Scholarship on information science, open knowledge, data science, and more
Saturday, March 5 is Open Data Day, an annual event that celebrates the use of open data and encourages the adoption of open data policies in government, business, and civil society. The MIT Press, a leader in open access publishing for over two decades, remains committed to paving the way for open access (OA). This year, we are publishing all of our spring 2022 monographs open access, and continue to support a growing number of open access initiatives through our journals publishing program.
To commemorate Open Data Day, we’ve compiled a list of just a few of our latest (open access) books on data science and open knowledge. If that’s not enough, discover more of our books on data science here, learn more about our open access model Direct to Open, and sign up for our newsletter to hear more about our new open access initiatives as they launch.
Making Open Development Inclusive: Lessons from IDRC Research edited by Matthew L. Smith and Ruhiya Kristine Seward
A decade ago, a significant trend toward openness emerged in international development. “Open development” can describe initiatives as disparate as open government, open health data, open science, open education, and open innovation. The theory was that open systems related to data, science, and innovation would enable more inclusive processes of human development. This volume, drawing on ten years of empirical work and research, analyzes how open development has played out in practice.
Data Feminism by Catherine D’Ignazio and Lauren F. Klein
Open access edition available on MIT Press Direct, courtesy of the MIT Libraries Experimental Collections Fund
Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D’Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought.
“Without ever finger-wagging, Data Feminism reveals inequities and offers a way out of a broken system in which the numbers are allowed to lie.” —WIRED
Critical Perspectives on Open Development: Empirical Interrogation of Theory Construction edited by Arul Chib, Caitlin M. Bentley, and Matthew L. Smith
Over the last ten years, “open” innovations—the sharing of information and communications resources without access restrictions or cost—have emerged within international development. But do these innovations empower poor and marginalized populations? This book examines whether, for whom, and under what circumstances the free, networked, public sharing of information and communication resources contribute (or not) toward a process of positive social transformation. The contributors offer cross-cutting theoretical frameworks and empirical analyses that cover a broad range of applications, emphasizing the underlying aspects of open innovations that are shared across contexts and domains. Taken together, the chapters offer an empirically tested theoretical direction for the field.
Open Knowledge Institutions: Reinventing Universities by Lucy Montgomery, John Hartley, Cameron Neylon, et. al.
In this book, a diverse group of authors—including open access pioneers, science communicators, scholars, researchers, and university administrators—offer a bold proposition: universities should become open knowledge institutions, acting with principles of openness at their center and working across boundaries and with broad communities to generate shared knowledge resources for the benefit of humanity. Calling on universities to adopt transparent protocols for the creation, use, and governance of these resources, the authors draw on cutting-edge theoretical work, offer real-world case studies, and outline ways to assess universities’ attempts to achieve openness.
“This book provides nuanced discussion around the moral, logistical, and practical challenges associated with embedding open science principles in public research organizations. With every syllable, I was nodding in agreement.” —Gemma Derrick, Lancaster University
The Open Handbook of Linguistic Data Management edited by Andrea L. Berez-Kroeker, Bradley McDonnell, Eve Koller and Lauren B. Collister
“Doing language science” depends on collecting, transcribing, annotating, analyzing, storing, and sharing linguistic research data. This volume offers a guide to linguistic data management, engaging with current trends toward the transformation of linguistics into a more data-driven and reproducible scientific endeavor. It offers both principles and methods, presenting the conceptual foundations of linguistic data management and a series of case studies, each of which demonstrates a concrete application of abstract principles in a current practice. The Open Handbook of Linguistic Data Management is an essential addition to the toolkit of every linguist, guiding researchers toward making their data FAIR: Findable, Accessible, Interoperable, and Reusable.
Data Intelligence, cosponsored by the National Science Library, the Chinese Academy of Sciences, and the China National Publications Import and Export (Group) Corporation, is an open-access, metadata-centric journal intended for data creators, curators, stewards, policymakers, and domain scientists as well as communities interested in sharing data. DI informs industry leaders, researchers, and scientists engaged in sharing and reusing data, metadata, knowledge bases, and data visualization tools. In addition to traditional articles addressing methodologies and/or resources, the journal also publishes “data articles” in the form of knowledge graphs, ontologies, and linked datasets.
As an open access platform of the Harvard Data Science Initiative, the Harvard Data Science Review features foundational thinking, research milestones, educational innovations, and major applications, with a primary emphasis on reproducibility, replicability, and readability. It aims to publish contents that help to 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.
Drawing on the nation’s most prominent thinkers in the arts, sciences, humanities, and social sciences, as well as the professions and public life, Daedalus, the open access journal of the American Academy of Arts and Sciences, explores the frontiers of knowledge and issues of public importance. Recent issues have examined Access to Justice; Inequality as a Multidimensional Process; Science and the Legal System; Why Jazz Still Matters; Political Leadership; Ethics, Technology, and War; Russia Beyond Putin; and The Prospects and Limits of Deliberative Democracy.
Quantitative Science Studies is the official open access journal of the International Society for Scientometrics and Informetrics (ISSI). It publishes theoretical and empirical research on science and the scientific workforce. Emphasis is placed on studies that provide insight into the system of science, general laws of scientific work, scholarly communication, science indicators, science policy, and the scientific workforce.