About | Areas of interest | Article types | Peer review process | Open Access | Transparency and openness promotion | Data for Policy Zenodo repository | Pre-print policy | Article preparation | Publishing ethics | Digital preservation
Data & Policy is a peer-reviewed, open-access journal concerned with the impact of data science on policy, governance and public administration. It aims to promote a deeper understanding of what the opening editorial calls "policy-data interactions" by publishing work that takes a “consistent, symmetrical approach to consideration of systems of policy and data, [and] how they interact with one another."
Data & Policy emerged from the Data for Policy Conference (dataforpolicy.org), an annual interdisciplinary and cross-sector forum that brings together experts in academia, government, international organisations, non-profit and private sectors. The Journal extends beyond this basis by welcoming submissions irrespective of whether they are connected to the Conference. Freely available to read and redistribute, Data & Policy publishes on a continuous rather than issue-based schedule.
In 2021, Data & Policy moved into a new phase of strategic development by establishing six non-domain specific and overarching Areas of Interest. Authors are encouraged to select the area most relevant to their article when they submit to the journal. The categories are interrelated and they do not indicate siloed activity. They are rather an articulation of the breadth and depth of the vision and mission for improved data-driven decisions and policymaking, which is the ethos of both Data & Policy and the Data for Policy Conference.
See the Editorial Committees page for the Editors responsible for developing each area:
Area 1: Data-driven Transformations in Policy and Governance – this area focuses on the high-level vision for philosophy, ideation, formulation and implementation of new approaches leading to paradigm shifts, innovation and efficiency gains in collective decision making processes. Topics may include:
- Data-driven innovation in public, private and voluntary sector governance and policy-making at all levels (international; national and local): applications for real-time management, future planning, and rethinking/reframing governance and policy-making in the digital era;
- Data and evidence-based policy-making;
- Government-private sector-citizen interactions: data and digital power dynamics, asymmetry of information; democracy, public opinion and deliberation; citizen services;
- Interactions between human, institutional and algorithmic decision-making processes, psychology and behaviour of decision-making;
- Global policy-making: global existential debates on utilizing data-driven innovation with impact beyond individual institutions and states;
- Socio-technical and cyber-physical systems, and their policy and governance implications.
The remaining areas represent more specifically the current applications, methodologies, strategies which underpin the broad aims of Data & Policy's vision:
Area 2: Data Technologies and Analytics for Policy and Governance – this area is concerned with data in its variety of forms and sources, and infrastructure and methods for its utilisation in policy and governance:
- Data sources: Personal and proprietary data, administrative data and official statistics, open and public data, organic vs designed data, sensory and mobile data, digital footprints, crowdsourced data, and other relevant data;
- Technologies: Artificial Intelligence, Blockchain, Internet of Things, Platform Technologies, Digital Twins, Visualisation and User Interaction Technologies, data and analytics infrastructures, cloud and mobile technologies;
- Methodologies and Analytics: Mathematical and Statistical models, Computational Statistics, Machine Learning, Edge Analytics, Federated Learning, theory and data-driven knowledge generation, multiple disciplinary methodologies, real-time and historical data processing, geospatial analysis, simulation, gaps in theory and practice.
Area 3: Policy Frameworks, Governance and Management of Data-driven Innovations – this area focusses on governance practices and management issues involved in implementation of data-driven solutions:
- Data and algorithm design principles and accountability
- Local, national and international governance models and frameworks for data and associated technologies;
- Data and algorithms in the law;
- General Data Protection Regulation (GDPR) and other regulatory frameworks;
- Data intermediaries, trusts and collaboratives;
- Meta-data, interoperability and standards;
- Data ownership, provenance, sharing, supply chains, linkage, curation and expiration;
- Data sovereignty and data spaces.
