Why FAIR data?

Research & Development (R&D) activities often result in data and datasets which are useful for further and future research. If this data cannot be found,  accessed, and does not interoperate, re-use is prohibited. This results in a huge financial loss for global research as well as a setback to scientific progress. Read more…

With the continued digitization of the energy sector, the problem of sunken scholarly data investments and forgone opportunities of harvesting existing data is exacerbating. It adds to the problem that the reproduction of knowledge is incomplete, impeding the transparency of science-based evidence for the choices made in the energy transition.

To apply the FAIR data principles, many changes are required in data processes, technology and people. Initial FAIR implementation costs can be significant depending on the amount of scientific data and the effort required. However, the benefits far outweigh the costs. These benefits can be seen  at three levels.

What are the individual benefits of FAIR data?
What are the research community benefits?
What are the societal benefits (e.g., regarding the low-carbon energy transition)?

 

A non-exhausting list of benefits (in no specific order):

  • Achieving more collaboration, community engagement, training, outreach, and support across multiple levels of expertise
  • Increasing the community-wide R&D efficiency by promoting FAIR assessment tools
  • Increasing the value of data through standardisation and certification
  • Building infrastructures to search and index digital objects 
  • Deploying new big data analysis methods 
  • Setting up a Use Case library detailing user narratives, objectives and cross-disciplinary workflows
  • Enabling interoperability across multiple software and energy devices
  • Establishing registries for identifiers and Application Programm Interfaces (APIs) 
  • Improving knowledge sharing by metadata documentation 
  • Enabling new business models based on the recognised value of data

See also: NIH Data Commons