We are the Data Curation Network

As professional data curators, research data librarians, academic library administrators, directors of international data repositories, disciplinary subject experts, and scholars we represent academic institutions and non-profit societies that make research data available to the public.

What we do

Data curators prepare and enrich research data to make them findable, accessible, interoperable and reusable (FAIR). Sharing our data curation staff across DCN partner institutions enables data repositories to collectively, and more effectively, curate a wider variety of data types (e.g., discipline, file format, etc.) that expands beyond what any single institution might offer alone.

Join Us

We strive to build services that are compatible across all technology systems and software formats. With a proven and appealing value-proposition, the Data Curation Network will expand into a sustainable entity that grows beyond our initial partner institutions.

Our Values

Standards-driven. DCN data curation techniques strive to be practical and transparent.

Trusted. We strive to add value to data while maintaining data integrity and the upholding ethical responsibilities of data sharing.

Inquisitive. Our research aims to explore how well-curated data are used to advance research and education in ways that are measurably of greater reuse value than non-curated data.

Empowering. By sharing tools, providing a pipeline for training data curators, and promoting data curation practices across the profession the Data Curation Network will build an innovative community that enriches capacities for data curation writ large.

Our Workflow

The DCN developed a standardized set of C-U-R-A-T-E steps and checklists to ensure that all datasets submitted to the Network receive consistent treatment. The CURATE Checklists (pdf) were drafted in the planning phase of the project (read the 2018 post) and further enhanced by members of the DCN at the First Annual All Hands Meeting in July, 2018. These checklists are works in progress.

Check files and read documentation (risk mitigation, file inventory, appraisal/selection) – read more

Understand the data (or try to), if not… (run files/environment, QA/QC issues, readmes) – read more.

Request missing information or changes (tracking provenance of any changes and why) – read more

Augment metadata for findability (DOIs, metadata standards, discoverability) – read more

Transform file formats for reuse (data preservation, conversion tools, data visualization) – read more

Evaluate for FAIRness (transparent usage licenses, responsibility standards, metrics for tracking use)– read more