The Harvard Data Science Initiative and The MIT Press launch the HARVARD DATA SCIENCE REVIEW

The Harvard Data Science Initiative and The MIT Press launch the HARVARD DATA SCIENCE REVIEW

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The Harvard Data Science Initiative (HDSI) and the MIT Press are pleased to announce today the launch of the Harvard Data Science Review (HDSR). The multimedia platform will feature leading global thinkers in the burgeoning field of data science, making research, educational resources, and commentary accessible to academics, professionals, and the interested public. With demand for data scientists booming, HDSR will provide a centralized, authoritative, and peer-reviewed publishing community to service the growing profession.

The first issue features articles on topics ranging from authorship attribution of Lennon-McCartney songs to machine learning models for predicting drug approvals to artificial intelligence (AI). Future content will have a similar range of general interest, academic, and professional content intended to foster dialogue among researchers, educators, and practitioners about data science research, practice, literacy, and workforce development. HDSR will prioritize quality over quantity, with a primary emphasis on substance and readability, attracting readers via inspiring, informative, and intriguing papers, essays, stories, interviews, debates, guest columns, and data science news. By doing so, HDSR intends to help define and shape the profession as a scientifically rigorous and globally impactful multidisciplinary field.

Combining features of a premier research journal, a leading educational publication, and a popular magazine, HDSR will leverage digital technologies and advances to facilitate author-reader interactions globally and learning across various media.

Harvard Data Science Review Key Features:

  • The Harvard Business Review has called data science the “sexiest job of the 21st century” and theHarvard Data Science Review will serve as a hub for high-quality work in this growing field.
  • Features contributions from leading thinkers with direct applications for teaching, research, business, government, and more.
  • Publishes articles that provide expert overviews of complex ideas and topics.
  • Includes content in the following categories:
    • Commentaries, overviews, and debates intended for a wide readership
    • Fundamental philosophical, theoretical, and methodological research
    • Innovations and advances in learning, teaching, and communicating data science
    • Short communications and letters to the editor
  • The dynamic digital edition is available open access on the PubPub platform to readers around the globe.

Francesca Dominici, Clarence James Gamble Professor of Biostatistics, Population and Data Science, and David Parkes, George F. Colony Professor of Computer Science, both at Harvard, announce, “As codirectors of the Harvard Data Science Initiative, we’re thrilled for the launch of this new journal. With its rigorous and cross-disciplinary thinking, the Harvard Data Science Review will advance the new science of data. By sharing stories of positive transformational impact as well as raising questions, this collective endeavor will reveal the contours that will shape future research and practice.”

Xiao-li Meng, Whipple V.N. Jones Professor of Statistics at Harvard and founding editor-in-chief of HDSR, explains, “The revolutionary ability to collect, process, and apply new analytics to extract powerful insights from data has a tremendous influence on our lives. However, hype and misinformation have emerged as unfortunate side effects of data science’s meteoric rise. The Harvard Data ScienceReview is designed to cut through the hype to engage readers with substantive and informed articles from the leading data science experts and practitioners, ranging from philosophers of ethics and historians of science to AI researchers and data science educators. In short, it is ‘everything data science and data science for everyone.’”

Amy Brand, director of the MIT Press, adds, “For too long the important work of data scientists has been opaque, appearing mainly in academic journals with limited reach. We are thrilled to partner with the Harvard Data Science Initiative to publish work that will have a deep impact on popular understanding of the growing field of data science. The Review will be an unparalleled resource for advancing data literacy in society.”

The inaugural issue of HDSR will publish contributions from internationally renowned scholars and educators, as well as leading researchers in industry and government, such as Christine Borgman(UCLA), Rodney Brooks (MIT), Emmanuel Candes (Stanford University), David Donoho (Stanford University), Luciano Floridi (Oxford/The Alan Turing Institute), Alan M. Garber (Harvard), Barbara J. Grosz (Harvard), Alfred Hero (University of Michigan), Sabina Leonelli (University of Exeter), Michael I. Jordan (University of California, Berkeley), Andrew Lo (MIT), Maja Matarić (University of Southern California), Brendan McCord (US Department of Defense), Nathan Sanders (Warner Media), Rebecca Willett (University of Chicago), and Jeannette Wing (Columbia University).

Sample articles in the first issue:

  • A trio of articles on “Data Life Cycle” by Christine Borgman, UCLA, Sabina Leonelli University of Exeter, and Jeannette Wing, Columbia University 
  • A Data in the Life: Authorship Attribution in Lennon-McCartney Songs” by Mark Glickman, Harvard University; Jason Brown, Dalhousie University; Ryan Song, Harvard University
  • Machine-learning with Statistical Imputation for Predicting Drug Approvals” by Andrew W. Lo, MIT; Kien Wei Siah, MIT; and Chi Heem Wong, MIT

Featured articles on AI:

  • Artificial Intelligence—The Revolution Hasn’t Happened Yet” by Michael I. Jordan, University of California, Berkeley, with 11 discussants from pioneering roboticists to leading AI researchers and Jordan’s rejoinder.
  • A Unified Framework of Five Principles for AI in Society”­ by Luciano Floridi and Josh Cowls, University of Oxford and The Alan Turing Institute
  • Mining the Past: Artificial Intelligence”, by Stephanie Dick, University of Pennsylvania
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