Harvard Data Science Review is committed to publishing top-quality content and conducting top-quality review of all submitted work (including all column articles). Our editorial board reflects a wide range of areas of expertise to facilitate our mission of publishing everything data science, and data science for everyone.

HDSR publishes four regular issues per year, plus special issues, on the digital platform. We operate as an open access platform and are the inaugural publication of the Harvard Data Science Initiative.

Our platform publishes a wide range of content, including full articles, perspectives, columns, interviews, tutorials, pedagogical materials, and more, as highlighted below.

The complex data science ecosystem requires wide-ranging discussions and in-depth introspection from all the varieties of its inhabitants in order to ensure its harmonious evolution. Panorama content aims to provide a global forum for broad overviews, deep reflections, inspiring visions, insightful blueprints, and informative debates stimulated by fresh perspectives from leaders and builders from all walks of data science. The forum aims to intrigue and engage experts and the public alike about the evolution of data science and its consequences, both positive and negative, in a thought-provoking and hype-resisting manner.  

The main source of nourishment to the ever-evolving data science ecosystem comes from the direct and broad impact it has on the world. Cornucopia content showcases impactful applications and innovative implementations of data science theory and methods to solve problems of importance to human society and nature, and to address issues of intellectual and general interest. It prioritizes articles where the knowledge transfer processes themselves can be transferred to tackle other problems, and applications that help to further advance data science research. Articles in this section may feature data science in industry, government, NGOs, such as those pertaining to 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; and voting and election systems.

Data science and artificial intelligence are advanced by human intelligence and diligence; harvesting and optimizing their benefits requires penetrating experts, a skilled workforce, and educated citizens. Stepping Stones content explores educational policies, infrastructures, contents, and innovations that can enhance and deepen data science education and training at all levels, from playroom to boardroom. It provides pedagogical strategies, methods, curricula, and lessons, as well as success stories that demonstrate the broad benefit of effective teaching and communicating data science. 

Data science, as a foundation for knowledge discovery and augmentation in the digital age, is itself built upon fundamental research from many contributing disciplines. Milestones and Millstones content features foundational achievements, theoretical breakthroughs, and methodological marvels pertaining to data science, from ethical conundrums to quantum computing and everything in between. It aims to push the frontiers of research on some of the most perplexing problems, to generate new directions of inquiry, and to motivate inquisitive minds worldwide to challenge themselves to tackle similar provocations put forward by data science. Articles in this section tackle issues such as 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, reusable) data; algorithm fairness and accountability; data flows and markets; and research reproducibility, replicability and triangulation.

The power of data science in the business realm is tremendous, but the hype and complexity surrounding it can be overwhelming. The "Active Industrial Learning" column focuses on applications of data science to businesses, with an eye toward practical considerations that can make data science initiatives more likely to succeed. The aim is to translate business concerns into the language of data science, and vice versa, to empower data science leaders at companies of all sizes and data maturity levels.

Causal inference is a pivotal element in disciplines focused on unraveling the cause-effect relationships that underpin outcomes. The “Catalytic Causal Conversations” column provides approachable insights and varied perspectives on the evolving landscape of causal inference. Its primary objective is to clarify and explore the complexities of causal inference, thereby making it relatable and compelling for a broad audience, and to showcase cutting-edge methods and practical applications in this ever-evolving field.

Data science is a diverse field, and one which is changing rapidly in response to developments in theory and practical capabilities, as well as to the challenges of the great many domains in which it is applied. The “Diving into Data” column presents short articles describing ideas, concepts, methods, and tools used in data science. The overriding aim is that the articles should be enlightening, useful, and accessible, avoiding obscure technicalities.

Governments of all scales increasingly rely on data to form, implement, and evaluate policies, and to seek more effective ways to collect, utilize and disseminate data for better governance and societal engagements. The "Effective Policy Learning" column features engaging articles that highlight current developments in data science as well as efforts made in government sector around the world to uncover insights for evidence-based policy making. The aim is to equip policy makers with better understanding of the power and perils of data science, and to inspire data scientists to consider the public sector as a vital place to employ their skills and to maximize their impact. 

Data science and AI raise countless big questions about the goals, structure, functions, methods, meaning and social roles of research. The “Meta Data Science” column presents short articles that discuss philosophical issues around the practice and theory of data science—ranging from ethical to epistemological and metaphysical questions. It aims to promote philosophy-minded data science and data-science-minded philosophy, and to bring philosophical debate around AI, statistics, and all things about data to a wider audience.

Data science is one of the fastest growing areas of employment in virtually all sectors, demanding many more qualified young talents than traditional training can supply. The “Minding the Future” column features pedagogical content for and about the younger audience, whether by age or experience, with a specific focus on students in their preteen and teen years via engaging their teachers and parents. It aims to inspire young minds to try out their first substantial data science-oriented studies, by computer simulations, classroom activities, or individual explorations.

To fully understand the practice and theory of modern data science, we must understand why the field developed as it did, with historical roots in mathematics, philosophy, statistics, and technology. The "Mining the Past" column presents short articles that explore these diverse histories and asks how they might inform contemporary debates, perspectives, and goals. The aim is to provide enlightening and useful historical perspectives that contextualize the field, enrich technical discourse, and enhance the public understanding of data science.

Data science has applicability that goes well beyond technological and academic domains. The "Recreations in Randomness" column presents short articles that demonstrate the scope, power and relevance of data science to the leisurely activities in our lives. The aim is to present explorations of various recreational activities, pastimes, hobbies, and games and sports, to highlight the surprising diversity of data science, and to engage the general public to better appreciate the value (and enjoyment) of proper reasoning under uncertainty. 

Transparent and credible research relies not only on computational reproducibility or availability of materials, but on a multitude of individual and disciplinary conditions, infrastructure, and regulations, in order to enhance scientific replicability and ultimately the trust in (data) science. Many scientists don't have the time to explore all of these topics. The “Reinforcing Reproducibility and Replicability” column will explore topics in this space via short articles, enriching the discussion within and across disciplines, and highlighting sometimes neglected topics.

Data science as an ecosystem relies on effective and timely communications among all its inhabitants to advance and sustain its healthy evolution. Bits and Bites collects timely reflections, enlightening stories, Letters to the Editor, and other succinct writings that can enhance such communications. The aim is to facilitate communication between authors of varied backgrounds or interests with the broader data science community via enticing topics and newsworthy reports, augmenting readers’ appreciation of the kaleidoscopic nature of data science activities and schools of thoughts.