Area 4: Ethics, Equity and Trust in Policy Data Interactions – this area examines the issues which must be considered in technology design and assessment:
- Digital Ethics: Data, algorithms, models and dynamic interactions between them;
- Digital trust, and human-data-machine interactions in policy context;
- Responsible technology design and assessment;
- Privacy and data sharing;
- Digital identification, personhood, and services;
- Uncertainties, bias, and imperfections in data and data-driven systems;
- Algorithmic behaviour: equity and fairness, transparency and explainability, accountability, and interpretability;
- Human-machine collaboration in strategic decision making and algorithm agency;
- Human control, rights, democratic values, and self-determination.
Area 5: Algorithmic Governance:
- Data-driven insights in governance decision making, black-box processing;
- Algorithm agency along with human and institutional decision-making processes;
- Government automation: citizen service delivery, supporting civil servants, managing national public records and physical infrastructure, statutes and compliance, and public policy development;
- Algorithmic ‘good’ governance: participation, consensus orientation, accountability, transparency, responsiveness, effectiveness and efficiency, equity and inclusiveness, and the rule of law.
Area 6: Data to Tackle Global Issues and Dynamic Societal Threats:
- Human existence and the planet;
- International collaboration for global risk management and disaster recovery;
- Global health, emergency response, Covid-19 and pandemics;
- Sustainable development, climate change and the environment;
- Humanitarian data science, international migration, gender-based issues and racial justice;
- International competition and cultures of digital transformation.
Data & Policy welcomes the submission of the following:
- Research articles that use rigorous methods that demonstrate how data science can inform or impact policy by, for example, improving situation analysis, predictions, public service design, and/or the legitimacy and/or effectiveness of policy making. Published research articles are typically reviewed by three peer reviewers: two assessing the academic or methodological rigour of the paper; and one providing an interdisciplinary or policy-specific perspective. (Approx 8,000 words in length).
- Commentaries are shorter articles that discuss and/or problematize an issue relevant to the Data & Policy scope. Commentaries are typically reviewed by two peer reviewers. (Approx 4,000 words in length).
- Translational articles are contributions that show how data science principles, techniques and technologies are being used in practice in organisational settings to improve policy outcomes. They may present original findings but are less embedded in the scholarly literature as research articles. They are typically reviewed by two peer reviewers, who assess the rigour and policy significance of the paper. (Approx 8,000 words in length).
- Replication studies examine previously published research, whether in Data & Policy or elsewhere, and report on an attempt to replicate findings. (Approx 8,000 words in length).
The journal also commissions Data & Policy Reports: articles invited by the journal to survey the landscape of data-policy interactions. Data & Policy Reports are independently reviewed by a reviewer not connected to the journal before publication. (Approx 8,000 words in length).
We welcome case studies, whether they are positive or negative examples of the use of data science for policy. We do not have a distinct case study category. A case study could be submitted as a research article - if presenting original findings and embedded in the academic literature - or a translational article, if it less embedded in the academic literature and primarily concerned with the organisational setting, or a commentary if it is a shorter discussion paper
The word length given above for each category is a guideline rather than a strict limit. If articles seem excessively long, we may ask you to shorten the article if possible without compromising the integrity of the argument in the paper.
Proposals for special collections of articles - for example originating from a workshop, conference or event - are also considered. See the instructions for submitting a special collection proposal.
Articles submitted to Data & Policy are subject to a single-blind peer review process. Articles are assigned to a Data & Policy Editor-in-Chief, who exercises oversight. The Editor-in-Chief assigns the article to an Editor - one of our Area Editors or Editorial Board members - to handle the peer review process. This Editor may recommend a reject without review if the article is clearly inappropriate or not in scope, but all decisions are approved by the Editor-in-Chief.
Peer review of research articles involves securing the input of three reviewers. The Editor obtains two reviews from people with relevant academic expertise who are capable of commenting on the rigour of the research. Given that Data & Policy is an interdisciplinary journal, the Editor also obtains a review from someone providing a different perspective: a reviewer with relevant policy expertise or expertise from a relevant but different discipline. After receiving the reviews the Editor forms a decision, which is approved by the Data & Policy Editor-in-Chief.
