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Research transparency

Making Data Available for Replication

Political Analysis requires that authors make replication materials publicly available prior to publication. The data and code for your article must be uploaded to the Political Analysis Dataverse at http://dvn.iq.harvard.edu/dvn/dv/pan and cited in the final version of your manuscript. Below are instructions on how to do this. We will formally accept and then forward your final manuscript to production only after we have verified your compliance with this requirement.

Authors are encouraged to spend time preparing their replication materials, in particular developing usable documentation for eventual users of their replication package. A replication package should contain:

  • A brief “readme” file that summarizes the materials that are part of the replication package.
  • Well-documented and well-named code for producing the results reported in the tables and figures of the paper.
  • Specialized software packages, modules, or routines that are not a standard component of public-release, off-the-shelf software.
  • The data necessary to reproduce the results reported in the paper.
  • Documentation so that users know how to use the code and data to reproduce the results reported in the paper.


The replication materials will be released and permanently archived on the journal’s Dataverse. Thus, authors should bear in mind that code, documentation and data will be in the public domain, and thus all should be edited carefully. In particular, all data made available in replication packages should be made anonymous, and in general no individually-identifying information should be present in replication datasets. Thus, if your data require confidentiality, you should anonymize the relevant variables or cell values.

Replication materials for all analyses reported in the published version of the paper are subject to this requirement, including (but not limited to) quantitative results, simulations, and qualitative analyses. You are only required to provide enough information to replicate the results in your article, not all the data in your possession or even in your data set. However, the more information you provide, the more likely someone will follow upon your article, which would be good for you, your article, and PA. If you wish to request an exception to this policy, please contact the editors.

Political Analysis Dataverse instructions:

  • Go to http://dvn.iq.harvard.edu/dvn/dv/pan. Click the "Add Data" button and select “New Dataset” in the dropdown menu.
  • Enter cataloguing fields to describe your data file(s), such as title, author name(s), abstract, year, citation to article, etc.
  • Scroll down to the “Files” section and click on “Select Files to Add” to upload your data files, code, documentation, and an explanation of what each of the files are. We recommend you upload tabular data files in one of the formats Dataverse presently recognizes, in which case it will process the files and provide additional formats to the end user.
  • Click the “Save Dataset” button when you are done. Your unpublished dataset is now created. When the dataset is ready, click "Publish Dataset", then "Send for review" to submit the draft version of the dataset for replication.
  • You will receive the citation to your replication data set when you upload it. Please insert the complete citation in your manuscript’s references, and refer to this in both the “Data Availability Statement” section within your manuscript and in a footnote in your manuscript around where you first describe your data or analysis.
  • When you have completed this process please email confirmation to [email protected].


After uploading the replication materials, they will be reviewed for completeness and eventually released for public use on the Political Analysis Dataverse page. Authors are encouraged to return to their study’s Dataverse entry after their paper has been published, and to update the Dataverse entry.

Research Preregistration

Political Analysis encourages authors to consider preregistering their studies, when appropriate. Preregistration is the act of archiving a research design with a third party prior to observing a project’s outcome variables. Releasing precise information about hypotheses, how they will be tested, and any pre-outcome data all serve to raise the level of transparency in the project. The goal of preregistering a study is to communicate research goals and strategies as clearly as possible before the outcome variable is observed, allowing readers to distinguish between analyses specified ex ante from those crafted as a function of outcomes.

Research designs should be deposited prior to analysis with a registry that: is open to all prospective registrants; requires that at minimum researchers provide a description of the intended research, a description of hypotheses or other conclusions that the research seeks to examine, a description of data sources including, as applicable, site, subjects, and timeframe, a description of the methods to be used, a description of whether outcomes have been realized prior to registration, and contact information for a lead researcher; records the date and time of all registered research designs and subsequent modifications of designs; provides all registered designs with a unique identifier; makes metadata publicly and freely accessible; and can provide journals with access to complete data at the time of article submission, and to the public within at least two years of completion of data collection.

At this point in time, we encourage the use of the Political Science Registered Studies Dataverse (http://thedata.harvard.edu/dvn/dv/registration); the American Economic Association’s RCT Registry (https://www.socialscienceregistry.org); the Experiments in Governance and Politics [egap] registry (http://e-gap.org/design-registration); or the Registry for International Development Impact Evaluations [RIDIE] (http://ridie.3ieipact.org).

A link to the preregistered study should be provided to the editors upon submission of the paper. The author should indicate if they would like reviewers to be able to review the preregistered information, and whether or not the preregistered information has been made anonymous. Preregistered studies also should include a link to the preregistered information in the final published article, in the same footnote as the link to the registration data. Authors should discuss in detail any deviations from the registered design, their rationale for those deviations, and the implications of these deviations on the reported results.