsible-data-science"],"label":"Bits and Bites"},{"id":"e9b6f961-69ca-42e2-88d0-aefb487f6562","slug":"2nyke6dy","title":"A Conversation on COVID-19 With Brief19 Editor-in-Chief and ER Physician Jeremy Faust","dek":"An interview with Jeremy Faust by Xiao-Li Meng and Liberty Vittert","authors":[{"name":"Jeremy Faust","slug":"jeremy-faust"},{"name":"Xiao-Li Meng","slug":"xiao-li-meng"},{"name":"Liberty Vittert","slug":"liberty-vittert"}],"date":"2020-07-24","section":null,"issue":"specialissue1","doi":"10.1162/99608f92.e9b6f961","abstract":"On May 11, 2020, The Harvard Data Science Review’s Editor-in-Chief, Xiao-Li Meng, and Media Feature Editor, Liberty Vittert, conducted a virtual interview with Dr. Jeremy Faust, emergency physician at Boston’s Brigham & Women’s Hospital to discuss his take on the data behind COVID-19. The conversation’s topics range from data quality when examining the number of cases and deaths, to the viable metrics behind when to ","keywords":[],"image":"/media/remote/c44efd88e42db179-720.webp","collections":[],"label":"Interview"},{"id":"f01ee285-c067-4b70-99dd-5d1f7e74206c","slug":"y9vc2u36","title":"On Identifying and Mitigating Bias in the Estimation of the COVID-19 Case Fatality Rate","dek":null,"authors":[{"name":"Anastasios Nikolas Angelopoulos","slug":"anastasios-angelopoulos"},{"name":"Reese Pathak","slug":"reese-pathak"},{"name":"Rohit Varma","slug":"rohit-varma"},{"name":"Michael I. Jordan","slug":"michael-i-jordan"}],"date":"2020-07-16","section":null,"issue":"specialissue1","doi":"10.1162/99608f92.f01ee285","abstract":"The relative case fatality rates (CFRs) between groups and countries are key measures of relative risk that guide policy decisions regarding scarce medical resource allocation during the ongoing COVID-19 pandemic. In the middle of an active outbreak when surveillance data is the primary source of information, estimating these quantities involves compensating for competing biases in time series of deaths, cases, and r","keywords":["case fatality rate and infection fatality rate","COVID-19","contact tracing","random sampling","unbiased estimation","infectious disease surveillance data"],"image":"/media/remote/97fe889cc39a1873-720.webp","collections":[],"label":"Article"},{"id":"1a99e67a-3b7e-4721-8eae-6af6a6c35898","slug":"6wx0qmkl","title":"Toward Foundations for Data Science and Analytics: A Knowledge Framework for Professional Standards","dek":null,"authors":[{"name":"Usama Fayyad","slug":"usama-fayyad"},{"name":"Hamit Hamutcu","slug":"hamit-hamutcu"}],"date":"2020-06-30","section":"panorama","issue":"volume2issue2","doi":"10.1162/99608f92.1a99e67a","abstract":"As the industry is racing to harness the power of data, demand for data science professionals is growing at an increasing rate. However, almost every organization has a unique way of defining roles in data science and associated skills and knowledge. This has resulted in a confusing industry landscape for employers, academic and training institutions, and existing and aspiring data science professionals. This article","keywords":["data science roles","skills and knowledge","hiring and assessment","industry standards","data analysis","data science","analytics"],"image":"/media/remote/78d3e3d2fe9eb9cb-720.webp","collections":["data-science-education","data-science-for-business-and-industry"],"label":"Panorama"},{"id":"60e08ed5-b65f-43b8-85b8-09c85faeff4a","slug":"r1qq01kw","title":"Predictions, Role of Interventions, and Effects of a Historic National Lockdown in India's Response to the COVID-19 Pandemic: Data Science Call to Arms","dek":null,"authors":[{"name":"Debashree Ray","slug":"debashree-ray"},{"name":"Maxwell Salvatore","slug":"maxwell-salvatore"},{"name":"Rupam Bhattacharyya","slug":"rupam-bhattacharyya"},{"name":"Lili Wang","slug":"lili-wang"},{"name":"Jiacong Du","slug":"jiacong-du"},{"name":"Shariq Mohammed","slug":"shariq-mohammed"},{"name":"Soumik Purkayastha","slug":"soumik-purkayastha"},{"name":"Aritra Halder","slug":"aritra-halder"},{"name":"Alexander Rix","slug":"alexander-rix"},{"name":"Daniel Barker","slug":"daniel-barker"},{"name":"Michael Kleinsasser","slug":"michael-kleinsasser"},{"name":"Yiwang Zhou","slug":"yiwang-zhou"},{"name":"Debraj Bose","slug":"debraj-bose"},{"name":"Peter Song","slug":"peter-song"},{"name":"Mousumi Banerjee","slug":"mousumi-banerjee"},{"name":"Veerabhadran Baladandayuthapani","slug":"veerabhadran-baladandayuthapani"},{"name":"Parikshit Ghosh","slug":"parikshit-ghosh"},{"name":"Bhramar Mukherjee","slug":"bhramar-mukherjee"}],"date":"2020-06-09","section":null,"issue":"specialissue1","doi":"10.1162/99608f92.60e08ed5","abstract":"With only 536 COVID-19 cases and 11 fatalities, India took the historic decision of a 21-day national lockdown on March 25, 2020. The lockdown was first extended to May 3 soon after the analysis of this article was completed, and then to May 18 while this article was being revised. In this article, we use a Bayesian extension of the susceptible-infected-removed (eSIR) model designed for intervention forecasting to st","keywords":["basic reproduction number","coronavirus","credible interval","India","intervention forecasting","SIR model"],"image":"/media/remote/68854caa7840e03d-720.webp","collections":[],"label":"Article"},{"id":"4bb9d7a7-d14f-477a-8cab-6911b249a286","slug":"as1p81um","title":"Tackling COVID-19 Through Responsible AI Innovation: Five Steps in the Right Direction","dek":null,"authors":[{"name":"David Leslie","slug":"david-leslie"}],"date":"2020-06-05","section":null,"issue":"specialissue1","doi":"10.1162/99608f92.4bb9d7a7","abstract":"Innovations in data science and artificial intelligence/machine learning (AI/ML) have a central role to play in supporting global efforts to combat COVID-19. The versatility of AI/ML technologies enables scientists and technologists to address an impressively broad range of biomedical, epidemiological, and socioeconomic challenges. This wide-reaching scientific capacity, however, also raises a diverse array of ethica","keywords":["COVID-19","AI ethics","responsible research and innovation","open science","digital contact tracing","public trust"],"image":"/media/remote/acce7077bd05cf97-720.webp","collections":["ai-and-responsible-data-science"],"label":"Article"},{"id":"268cc8e4-d741-4cd8-9b7a-57130b77c666","slug":"xsrt4zs2","title":"Doing Data Science on the Shoulders of Giants: The Value of Open Source Software for the Data Science Community","dek":null,"authors":[{"name":"Katie Malone","slug":"katie-malone"},{"name":"Rich Wolski","slug":"rich-wolski"}],"date":"2020-05-31","section":"panorama","issue":"volume2issue2","doi":"10.1162/99608f92.268cc8e4","abstract":"Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another. Open source projects, however, are disproportionately maintained by a small number of individuals, some of whom are institutionally supported, but many of whom do this maintenance on a purely volunteer basis. The health of the data science ecosystem depends on the support of open sou","keywords":["open source software","data science community","software licenses","computing"],"image":"/media/remote/e7bf1e8fa367348e-720.webp","collections":["open-data-science"],"label":"Panorama"},{"id":"a6e7a24e-5fc4-42cb-9b74-998ecde44e89","slug":"yrzpcrus","title":"Data, Data, Everywhere . . .","dek":null,"authors":[{"name":"Nicole Lazar","slug":"nicole-lazar"}],"date":"2020-05-27","section":"minding-the-future","issue":"volume2issue2","doi":"10.1162/99608f92.a6e7a24e","abstract":null,"keywords":[],"image":"/media/remote/1a0d0a2b356a6e3a-720.webp","collections":["data-science-education"],"label":"Minding the Future"},{"id":"962cb309-a4c4-40c2-b2f2-55b68ac0a6e5","slug":"5zxrvthz","title":"Error, Uncertainty, and the Shifting Ground of Census Data","dek":null,"authors":[{"name":"Dan Bouk","slug":"dan-bouk"}],"date":"2020-05-26","section":"mining-the-past","issue":"volume2issue2","doi":"10.1162/99608f92.962cb309","abstract":null,"keywords":[],"image":"/media/remote/5a225ba6e54d33a8-720.webp","collections":["data-science-for-governing-and-policy-making"],"label":"Mining the Past"},{"id":"7fa08812-6e0a-47a2-b0ee-eb84b488a33f","slug":"zp886sce","title":"A Conversation About COVID-19 With Economists, Sociologists, Statisticians, and Operations Researchers","dek":"An interview with Laura Albert, Jonathan Caulkins, Fiona Greig, and Adrian Raftery by David Banks with comments by Sylvia Frühwirth-Schnatter and Duncan Thomas","authors":[{"name":"David Banks","slug":"david-banks"},{"name":"Laura A. Albert","slug":"laura-a-albert"},{"name":"Jonathan P. Caulkins","slug":"jonathan-p-caulkins"},{"name":"Sylvia Frühwirth-Schnatter","slug":"sylvia-fruhwirth-schnatter"},{"name":"Fiona Greig","slug":"fiona-greig"},{"name":"Adrian Raftery","slug":"adrian-raftery"},{"name":"Duncan Thomas","slug":"duncan-thomas"}],"date":"2020-05-22","section":null,"issue":"specialissue1","doi":"10.1162/99608f92.7fa08812","abstract":"The COVID-19 pandemic is causing economic and social change. Moderated by David Banks, the Director of the Statistical and Applied Mathematical Sciences Institute (SAMSI), six eminent scientists who study different aspects of social change and public policy came together to discuss the impacts of the COVID-19 pandemic on the U.S. and the world. The discussion took a range of quantitative perspectives on how to respon","keywords":["cost-benefit","COVID-19","economic recovery","re-engineering work","social inequity"],"image":"/media/remote/c6765be68397098a-720.webp","collections":[],"label":"Interview"},{"id":"55546b4a-1643-417f-aeae-6994f12b8858","slug":"t37qjoi7","title":"Beyond Unicorns: Educating, Classifying, and