For other article types, the Editor secures input from two reviewers.
Articles are published on an Open Access (OA) basis: made freely accessible immediately on publication under a Creative Commons license that allows users to re-distribute and re-use the article. The standard license that Data & Policy uses is CC-BY 4.0, but authors are able to select other Creative Commons licenses in the publishing agreement that is signed on acceptance.
Funding Open Access
To contribute to the cost associated with publishing in Data & Policy, we ask authors of research articles who have access to grant funding, or an institutional OA fund, or who are based at commercial organisations to pay an article processing charge (APC) of £1300 GBP / $1660 USD.
As a matter of standard policy:
- Authors in low and middle-income countries as defined by Research4Life automatically receive either a complete waiver (Group A countries) or a discount (Group B) to the APC. See this page for more details;
- Authors affiliated with institutions that fall under the Read-and-Publish agreements that Cambridge has formed with national consortia and institutions (e.g. JISC in the UK; Bibsam in Sweden; VSNU in the Netherlands; Max Planck and BSB in Germany; the University of California Digital Library) will be eligible for a discount or waiver to the APC, depending upon the agreement. Read here for more details;
- Authors affiliated with institutions that are sustainer partners of Data & Policy do not have to pay an APC: University College London, the Office for National Statistics and the Alan Turing Institute.
In addition to these arrangements Data & Policy is able to offer waivers to any author who is unable to access funding for an APC, as a consequence of the partnerships we have formed. When authors submit through the Data & Policy ScholarOne system they are asked to provide details about their funding and to confirm whether or not a waiver to the APC is required.
Please note that the decision to accept or reject a paper for publication is not made with reference to the funding situation of the authors, or their ability to pay an APC. Editorial decisions rest solely with the Editors-in-Chief. Payments and waivers are handled by Cambridge University Press.
Copyright and licensing
Authors accepted in Data & Policy are are asked to sign a publishing agreement, which can be found here.
Articles will be published under a Creative Commons Attribution license (CC-BY) by default. This means that the article is freely available to read, copy and redistribute, and can also be adapted (users can "remix, transform, and build upon" the work) for any commercial or non-commercial purpose, as long as proper attribution is given. Authors can, in the publishing agreement form, choose a different kind of Creative Commons license (including those prohibiting non-commercial and derivative use) if they prefer.
For more information Creative Commons licensing, please visit creativecommons.org/licenses.
Data & Policy supports the idea that research articles should contain sufficient information to allow others to understand, verify and replicate the findings.
We require authors to provide a Data Availability Statement in the manuscript, describing how readers can access the resources necessary to replicate the findings if they are publicly accessible. If these resources are under embargo, or cannot be publicly released for legal, ethical, commercial or other reasons, the Data Availability Statement should make this clear with a brief explanation.
For more details, see the full Data & Policy Transparency and Openness Promotion policy.
Data & Policy recognises exemplary scientific practices by awarding Open Science Badges to authors who openly share their data and research materials.
Authors can apply for an Open Data and/or an Open Materials badge, which are described in detail below. If awarded, these badges display prominently in the published article as a visible reward to the author and clear indication to readers that the article contains links to related data and materials.
Open Science Badges are an initiative of the Center for Open Science – a non-profit aiming to increase the openness, integrity, and reproducibility of scientific research – and have been adopted by a number of leading journals in different disciplines.
What do the badges mean?
We award an Open Data badge to authors who deposit data in an open-access repository. Authors can satisfy this requirement by depositing their entire dataset or by depositing a slice of it, as long as it allows an independent researcher to reproduce the reported results. If confidentiality is sought, authors may deposit a transformed dataset, as long as it allows reproduction of the reported results (Reiter, 2002). Depending on the methodology, deposited data may include quantitative and qualitative materials, but may not compromise the anonymity of participants or undermine promises of confidentiality. Often, it is easy to remove such identifying information from the dataset while preserving the ability of an independent researcher to reproduce the results. But if access to such identifying information is necessary to reproduce the reported results, then authors are not eligible for an open data badge. The criteria for Open Data are here: https://osf.io/g6u5k/
If the data are statistical, authors are expected to deposit the code necessary to generate the results. Once the data and the code are available, authors may, but are not required to, assist others in using the deposited materials.
