As an open access platform of the Harvard Data Science Initiative, Harvard Data Science Review (HDSR) features foundational thinking, research milestones, educational innovations, and major applications, with a primary emphasis on reproducibility, replicability, and readability. We aim to publish content that helps define and shape data science as a scientifically rigorous and globally impactful multidisciplinary field based on the principled and purposed production, processing, parsing, and analysis of data. By uniting the strengths of a premier research journal, a cutting-edge educational publication, and a popular magazine, HDSR provides a crossroads at which fundamental data science research and education intersect directly with societally-important applications from industry, governments, NGOs, and others. By disseminating inspiring, informative, and intriguing articles and media materials, HDSR aspires to be a global forum on everything data science and data science for everyone.

HDSR prioritizes quality over quantity, with a primary emphasis on reproducibility, replicability, and readability; attracting readers via inspiring, informative, and intriguing articles; and authors via our multi-domain reader outreach platform, speedy review process, and flexibility in presentation styles. Creative presentations are especially encouraged. We leverage digital technologies and advances to emphasize multimedia dissemination, learning-oriented visualization, enhanced author-reader interactions, and a global presence. In particular, our editorial board, authors, and external referees are drawn globally with the intention to progressively increase coverage and diversity. Submissions are by invitation only after a screening process. HDSR’s publications will be in a variety of forms, including research articles, discussion papers, special columns, interviews, conversations, tutorials, short essays, personal stories, and letters to the editor. HDSR publishes quarterly in digital forms, and also in print for special issues and collections.