Please pardon any cross-posting.

The Harvard Data Science Initiative (HDSI) and the MIT Press have just launched the Harvard Data Science Review (HDSR), hosted on PubPub an open source community platform. Combining features of a premier research journal, a leading educational publication, and a popular magazine, HDSR provides a centralized, authoritative, and peer-reviewed publishing community to service the growing profession.

The first issue of the freely available digital edition 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.

Sample articles in the inaugural 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

●      “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

●      “Highlights of the National Academics Report on ‘Data Science for Undergraduates: Opportunities and Options’” Laura Haas, University of Massachusetts, and Alfred Hero, University of Michigan, interviewed by Rob Lue, Harvard University

●      “A Balanced Perspective on Prediction and Inference for Data Science in Industry” Nathan E. Sanders, WarnerMedia Applied Analytics

Read the full issue now at hdsr.mitpress.mit.edu.

Thanks,

Heather