Certifying Business Data Scientists","dek":null,"authors":[{"name":"Thomas Davenport","slug":"thomas-davenport"}],"date":"2020-05-19","section":"stepping-stones","issue":"volume2issue2","doi":"10.1162/99608f92.55546b4a","abstract":"There is increasing recognition that the data scientist ‘unicorn’—one who can master all the necessary skills of data science required by businesses—exists only rarely, if at all. Successful data science teams in business organizations, then, need to assemble people with a variety of different skills. This is only possible at scale with clear classification and certification of skills. While such certifications and c","keywords":["data science skills","data science job definitions","analytical skills","skill classification","certification"],"image":"/media/remote/b945099d13eb00f6-720.webp","collections":["data-science-education","data-science-for-business-and-industry"],"label":"Stepping Stones"},{"id":"1b77b932-599e-4938-aac1-7677b8283f92","slug":"l7a2t45s","title":"COVID-19: A Massive Stress Test With Many Unexpected Opportunities (for Data Science)","dek":null,"authors":[{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2020-05-14","section":"editorscolumn","issue":"specialissue1","doi":"10.1162/99608f92.1b77b932","abstract":null,"keywords":[],"image":"/media/remote/d84106de5d36b8d1-720.webp","collections":["perspectives-on-data-and-data-science"],"label":"Editor’s Column"},{"id":"7656c213-07c0-42e1-b25f-2fe19a441c69","slug":"non4rfk6","title":"Bayesian Adaptive Clinical Trials for Anti-Infective Therapeutics During Epidemic Outbreaks","dek":null,"authors":[{"name":"Shomesh Chaudhuri","slug":"shomesh-chaudhuri"},{"name":"Andrew W. Lo","slug":"andrew-lo"},{"name":"Danying Xiao","slug":"danying-xiao"},{"name":"Qingyang Xu","slug":"qingyang-xu"}],"date":"2020-05-14","section":null,"issue":"specialissue1","doi":"10.1162/99608f92.7656c213","abstract":"In the midst of epidemics such as COVID-19, therapeutic candidates are unlikely to be able to complete the usual multiyear clinical trial and regulatory approval process within the course of an outbreak. We apply a Bayesian adaptive patient-centered model—which minimizes the expected harm of false positives and false negatives—to optimize the clinical trial development path during such outbreaks. When the epidemic is","keywords":["COVID-19","Bayesian decision analysis","adaptive clinical trial","anti-infective therapeutics","vaccine","multigroup SEIR epidemic model"],"image":"/media/remote/dcb273dca9dd918f-720.webp","collections":[],"label":"Article"},{"id":"e0c150e8-8a20-4a91-97a0-f9ac0b356f36","slug":"pnp0pr4j","title":"Estimating Probabilities of Success of Vaccine and Other Anti-Infective Therapeutic Development Programs","dek":null,"authors":[{"name":"Andrew W. Lo","slug":"andrew-lo"},{"name":"Kien Wei Siah","slug":"kien-wei-siah"},{"name":"Chi Heem Wong","slug":"chi-heem-wong"}],"date":"2020-05-14","section":null,"issue":"specialissue1","doi":"10.1162/99608f92.e0c150e8","abstract":"A key driver in biopharmaceutical investment decisions is the probability of success of a drug development program. We estimate the probabilities of success (PoSs) of clinical trials for vaccines and other anti-infective therapeutics using 43,414 unique triplets of clinical trial, drug, and disease between January 1, 2000, and January 7, 2020, yielding 2,544 vaccine programs and 6,829 nonvaccine programs targeting in","keywords":["vaccines","infectious diseases","probabilities of success","randomized clinical trials","health care finance"],"image":"/media/remote/9165bc28d9ac996d-720.webp","collections":[],"label":"Article"},{"id":"a1f00368-b446-4dc4-8880-6c7f19baf8fe","slug":"9qtvk9qi","title":"A Conversation About COVID-19 With Biostatisticians and Epidemiologists","dek":"An interview with Susan Ellenberg, Thomas Fleming, M. Elizabeth Halloran, Andrew Lawson, and Lance Waller by David Banks","authors":[{"name":"David Banks","slug":"david-banks"},{"name":"Susan S. Ellenberg","slug":"susan-s-ellenberg"},{"name":"Thomas R. Fleming","slug":"thomas-r-fleming"},{"name":"M. Elizabeth Halloran","slug":"m-elizabeth-halloran"},{"name":"Andrew B. Lawson","slug":"andrew-b-lawson"},{"name":"Lance Waller","slug":"lance-waller"}],"date":"2020-05-14","section":null,"issue":"specialissue1","doi":"10.1162/99608f92.a1f00368","abstract":"The COVID-19 pandemic is an almost unprecedented challenge. Moderated by David Banks, Director of the Statistical and Applied Mathematical Sciences Institute (SAMSI), five leading biostatisticians and epidemiologists discuss the probable scope and duration of the pandemic, the kinds of medical responses that we need, and some of the impacts they foresee on the U.S. and on the world. They also discuss the pandemic’s l","keywords":["disease modeling","higher education","prevalence","sampling","spatio-temporal models"],"image":"/media/remote/fb0310cd297b932c-720.webp","collections":[],"label":"Interview"},{"id":"fec69745-43da-4dc9-97f0-79b783aa6336","slug":"imsfxwvi","title":"The Coronavirus Exponential: A Preliminary Investigation Into the Public's Understanding","dek":null,"authors":[{"name":"Alexander Podkul","slug":"alexander-podkul"},{"name":"Liberty Vittert","slug":"liberty-vittert"},{"name":"Scott Tranter","slug":"scott-tranter"},{"name":"Alex Alduncin","slug":"alex-alduncin"}],"date":"2020-05-14","section":null,"issue":"specialissue1","doi":"10.1162/99608f92.fec69745","abstract":"In this period of global uncertainty, the public is being inundated by information (and misinformation) from news sources, social media, and the community about the spread of COVID-19 and disease more generally. Moreover, unlike with most widespread news coverage, data and models are being used to explain the story by news organizations, health organizations, and governments. The reasons we are presently socially dis","keywords":["coronavirus","COVID-19","exponential growth","linear growth","public understanding","numerical literacy","statistical literacy"],"image":"/media/remote/05fff86b6d36a6e3-720.webp","collections":["data-science-education"],"label":"Article"},{"id":"a9c8d801-5638-45f9-99aa-6130182df26b","slug":"3sifzfua","title":"A Conversation With Larry Bacow on the Impact of COVID-19","dek":"An interview with Larry Bacow by Xiao-Li Meng and Liberty Vittert","authors":[{"name":"Lawrence Bacow","slug":"lawrence-bacow"},{"name":"Xiao-Li Meng","slug":"xiao-li-meng"},{"name":"Liberty Vittert","slug":"liberty-vittert"}],"date":"2020-05-14","section":null,"issue":"specialissue1","doi":"10.1162/99608f92.a9c8d801","abstract":"Harvard Data Science Review’s Editor-in-Chief, Xiao-Li Meng, and Media Feature Editor, Liberty Vittert, sat down with Lawrence Bacow, the 29th President of Harvard University. Following President Bacow’s COVID-19 recovery, he joined the editors to discuss the data used behind both the decisions to close and to open the university in stages, how economists contemplate the concept of risk and the value of life, and the","keywords":[],"image":"/media/remote/23a8ce0a5eb59ae0-720.webp","collections":["conversations-with-leaders"],"label":"Interview"},{"id":"834c6595-19ae-473c-bf05-4f2c8a91c037","slug":"7e1zrs70","title":"Data Science in Heavy Industry and the Internet of Things","dek":null,"authors":[{"name":"Michael Horrell","slug":"michael-horrell"},{"name":"Larry Reynolds","slug":"larry-reynolds"},{"name":"Adam McElhinney","slug":"adam-mcelhinney"}],"date":"2020-04-30","section":"panorama","issue":"volume2issue2","doi":"10.1162/99608f92.834c6595","abstract":"Increasingly cheap and available sensors enable new applications of data science for heavy industries. From locomotives to wind turbines to solar farms, data scientists close to these industries will be the first to attempt to turn the data collected on these machines into consistent sources of economic value. This article discusses an approach to framing industrial analytics problems and goes into detail on one prob","keywords":["Internet of Things (IoT)","industrial internet","failure prediction","prognostics","telematics","machine learning","predictive maintenance"],"image":"/media/remote/c047ee8b791124ae-720.webp","collections":["data-science-for-business-and-industry"],"label":"Panorama"},{"id":"7f90ce96-3a37-4252-94d3-f18892ee30fd","slug":"1vdc2z91","title":"Can Machine Learning Predict the Price of Art at Auction?","dek":null,"authors":[{"name":"Jason Bailey","slug":"jason-bailey"}],"date":"2020-04-30","section":"recreations-in-randomness","issue":"volume2issue2","doi":"10.1162/99608f92.7f90ce96","abstract":null,"keywords":[],"image":"/media/remote/0ad9a44a68e12b9a-720.webp","collections":[],"label":"Recreations in Randomness"},{"id":"ccd17b00-477f-4da0-aca9-6445e9f01327","slug":"enheggqr","title":"Measuring and Mapping Data Reuse: Findings From an Interactive Workshop on Data Citation and Metrics for Data Reuse","dek":null,"authors":[{"name":"Lisa Federer","slug":"lisa-federer"}],"date":"2020-04-30","section":"cornucopia","issue":"volume2issue2","doi":"10.1162/99608f92.ccd17b00","abstract":"Widely adopted standards for data citation are foundational to efforts to track and quantify data reuse. Without the means to track data reuse and metrics to measure its impact, it is difficult to reward researchers who share high-value data with meaningful credit for their contribution. Despite initial work on developing guidelines for data citation and metrics, standards have not yet been universally adopted. This ","keywords":["data citation","data metrics","data publication","open science","research