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Research Data Management at UF: Home

This is a guide on resources available at the University of Florida and beyond on research data management. It includes information about tools for data management planning, data and file sharing, metadata and data standards, and data storage.

Data Management Basics

See guide tabs for additional information on these data management topics:

Other Training On Campus

UF Research Computing provides training on use of its resources.

UF Informatics Institute hosts a wide variety of talks as well as an Annual Symposium, covering big data and more.  See their schedule.

Related LibGuides

Research Data

"Research data is defined as the recorded factual material commonly accepted in the scientific community as necessary to validate research findings" (OMB, Circular A-110)

What constitutes “data” covered by a Data Management Plan? 

  • "What constitutes such data will be determined by the community of interest through the process of peer review and program management. This may include, but is not limited to: data, publications, samples, physical collections, software and models" (NSF, Data Management & Sharing Frequently Asked Questions, 2010)
  • "Recorded factual material commonly accepted in the scientific community as necessary to document and support research findings. This does not mean summary statistics or tables; rather, it means the data on which summary statistics and tables are based. For the purposes of this policy, final research data do not include laboratory notebooks, partial datasets, preliminary analyses, drafts of scientific papers, plans for future research, peer review reports, communications with colleagues, or physical objects, such as gels or laboratory specimens" (NIH, Data Sharing Policy and Implementation Guidance, 2003)

Looking for more data-related definitions?

Check out the Data Thesaurus from the National Network of Libraries of Medicine.

 

 

Benefits of Proper Data Management

Some of the benefits of proper data management are:

  • Confirmation of published research claims, peer-review, and validation of data
  • Reuse and repurpose of data (e.g. reanalyses and meta-analyses) beyond the primary objective of the data collector
  • Increase the discoverability of research, citations and new collaborations (research impact)
  • Avoids redundant data collection
  • Preservation and protection of data
  • Efficient use of research funding
  • Increase public trust in science
  • Data available for educational purposes
  • Research excellence and advancing of science

Modified from Beagrie et al. (2009) Keeping Research Data Safe 2

Learn More - Web-based Training Opportunities

Hazards of Not Having a Data Management Plan

from NYU Health Sciences Libraries, "a mini series showing a data management horror story."

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