We also award an Open Materials badge to authors who deposit their research materials (the components of the research methodology, including code) in an open-access repository. The deposited materials should be as complete as possible, to allow an independent researcher to reproduce the reported procedure and analysis. Depending on the methodology, materials may include statistical code, questionnaires, interview questions, experimental procedures, and participant instructions (but not data). The criteria for Open Materials are here: https://osf.io/gc2g8/
Where to deposit the data and code?
Data & Policyrecommends that authors make data and code available via public repositories that:
- Are committed to the long-term preservation and accessibility of their content.
- Are supported and recognised by the community as appropriate for the resources they hold.
- Provide stable, unique identifiers for the information they hold.
- Support linking between their database records and associated published research articles.
- Allow free public access to their holdings, with reasonable exceptions (such as administration charges for the distribution of physical materials).
Applying for the badges
Authors asked during submission process whether they want to apply for an Open Data and/or an Open Materials badge. The Data Availability Statement should be used to provide the link to the publicly accessible data and materials and provide any additional explanation.
Data & Policy is following the disclosure model in its award of the Open Data and Open Materials badges: authors affirm that they meet the badge criteria through the submission system and their use of the Data Availability Statement. Data & Policy, as the awarding journal, makes a cursory evaluation of the data and materials. This includes checking that the provided link leads to the data or materials in an open repository, that they look appropriate and that they relate to the article. Data & Policy does not perform a peer review of the data or materials. The onus is on authors to follow the criteria for each badge and they are accountable to the community for the accuracy of their statements.
In applying for the Open Data badge authors are disclosing and confirming that:
- They have provided the URL, DOI, or other permanent path for accessing the data in a public, open access repository in the Data Availability Statement.
- There is sufficient information for an independent researcher to reproduce the reported results
In applying for the Open Materials badge authors are disclosing and confirming that:
- They have provided the URL, DOI, or other permanent path for accessing the materials in a public, open access repository in the Data Availability Statement.
- There is sufficient information for an independent researcher to reproduce the reported methodology.
There is a Data for Policy community on Zenodo, a repository which is funded by the European Commission, CERN and OpenAIRE.
Data for Policy's Zenodo site is being used to host Data for Policy conference papers and posters. But we also encourage authors submitting to Data & Policy to make use of Zenodo to host materials associated with their article. As explained in the article preparation section, authors submitting articles to Data & Policy are required to provide a Data Availability Statement (see disclosures) that contains details about the data and other materials necessary to replicate the findings. Zenodo can be used as a repository to host data and other materials referred to in the Data Availability Statement.
Other types of content that can be uploaded to Zenodo include: presentations; datasets; data management plans; code; software documentation; audio and video files; proposals; reports; technical notes. Early versions of articles submitted to Data & Policy can be made available via Zenodo as pre-prints. We ask authors to cite any relevant material in the article using the DOI that Zenodo assigns to the uploaded content.
All submissions should be made through the Data & Policy ScholarOne system.
Article template files
Authors are not required to use the following Data & Policy article templates, but they may help you with your submission:
Authors are prompted to provide a short cover letter to the editors through a form in the ScholarOne system.
The first page of the main manuscript must include:
- A concise but informative title, reflective of the content
- The names and institutional affiliations for the authors, indicating with asterisk the corresponding author and his/her email address
- Any ORCIDs of the authors
- Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work;
- Drafting the work or revising it critically for important intellectual content;
- Final approval of the version to be published;
- Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
In the process of submitting the article through the Data & Policy ScholarOne system, the corresponding author is prompted to provide further details about contributions to the article using the CRediT taxonomy. People who have contributed to the article but do not meet the full criteria for authorship should be recognised in the acknowledgements section; their contribution can be described in terms of the CRediT taxonomy.