data"],"image":"/media/remote/a2110567a7352b23-720.webp","collections":["open-data-science"],"label":"Cornucopia"},{"id":"03f00592-e90f-45b4-b056-538027bef199","slug":"qedrwcz3","title":"With Malice Toward None: Assessing Uncertainty via Equalized Coverage","dek":null,"authors":[{"name":"Yaniv Romano","slug":"yaniv-romano"},{"name":"Rina Foygel Barber","slug":"rina-foygel-barber"},{"name":"Chiara Sabatti","slug":"chiara-sabatti"},{"name":"Emmanuel Candès","slug":"emmanuel-candes"}],"date":"2020-04-30","section":"milestones-and-millstones","issue":"volume2issue2","doi":"10.1162/99608f92.03f00592","abstract":"An important factor to guarantee a fair use of data-driven recommendation systems is that we should be able to communicate their uncertainty to decision makers. This can be accomplished by constructing prediction intervals, which provide an intuitive measure of the limits of predictive performance. To support equitable treatment, we force the construction of such intervals to be unbiased in the sense that their cover","keywords":["conformal prediction","calibration","uncertainty quantification","fairness","quantile regression","unbiased predictions."],"image":"/media/remote/d38cbec59902013b-720.webp","collections":["ai-and-responsible-data-science"],"label":"Milestones and Millstones"},{"id":"c108a25b-6c92-4c33-be19-11e5d37d016a","slug":"s1mnsz41","title":"Information and Uncertainty: Two Sides of the Same Coin","dek":null,"authors":[{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2020-04-30","section":"editorscolumn","issue":"volume2issue2","doi":"10.1162/99608f92.c108a25b","abstract":null,"keywords":[],"image":"/media/remote/4bf7852703b11edb-720.webp","collections":["perspectives-on-data-and-data-science"],"label":"Editor’s Column"},{"id":"353c6f99-33c5-4ba1-ba5b-019036d17145","slug":"dgg03vo6","title":"Implementing Differential Privacy: Seven Lessons From the 2020 United States Census","dek":null,"authors":[{"name":"Michael B. Hawes","slug":"michael-hawes"}],"date":"2020-04-30","section":"panorama","issue":"specialissue2","doi":"10.1162/99608f92.353c6f99","abstract":"With the 2020 Census now underway, there is substantial national and global interest in the U.S. Census Bureau’s decision to modernize the statistical safeguards that will be used to protect respondent privacy. The Census Bureau’s adoption of differential privacy for the 2020 Census marks a transformational moment for official statistics. But, the transition to differential privacy has raised a number of questions ab","keywords":["census","differential privacy","disclosure avoidance","statistical infrastructure","official statistics","data privacy"],"image":"/media/remote/2f999e05a7c14ed2-720.webp","collections":["data-science-for-governing-and-policy-making"],"label":"Panorama"},{"id":"9f0215e6-10c7-42b3-a4aa-605c102c45da","slug":"twqhhlhr","title":"On the Convergence of Epidemiology, Biostatistics, and Data Science","dek":null,"authors":[{"name":"Neal D. Goldstein","slug":"neal-d-goldstein"},{"name":"Michael LeVasseur","slug":"michael-levasseur"},{"name":"Leslie A. McClure","slug":"leslie-a-mcclure"}],"date":"2020-04-30","section":"panorama","issue":"volume2issue2","doi":"10.1162/99608f92.9f0215e6","abstract":"Epidemiology, biostatistics, and data science are broad disciplines that incorporate a variety of substantive areas. Common among them is a focus on quantitative approaches for solving intricate problems. When the substantive area is health and health care, the overlap is further cemented. Researchers in these disciplines are fluent in statistics, data management and analysis, and health and medicine, to name but a f","keywords":["epidemiology","biostatistics","data science","training and education","causal inference","study design","electronic medical records"],"image":"/media/remote/a4b56eafde3d637b-720.webp","collections":["data-science-education"],"label":"Panorama"},{"id":"e38165eb-ef5f-463e-9ddc-e7ae48a9a4aa","slug":"gw3r97ht","title":"Lost or Found? Discovering Data Needed for Research","dek":null,"authors":[{"name":"Kathleen Gregory","slug":"kathleen-gregory"},{"name":"Paul Groth","slug":"paul-groth"},{"name":"Andrea Scharnhorst","slug":"andrea-scharnhorst"},{"name":"Sally Wyatt","slug":"sally-wyatt"}],"date":"2020-04-30","section":"cornucopia","issue":"volume2issue2","doi":"10.1162/99608f92.e38165eb","abstract":"Finding data is a necessary precursor to being able to reuse data, although relatively little large-scale empirical evidence exists about how researchers discover, make sense of, and (re)use data for research. This study presents evidence from the largest known survey investigating how researchers discover and use data that they do not create themselves. We examine the data needs and discovery strategies of responden","keywords":["data discovery","data search","data reuse","research practices","open data policy","research communities"],"image":"/media/remote/28b8703d57dbcd63-720.webp","collections":["open-data-science"],"label":"Cornucopia"},{"id":"a577296b-2eb3-4a9f-a2df-ea40c92654c1","slug":"mo0btsld","title":"Can the Coronavirus Prompt a Global Outbreak of “Distributional Thinking” in Organizations?","dek":null,"authors":[{"name":"Nathan E. Sanders","slug":"nathan-sanders"}],"date":"2020-04-30","section":"stepping-stones","issue":"volume2issue2","doi":"10.1162/99608f92.a577296b","abstract":"Media reporting and public discourse in the spring of 2020 has been dominated by the discussion of statistics relating to the COVID-19 outbreak and how to intepret them. Reasoning about these numbers has inspired fear as well as hope in communities worldwide. This environment provides a lens, and a rare scale, for data scientists to investigate how complex statistical topics are communicated to, understood by, and ac","keywords":["communication","industry","coronavirus","equity"],"image":"/media/remote/c6dba86d6ed87ceb-720.webp","collections":["data-science-education"],"label":"Stepping Stones"},{"id":"30a5e938-6a5c-48b4-a802-62fe98c9a8c4","slug":"3howud7k","title":"Lost or Found? Discovering Data Needed for Research: Supplementary Materials","dek":null,"authors":[{"name":"Kathleen Gregory","slug":"kathleen-gregory"},{"name":"Paul Groth","slug":"paul-groth"},{"name":"Andrea Scharnhorst","slug":"andrea-scharnhorst"},{"name":"Sally Wyatt","slug":"sally-wyatt"}],"date":"2020-04-30","section":null,"issue":"volume2issue2","doi":"10.1162/99608f92.30a5e938","abstract":null,"keywords":[],"image":"/media/remote/55f09a2d4d6e5c23-720.webp","collections":[],"label":"Article"},{"id":"6ed64b30-faeb-4c8c-98ca-7326b4d78815","slug":"7z10o269","title":"The Age of Secrecy and Unfairness in Recidivism Prediction","dek":null,"authors":[{"name":"Cynthia Rudin","slug":"cynthia-rudin"},{"name":"Caroline Wang","slug":"caroline-wang"},{"name":"Beau Coker","slug":"beau-coker"}],"date":"2020-03-31","section":"milestones-and-millstones","issue":"volume2issue1","doi":"10.1162/99608f92.6ed64b30","abstract":"In our current society, secret algorithms make important decisions about individuals. There has been substantial discussion about whether these algorithms are unfair to groups of individuals. While noble, this pursuit is complex and ultimately stagnating because there is no clear definition of fairness and competing definitions are largely incompatible. We argue that the focus on the question of fairness is misplaced","keywords":["transparency in predictive modeling","criminal justice","risk assessment","machine learning","trustworthiness"],"image":"/media/remote/e98fb0db42ad619a-720.webp","collections":["ai-and-responsible-data-science"],"label":"Milestones and Millstones"},{"id":"468468af-f7e8-4a98-a530-b059b2f259c6","slug":"dudgcmk3","title":"“Nothing Is More Opaque Than Absolute Transparency”: The Use of Prior History to Guide Sentencing","dek":null,"authors":[{"name":"Shawn D. Bushway","slug":"shawn-d-bushway"}],"date":"2020-03-31","section":"milestones-and-millstones","issue":"volume2issue1","doi":"10.1162/99608f92.468468af","abstract":null,"keywords":[],"image":"/media/remote/326ff73e1456e17e-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"b9343eec-ccd2-4d39-82f0-5c0138e9b760","slug":"xlfiu7za","title":"Transparency and Simplicity in Criminal Risk Assessment","dek":null,"authors":[{"name":"Alexandra Chouldechova","slug":"alexandra-chouldechova"}],"date":"2020-03-31","section":"milestones-and-millstones","issue":"volume2issue1","doi":"10.1162/99608f92.b9343eec","abstract":null,"keywords":[],"image":"/media/remote/736c764d5d71fb93-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"181cd09f-8b2d-404a-9084-542aee7218da","slug":"60jfy7hm","title":"The Role of Risk Assessment in the Criminal Justice System: Moving Beyond a Return to the Status Quo","dek":null,"authors":[{"name":"Sarah L. Desmarais","slug":"sarah-l-desmarais"}],"date":"2020-03-31","section":"milestones-and-millstones","issue":"volume2issue1","doi":"10.1162/99608f92.181cd09f","abstract":null,"keywords":[],"image":"/media/remote/748a82d85839556a-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"a6b843af-8e1e-4dbe-99a4-f0d515f10b58","slug":"p0wzaz31","title":"Justice in Forensic Algorithms","dek":null,"authors":[{"name":"Brandon L. Garrett","slug":"brandon-l-garrett"}],"date":"2020-03-31","section":"milestones-and-millstones","issue":"volume2issue1","doi":"10.1162/99608f92.a6b843af","abstract":null,"keywords":[],"image":"/media/remote/780b1d6ab92690df-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"1b3dadaa-49d0-4ed1-b9fb-cf11bb58decb","slug":"hzwo7ax4","title":"Setting the Record Straight: What the COMPAS Core Risk and Need Assessment Is and Is Not","dek":null,"authors":[{"name":"Eugenie Jackson","slug":"eugenie-jackson"},{"name":"Christina