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Working Group Recommendation October 3, 2025

Best Practice Recommendations for improving FAIR data maturity in wind energy

  • Output Type: Working Group Recommendation
  • Output Status: Endorsed
  • Review Period End: 2026-01-05
  • DOI: 10.15497/RDA00141
  • Primary Domain: Engineering and Technology
  • RDA Pathways: FAIR, CARE, TRUST - Evaluation and Policy,FAIR, CARE, TRUST - Adoption, Implementation, and Deployment
  • Group Technology Focus: Depositing Research Outputs
  • Stakeholders: Early Career Individuals, Funders & Policy makers, Industry & Private Sector, Infrastructure Providers, Libraries, Regions & Nations, Research Performing Organisations, Researchers & Scientists
  • Sustainable Development Goals: Affordable and Clean Energy
  • Language: English

Abstract

The RDA Wind Energy Community Standards Working Group has created these new best practice recommendations for improving FAIR data maturity in wind energy in practice. It addresses key challenges in the sector, which are to foster data exchange and re-use as well as to create more value from data by increasing the efficiency of data cleaning and analytics processes. The Working Group first reviewed and evaluated the current data sharing and publishing landscape in wind energy, finding that there is no widely recognised community ontology at the sector level, and alignment with upper-level ontologies is largely absent. A review of existing open wind energy data led us to the conclusion that the platform itself tends to dictate the FAIRness of a published dataset. It was therefore decided to analyse the potential FAIRness of a range of different platforms (divided into catalogues, repositories, knowledge hubs, organisation websites and dashboards) relevant to the wind energy sector. After shortlisting 15 relevant data sources covering these different types, we evaluated potential FAIRness assessment tools, and chose to work with the FAIR Data Maturity
Model due to previous positive experience with it. However, the criteria were adapted slightly for our needs, including reducing the evaluation scale to a Boolean “1/0” choice, rather than using the suggested scale. We also added some specific criteria, including the capability to link to defined wind energy specific terms, to access a preview plot of the data and to access a preview of the file structure. The results showed that the repositories all score similarly, mostly between 74% and 83%, the knowledge hubs also score slightly lower (69-71%), with the company websites (43% and 0%) and the dashboards (34-66%) the lowest. A more detailed analysis of the  ifferent criteria allowed us to define recommendations for wind energy data publishing platform developers for wind energy data publishers and for the general wind energy community. Finally, the project allowed us to define some lessons learned, both in terms of community building and interdisciplinary collaboration, as well as in the application of the FAIR Data Maturity Model.

Impact Statement

These recommendations enable the wind energy community to improve the FAIRness of sectoral data platforms and practices, fostering more efficient data exchange, reuse, and analytics. By providing clear guidance on evaluation criteria and platform design, they support developers, publishers, and researchers in aligning with FAIR principles, thereby unlocking greater value from data and strengthening reproducibility, interoperability, and collaboration in wind energy research and innovation.

Explanation of Sustainable Development Goals

These recommendations contribute to SDG7 (Affordable and Clean Energy) by enhancing the accessibility, interoperability, and reuse of wind energy data, thereby accelerating innovation, improving efficiency, and supporting the global transition to sustainable energy systems.

Citations

Sarah Barber, Sirko Schindler, Catherine Jones, Pablo Rodríguez-Sánchez, Yuriy Marykovskiy, RDA Wind Energy Community Standards WG,
Version 1.0.0 of Recommendations published 2025 via Research Data Alliance

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Comments

  • Profile Picture

    December 15, 2025 at 1:26 pm

    Sarah Barber says:

    Thanks for the comments, Stephen. We have made the following changes:
    - We have numbered the recommendations.
    - We added a few words to explain OAI-PHM within R1.9.
    - We tried to help readers understand the recommendations better by adding examples to all of the recommendations.

    Reply
  • Profile Picture

    December 15, 2025 at 1:26 pm

    Sarah Barber says:

    Thanks for the feedback Harry! We have made the following updates in the new version of the recommendations:


    • We have mentioned RDF-A or JSON-LD in R1.7 as follows: "Wind energy data publishing platforms should ensure that their records can be harvested by data catalogues, such as Google Dataset Search, and the records can be parsed by automated FAIR assessment tools like F-UJI. Example: Using schema.org annotations embedded as RDFa, Microdata, or JSON-LD on the landing page for a dataset provides structured, semantically annotated information about the dataset for systems to automatically process."

    • We have updated the analysis for RDA-I1-02M in Section 3 to assess the use of RDF-A or JSON-LD accordingly.

    • We have included a suggestion for using CoreTrustSeal in R1.2.

    Reply
  • Profile Picture

    October 13, 2025 at 1:26 pm

    Harry Richardson says:

    - Production of guidance and support to encourage data publishing platforms to be ingested into Google Dataset Search and automated tools
    - Could include the endorsement and encouragement of things like Core Trust Sealed for Wind Energy Data Repositories
     
    + Support the encouragement for platforms to perform their own FAIRness evaluations is great
    + Agree with creation and support of community for data publishing platforms - as ever the issue is time and finding someone to facilitate and bring all relevant parties in - why this working group is beneficial

    Reply
  • Profile Picture

    October 10, 2025 at 1:26 pm

    Sarah Barber says:

    Comment from Stephen Holleran:


    1. It would be good if the recommendations were numbered so I could refer to them easily.

    2. Being a wind analyst I am lost with some of the recommendations. Some examples would be good to explain what is meant. E.g.  "Data publishers should use a repository that relies on controlled vocabularies or even ontology terms to provide metadata attributes like subject or keywords." I'm not sure what "subject" and "keywords" mean here.

      1. I don't know what "subject" relates to in the next recommendation either?

      2. "For central search interfaces, metadata harvesting schemes like OAI-PMH23 should be provided to allow federated data infrastructures. A." I don't know what this is but maybe explaining this is outside the scope.



    3. "Data publishers should select terms from subject, and other, controlled vocabularies when available within the platform." Controlled vocabularies are mentioned a few times. What are relevant controlled vocabularies in the wind industry?

    4. You have included some good recommendations.

    Reply
  • Profile Picture

    October 9, 2025 at 1:26 pm

    HArry Richardson says:

    Hello all, Thank you Sarah. The Recommendations make for a very interesting read. What forum is best to review and leave feedback - is this comment section a suitable method?

    Reply
    • Profile Picture

      October 9, 2025 at 1:26 pm

      Sarah Barber says:

      Yes, I think you can just write your comments here!

      Reply
  • Profile Picture

    October 8, 2025 at 1:26 pm

    Charles MaGill says:

    This is a timely and valuable contribution, especially as the wind energy field scales up with increasing emphasis on data-driven innovation, reproducibility, and cross-sector collaboration. The document effectively bridges the gap between general FAIR principles and domain-specific needs in wind energy, providing actionable guidance for developers, publishers, and researchers. It's well-structured, with clear sections on the current landscape, analysis, and recommendations, and the use of visuals (e.g., Sankey diagrams and bar charts) makes complex assessments more digestible. The focus on practical tools like the FAIR Data Maturity Model (FDMM) and F-UJI assessments is particularly strong, demonstrating a rigorous, evidence-based approach.

    Reply

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