Learning from the Data Curation Network

Last week, I attended the Data Curation Network (DCN) Workshop at Johns Hopkins University in Baltimore, MD. The DCN is a consortium of libraries that work together to help curate research data in order to make that data more Findable, Accessible, Interoperable, and Reusable (FAIR).

Tweet from Cynthia Hudson-Vitale (Head of Research Informatics and Publishing at Penn State) about the workshop

Tweet from Cynthia Hudson-Vitale (Head of Research Informatics and Publishing at Penn State) about the workshop

The objectives of the DCN and the DCCP are closely aligned. With their hands on the data, DCN curators work with researchers to ensure that the data put into repositories is FAIR. The DCCP, on the other hand, works with researchers to make their data discoverable, even if that data is not in a repository. In other words, where the DCN deals with data curation as a whole, including cataloging the data, the DCCP focuses primarily on cataloging only. I attended the workshop not just to learn more about data curation myself but also to see what lessons the DCCP can learn from the DCN.

The DCN created and uses a workflow that they call CURATE. This workflow walks curators through the steps of making a dataset FAIR and provides checklists for each step to act as a guide. Because the DCCP works with data that is not necessarily in a repository, not all aspects of the workflow are applicable to our model. However, these checklists provide an excellent resource as we at the DCCP work to improve our data cataloging and provide guidance to new members.

We at the DCCP look forward to reviewing all the resources created by DCN members and working together to ensure that data is FAIR, no matter where it is stored.