Our default position is that the corresponding author has the authority to act on behalf of all co-authors, and we expect the corresponding author to confirm this at the beginning of the submission process.
This must summarise the purpose of the paper and be no more than 250 words in length.
Beneath the abstract authors must provide a 120-word statement that summarises the significance of their research for policymakers, written at a level understandable to a broad audience. This will be published in the article itself.
Provide up to five keywords, separated by semi colons.
The body of the article, which can be separated using headings and subheadings.
In published articles, Data & Policy uses the American Psychological Association style, Sixth Edition for citations and references, because this is recognised widely across disciplines. Authors are not required to submit articles with references in this style; the necessary adjustments will be made by the typesetter if the article is accepted.
For papers with one author, list the author’s last name, followed by a comma, a space and the year, throughout the paper: (Moreno, 1953)
For papers with one or two authors, use an ampersand between author names throughout the paper: (Hutchins & Benham-Hutchins, 2010)
For papers with three or more authors, et al. is used after the first author throughout the paper: (Doe et al., 2012)
If abbreviations are used in the text they should be defined in the text at first use. A list of abbreviations should be provided at the end of the main text.
Following the main text, articles must include the following disclosure statements in the interest of transparency:
This should recognize help and advice from associates and colleagues who contributed to the article but do not meet the criteria for authorship, as well as other kinds of non-financial support from individuals and organisations.
Research articles must contain sufficient information to allow others to understand, verify, and replicate findings. See the Data & Policy Transparency and Openness Promotion (TOP) policy for more details.
The article must contain a Data Availability Statement explaining how data and other resources were created, from where they are available, along with information about any restrictions on the accessibility of data and other resources.
Data availability: The data that support the findings of this study are openly available in [repository name] at http://doi.org/[doi], reference number [reference number].
Data availability: The data that support the findings will be available in [repository name] at [URL / DOI link] following a [6 month] embargo from the date of publication to allow for commercialisation of research findings.
Data availability: The data that support the findings of this study are available from [third party]. Restrictions apply to the availability of these data, which were used under licence for this study. Data are available [from the authors / at URL] with the permission of [third party].
The Data for Policy Zenodo site provides one way of making data and additional materials available and citable.
This must detail the sources of financial support for all authors in relation to the article, including grant numbers, or declare that no specific funding exists. The statement should also make it clear whether the funder had a role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
“This work was supported by the National Science Foundation (NSF) under research grant XXXX. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.”
Where no specific funding has been provided for research, please provide the following statement: “This work received no specific grant from any funding agency, commercial or not-for-profit sectors.”
Authors should include a Competing Interest statement in their manuscript. If authors do not include this, their submission will not proceed to peer review.
- Competing Interests are situations that could be perceived to exert an undue influence on an author’s presentation of their work. They may include, but are not limited to, financial, professional, contractual or personal relationships or situations.
- Competing Interests do not necessarily mean that an author’s work has been compromised. Authors should declare any real or perceived Competing Interests in order to be transparent about the context of their work.
- If the manuscript has multiple authors, the author submitting the manuscript must include Competing Interest declarations relevant to all contributing authors.
- Example wording for a Competing Interest declaration is as follows: “Competing Interests: Author A is employed at company B. Author C owns shares in company D, is on the Board of company E and is a member of organisation F. Author G has received grants from company H.” If no Competing Interests exist, the declaration should state “Competing Interest: Author A and Author B declare none”.
References must appear in the order in which they first appear in the text, following the American Psychological Association style, Sixth Edition. See the examples below.
Moreno, J. L. (1953). Who shall survive? Foundations of sociometry, group psychotherapy and socio- drama (2nd ed.). Oxford, England: Beacon House.
Borgatti, S. P., & Everett, M. G. (2005). Extending centrality. In P. J. Carrington, J. Scott & S. Wasserman (Eds.), Models and methods in social network analysis (pp. 57-76). Cambridge: Cambridge University Press.
