Published May 2, 2022 | Version v1
Technical note Open

The implementation of FAIR data principles in the IPCC AR6 assessment process

  • 1. ICTP, Trieste, Italy
  • 2. Working Group II Technical Support Unit, IPCC; Alfred Wegener Institute, Germany
  • 3. Centre for Environmental Policy, Imperial College, London, UK
  • 4. Directorate General of Climate Change, Ministry of Environment and Forestry, Republic of Indonesia
  • 5. Instituto de Física de Cantabria (IFCA), CSIC - Universidad de Cantabria. Santander. Spain
  • 6. Alaska Fisheries Science Center, USA
  • 7. Ouranos, Canada
  • 8. UKRI Science and Technology Facilities Council, UK
  • 9. Research Center for Environmental Modeling and Application Research Institute for Global Environment Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Japan
  • 10. University of Ghana, Ghana
  • 11. IIASA, Vienna, Austria
  • 12. University of East London, UK
  • 13. MetaDataWorks, UK
  • 14. CEDA, NCAS, STFC Rutherford Appleton Laboratory, UK
  • 15. Koninklijk Nederlands Meteorologisch Instituut, De Bilt, The Netherlands
  • 16. German Climate Computing Center, Hamburg, Germany
  • 17. Center for International Earth Sciences Information Network (CIESIN) Columbia Climate School, Columbia University, New York, U.S.A


The IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) recommends the implementation of FAIR (Findable, Accessible, Interoperable, Reusable) data principles and best practices for the documentation and curation of data that is assessed by the IPCC. The motivation to implement FAIR in the IPCC is to increase transparency and accessibility of the assessment, the implementation of the IPCC Error Protocol, and the long-term curation of the assessed digital information.

This document introduces the implementation of FAIR data principles into the IPCC process and reflects the four elements of FAIR to find the data, produce and reproduce figures and, finally, to document the provenance for reusability. It presents standard (basic) measures that are recommended for all digital data that is assessed, intermediate measures that achieve reproducibility of assessed digital information, for example through the use of collaborative platforms for figure development, and also full (advanced) measures to achieve reusability of digital products with complete provenance documentation.



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