UNF Digital Commons and DC Data Repositories

Digital Commons Data (DCD), offered by the University of North Florida’s Thomas G. Carpenter Library, is a generalist data repository for faculty to deposit datasets and supporting files in compliance with funder and publisher policies. It provides a means through which faculty researchers and administrators may store, collaborate on, manage, publish, and preserve datasets.

• Researchers may collaborate on data with internal and external collaborators and apply access controls.
• When datasets are uploaded, a DOI is automatically reserved and becomes active upon publication of the dataset.

To learn more, please reach out to lib-digital@unf.edu.

Formats

Many file and data types may be deposited, including text, structured and tabular data files, audio-visual media and image files, geospatial data, 3D data files, and more. We're happy to discuss your particular project needs and answer any questions.

Currently, each dataset may be a maximum of 100GB, but a project may contain more than one dataset.

Let us know at lib-digital@unf.edu if you have questions!

Metadata

When depositing a dataset in DC Data, be prepared with the following metadata, as applicable:

• Title
• Contributors, their email addresses, and any scholarly identifiers (e.g. ORCID, ISNI, Scopus IDs, VIAF, etc.)
• Description
• Data files to upload
• Institutions
• Broad subject categories as well as more specific topics that will help other researchers discover your data
• Funding
• Steps to reproduce
• Spatial (e.g. county, state, country, countries, or any other geographic information relevant to dataset)
• Dates of data collection or compilation
• Collection Methods
• Answer Y/N - Was this data produced through publicly funded research?
• File formats
• Specialized Hardware or Software Required to Access or Open the Files
• Note any other versions available
• Related links such as related publications or datasets

Deposited datasets should not contain any copyrighted or sensitive data.
Deposited datasets should be unique to this repository, not duplicative of datasets already deposited elsewhere, and should not already have a DOI.