Data Feminism

Data Feminism

A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism.Today, data science is a form of power. I

Data Feminism

A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism.

MyData

The human-centric approach to data is aimed at a fair, sustainable, and prosperous digital society. In such a society, people get value from their data and set the agenda on how it is used. And for organisations, the ethical use of data is always the most attractive option.

Innovation in Data Science Education

Data science is a new field of research focuses on extracting knowledge and value from data. The radical growth in recent years in the availability of data and of the computational resources required to process them, has led to a corresponding increase ...

Feminist Data Manifest-No

1. We refuse to operate under the assumption that risk and harm associated with data practices can be bounded to mean the same thing for everyone, everywhere, at every time. We commit to acknowledging how historical and systemic patterns of violence and exploitation produce differential vulnerabilities for communities.

Living in Data: A Citizen's Guide to a Better Information Future (Paperback)

Jer Thorp’s analysis of the word “data” in 10,325 New York Times stories written between 1984 and 2018 shows a distinct trend: among the words most closely associated with “data,” we find not only its classic companions “information” and “digital,” but also a variety of new neighbors—from “scandal” and “misinformation” to “ethics,” “friends,” and “play.”To live in data in the twenty-first century

Data Science in 2020: Computing, Curricula, and Challenges for the Next 10 Years

In the past 10 years, new data science courses and programs have proliferated at the collegiate level. As faculty and administrators enter the race to provide data science training and attract new ...

Governing by dismantling: tech oligarchy and the stifling of public data infrastructure

Published in Science as Culture (Ahead of Print, 2026)

Foundations of Data Science for Students in Grades K-12

On September 13 and 14, 2022, the Board on Science Education at the National Academies of Sciences, Engineering, and Medicine held a workshop entitled Foundations of Data Science for Students in Grades K–12. Speakers and participants explored the rapidly growing field of K-12 data science education, by surveying the current landscape, surfacing what is known, and identifying what is needed to support student learning, develop curriculum and tools, and prepare educators. To support these conversations, four papers were commissioned and discussed during the workshop. This publication summarizes the presentations and discussion of the workshop.

Accelerating the Advancement of Data Science Education

We outline a synthesis of strategies created in collaboration with 35+ colleges and universities on how to advance undergraduate data science education on a national scale. The four core pillars of this strategy include the integration of data science education across all domains, establishing adoptable and scalable cyberinfrastructure, applying data science to non-traditional domains, and incorporating ethical content into data science curricula.

We Need A New Kind Of Data Rights

Associational data rights would empower communities to reclaim power from Big Tech

Better Deal for Data - Practical Data Governance for the Social Sector

Better Deal for Data - practical data governance for the social sector.

Home - Data & Society

Data & Society believes that technology policy must be grounded in research, account for tech’s real-world impacts, and serve the public.

Data Cívica | Organización feminista que utiliza los datos y la tecnología como herramientas para la defensa de los derechos humanos y la justicia en México.

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Doing Data Science on the Shoulders of Giants: The Value of Open Source Software for the Data Science Community · Issue 2.2, Spring 2020

Open source software is ubiquitous throughout data science, and enables the work of nearly every data scientist in some way or another. Open source projects, however, are disproportionately maintained by a small number of individuals, some of whom are institutionally supported, but many of whom do this maintenance on a purely volunteer basis. The health of the data science ecosystem depends on the support of open source projects, on an individual and institutional level.

Movement Data Science: Practices for Working in Solidarity with Social Justice Efforts

Although many data scientists are eager to promote social justice, their efforts often fall short, and in some cases even exacerbate existing injustices. This gap between intentions and impacts follows from issues in how data scientists conceptualize their role in social justice efforts: they typically act as technicians, aiming to develop technical tools that will produce social justice. This default role generally results in depoliticized techno-fixes that fail to produce substantive changes. In this paper, we contribute “movement data science” as a framework to guide data scientists in taking on more productive and equitable roles in social justice efforts. Our framework is inspired by movement lawyering, a practice used by lawyers to situate themselves in grassroots social movements. Movement data science suggests that data scientists should ground their efforts to advance justice in sustained and solidaristic relationships with social movements. We contribute a process with four essential practices to help data scientists build these relationships and identify roles to play within specific social justice efforts. Movement data science does not provide a simple blueprint for data scientists to follow, but it can help them support social justice movements in substantive ways that go beyond attempting to solve injustices with technical tools.