Data Management Resources

Repositories are places where researchers can deposit their data for preservation and sharing at the conclusion of a project. Using an established repository as a mode of sharing is encouraged or required by many funders and publishers. In addition, depositing data into a repository increases your associated research citations (Drachen, Ellegaard, Larsen, & Dorch, 2016).

Repository services may include:

• Providing the dataset with a persistent identifier (ex: DOI)
• Data curation, where data is reviewed for ways to increase reuse potential
  • Curation is not a judgement of the content within the dataset. Curation reviews the structure and format of the data (ex: file formats) to ensure the best possible use by other researchers. 
• Embargoing data until it can be publicly available for access (usually up to 1 year) 
• Applying a license to the data to clarify future use
• Guaranteed availability for a certain time frame
• Preservation and storage services, such as duplicate backups and file integrity checks
• Access protection, especially in human subject research
  • Only specialized repositories have the ability to provide access restrictions, such as QDR and ICPSR. Most other repositories make data openly available to support the mission of open data and open scholarship.  

The University of Pennsylvania Libraries is an institutional member of the Qualitative Data Repository (QDR), a dedicated archive for storing and sharing qualitative and multi-method data. Our membership supports the curation and preservation of data projects deposited by Penn faculty, students, and research staff. Through this benefit, individual researchers can consult with experts in qualitative data curation, including discussions about disclosure issues, metadata creation, and more. Researchers should start early in consulting with QDR, ideally at the beginning of a project before data collection or during the data management planning stage for any grant submissions. If you are applying for a large or multi-year grant, please consult with QDR before designating them as a repository. There may be additional costs for large-scale or multi-year projects. If you are a researcher working in qualitative methods, please contact Lynda Kellam (LibraryRDDS@pobox.upenn.edu) for more information.

For more information about QDR and qualitative data analysis, check out our Qualitative Data Analysis Tools and Resources guide. There is even a recording of a QDR workshop for in depth information on the repository.  

ScholarlyCommons is the University of Pennsylvania's open access institutional repository for gathering, indexing, storing, and making widely available the scholarly output of the Penn community. ScholarlyCommons accepts datasets from the Penn community that meet submission policies. 

Our policies and instructions are available on our ScholarlyCommons Guide, but below we have highlighted relevant information for datasets: 

• We have a limited file submission size of 5GB per file, and total submissions must be under 50GB.
• All submissions must be free of Personally Identifiable Information (PII) as our repository is completely open access (meaning anyone can download anything).
• We automatically assign a Handle (a persistent and unique identifier) to all submissions, but if you would like a DOI (a specific type of Handle), you can email us at libraryRDDS@pobox.upenn.edu to request one at the time of your submission. 
• We have a basic list of preferred file formats for submission as they are more preservation friendly. While this list is not comprehensive, overall we prefer files that are not proprietary, so you don’t need a specific software to open and use them.
• Expert data librarians can provide data curation for dataset submissions if desired by the submitter. Staff follow the CURATED workflow created by the Data Curation Network. Please request curation through our email libraryRDDS@pobox.upenn.edu at the time of submission.

For more information, see our guide or email repository@pobox.upenn.edu. Feel free to use our boilerplate text to describe ScholarlyCommons in your DMP. 

Dryad is an open data publishing platform and a community committed to the open availability and routine re-use of all research data. It originally began preserving ecology and environmental biology data, but has since then broadened out its collection policy to accept any data that meets its policies (such as no personally identifiable human subject data). The University of Pennsylvania has an institutional membership with Dryad, which covers the cost of curation and deposit of up to 300GB of data. 

Below are some important points to know about Dryad. For more information, review Dryad's thorough FAQ and our Dryad DMP boilerplate language. Any questions can be directed to LibraryRDDS@pobox.upenn.edu. 

• You need an ORCID identifier to login to Dryad. View the ORCID guide for more information. The first time you sign in, you will either create an ORCID, if you do not have one, or connect your existing account. You will then choose the University of Pennsylvania as your institutional member organization and sign on with your Penn SSO. 
  • Connecting to ORCID pushes anything you publish with Dryad to your ORCID profile automatically.
• All data deposits have a mandatory Creative Commons Zero license, which allows for the widest reuse. Assigning a work a CC0 license does not remove expectations for future users to follow professional norms of citation and attribution. 
• While deposits up to 300GB of data are covered under our membership, they accept deposits up to 1TB. The additional data volume will cost extra, which will be the responsibility of the PI to pay. 
• Dryad is a curated repository which means they do curation checks on each deposit. They will review your metadata and files to ensure they are readable, well documented, and validated. If they have questions, problems, or suggested area of improvement for your dataset, they will reach out to the depositor directly. 
• As an open access repository, Dryad requires that all Personal Health Information (PHI), Personally Identifiable Information (PII), and other types of sensitive data be removed from the dataset. Make sure that you can meet these Human Subjects Data expectations. If you cannot meet these requirements, then you must deposit in a controlled access repository or update your data. 

You can watch a recording of our Dryad for STEM Researchers workshop for even more information on how to login and the more advanced options that Dryad offers! The slides are available for viewing as well.