The FAIR data principles are a set of guiding principles that foster discoverability, accessibility, interoperability, and reusability of research data. Funders now require FAIR practices in the development of funded research data. The FAIR Guiding Principles for scientific data management and stewardship (Wilkinson et al., 2016) broadened awareness of FAIR in research data. Scientific Data compiled a list of Recommended Repositories resources available online via https://www.nature.com/sdata/policies/repositories#general.
- Data Repository Guidance (scientific data) – https://www.nature.com/sdata/policies/repositories
- Electronic lab notebook (ELNs) which have been approved by UF Integrated Risk Management (IRM) Fast Path Solutions (FPS) for open data. The following resource is listed on the UF IRM FPS resource page.
- LabArchives – https://www.labarchives.com/
- Generalist data repositories which have been approved by UF Integrated Risk Management (IRM) Fast Path Solutions (FPS) for open data:
- Figshare – https://figshare.com/
- Zenodo – https://zenodo.org/
- National Institutes of Health (NIH) National Library of Medicine (NLM) Data Sharing Resources – https://www.nlm.nih.gov/NIHbmic/nih_data_sharing_repositories.html
Benefits of sharing data:
- Analyses can be reproduced and/or validated by others
- Data can be checked for cleanliness and accuracy
- Data can be used for educational purposes
- Help the development and evaluation of novel statistical methods
- Allows testing of secondary or new hypotheses
- Helps in designing new experiments
- Simplifies data acquisition for meta-analysis
- Helps to prevent research misconduct and selective reporting of results
Modified from (Vickers, A (2006) Whose data is it anyway? Sharing raw data from randomized trials. Trials 7: 15).
View this Data Sharing and Management Snafu video for a great example of the challenges of utilizing someone else’s data:
Standards for sharing data:
Without standards, your data may be unusable or unidentifiable. Metadata is descriptive or contextual information that refers to your data. The level of information you keep about your data will depend on the type of data you collect and expected re-purposing.
Use standardized taxonomies and controlled vocabularies to better capture, manage, and archive your data. The most basic type of metadata to capture includes:
- Descriptive (title, time, author, keywords, relations to other data, what the data is about)
- Technical (process used to produce the data, or process required to use the data)
- Administrative (ownership, use permissions)
- Provenance (data origination, history of changes, versions)
- Discipline specific (format, subject, taxonomies, etc.)
For more information on subject-specific metadata (taxonomies, thesauri, ontologies, etc.), see the University of Pittsburgh Metadata & Discovery Guide or the Digital Curation Center Guide on Metadata Standards.
UF Institutional Review Board (IRB) resources related to data:
- Research Data is Institutional Property – https://irb.ufl.edu/index/data.html
- Data/Record Storage and Security – https://irb.ufl.edu/index/data/1981-2.html
- Destruction of Data – https://irb.ufl.edu/index/data/2019-2.html
UF Research Education and Training
UF Research Data Management Sharing Plans & Repositories – https://research.ufl.edu/education-and-training/data-management-sharing-plans-repositories.html
Copyright & Ethical Use of Data
UF Office of Clinical Research (OCR) Data Use Agreements (DUA) – https://clinicalresearch.ctsi.ufl.edu/services/contracting/data-use-agreements/.
Copyright and Data as Intellectual Property
For more information on copyright and data management, or to request a workshop, please see the UF Libraries Copyright on Campus guide.
Citing Data: If you are using another researcher’s data, you must properly cite the data. For more information on citing datasets.
Privacy and Data Security
See University of Florida IT Data Security Standard.