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Research Data Services at Penn State Harrisburg

This guide provides an overview of research data services available in the Madlyn L. Hanes Library at Penn State Harrisburg.

Data should be FAIR

Data librarians and data scientists often refer to the "FAIR Guiding Principles for scientific data management and stewardship", published in Scientific Data 2016. In order for research data to be as useful and relevant as it can be, data should follow the FAIR Principles as close as possible. FAIR stands for: Findable, Accessible, Interoperable, and Reusable. A brief outline is included below.

Findable - Both people and machines should be able to find the data. This implies persistent identifiers – like a “digital object identifier” (DOI). You also want to make sure that your metadata is suitable for others to find/discover it. And to be findable, your data should be in a visible catalog or data repository.

Accessible - Both people and machines should be able to access the data through clearly defined, open protocols. Once a user finds the data, they need to know how they can be accessed.

Interoperable - Data usually needs to be integrated with other data throughout the research process for analysis, storage, and processing. A researcher should strive to follow disciplinary standards when describing their data and metadata. Consider the vocabularies/metadata schema that specific disciplines utilize.

Reusable - To make data reusable, it must be clearly and fully documented from beginning to end. Be sure to include a specific usage license and relevant provenance. For example: who created it, when did they create it, and how was it created. All of this information will be relevant to others who want to reuse your data.

Data management

Data management is how you handle, organize, and structure your research data throughout all stages of the research process. This includes data collection, data analysis, long-term storage of your data, and the re-use of your data. Review the Introduction to Research Data Management tab along the left-side of the page for a more detailed look at this topic.

What is your data management workflow? Take a few minutes to draw your data workflow diagram. Consider these questions:

  • What data tools or technology do I use? What tools are available to me?
  • Does my data have security concerns?
  • How much do I collaborate? Internal and external partners?
  • What problems do I encounter when practicing good data management?

Data sharing

Often, federal funding agencies stipulate where, when, and how a researcher must share their research data openly and publicly. This can vary by funding agency and if you suspect that a funding agency has requirements that you are unfamiliar with, check out the Public Access Policies for Federally Funded Research tab along the left side of this guide.

If your funding agency requires you to share your data, but they do not stipulate where it must be shared, consider sharing your data in a disciplinary data repository where your data may be useful to others in your discipline. More information about data repositories can be found in the Data Discovery and Storage tab along the left side of this guide.

If you are not required to share your data, but you are interested in learning more about how to do so, please contact Andrea Pritt at for assistance.