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Support for Research Data Management

“Manuscripts and records were, more or less, raw bits of data until a researcher ‘mined’ them to answer a research question. Therefore, libraries and archives had been partners in data management for a long time.”
-- James L. Mullins, Dean of Purdue Libraries

The ULS recognizes that researchers’ datasets are -- like books, journal articles, and conference proceedings -- scholarly output that hold great potential for facilitating and informing future discoveries and knowledge creation. Among the hallmarks of all good research is good research data management practice. In alignment with its commitment to foster scholarly progress, the ULS is building support around research data management. The ULS can help you with the management of your data by meeting with you for a consultation or by connecting you to relevant resources. 

This page highlights some areas related to research data management where library staff are available to meet with you for consultations.

Contact us about:

Creating a data management plan

Thinking about a new project proposal? Submitting an application to the NSF or another granting agency?

Data management begins with a plan. Increasingly funders are requiring that researchers submit formal data management plans with grant applications, but the creation of a data management plan is good practice even if it is not required by a funder. We are happy to consult with you on data management plan and to point you to guidance that may be useful to you as you prepare for managing your research data.

Understanding funder mandates for data management planning and sharing

Funder requirements around research data management plans and what must be done with the data after the completion of the research project differ. We can help you to identify requirements affecting your project and refer you to resources that will help you to meet these requirements.

Understanding publisher mandates for data sharing

Some academic journals -- Nature and Science among them -- are requiring that researchers share their data as a condition of publishing. We can discuss with you publisher requirements for journals you are publishing in or are considering publishing in and ways that you can fulfill them.

“When datasets were shared only among colleagues known to each other, trust was implicit. If data are to be made widely available and used by people with no personal knowledge of their creation, and for different purposes than those for which they were created, then trust must derive from how the data are managed and documented.” 
-- Joyce M. Ray of Johns Hopkins University

Describing your data

The information that provides context and supports discoverability and reuse of your data is called metadata. Contact us about describing and documenting your data so that others can understand your data files.

Choosing sustainable formats for your data

There are factors to consider when determining file formats for your data. In order to best assure the long-term preservation of your data, choose open standards that are non-proprietary and that can be run on more than one software platform.

Locating repositories to access datasets and to deposit your own data

Looking for shared datasets to reuse in your own research? Or interested in sharing your data with others? We can work with you to locate repositories where they can access relevant shared datasets and share their own data. D-Scholarship, the University of Pittsburgh’s institutional repository, offers long-term storage for scholarly output. Pitt researchers can upload their published or unpublished work to D-Scholarship and this includes datasets. Users can submit nearly any format of file and compressed file formats to D-Scholarship. DScholarship 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-sized datasets. Online help and an FAQ page are available at D-Scholarship@Pitt.

Citing datasets

If you’re reusing a dataset to inform your own work, you’ll want to make sure that you are providing proper recognition. Datasets are scholarly products and should be cited as such. If you are using a dataset that was deposited in a disciplinary data repository, you may find that the repository has a recommended citation standard. Contact us if you would like to discuss dataset citation.

Customized training

The University Library System has developed a series of short modules that focus on research data management topics. Library staff members are available to deliver individual or combined modules in person and to customize these modules to meet the needs of departments, labs, courses, and students groups.


James L. Mullins, “The Policy and Institutional Framework,” in Research Data Management: Practical Strategies for Information Professionals, ed. Joyce M. Ray (Ashland, OH: Purdue University Press), 31.

Joyce M. Ray, “Introduction to Research Data Management,” 2.