If you have large sets of research data that you want to share, consider publishing them in a data journal. Data journals are typically open access, peer-reviewed publications. Data creators submit their datasets to be peer-reviewed and published. The work is then citable by others and can be tracked for impact. Open access data journals include biomedical, public health, and psychosocial coverage.

The list of data journals is rapidly growing. The following are a small number of data journals that researchers on the UMB campus may find useful:

F1000Research is an Open Science publishing platform for life scientists, offering immediate publication and transparent refereeing. Peer review is as formal as that of a traditional journal, but the reviewer names, affiliations, and comments are published with the article.

Genomics Data is an open access journal that considers articles on all aspects of genome-scale analysis.

GigaScience publishes datasets from life and biomedical sciences research. The journal links standard manuscript publication with a database that hosts all associated data and provides data analysis tools and cloud-computing resources.

The Journal of Open Psychology Data features peer-reviewed papers describing psychology datasets with high reuse potential. Data papers may describe data from unpublished work, including replication research, or from papers published previously in a traditional journal. The data and papers are citable, and reuse is tracked.

Open Health Data features peer-reviewed data papers describing health datasets with high reuse potential. The publishers are working with specialist and institutional data repositories to ensure that the associated data are professionally archived, preserved, and openly available. Open Health Data also encourages the deposition of grey literature, such as research study protocols, data management plans, consent forms, participant guidance documents, and white paper reports.

Scientific Data, a Nature publication, is an open-access, peer-reviewed publication for descriptions of scientifically valuable datasets. The journal’s primary article type, the Data Descriptor, is designed to make data discoverable, interpretable, and reusable.

For additional information on sharing and managing data, take a look at HSHSL’s Data Management Best Practices Guide.