Research Data Management
What is Data?
The definition for "research data" varies by discipline as well as by regulatory agency. However, in general it refers to data that has been systematically collected for analysis to support research, scholarship, or artistic activity.
Data management is a continuing process that refers to the collection of tasks practiced throughout the research life cycle that make data easier to find, understand, navigate and use.
Data is categorized by Data Management Planning Tool (DMPTool) as
examples: sensor readings, telemetry, survey results, images
- Captured in real-time, typically outside the lab
- Usually irreplaceable and therefore important to safeguard
examples: gene sequences, chromatograms, magnetic field readings
- Typically generated in the lab or under controlled conditions
- Often reproducible, but can be expensive or time-consuming
examples: climate models, economic models
- Machine generated from test models
- Likely to be reproducible if the model and inputs are preserved
Derived or Compiled
examples: text and data mining, compiled database, 3D models
- Generated from existing datasets
- Reproducible, but can be very expensive and time-consuming
Image by João Batista Neto, from Wikimedia Commons
Why Manage Data?
Increase the research impact of your work
Effective data curation makes your data discoverable by other researchers thus increasing visibility and relevance. Publications with transparent data are also cited more frequently.
Save time and simplify the research workflow
Planning for data management saves time and resources by allowing researchers to focus on working with the data rather than looking for it.
Preserve data for perpetuity
Storing your data in a repository protects your work and preserves your data for future reuse and discovery.
Maintain the integrity of your data
Managing your data the right way across its life cycle will improve reviewability for prospective research by you and others.
Meet grant requirements and make your application more competitive
A large number of funding agencies now require data curation as part of grant proposals and others favor proposals with data management plans in place.
Why Share Data?
Make your research more desirable to publishers
Publishers are increasingly requiring open data policies prior to publication in order to increase transparency and reproducibility.
Promote new discoveries
Disseminating data to other researchers leads to new perspectives on your work and subject that spur new discoveries and innovations as well as providing research resources for others with less funding.
Support open access
Show support for open access by sharing your data.
Image by Tumisu, from Pixabay