Data and model management is the process of controlling information that is generated during research projects. All research projects require some degree of data and model management, whether it is simple spreadsheet storage, or the complex management of high-throughput data stored and shared among geographically distributed partners. Establishing good data and model management practices ensures that data and models:

  1. can be searched for by the community after publication (Findable)
  2. can be read/downloaded by other researchers (Accessible)
  3. can be understood clearly in the context of the original experiment (Interoperable) and
  4. can be used by other researchers (Re-usable).

The acronym FAIR is used to denote data and models that adhere to good management practices. Good practices are vital for research, as they ensure that the maximal potential impact can be garnered from investment within the research field.

The impact is not the only driver for good data management practices, FAIR data, and models are inherently more robust for exchanging between researchers themselves. Systems biology is an interdisciplinary subject, and as such requires data to be exchanged between researchers from different specialty fields, for integration into models. This innate need for robust exchange between researchers has driven a strong grass-roots movement of field experts who have, and continue to, generate the key tools required for FAIR data and model production: standards, specialized software, and repositories and commons.

A more detailed description of the FAIR guiding principles has been published by Mark Wilkinson et al. in Nature’s Scientific Data.

You can identify a basic data and model management plan using our checklist.