Note: Interested authors should submit an abstract by the October 26th deadline before providing a full paper. Use this Google Form to submit an abstract. 

Jump to:

• Introduction
• Topics
• Timetable
• Research Transparency
• Open Access
• Why Submit to Data & Policy
• Guest Editors

 

Introduction

The growing availability of linked datasets is transforming research and policy analysis across the social sciences and beyond. Researchers increasingly combine survey responses with administrative records, health and education data, digital trace data, platform data, transactional records, and other forms of observational information to generate richer insights into social, political, economic, and behavioural processes. These developments have created significant opportunities for advancing evidence-based policymaking and scientific knowledge, while simultaneously raising new challenges around governance, privacy, consent, transparency, ethics, reproducibility, and public trust.

As linked data become more prevalent, questions about how such resources should be collected, managed, shared, regulated, and reused have become increasingly urgent. Traditional governance frameworks often struggle to accommodate the distinctive risks and opportunities associated with linked datasets, particularly when data originate from multiple sources collected under different legal, ethical, and institutional arrangements. At the same time, emerging technologies, including artificial intelligence and machine learning, are creating new possibilities for analysing linked data while also introducing additional concerns regarding accountability, bias, explainability, and disclosure risk.

This special collection seeks to advance understanding of the governance of linked datasets and the ways in which linked data can be used responsibly to inform policy and research. We welcome contributions that address conceptual, methodological, empirical, legal, ethical, technical, and policy dimensions of linked data. Of particular interest are papers that move beyond narrowly technical discussions to engage with broader questions of governance, public value, trust, and societal impact.

Topics

We are thus keen to receive contributions addressing topics including, but not limited to:

• Governance frameworks for linked data and data infrastructures
• Linking survey, administrative, health, education, and digital trace datasets
• Legal and ethical frameworks for data linkage
• Consent, transparency, and public trust in linked data research
• Privacy, anonymisation, disclosure risk, and data protection
• Access, stewardship, and management of secure research environments
• FAIR principles and responsible data sharing
• The governance of smart data and platform data
• AI and machine learning applications in linked data environments
• Algorithmic accountability and bias in linked datasets
• Cross-national and comparative approaches to linked data governance
• Public attitudes toward data linkage and data reuse
• Data linkage in public policy evaluation and evidence-informed policymaking
• Challenges and opportunities arising from emerging digital data sources
• Reproducibility, documentation, and metadata standards for linked data research

Comparative and interdisciplinary studies are particularly encouraged. Papers may employ qualitative, quantitative, computational, mixed-methods, or participatory approaches. We particularly encourage submissions from interdisciplinary teams working across the social sciences, data science, law, public policy, health research, and information governance, as well as early-career researchers and scholars developing innovative approaches to linked data research.

Timetable 

Interested authors must submit an abstract via this Google Form by 26 October 2026. 

Invitations with full instructions to submit draft papers will be sent to authors in early November. 

Full paper submission: 29 March 2027 with publications anticipated by the end of 2027.

Open Access

Any author can publish on an open access basis in Data & Policy, irrespective of their funding situation or institutional affiliation. Many articles have publishing costs covered through the Transformative Agreements that Cambridge has set up with universities worldwide. Authors not affiliated with these agreements can still publish open access. Authors who have a grant that specifically budgets for open access publication are expected to pay an article processing charge (APC). However, if an author has no funding to pay an APC and no institutional agreement, the charge will be waived. Please feel free to submit to this special collection irrespective of where you are based and whether or not you have funding.

Research Transparency 

All authors are required to provide a Data Availability Statement with their full submission, as outlined in the journal's Research Transparency policy. The journal expects authors to make data, code and other replication materials openly available when possible, via an open repository such as Zenodo. Authors using GitHub should consider archiving their materials in Zenodo via this integration, in order to provide them with a citable DOI. 

The Data Availability Statement should cite any replication materials or provide an explanation why such materials cannot be made openly available. Example Data Availability Statements can be found on Cambridge Core. Data & Policy uses the Centre for Open Science's Open Practice Badges to indicate when articles have related open data or materials.

Why Submit to Data & Policy?

✔ A venue developed for and expanding the community working at the data science for governance interface, established by the Data for Policy Conference.
✔ Welcomes research, translational articles, commentaries and data papers, plus the Data & Policy blog for more immediate reflections.
✔ Well-cited (2025 Impact Factor: 2.6 and Cite Score: 4.2) and indexed in Web of Science, Scopus and Directory of Open Access Journals.
✔ Open Access with support for authors who do not have access to funding to pay publishing charges.
✔ Promotes open sharing of data and code through Open Science Badges.

Guest Editors

• Prof. Rachel Gibson (University of Manchester)
• Dr. Filip Bialy (Liverpool John Moores University)
• Dr. Marta Cantijoch (University of Manchester)
• Dr. Andreu Casas (University of Oxford)
• Dr. Conor Gaughan (University of Manchester)