This poster illustrates the adoption and customisation of a process for aligning existing data objects with the FAIR data principles (Findability, Accessibility, Interoperability, and Reuse [3, 6]). This is a preparatory step for creating the Catalysis Data Infrastructure (CDI), an online repository that stores persistent relationships between different types of data objects generated by UK Catalysis Hub projects. Achieving data ‘FAIRness’ eases data integration, which in turn supports cross-disciplinary sharing and use of data. In Illustrating this, we aim to also highlight in practice the benefits of the alignment to the FAIR data principles.
The FAIR data principles for data management and stewardship are intended to provide guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets. These principles emphasise machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity, and creation speed of data . The GO FAIR initiative has been stablished to facilitate the implementation of the FAIR data principles, making data Findable, Accessible, Interoperable and Reusable (FAIR) . The alignment of repositories with the FAIR principles is supported by a seven step process called the “FAIRification process” (FP) . The seven steps of the process are shown in Figure 1.
The adoption and customisation of the process was performed in four steps: (1) localise data objects, (2) retrieve/create metadata for data objects (3) Analysis of the characteristics of published data objects to determine if their current FAIRness and (4) verify how the application of the FAIRification process could improve the objects FAIRNess score.
The results obtained indicate that, while the rate of the articles including some form of data has increased, the alignment of those data to FAIR data principles is still in very early stages. Consequently, a deliberate effort is required to ensure that the data from UK Catalysis Hub projects aligns with FAIR principles.
The development of the CDI provides a unique opportunity for promoting this approach. We think that the CDI will play the roles of resource manager, data publisher, and collaboration space [4, 5]. As resource manager, the CDI manages access to distributed data repositories (institutional repositories, public databases, and specialised repositories). As data publisher, the CDI provides access to data assets from institutional and shared data archives. As collaboration space, the CDI supports users’ accessing, sharing and (re)using data assets, and derived data products and services. Adopting the collaboration space and data publisher roles, the CDI implements interfaces that expose data resources to the research community, fulfilling the findability, accessibility, and reuse principles. Adopting the resource manager roles, the CDI creates meaningful and persistent relationships required to link data resources, fulfilling the interoperability principle.
- Nieva de la Hidalga A., Magagna B., Stocker, M., Hardisty A., Martin P., Zhao Z., Atkinson, M., Jefferey, K. (2017) ENVRI Reference Model V2.2. ENVRIplus project. https://confluence.egi.eu/display/EC/ENVRI+Reference+Model
- Nieva de la Hidalga A., Hardisty A., Martin P., Magagna B., Zhao Z. (2020) The ENVRI Reference Model. In: Zhao Z., Hellström M. (eds) Towards Interoperable Research Infrastructures for Environmental and Earth Sciences. Lecture Notes in Computer Science, vol 12003. Springer, Cham. https://doi.org/10.1007/978-3-030-52829-4_4
- Wilkinson MD, et.al. (2016) The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 3 URL: https://www.nature.com/articles/sdata201618
Abraham Nieva de la Hidalga (Cardiff)
Abraham Nieva de la Hidalga1*, C. Richard A. Catlow1, Brian Matthews2
1UK Catalysis Hub, Research Complex at Harwell, Rutherford Appleton Laboratory, R92 Harwell Oxford Oxfordshire OX11 0FA
2Scientific Computing Department STFC, Rutherford Appleton Laboratory, Harwell Campus, Didcot, OX11 0QX, UK