Mendoza","slug":"christina-mendoza"}],"date":"2020-03-31","section":"milestones-and-millstones","issue":"volume2issue1","doi":"10.1162/99608f92.1b3dadaa","abstract":null,"keywords":[],"image":"/media/remote/75b26b79f9a4e729-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"038c43fe-0204-466b-b875-a0f22c5f3525","slug":"8jy98s9q","title":"Broader Issues Surrounding Model Transparency in Criminal Justice Risk Scoring","dek":null,"authors":[{"name":"Cynthia Rudin","slug":"cynthia-rudin"},{"name":"Caroline Wang","slug":"caroline-wang"},{"name":"Beau Coker","slug":"beau-coker"}],"date":"2020-03-31","section":"milestones-and-millstones","issue":"volume2issue1","doi":"10.1162/99608f92.038c43fe","abstract":null,"keywords":[],"image":"/media/remote/61d6f0b8f2b3e08e-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"42d961b6-74da-4b3b-baad-b41f763f82a8","slug":"h7kdirec","title":"2019 Symposium","dek":"Schedule, Videos, and Slide Decks | Panels: Differential Privacy for U.S. 2020 Census, Redistricting in 2020, and Data Science Education: 2020 Visions and Versions","authors":[],"date":"2020-03-13","section":null,"issue":null,"doi":null,"abstract":null,"keywords":[],"image":"/media/remote/524c7c32bdc8ce6d-720.webp","collections":["2019-hdsr-symposium"],"label":"Article"},{"id":"2d83f7f5-2efe-4e13-89c6-cb886a459c72","slug":"hnptx6lq","title":"Doing Data Science: A Framework and Case Study","dek":null,"authors":[{"name":"Sallie Ann Keller","slug":"sallie-ann-keller"},{"name":"Stephanie S. Shipp","slug":"stephanie-s-shipp"},{"name":"Aaron D. Schroeder","slug":"aaron-d-schroeder"},{"name":"Gizem Korkmaz","slug":"gizem-korkmaz"}],"date":"2020-02-21","section":"cornucopia","issue":"volume2issue1","doi":"10.1162/99608f92.2d83f7f5","abstract":"Today’s data revolution is not just about big data, it is about data of all sizes and types. While the issues of volume and velocity presented by the ingestion of massive amounts of data remain prevalent, it is the rapidly developing challenges being presented by the third v, variety, that necessitates more attention. The need for a comprehensive approach to discover, access, repurpose, and statistically integrate al","keywords":["data science framework","data discovery","ethics","data acumen","workforce"],"image":"/media/remote/4a01baa37b91d03c-720.webp","collections":["perspectives-on-data-and-data-science"],"label":"Cornucopia"},{"id":"6230ce9f-9902-4717-b885-733cb8116b39","slug":"6lpet0sx","title":"Oscar Seasons: The Intersection of Data and the Academy Awards","dek":null,"authors":[{"name":"Ben Zauzmer","slug":"ben-zauzmer"}],"date":"2020-01-31","section":"recreations-in-randomness","issue":"volume2issue1","doi":"10.1162/99608f92.6230ce9f","abstract":null,"keywords":[],"image":"/media/remote/30d1cb423fdb5e76-720.webp","collections":[],"label":"Recreations in Randomness"},{"id":"2ce040dc-0ffe-49ec-b87d-2ce0eea64faf","slug":"bvzrp16k","title":"2020: A Very Busy Year for Data Science (and for HDSR)","dek":null,"authors":[{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2020-01-31","section":"editorscolumn","issue":"volume2issue1","doi":"10.1162/99608f92.2ce040dc","abstract":null,"keywords":[],"image":"/media/remote/6de6822bb7c1a481-720.webp","collections":[],"label":"Editor’s Column"},{"id":"c871f9e0-37de-41eb-b8cf-8642f48c975e","slug":"1g1cbvkv","title":"Coming to Our Census: How Social Statistics Underpin Our Democracy (and Republic)","dek":null,"authors":[{"name":"Teresa A. Sullivan","slug":"teresa-a-sullivan"}],"date":"2020-01-31","section":"panorama","issue":"specialissue2","doi":"10.1162/99608f92.c871f9e0","abstract":"The 2020 Census provides the opportunity to reflect on the key role statisticians, demographers, and other social scientists play in safeguarding American democracy. Democracy requires numbers for its proper functioning, and there is now a large statistical infrastructure of which the constitutionally mandated census is the keystone. Mistrust of the government is a major obstacle for the census, potentially affecting","keywords":[],"image":"/media/remote/8976eef61eb06c88-720.webp","collections":["data-science-for-governing-and-policy-making"],"label":"Panorama"},{"id":"63a22079-c310-4e1d-b0d0-22b17b222da1","slug":"g9o4z8au","title":"Differential Privacy and Social Science: An Urgent Puzzle","dek":null,"authors":[{"name":"Daniel L. Oberski","slug":"daniel-l-oberski"},{"name":"Frauke Kreuter","slug":"frauke-kreuter"}],"date":"2020-01-31","section":"panorama","issue":"specialissue2","doi":"10.1162/99608f92.63a22079","abstract":"Accessing and combining large amounts of data is important for quantitative social scientists, but increasing amounts of data also increase privacy risks. To mitigate these risks, important players in official statistics, academia, and business see a solution in the concept of differential privacy. In this opinion piece, we ask how differential privacy can benefit from social-scientific insights, and, conversely, how","keywords":["differential privacy","social science","data science","open data","robustness","data protection","confidentiality","GDPR"],"image":"/media/remote/f5bfac1a70ded6f8-720.webp","collections":["data-science-for-governing-and-policy-making"],"label":"Panorama"},{"id":"cb91a35a-2725-4d26-85a3-72581f7ac668","slug":"56lnenzj","title":"Should We Trust Algorithms?","dek":null,"authors":[{"name":"David Spiegelhalter","slug":"david-spiegelhalter"}],"date":"2020-01-31","section":"panorama","issue":"volume2issue1","doi":"10.1162/99608f92.cb91a35a","abstract":"There is increasing use of algorithms in the health care and criminal justice systems, and corresponding increased concern with their ethical use. But perhaps a more basic issue is whether we should believe what we hear about them and what the algorithm tells us. It is illuminating to distinguish between the trustworthiness of claims made about an algorithm, and those made by an algorithm, which reveals the potential","keywords":["trustworthiness","evaluation","AI","criminal justice system","medical AI","transparency"],"image":"/media/remote/1b5c3dab77239614-720.webp","collections":["ai-and-responsible-data-science"],"label":"Panorama"},{"id":"e2473f6a-00c0-43d7-803b-bf13a1b2146b","slug":"bd5k4gzf","title":"P-Values on Trial: Selective Reporting of (Best Practice Guides Against) Selective Reporting","dek":null,"authors":[{"name":"Deborah Mayo","slug":"deborah-mayo"}],"date":"2020-01-31","section":"panorama","issue":"volume2issue1","doi":"10.1162/99608f92.e2473f6a","abstract":"In an attempt to stem the practice of reporting impressive-looking findings based on data dredging and multiple testing, the American Statistical Association's (ASA) 2016 guide to interpreting p values (Wasserstein & Lazar) warns that engaging in such practices “renders the reported p-values essentially uninterpretable” (pp. 131-132). Yet some argue that the ASA statement actually frees researchers from culpability f","keywords":["statistical significance","p values","data dredging","multiple testing","ASA guide to p values","selective reporting"],"image":"/media/remote/7ef44f49cf87edb1-720.webp","collections":[],"label":"Panorama"},{"id":"5e126552-2e85-4924-8367-3b804cd8b642","slug":"i4eo09f0","title":"The Research Data Alliance: Benefits and Challenges of Building a Community Organization","dek":"An interview with Francine Berman by Mercè Crosas","authors":[{"name":"Francine Berman","slug":"francine-berman"},{"name":"Merce Crosas","slug":"merce-crosas"}],"date":"2020-01-31","section":"panorama","issue":"volume2issue1","doi":"10.1162/99608f92.5e126552","abstract":"The Research Data Alliance (RDA) is a community-driven organization dedicated to the development and use of technical, social, and community infrastructure promoting data sharing and data-driven exploration. RDA is particularly important for the global academic community where research infrastructure is often ad hoc, may have a short shelf-life, and hard to fund.At its launch in 2013, RDA struck a chord. Since then, ","keywords":["infrastructure","data sharing","interoperability","stewardship","research data","community organization","data management"],"image":"/media/remote/1d3d18edfe4a8b9a-720.webp","collections":["open-data-science"],"label":"Panorama"},{"id":"147cc28e-66ce-47d4-8824-2c0350ecd0fb","slug":"fxz7kr65","title":"How Confident Are We About Observational Findings in Health Care: A Benchmark Study","dek":null,"authors":[{"name":"Martijn J. Schuemie","slug":"martijn-j-schuemie"},{"name":"M. Soledad Cepeda","slug":"m-soledad-cepeda"},{"name":"Marc A. Suchard","slug":"marc-a-suchard"},{"name":"Jianxiao Yang","slug":"jianxiao-yang"},{"name":"Yuxi Tian","slug":"yuxi-tian"},{"name":"Alejandro Schuler","slug":"alejandro-schuler"},{"name":"Patrick B. Ryan","slug":"patrick-b-ryan"},{"name":"David Madigan","slug":"david-madigan"},{"name":"George Hripcsak","slug":"george-hripcsak"}],"date":"2020-01-31","section":"cornucopia","issue":"volume2issue1","doi":"10.1162/99608f92.147cc28e","abstract":"Health care professionals increasingly rely on observational health care data, such as administrative claims and electronic health records, to estimate the causal effects of interventions. However, limited prior studies raise concerns about the real-world performance of the statistical and epidemiological methods that are used. We present the “Observational Health Data Sciences and Informatics (OHDSI) Methods Benchma","keywords":["evaluation","methods","causal effect estimation","observational research"],"image":"/media/remote/ca932d19e545b394-720.webp","collections":[],"label":"Cornucopia"},{"id":"dd363929-7d24-4847-bfb4-53f4da1c9050","slug":"gg6swfqh","title":"The Role of Academia in Data