It looks like you're using Internet Explorer 11 or older. This website works best with modern browsers such as the latest versions of Chrome, Firefox, Safari, and Edge. If you continue with this browser, you may see unexpected results.
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.
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.
"Research datais 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
Developed by the University of Edinburgh, this free training is designed for anyone who manages digital data as art of their research project. Includes topical learning units and selections based on role (research student, senior academic, etc.)
Produced by NIH as part of their initiative to enhance rigor and reproducibility in research. Graduate students, postdoctoral fellows and early stage investigators are the primary audiences for these training modules.
Data Carpentry workshops are domain-specific, so that they teach researchers the skills most relevant to their domain and use examples from their type of work. Domains currently covered include ecology, genomics, and social sciences.