Research data management (or RDM) is a term that describes the organization, storage, preservation, and sharing of data collected and used in a research project. It involves the everyday management of research data during the lifetime of a research project (for example, using consistent file naming conventions). It also involves decisions about how data will be preserved and shared after the project is completed (for example, depositing the data in a repository for long-term archiving and access).

Managing your research data is important for several reasons:

• Effective data management from the outset of your project saves time and resources in the long run.
• Good data management practices help to prevent data loss and errors, increasing the quality of your analyses.
• Funders are increasing requiring research data management plans and data sharing.
• Well-managed, accessible data allows others to validate and replicate findings, and can lead to valuable discoveries by others outside of the original research team.

The ULS supports research data management at the University of Pittsburgh. Contact us for assistance, or browse our research guide at http://pitt.libguides.com/managedata.

Data management plans (DMPs) are documents prepared by researchers as they are planning a project. Funders, particularly federal agencies, are increasingly requiring DMPs in grant proposals. DMPs typically address

• The types and formats of data to be collected
• Plans for creating metadata and documentation for the data
• Plans for sharing data, including policies affecting access and reuse of the data by other researchers
• Plans for archiving and preserving the data

DMPTool can guide you through the creation of a data management plan that will meet your funder's requirements. DMPTool is widely used by researchers at institutions around the U.S.

To use DMPTool, click on the DMPTool icon above, click Get Started, and select the University of Pittsburgh from the drop-down list.

For assistance with writing a DMP, contact us.

If you’re reusing data from an outside source in your research, you’ll want to make sure that you are providing proper attribution. The Inter-university Consortium for Political and Social Research (ICPSR) suggests that a citation for a dataset should include the following basic elements:

• Title
• Author
• Date
• Version
• Persistent identifier, such as the Digital Object Identifier (DOI)

For more information about citing datasets, see the following resources:

• Data Citations (ICPSR)
• Cite Your Data (DataCite)
• How to Cite Datasets and Link to Publications (DCC)
• How to Cite Data (Michigan State University Libraries)
• Citing Data​ (Purdue Libraries)

If you are using a dataset from a repository, you may find that the repository provides a recommended citation.

The University of Pittsburgh’s institutional repository D-Scholarship@Pitt offers long-term storage for scholarly output. Pitt researchers can upload their published or unpublished work to D-Scholarship, including datasets.

Users can submit nearly any format of file and compressed file formats to D-Scholarship@Pitt. D-Scholarship@Pitt is best suited for datasets that are in an inactive state (i.e., after a research project is completed) and may not be able to accommodate large datasets. 

Online help and an FAQ page are available at the D-Scholarship@Pitt website.