Content Guidelines

HDSR aims to publish the highest quality articles in data science in at least the following four categories.


  • Commentaries, overviews and debates pertaining to the history, state-of-the-art, grand challenges, and opportunities in major areas of data science, with attention to their broad societal and policy-related implications (e.g., artificial intelligence vs human intelligence; cybersecurity; algorithms in society; GDPR and its global impact).  These articles could provide the content for compact multimedia learning modules designed for broad reuse as case studies or illustrations in online and on-campus courses.


  • Fundamental philosophical, theoretical, and methodological research, especially that which reflects deeply and broadly the synergistic nature of data science, engaging issues such as---just to name a few---the tradeoff between computational and statistical efficiency; digital humanities; data privacy versus data utility; deep learning versus deep understanding; learning causality from massive online data; data curation and provenance; information governance; FAIR (findable, accessible, interoperable, re-usable) data; algorithm fairness and accountability; data flows and markets; research reproducibility, replicability and triangulation.


  • Advances, challenges, and opportunities in learning, teaching, and communicating data science, especially those that are innovative (e.g., the Berkeley “connector” courses) and those that can have broad and lasting impact, as well as major pedagogical research findings such as the effectiveness of online courses and how they inform residential learning and vice versa. These articles could provide educational frameworks, course syllabi, and programmatic curricula for advancing data science education at all levels, from data literacy to pre-colleague education to university programs and to executive training.


  • Major innovations, practices and knowledge transfers of data science in industry, government, NGOs, such as those pertaining to—again to name a small sample---AI, personalized health and medical advances; nano technologies; biomedical engineering; business analytics; social policy; economic developments; smart cities; food supply; human rights; counter terrorism; international relationships; climate change and environmental protections; consumer products and retail; sports and entertainment industry; voting and election systems. Preferences are given to those cases that reflect the principled application of data science methods, generating philosophical, ethical, theoretical, methodological, and/or pedagogical challenges and insights, and those upon which educational case studies can be built effectively (as inspired by Harvard Business Review).