Hutchins, C., & Benham-Hutchins, M. (2010). Hiding in plain sight: Criminal network analysis. Computational & Mathematical Organization Theory, 16(1), 89-111.
Frantz, T. L., & Carley, K. (2005).
A formal characterization of cellular networks (CASOS - Center for Computational Analysis of Social and Organizational Systems, Trans.) CASOS Technical Report (pp. 14): Carnegie Mellon University.
Clancey, W. J. 1984. Classification Problem Solving. In Proceedings of the Fourth National Conference on Artificial Intelligence, 49-54. Menlo Park, Calif.: AAAI Press.
[dataset] Adolph, Christopher; Breunig, Christian; Koski, Chris, 2018, "Replication Data for: The Political Economy of Budget Trade-offs", https://doi.org/10.7910/DVN/RXMV9W, Harvard Dataverse, V1, UNF:6:cdCGf3H0GUX64Tn4kEvVGg==
If your article is provisionally accepted pending minor revisions, we require you to upload the figures as separate files. This is not necessary with the original submission. Submitting your figures, illustrations, pictures and other artwork (such as multimedia and supplementary files) in an electronic format alongside the main article file helps us produce your work to the best possible standards, ensuring accuracy, clarity, and a high level of detail.
As a peer-reviewed open access journal, articles submitted to Data & Policy are subject to a process that takes time to complete before a decision is made (see the details about our peer review process).
Authors submitting their work to Data & Policy but seeking a fast way to disseminate their findings are free to make a version of their article available via a pre-print repository, such as arXiv, bioRxiv or the Open Science Framework. This is consistent with the general Cambridge University Press policy on pre-prints.
If you make an early version of your article available via a pre-print server, we ask that you make reference to the pre-print in your submitted article so that the pre-print and peer-reviewed versions are linked together. This can be done by:
1. Mentioning the pre-print in your acknowledgments section and providing its DOI or permanent identifier; and
2. Listing the pre-print in the references and signalling it is a pre-print, e.g. as follows:
Bar DZ, Atkatsh K, Tavarez U, Erdos MR, Gruenbaum Y, Collins FS. Biotinylation by antibody recognition- A novel method for proximity labeling. BioRxiv 069187 [Preprint]. August 11, 2016 [cited 2017 Jan 12]. Available from: https://doi.org/10.1101/069187.
Authors are also encouraged to ensure that the preprint record is later updated with a DOI and a URL link to the published version of the article if their article is accepted.
Cambridge University Press is a member of the Committee on Publication Ethics (COPE) and follows the COPE Guidelines for resolving authorship disputes and other ethical issues in relation to publishing.
Articles submitted to Data & Policy are run through the iThenticate tool for plagiarism detection.
Data & Policy articles are deposited in the following digital archives to guarantee their long-term digital preservation:
Data and Policy now requires that all corresponding authors identify themselves using their ORCID iD when submitting a manuscript to the journal. ORCID provides a unique identifier for researchers and, through integration in key research workflows such as manuscript submission and grant applications, provides the following benefits:
- Discoverability: ORCID increases the discoverability of your publications, by enabling smarter publisher systems and by helping readers to reliably find work that you’ve authored.
- Convenience: As more organisations use ORCID, providing your iD or using it to register for services will automatically link activities to your ORCID record, and will enable you to share this information with other systems and platforms you use, saving you re-keying information multiple times.
- Keeping track: Your ORCID record is a neat place to store and (if you choose) share validated information about your research activities and affiliations.
If you don’t already have an iD, you’ll need to create one if you decide to submit a manuscript to Data and Policy . You can register for one directly from your user account on Scholar One or via https://ORCID.org/register.
If you already have an iD, please use this when submitting, either by linking it to your Scholar One account or supplying it during submission by using the “Associate your existing ORCID ID” button.
Last updated 25 January 2021