Science Education","dek":null,"authors":[{"name":"Rafael A. Irizarry","slug":"rafael-a-irizarry"}],"date":"2020-01-31","section":"stepping-stones","issue":"volume2issue1","doi":"10.1162/99608f92.dd363929","abstract":"As the demand for data scientists continues to grow, universities are trying to figure out how to best contribute to the training of a workforce. However, there does not appear to be a consensus on the fundamental principles, expertise, skills, or knowledge-base needed to define an academic discipline. We argue that data science is not a discipline but rather an umbrella term used to describe a complex process involv","keywords":["applied statistics","data science","data science curriculum","data wrangling","machine learning","software engineering"],"image":"/media/remote/921d11a0a3fc0557-720.webp","collections":["data-science-education"],"label":"Stepping Stones"},{"id":"8ae4d525-0f9c-41a9-831d-ee89c8abf9ca","slug":"zok97i7p","title":"Why Is Data Visualization Important? What Is Important in Data Visualization?","dek":null,"authors":[{"name":"Antony Unwin","slug":"anthony-unwin"}],"date":"2020-01-31","section":"diving-into-data","issue":"volume2issue1","doi":"10.1162/99608f92.8ae4d525","abstract":null,"keywords":[],"image":"/media/remote/befd7a53cda3215f-720.webp","collections":[],"label":"Diving into Data"},{"id":"3e85b56a-eb87-4fdf-acf9-d973b05bd9cd","slug":"y7r65r4k","title":"Precision Medicine and Its Imprecise History","dek":null,"authors":[{"name":"Christopher J. Phillips","slug":"chris-phillips"}],"date":"2020-01-31","section":"mining-the-past","issue":"volume2issue1","doi":"10.1162/99608f92.3e85b56a","abstract":null,"keywords":[],"image":"/media/remote/5eee587513fc00d0-720.webp","collections":[],"label":"Mining the Past"},{"id":"c2fc310d-a911-43fb-a812-3a1600d5020e","slug":"bfeyfx22","title":"When Translation Problems Arise Between Data Scientists and Business Stakeholders, Revisit Your Metrics","dek":null,"authors":[{"name":"Katie Malone","slug":"katie-malone"}],"date":"2020-01-31","section":"active-industrial-learning","issue":"volume2issue1","doi":"10.1162/99608f92.c2fc310d","abstract":null,"keywords":[],"image":"/media/remote/23b9232be7d7be8a-720.webp","collections":["data-science-for-business-and-industry"],"label":"Active Industrial Learning"},{"id":"414caadb-9204-42bc-b6fe-d55a1e91700c","slug":"5pkkan15","title":"Measuring the Gross Domestic Product (GDP): The Ultimate Data Science Project","dek":null,"authors":[{"name":"Brian C. Moyer","slug":"brian-c-moyer"},{"name":"Abe Dunn","slug":"abe-dunn"}],"date":"2020-01-31","section":"effective-policy-learning","issue":"volume2issue1","doi":"10.1162/99608f92.414caadb","abstract":null,"keywords":[],"image":"/media/remote/f4813106ca0bab8f-720.webp","collections":["data-science-for-governing-and-policy-making"],"label":"Effective Policy Learning"},{"id":"cb0f8674-846a-4b63-b2ef-4c83e5b0bf44","slug":"vu6rc1yv","title":"Transparency, Statistics, and Justice System Knowledge Is Essential for Science of Risk Assessment","dek":null,"authors":[{"name":"Greg Ridgeway","slug":"greg-ridgeway"}],"date":"2020-01-31","section":"milestones-and-millstones","issue":"volume2issue1","doi":"10.1162/99608f92.cb0f8674","abstract":null,"keywords":[],"image":"/media/remote/30f2f3cdedd48945-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"490ac65e-45b9-4fb5-ab21-ef8dc3b5f32d","slug":"ofozx8tv","title":"Historical Lessons: How Statistics Underpin Our Democracy","dek":null,"authors":[{"name":"Margo J. Anderson","slug":"margo-j-anderson"}],"date":"2020-01-31","section":null,"issue":"specialissue2","doi":"10.1162/99608f92.490ac65e","abstract":null,"keywords":[],"image":"/media/remote/069d835648077a0a-720.webp","collections":[],"label":"Commentary"},{"id":"bac26d97-a705-401a-9862-5bd63664130c","slug":"5rdl7fyi","title":"The U.S. Census as a Crucible for Trust in Government","dek":null,"authors":[{"name":"Thomas R. Belin","slug":"thomas-r-belin"}],"date":"2020-01-31","section":null,"issue":"specialissue2","doi":"10.1162/99608f92.bac26d97","abstract":null,"keywords":[],"image":"/media/remote/1fe59185754dd85f-720.webp","collections":[],"label":"Commentary"},{"id":"b0966dd6-1f50-4f1a-b8ce-2c208e169c58","slug":"n6sp95q1","title":"Should the Census Have More Spine?","dek":null,"authors":[{"name":"Ray Chambers","slug":"ray-chambers"}],"date":"2020-01-31","section":null,"issue":"specialissue2","doi":"10.1162/99608f92.b0966dd6","abstract":null,"keywords":[],"image":"/media/remote/74f84989f9840dc3-720.webp","collections":[],"label":"Commentary"},{"id":"1a3cd97f-0258-447d-94d4-d5f107d5ba41","slug":"ec742hpw","title":"Are We Up to the Challenges of Protecting Federal Statistics?","dek":null,"authors":[{"name":"Constance F. Citro","slug":"constance-f-citro"}],"date":"2020-01-31","section":null,"issue":"specialissue2","doi":"10.1162/99608f92.1a3cd97f","abstract":null,"keywords":[],"image":"/media/remote/53d88237c02a8ce7-720.webp","collections":[],"label":"Commentary"},{"id":"8a0cc85c-dac3-4872-a352-18d8a08567f7","slug":"rosc6trb","title":"The Importance of Census 2020 and the Challenges of Getting a Complete Count","dek":null,"authors":[{"name":"Reynolds Farley","slug":"reynolds-farley"}],"date":"2020-01-31","section":null,"issue":"specialissue2","doi":"10.1162/99608f92.8a0cc85c","abstract":null,"keywords":[],"image":"/media/remote/dfef8a431e7787ef-720.webp","collections":[],"label":"Commentary"},{"id":"11f3e977-dfc0-4d32-bf4a-d1a6f3522be6","slug":"z4115tbw","title":"Distrust in the Governments Brings Risk to the Census","dek":null,"authors":[{"name":"Howard Hogan","slug":"howard-hogan"}],"date":"2020-01-31","section":null,"issue":"specialissue2","doi":"10.1162/99608f92.11f3e977","abstract":null,"keywords":[],"image":"/media/remote/484cd077651753ad-720.webp","collections":[],"label":"Commentary"},{"id":"aeb6e98b-2d82-42e1-b869-cd3d470a70b3","slug":"pfji5iif","title":"Statisticians’ Role in Ensuring Accuracy and Integrity of Federal Data","dek":null,"authors":[{"name":"Karen Kafadar","slug":"karen-kafadar"}],"date":"2020-01-31","section":null,"issue":"specialissue2","doi":"10.1162/99608f92.aeb6e98b","abstract":null,"keywords":[],"image":"/media/remote/5dc4847a3ec71c43-720.webp","collections":[],"label":"Commentary"},{"id":"2b99e39a-d7eb-4475-9799-545030e6f212","slug":"m0lpwb4x","title":"The Decennial Census and Congressional Apportionment","dek":null,"authors":[{"name":"Dudley L. Poston, Jr.","slug":"dudley-l-poston-jr"}],"date":"2020-01-31","section":null,"issue":"specialissue2","doi":"10.1162/99608f92.2b99e39a","abstract":null,"keywords":[],"image":"/media/remote/4ed3732595a2538c-720.webp","collections":[],"label":"Commentary"},{"id":"53fef7af-cb16-4e26-a5fd-6b8c668a3535","slug":"4v4jnt6g","title":"An Australian Perspective on Teresa Sullivan’s “Coming to Our Census”","dek":null,"authors":[{"name":"Dennis Trewin","slug":"dennis-trewin"}],"date":"2020-01-31","section":null,"issue":"specialissue2","doi":"10.1162/99608f92.53fef7af","abstract":null,"keywords":[],"image":"/media/remote/0dc6f35e390bb9d9-720.webp","collections":[],"label":"Commentary"},{"id":"addb8baf-3057-4aee-88d0-6a0979d400de","slug":"3saxaqb8","title":"“Coming to Our Census: How Social Statistics Underpin Our Democracy (and Republic)”: Author’s Response","dek":null,"authors":[{"name":"Teresa A. Sullivan","slug":"teresa-a-sullivan"}],"date":"2020-01-31","section":null,"issue":"specialissue2","doi":"10.1162/99608f92.addb8baf","abstract":null,"keywords":[],"image":"/media/remote/7f08287171b2b192-720.webp","collections":[],"label":"Commentary"},{"id":"282ad764-4a28-411a-8eb1-ddefeff2cb31","slug":"owxwohyz","title":"Beyond Translation: Language Hacking and Philology","dek":null,"authors":[{"name":"Gregory Crane","slug":"gregory-crane"}],"date":"2019-11-30","section":"milestones-and-millstones","issue":"volume1issue2","doi":"10.1162/99608f92.282ad764","abstract":"Among applications of machine learning, automatic language translation stands out for its spectacular gains from exploiting large data sets and its promise of cultural connection. Knowledge of a range of languages has been a marker of human erudition for centuries. The work described here outlines the rise of a third path, one situated between linguistic mastery and reliance upon translation. Manually annotated sourc","keywords":["digital humanities","digital classics","philology","translation studies","digital reading","smart texts","semantic annotations","language technologies"],"image":"/media/remote/81941a77badf30b7-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"5a8a3a3d-d7a0-43ab-a8ff-9db892ebaf87","slug":"f9kuryi8","title":"Why Are We Using Black Box Models in AI When We Don’t Need To? A Lesson From an Explainable AI Competition","dek":null,"authors":[{"name":"Cynthia Rudin","slug":"cynthia-rudin"},{"name":"Joanna Radin","slug":"joanna-radin"}],"date":"2019-11-22","section":"bits-and-bites","issue":"volume1issue2","doi":"10.1162/99608f92.5a8a3a3d","abstract":"In 2018, a landmark challenge in artificial intelligence (AI) took place, namely, the Explainable Machine Learning Challenge. The goal of the competition was to create a complicated black box model for the dataset and explain how it worked. One team did not follow the rules. Instead of sending in a black box, they created a model that was fully interpretable. This leads to the question of whether the real world of ma","keywords":["interpretability","explainability","machine learning","finance"],"image":"/media/remote/7b1119e22d9e4a3f-720.webp","collections":["ai-and-responsible-data-science"],"label":"Bits and Bites"},{"id":"c9267215-9708-4e43-9bb4-0a9c1d8359b6","slug":"rye9y1w0","title":"Data Science as