Research Data Management

Research Data Management (RDM) 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, use and reuse.

Below you'll find more information about what exactly research data is as well as reasons for managing and sharing research data.

Use the navigation bar to the left to learn more about how to properly manage and share your research data, as well as funder mandates and additional resources for RDM and creating Data Management Plans (DMPs, also referred to as Data Management & Sharing Plans)

While the definition for "research data" varies by discipline as well as by regulatory agency, in general it refers to data that has been systematically collected for analysis to support research, scholarship, or artistic activity.

Data is categorized by Data Management Planning Tool (DMPTool) as

Observational
     examples: sensor readings, telemetry, survey results, images

• Captured in real-time, typically outside the lab
• Usually irreplaceable and therefore important to safeguard

Experimental
     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

Simulated
     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

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.

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