- Course overview
- Search within this course
- What is data management?
- Managing and making the most of your data
- Why share your data?
- What happens to your data?
- Giving data context, structure and meaning
- Tools for data management planning
- Myths and best practice
- How and why to make your data open?
- BioSamples: a FAIR sample metadata archive
- Maintenance of life science data by biocurators
- Summary
- Quiz: test your knowledge
- Your feedback
- References
All materials are free cultural works licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, except where further licensing details are provided.
What is data management?
Data management is everything you do to plan, create, capture, record, analyse, store and share data. Data management can also include the discovery, reuse and citation of publicly available data.
Why should you care about data management?
There are many reasons!
- Having a plan helps you by improving your efficiency, reproducibility of your work and by keeping your data secure and safe
- Many funders now require a data management plan
- Making your data publicly available to the research community can help your career and spark new collaborations
How can I make sure my data is FAIR?
The FAIR principles can be used to guide us in thinking about good data management:1
Findable – ensure data can be found, for example by using unique identifiers and clear metadata.
Accessible – ensure data can be retrieved using the unique identifier, however this may require user authorisation where necessary.
Interoperable – ensure use of shared formats, vocabularies and ontologies.
Reusable – ensure data are clearly described, meet community standards and have a usage license.
Read this paper to find out more about the FAIR principles.