a Foundation for Inclusive Learning","dek":null,"authors":[{"name":"Robert A. Lue","slug":"robert-lue"}],"date":"2019-11-19","section":"stepping-stones","issue":"volume1issue2","doi":"10.1162/99608f92.c9267215","abstract":"STEM (science, technology, engineering, and mathematics) fields such as computer science and statistics have traditionally struggled to increase participation from underrepresented groups such as women and minorities. And while progress has been made in developing more inclusive classroom pedagogies and programs, rates of representation have remained low over the past 20 years. The emerging field of data science with","keywords":["education","inclusive teaching","pedagogy","STEM","curriculum","statistics","computer science"],"image":"/media/remote/feb0ed5356594ac1-720.webp","collections":["data-science-education"],"label":"Stepping Stones"},{"id":"fc14bf2d-fb55-4cea-9291-a66fd4ab5c7d","slug":"jduhd7og","title":"Uses and Reuses of Scientific Data: The Data Creators’ Advantage","dek":null,"authors":[{"name":"Irene V. Pasquetto","slug":"irene-pasquetto"},{"name":"Christine L. Borgman","slug":"christine-l-borgman"},{"name":"Morgan F. Wofford","slug":"morgan-wofford"}],"date":"2019-11-15","section":"milestones-and-millstones","issue":"volume1issue2","doi":"10.1162/99608f92.fc14bf2d","abstract":"Open access to data, as a core principle of open science, is predicated on assumptions that scientific data can be reused by other researchers. We test those assumptions by asking where scientists find reusable data, how they reuse those data, and how they interpret data they did not collect themselves. By conducting a qualitative meta-analysis of evidence on two long-term, distributed, interdisciplinary consortia, w","keywords":["data","science","reuse","biomedicine","environmental sciences","open science","data practices","science policy"],"image":"/media/remote/7354294233c1e352-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"2bb0a53a-f7ab-4879-80d7-e399410eac7d","slug":"tn4j86t1","title":"Uses and Reuses of Scientific Data: The Data Creators’ Advantage: Supplementary Materials","dek":null,"authors":[{"name":"Irene V. Pasquetto","slug":"irene-pasquetto"},{"name":"Christine L. Borgman","slug":"christine-l-borgman"},{"name":"Morgan F. Wofford","slug":"morgan-wofford"}],"date":"2019-11-15","section":null,"issue":"volume1issue2","doi":null,"abstract":null,"keywords":[],"image":"/media/remote/6641459eb2de8f5b-720.webp","collections":[],"label":"Article"},{"id":"ab81d0a9-5177-4b96-832a-35289a697d46","slug":"hx3pgxjc","title":"Five Immersive 3D Surroundings of Data Science","dek":null,"authors":[{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2019-11-01","section":"editorscolumn","issue":"volume1issue2","doi":"10.1162/99608f92.ab81d0a9","abstract":null,"keywords":[],"image":"/media/remote/534815dea26a47e6-720.webp","collections":["perspectives-on-data-and-data-science"],"label":"Editor’s Column"},{"id":"77e63f8f-4166-46df-b787-3307ead1a65e","slug":"2mkokrzb","title":"Deep Policy Learning: Opportunities and Challenges From the Evidence Act","dek":null,"authors":[{"name":"Nancy Potok","slug":"nancy-potok"}],"date":"2019-11-01","section":"panorama","issue":"volume1issue2","doi":"10.1162/99608f92.77e63f8f","abstract":null,"keywords":[],"image":"/media/remote/f55517ddbc49a9bc-720.webp","collections":["data-science-for-governing-and-policy-making"],"label":"Panorama"},{"id":"e0eaabfc-9a64-4c09-9088-d8508744eee2","slug":"5irjez4q","title":"What Does It Take to Be a Successful Data Scientist?","dek":null,"authors":[{"name":"Michael R. Berthold","slug":"michael-berthold"}],"date":"2019-11-01","section":"panorama","issue":"volume1issue2","doi":"10.1162/99608f92.e0eaabfc","abstract":"Given recent claims that data science can be fully automated or made accessible to nondata scientists through easy-to-use tools, I describe different types of data science roles within an organization. I then provide a view on the required skill sets of successful data scientists and how they can be obtained, concluding that data science requires both a profound understanding of the underlying methods as well as exha","keywords":["data science","analytics","practitioner","education","insights","discovery"],"image":"/media/remote/ca3fa58c218d00e3-720.webp","collections":["data-science-for-business-and-industry"],"label":"Panorama"},{"id":"f9a1e510-28c2-4e53-8cc0-f83c351e8343","slug":"iu26pfw1","title":"Data Have a Limited Shelf Life","dek":null,"authors":[{"name":"Stephen M. Stigler","slug":"steven-stigler"}],"date":"2019-11-01","section":"panorama","issue":"volume1issue2","doi":"10.1162/99608f92.f9a1e510","abstract":"Data, unlike some wines, do not improve with age. The contrary view, that data are immortal, a view that may underlie the often-observed tendency to recycle old examples in texts and presentations, is illustrated with three classical examples and rebutted by further examination. Some general lessons for data science are noted, as well as some history of statistical worries about the effect of data selection on induct","keywords":["dead data","zombie data","post-selection inference","history"],"image":"/media/remote/3c6d8ebb45a0be94-720.webp","collections":["perspectives-on-data-and-data-science"],"label":"Panorama"},{"id":"4a9eef8d-1501-477b-8d75-f41dd26bf9c4","slug":"qkgyckh8","title":"Immersive Collaboration on Data Science for Intelligence Analysis","dek":"An interview with Alyson Wilson and Matthew Schmidt by Lara Schmidt and Brent Winter","authors":[{"name":"Alyson Wilson","slug":"alyson-wilson"},{"name":"Matthew Schmidt","slug":"matthew-schmidt"},{"name":"Lara Schmidt","slug":"lara-schmidt"},{"name":"Brent Winter","slug":"brent-winter"}],"date":"2019-11-01","section":"panorama","issue":"volume1issue2","doi":"10.1162/99608f92.4a9eef8d","abstract":"In 2013, the National Security Agency (NSA) founded the Laboratory for Analytic Sciences (LAS) at North Carolina State University (NCSU) to help the Intelligence Community (IC) address the growing complexity of big data challenges. The goal of LAS is to partner experts and practitioners from academia, government, and industry to create tools and techniques that help intelligence analysts provide better information to","keywords":["anticipatory thinking","collaboration","innovation","intelligence community","structured analytic techniques"],"image":"/media/remote/e8c684c899c3d82b-720.webp","collections":["data-science-for-governing-and-policy-making"],"label":"Panorama"},{"id":"e45fcb79-e6b0-45f0-a960-00511877eaff","slug":"cdkvbb76","title":"Highlights of the Inaugural Data Science Leadership Summit","dek":"An interview with Jeanette M. Wing by David Banks","authors":[{"name":"Jeannette M. Wing","slug":"jeannette-m-wing"},{"name":"David Banks","slug":"david-banks"}],"date":"2019-11-01","section":"panorama","issue":"volume1issue2","doi":"10.1162/99608f92.e45fcb79","abstract":"On March 26, 2018, the Data Science Institute at Columbia University hosted the inaugural Data Science Leadership Summit. It drew together a cross-section of academic researchers and administrators to discuss how university practices could best adapt to the emerging field of data science. The summit was co-funded by the National Science Foundation, the Gordon and Betty Moore Foundation, and the Alfred P. Sloan Founda","keywords":["data science institute","education units","inter-disciplinary","multi-disciplinary","university administration"],"image":"/media/remote/613f8e0f90ab6ffc-720.webp","collections":["perspectives-on-data-and-data-science"],"label":"Panorama"},{"id":"6fdaa9de-a01b-428f-b750-592bfbdc8bd0","slug":"18lm7jrp","title":"Medications and Suicide: High Dimensional Empirical Bayes Screening (iDEAS)","dek":null,"authors":[{"name":"Robert Gibbons","slug":"robert-gibbons"},{"name":"Kwan Hur","slug":"kwan-hur"},{"name":"Jill Lavigne","slug":"jill-lavigne"},{"name":"Jiebiao Wang","slug":"jiebiao-wang"},{"name":"J. John Mann","slug":"j-john-mann"}],"date":"2019-11-01","section":"cornucopia","issue":"volume1issue2","doi":"10.1162/99608f92.6fdaa9de","abstract":"The rate of suicide has been rising for 16 years and it is the 10th leading cause of death in the United States. Most suicides occur in the context of a psychiatric disorder (Bachmann, 2018), and yet the effect of many medications, including psychotropic medications, on suicide risk is intensely debated. The objective of this study is to develop a statistical surveillance methodology based on generalized mixed-effect","keywords":["empirical Bayes","drug safety","suicide"],"image":"/media/remote/b01e8f1dbc883648-720.webp","collections":[],"label":"Cornucopia"},{"id":"ed353ae3-f786-4312-a93a-968666b0d74f","slug":"0mb0zzlc","title":"Change Through Data: A Data Analytics Training Program for Government Employees","dek":null,"authors":[{"name":"Frauke Kreuter","slug":"frauke-kreuter"},{"name":"Rayid Ghani","slug":"rayid-ghani"},{"name":"Julia Lane","slug":"julia-lane"}],"date":"2019-11-01","section":"stepping-stones","issue":"volume1issue2","doi":"10.1162/99608f92.ed353ae3","abstract":"From education to health to criminal justice, government regulation and policy decisions have important effects on social and individual experiences. New data science tools applied to data created by government agencies have the potential to enhance these meaningful decisions. However, certain institutional barriers limit the realization of this potential. First, we need to provide systematic training of government e","keywords":["training programs","evidence-based policy","confidential data","administrative data research facility","government