Scholarly Impact 5-Day Challenge

When readers are seeking the data supporting a published research finding, what does that look like? How do you discover data in your discipline?

Today we are going to examine data repositories as one of the most reliable methods for making your data discoverable and accessible.

Sharing data can take many forms, from emailing files to colleagues, to providing links on a personal website, or as supplemental files to journal articles. But even publishers are beginning to encourage researchers to deposit their data to repositories instead. One of the primary reasons is repositories’ use of persistent identifiers.

Persistent identifiers are important because they allow your data to be found if the URL for your dataset changes, or it’s transferred to another repository when one is shuttered, and so on. And with persistent identifiers like DOIs, it’s easy to track citations, shares, mentions, and other reuse and discussion of your data online.

Explore data hosted in an open repository, such as Dryad, openICPSR, or the Emory Dataverse.

View a dataset and its accompanying documentation (metadata, codebook, or README file) to get a feel for how the data are described and indexed in the repository.

Look at the usage metrics (views, downloads, any information on mentions) provided. Make a note of the DOI assigned to the dataset, and search the web for any references to it.

Ready to deposit your data?

• Register for an account with a data repository of your choice. Choose one that you explored in Step 1, ask a trusted colleague or advisor for a recommended repository, or check out the Registry of Research Data Repositories (re3data.org) for a comprehensive global list of repositories.
• Select a dataset to share. It can be a copy of supplementary data you’ve posted alongside a journal article, raw data, or data from a backburnered project you’ve never published. Be sure to add as much descriptive information as possible during the deposit. If README files are new to you, check out an example README template from Cornell University.

Datasets are recognized works in ORCID profiles. Once your dataset has a DOI, use the direct import feature of ORCID to search via CrossRef or DataCite, or use the identifier to add a dataset directly to your ORCID record.