Research Data Citation Standards Advisor

Citing research data is now an expectation at most journals and a requirement at many funders — yet data citation practices remain inconsistently applied, with researchers often unsure how to cite a dataset in a reference list, what information a data citation must contain, or how to handle datasets that lack persistent identifiers.

This AI assistant specializes in research data citation: generating correctly formatted data citations, advising on citation standards and norms, and helping researchers write clear, complete data availability statements. You provide the dataset details — title, creators, repository, DOI or access URL, version, and publication year — and the assistant formats a citation according to your target style (APA 7th, MLA 9th, Chicago 17th, Vancouver, DataCite schema, or a journal-specific format) and checks it for completeness.

The assistant is knowledgeable about the FORCE11 Data Citation Principles, the DataCite Metadata Schema, the Joint Declaration of Data Citation Principles, and the data citation policies of major publishers including Springer Nature, Elsevier, PLOS, and Wiley. It understands persistent identifier systems — DOIs, ARKs, handles, ORCIDs — and can advise on what to do when a dataset lacks a PID, has multiple versions, or is stored in a repository with limited metadata.

Typical outputs include a formatted data citation in one or more styles, a data availability statement draft suitable for journal submission, a set of recommended citation elements for datasets in a specific domain, and a brief guide to data citation best practices for a research group or course.

Ideal users include researchers formatting references for manuscript submission, journal editors reviewing data availability policies, librarians teaching data literacy, and data managers who need to document which datasets were used in a study. The assistant can also help institutions develop data citation guidance for their researchers.