data"],"image":"/media/remote/e365f7d31f75e2c3-720.webp","collections":["data-science-education","data-science-for-business-and-industry","data-science-for-governing-and-policy-making"],"label":"Stepping Stones"},{"id":"84deb5c4-c9dc-4517-8165-bb072f503596","slug":"8fm8lo1e","title":"Data Linkage: The Big Picture","dek":null,"authors":[{"name":"Peter Christen","slug":"peter-christen-2"}],"date":"2019-11-01","section":"diving-into-data","issue":"volume1issue2","doi":"10.1162/99608f92.84deb5c4","abstract":null,"keywords":[],"image":"/media/remote/6c051a630505a6d4-720.webp","collections":[],"label":"Diving into Data"},{"id":"5c483119-2233-4013-bd35-c4402fe2f9d6","slug":"3adoxb26","title":"The Bases of Data","dek":null,"authors":[{"name":"Christopher J. Phillips","slug":"chris-phillips"}],"date":"2019-11-01","section":"mining-the-past","issue":"volume1issue2","doi":"10.1162/99608f92.5c483119","abstract":null,"keywords":[],"image":null,"collections":[],"label":"Mining the Past"},{"id":"70ccf88a-4285-4e3c-9c74-c2402462ce8a","slug":"3cfhdct9","title":"Medications and Suicide: High Dimensional Empirical Bayes Screening (iDEAS): Supplementary File","dek":null,"authors":[{"name":"Robert Gibbons","slug":"robert-gibbons"},{"name":"Kwan Hur","slug":"kwan-hur"},{"name":"Jill Lavigne","slug":"jill-lavigne"},{"name":"Jiebiao Wang","slug":"jiebiao-wang"},{"name":"J. John Mann","slug":"j-john-mann"}],"date":"2019-11-01","section":null,"issue":"volume1issue2","doi":null,"abstract":null,"keywords":[],"image":"/media/remote/ded49fbc9e5685af-720.webp","collections":[],"label":"Article"},{"id":"130f856e-38db-4586-a498-4debbfe370b1","slug":"xcq8a1v1","title":"(A) Data in the Life: Authorship Attribution in Lennon-McCartney Songs","dek":null,"authors":[{"name":"Mark Glickman","slug":"mark-glickman"},{"name":"Jason Brown","slug":"jason-brown"},{"name":"Ryan Song","slug":"ryan-song"}],"date":"2019-07-02","section":"cornucopia","issue":"volume1issue1","doi":"10.1162/99608f92.130f856e","abstract":"The songwriting duo of John Lennon and Paul McCartney, the two founding members of the Beatles, composed some of the most popular and memorable songs of the last century. Despite having authored songs under the joint credit agreement of Lennon-McCartney, it is well-documented that most of their songs or portions of songs were primarily written by exactly one of the two. Furthermore, the authorship of some Lennon-McCa","keywords":["authorship","elastic net","logistic regression","music","regularization","stylometry","variable screening"],"image":"/media/remote/95755440ad658256-720.webp","collections":[],"label":"Cornucopia"},{"id":"4a85af74-c703-4027-87ad-e958cab03060","slug":"9qsbf3hz","title":"What Is the Purpose of Statistical Modeling?","dek":null,"authors":[{"name":"David Hand","slug":"david-hand"}],"date":"2019-07-01","section":"diving-into-data","issue":"volume1issue1","doi":"10.1162/99608f92.4a85af74","abstract":null,"keywords":[],"image":"/media/remote/34f5681b09494f77-720.webp","collections":[],"label":"Diving into Data"},{"id":"9a36bdb6-a5ea-4db8-a0e2-d05c42a59e31","slug":"4giycvvj","title":"The Lives and After Lives of Data","dek":null,"authors":[{"name":"Christine L. Borgman","slug":"christine-l-borgman"}],"date":"2019-07-01","section":"panorama","issue":"volume1issue1","doi":"10.1162/99608f92.9a36bdb6","abstract":"The most elusive term in data science is ‘data.’ While often treated as objects to be computed upon, data is a theory-laden concept with a long history. Data exist within knowledge infrastructures that govern how they are created, managed, and interpreted. By comparing models of data life cycles, implicit assumptions about data become apparent. In linear models, data pass through stages from beginning to end of life,","keywords":["astronomy","curation","data","digital curation","life cycles","observations","preservation","reuse","science","stewardship"],"image":"/media/remote/85af2672fd56dcff-720.webp","collections":["perspectives-on-data-and-data-science"],"label":"Panorama"},{"id":"e26845b4-9621-4b94-911e-381c41c7c037","slug":"577rq08d","title":"The Data Life Cycle","dek":null,"authors":[{"name":"Jeannette M. Wing","slug":"jeannette-m-wing"}],"date":"2019-07-01","section":"panorama","issue":"volume1issue1","doi":"10.1162/99608f92.e26845b4","abstract":"To put data science in context, we present phases of the data life cycle, from data generation to data interpretation. These phases transform raw bits into value for the end user. Data science is thus much more than data analysis, e.g., using techniques from machine learning and statistics; extracting this value takes a lot of work, before and after data analysis. Moreover, data privacy and data ethics need to be con","keywords":["analysis","collection","data life cycle","ethics","generation","interpretation","management","privacy","storage","story-telling","visualization"],"image":"/media/remote/2041afebad048509-720.webp","collections":["perspectives-on-data-and-data-science"],"label":"Panorama"},{"id":"5c5f0525-dd50-4ea2-886a-7db64633131a","slug":"ct67j043","title":"Machine Learning With Statistical Imputation for Predicting Drug Approvals","dek":null,"authors":[{"name":"Andrew W. Lo","slug":"andrew-lo"},{"name":"Kien Wei Siah","slug":"kien-wei-siah"},{"name":"Chi Heem Wong","slug":"chi-heem-wong"}],"date":"2019-07-01","section":"cornucopia","issue":"volume1issue1","doi":"10.1162/99608f92.5c5f0525","abstract":"We apply machine-learning techniques to predict drug approvals using drug-development and clinical-trial data from 2003 to 2015 involving several thousand drug-indication pairs with over 140 features across 15 disease groups. To deal with missing data, we use imputation methods that allow us to fully exploit the entire dataset, the largest of its kind. We show that our approach outperforms complete-case analysis, whi","keywords":["drug development","clinical trials","healthcare","machine learning","imputation"],"image":"/media/remote/5eb32f17896da582-720.webp","collections":[],"label":"Cornucopia"},{"id":"440445cb-c7f6-4745-84ae-cda098cee35f","slug":"v9fdn7n7","title":"The Hiring Gambit: In Search of the Twofer Data Scientist","dek":null,"authors":[{"name":"Radu V. Craiu","slug":"radu-craiu"}],"date":"2019-07-01","section":"bits-and-bites","issue":"volume1issue1","doi":"10.1162/99608f92.440445cb","abstract":null,"keywords":[],"image":"/media/remote/84ea6ce0ab4215ba-720.webp","collections":[],"label":"Bits and Bites"},{"id":"88ba42cb-5bc6-477c-bc9f-eb3f114a14b5","slug":"pjl0jtkp","title":"Data Science: What the Educated Citizen Needs to Know","dek":null,"authors":[{"name":"Alan M. Garber","slug":"alan-m-garber"}],"date":"2019-07-01","section":"panorama","issue":"volume1issue1","doi":"10.1162/99608f92.88ba42cb","abstract":null,"keywords":[],"image":"/media/remote/c0d97bffe7434e64-720.webp","collections":["data-science-education"],"label":"Panorama"},{"id":"8cd550d1-bf27-4bfe-9a2c-cc788afb0201","slug":"l0jsh9d1","title":"A Unified Framework of Five Principles for AI in Society","dek":null,"authors":[{"name":"Luciano Floridi","slug":"luciano-floridi"},{"name":"Josh Cowls","slug":"john-cowls"}],"date":"2019-07-01","section":"milestones-and-millstones","issue":"volume1issue1","doi":"10.1162/99608f92.8cd550d1","abstract":"Artificial Intelligence (AI) is already having a major impact on society. As a result, many organizations have launched a wide range of initiatives to establish ethical principles for the adoption of socially beneficial AI. Unfortunately, the sheer volume of proposed principles threatens to overwhelm and confuse. How might this problem of ‘principle proliferation’ be solved? In this paper, we report the results of a ","keywords":["accountability","autonomy","artificial intelligence","beneficence","ethics","explicability","fairness","intelligibility","justice","non-maleficence."],"image":"/media/remote/c624ec3b3f7ab99f-720.webp","collections":["ai-and-responsible-data-science"],"label":"Milestones and Millstones"},{"id":"92fe150c-fac7-4098-9868-5c7a24d4cb43","slug":"0aytgrau","title":"Artificial Intelligence","dek":null,"authors":[{"name":"Stephanie Dick","slug":"stephanie-dick"}],"date":"2019-07-01","section":"mining-the-past","issue":"volume1issue1","doi":"10.1162/99608f92.92fe150c","abstract":null,"keywords":[],"image":"/media/remote/632a0c94c4d47f8a-720.webp","collections":["ai-and-responsible-data-science"],"label":"Mining the Past"},{"id":"ff7ca64e-f682-4a23-96fc-4efebf0059a9","slug":"djb16hzl","title":"Statistics and the Oncoming AI Revolution","dek":null,"authors":[{"name":"Emmanuel Candès","slug":"emmanuel-candes"},{"name":"John Duchi","slug":"john-duchi"},{"name":"Chiara Sabatti","slug":"chiara-sabatti"}],"date":"2019-07-01","section":null,"issue":"volume1issue1","doi":"10.1162/99608f92.ff7ca64e","abstract":null,"keywords":[],"image":"/media/remote/29e051b00f15f225-720.webp","collections":[],"label":"Featured Discussion"},{"id":"9f99661b-e953-4656-9612-8213eb80a39a","slug":"pjebp7io","title":"Why Artificial Intelligence May Not Be as Useful or as Challenging as Artificial Stupidity","dek":null,"authors":[{"name":"Andrew W. Lo","slug":"andrew-lo"}],"date":"2019-07-01","section":null,"issue":"volume1issue1","doi":"10.1162/99608f92.9f99661b","abstract":null,"keywords":[],"image":"/media/remote/8252ad4e9ea37412-720.webp","collections":[],"label":"Featured Discussion"},{"id":"ee412edd-76b1-4a6b-a95e-8dc213a811d8","slug":"4s0269ne","title":"Taking Up the Revolutionary Call: Principles to Guide a Purpose-Driven AI Future","dek":null,"authors":[{"name":"Brendan McCord","slug":"brendan-mccord"}],"date":"2019-07-01","section":null,"issue":"volume1issue1","doi":"10.1162/99608f92.ee412edd","abstract":null,"keywords":[],"image":"/media/remote/3d2f59e9633dfe81-720.webp","collections":[],"label":"Featured Discussion"},{"id":"364ce476-879e-4f72-a98c-232858681925","slug":"u2econxe","title":"Artificial Intelligence Versus Intelligence Engineering","dek":null,"authors":[{"name":"Max Welling","slug":"max-welling"}],"date":"2019-07-01","section":null,"issue":"volume1issue1","doi":"10.1162/99608f92.364ce476","abstract":null,"keywords":[],"image":"/media/remote/ff7d427090437016-720.webp","collections":[],"label":"Featured Discussion"},{"id":"98280d4a-6132-49ce-a85f-d43b7f0e67c2","slug":"plto69bh","title":"Engineering Perspectives on AI","dek":null,"authors":[{"name":"Rebecca Willett","slug":"rebecca-willett"}],"date":"2019-07-01","section":null,"issue":"volume1issue1","doi":"10.1162/99608f92.98280d4a","abstract":null,"keywords":[],"image":"/media/remote/de933e0405632561-720.webp","collections":[],"label":"Featured Discussion"},{"id":"17405bb6-9bf4-42f6-8237-5a338e7a850f","slug":"4ovhpe3v","title":"Data Governance is Key to Interpretation: Reconceptualizing Data in Data Science","dek":null,"authors":[{"name":"Sabina Leonelli","slug":"sabina-leonelli"}],"date":"2019-07-01","section":"panorama","issue":"volume1issue1","doi":"10.1162/99608f92.17405bb6","abstract":"I provide a philosophical perspective on the characteristics of data-centric research and the conceptualization of data that underpins it. The transformative features of contemporary data science derive not only from the availability of Big Data and powerful computing, but also from a fundamental shift in the conceptualization of data as research materials and sources of evidence. A relational view of data is propose","keywords":["data philosophy","data history","data-centric research","inference","data management","data curation","modelling."],"image":"/media/remote/34df71664e967922-720.webp","collections":["perspectives-on-data-and-data-science"],"label":"Panorama"},{"id":"7ef56abe-989a-49c3-ad5a-773feaad29b8","slug":"tgl23krp","title":"Challenges for Human-Level Intelligence","dek":null,"authors":[{"name":"Rodney Brooks","slug":"rodney-brooks"}],"date":"2019-07-01","section":null,"issue":"volume1issue1","doi":"10.1162/99608f92.7ef56abe","abstract":null,"keywords":[],"image":"/media/remote/29968e3c440a91ca-720.webp","collections":[],"label":"Featured Discussion"},{"id":"f06c6e61-5f54-4739-8012-8954a55c4d38","slug":"wot7mkc1","title":"Artificial Intelligence—The Revolution Hasn’t Happened Yet","dek":null,"authors":[{"name":"Michael I. Jordan","slug":"michael-i-jordan"}],"date":"2019-07-01","section":"panorama","issue":"volume1issue1","doi":"10.1162/99608f92.f06c6e61","abstract":null,"keywords":[],"image":"/media/remote/3d07b656b579fd1f-720.webp","collections":["ai-and-responsible-data-science"],"label":"Panorama"},{"id":"b9006d09-835d-4a83-93fd-cf7bee335b49","slug":"2imtstfu","title":"Dr. AI or: How I Learned to Stop Worrying and Love Economics","dek":null,"authors":[{"name":"Michael I. Jordan","slug":"michael-i-jordan"}],"date":"2019-07-01","section":null,"issue":"volume1issue1","doi":"10.1162/99608f92.b9006d09","abstract":null,"keywords":[],"image":"/media/remote/af3b76f8264416ec-720.webp","collections":[],"label":"Featured Discussion"},{"id":"97b95546-f3bf-491c-94a2-3f025c200c25","slug":"wiq01ru6","title":"The AI Revolution Needs Expertise in People, Publics, and Societies","dek":null,"authors":[{"name":"Barbara J. Grosz","slug":"barbara-j-grosz"}],"date":"2019-07-01","section":null,"issue":"volume1issue1","doi":"10.1162/99608f92.97b95546","abstract":null,"keywords":[],"image":"/media/remote/0285aa43d12e5a52-720.webp","collections":[],"label":"Featured Discussion"},{"id":"0c23c330-3ff5-433f-965a-53c6033cbdcb","slug":"pqddq8u5","title":"AI: A New Engineering Discipline or the Alchemy of Our Time?","dek":null,"authors":[{"name":"Maria Fasli","slug":"maria-fasli"}],"date":"2019-07-01","section":null,"issue":"volume1issue1","doi":"10.1162/99608f92.0c23c330","abstract":null,"keywords":[],"image":"/media/remote/8f98d7acee7f02d2-720.webp","collections":[],"label":"Featured Discussion"},{"id":"38f16b68-b8a2-4b3e-a8c6-00ab2b59cefa","slug":"z4sb5j9l","title":"Highlights of the National Academies Report on \"Undergraduate Data Science: Opportunities and Options”","dek":"An interview with Laura Haas and Alfred Hero by Robert Lue","authors":[{"name":"Laura Haas","slug":"laura-haas"},{"name":"Alfred Hero","slug":"alfred-hero"},{"name":"Robert A. Lue","slug":"robert-lue"}],"date":"2019-07-01","section":"stepping-stones","issue":"volume1issue1","doi":"10.1162/99608f92.38f16b68","abstract":"In 2016, the National Academies of Sciences, Engineering, and Medicine of the United States established the Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective. The committee issued a 120-page report in 2018, setting forth a vision for the undergraduate education in data science. This interview of the co-chairs of the committee, Laura Haas, the Dean of the College of Information and Co","keywords":["data ccumen","data ethics","data science curriculum","data science programs","digital-age education","liberal arts education"],"image":"/media/remote/c9290a70d0163098-720.webp","collections":["data-science-education"],"label":"Stepping Stones"},{"id":"644ef4a4-5a71-43f8-9bcd-f2f6cb92ea65","slug":"a7gxkn0a","title":"A Balanced Perspective on Prediction and Inference for Data Science in Industry","dek":null,"authors":[{"name":"Nathan E. Sanders","slug":"nathan-sanders"}],"date":"2019-07-01","section":"panorama","issue":"volume1issue1","doi":"10.1162/99608f92.644ef4a4","abstract":"The strategic role of data science teams in industry is fundamentally to help businesses to make smarter decisions. This includes decisions on minuscule scales, such as what fraction of a cent to bid on an ad placement displayed in a web browser, whose importance is only manifest when scaled by orders of magnitude through machine automation. But it also extends to singular, monumental decisions made by businesses, su","keywords":["industry","entertainment","communication","inference","bibliometrics"],"image":"/media/remote/3555c015a52fe9ee-720.webp","collections":["data-science-for-business-and-industry"],"label":"Panorama"},{"id":"ddc4d18e-f3be-4476-8d7b-c234a7202621","slug":"esfh2s4w","title":"What Kinds of Intelligent Machines Really Make Life Better?","dek":null,"authors":[{"name":"Maja Matarić","slug":"maja-mataric"}],"date":"2019-07-01","section":null,"issue":"volume1issue1","doi":"10.1162/99608f92.ddc4d18e","abstract":null,"keywords":[],"image":"/media/remote/0bb53716aa094ad3-720.webp","collections":[],"label":"Featured Discussion"},{"id":"02ffc552-efc0-4c4e-8807-aa43f273d73d","slug":"9mdn32tq","title":"Ambitious Data Science Can Be Painless","dek":null,"authors":[{"name":"Hatef Monajemi","slug":"hatef-monajemi"},{"name":"Riccardo Murri","slug":"riccardo-murri"},{"name":"Eric Jonas","slug":"eric-jonas"},{"name":"Percy Liang","slug":"percy-liang"},{"name":"Victoria Stodden","slug":"victoria-stodden"},{"name":"David Donoho","slug":"david-donoho"}],"date":"2019-07-01","section":"milestones-and-millstones","issue":"volume1issue1","doi":"10.1162/99608f92.02ffc552","abstract":"Modern data science research, at the cutting edge, can involve massive computational experimentation; an ambitious PhD in computational fields may conduct experiments consuming several million CPU hours. Traditional computing practices, in which researchers use laptops, PCs, or campus-resident resources with shared policies, are awkward or inadequate for experiments at the massive scale and varied scope that we now s","keywords":["ambitious data science","painless computing stacks","cloud computing","experiment management system","massive computational experiments"],"image":"/media/remote/2e4073c644539107-720.webp","collections":[],"label":"Milestones and Millstones"},{"id":"e32f6dec-ec0a-48a4-91ab-fd06df0dba54","slug":"kyzf7fjv","title":"AI, Language, and the Humanities","dek":null,"authors":[{"name":"Gregory Crane","slug":"gregory-crane"}],"date":"2019-07-01","section":null,"issue":"volume1issue1","doi":"10.1162/99608f92.e32f6dec","abstract":null,"keywords":[],"image":"/media/remote/6a347892af8577c3-720.webp","collections":[],"label":"Featured Discussion"},{"id":"ba20f892-36e0-4fad-9c34-6e3fbf906cb3","slug":"jhy4g6eg","title":"Data Science: An Artificial Ecosystem","dek":null,"authors":[{"name":"Xiao-Li Meng","slug":"xiao-li-meng"}],"date":"2019-07-01","section":"editorscolumn","issue":"volume1issue1","doi":"10.1162/99608f92.ba20f892","abstract":null,"keywords":[],"image":"/media/remote/4771efd013211257-720.webp","collections":["perspectives-on-data-and-data-science"],"label":"Editor’s Column"},{"id":"c698b3a7-c9df-49f4-9ae8-188e62705aaa","slug":"rim3pvdw","title":"What’s Missing From Today’s Machine Intelligence Juggernaut?","dek":null,"authors":[{"name":"David Donoho","slug":"david-donoho"}],"date":"2019-07-01","section":null,"issue":"volume1issue1","doi":"10.1162/99608f92.c698b3a7","abstract":null,"keywords":[],"image":"/media/remote/8d87c9c69bd4ea8f-720.webp","collections":[],"label":"Featured Discussion"},{"id":"2a3160be-8ad4-45e0-8cd0-6f0775ac53ed","slug":"4tx7h11w","title":"Machine Learning with Statistical Imputation for Predicting Drug Approvals: Supplementary Materials","dek":null,"authors":[{"name":"Andrew W. Lo","slug":"andrew-lo"},{"name":"Kien Wei Siah","slug":"kien-wei-siah"},{"name":"Chi Heem Wong","slug":"chi-heem-wong"}],"date":"2019-07-01","section":null,"issue":"volume1issue1","doi":"10.1162/99608f92.5c5f0525/2a3160be","abstract":null,"keywords":[],"image":"/media/remote/a86747490197baa3-720.webp","collections":[],"label":"Article"}]