Towards Rigorous Data Upcycling using the FAIR Implementation Profile for the SSHOC-NL Socio-Economic History Community

By Angelica Maineri, Shuai Wang, and Tycho Hofstra


Despite the wide endorsement of the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles [1] by research institutions and funders, the principles are implemented heterogeneously by research communities. FAIR Implementation Profiles (FIPs) were proposed as a FAIR Digital Object that captures communities' decisions about FAIR implementation [2]. The SSHOC-NL Socio-Economic History (SSHOC-NL-SEH) community is a community consisting of researchers and data experts from various institutes in the Netherlands. The research outputs of the community include historical and upcycled datasets, as well as analytical results about social and economic history. The FIPs for the SSHOC-NL-SEH community along with five other social science communities have been published recently [3].

We study the upcycling of legacy datasets using the FIP. By comparing their current status, we demonstrate that, when compared with FIP, one can rely on the decisions of the FIP for suggesting data management decisions and future steps to upcycle these datasets and align them with community standards. For example, Volkstellingen is published on EASY, which has been discontinued and is replaced by the DANS SSH Data Station, and its use of URN is not aligned with the FIP. Our examination shows that Gementegeschiedenis is the only dataset whose licence is aligned with the FIP. Due to the use of Dataverse, the metadata knowledge representation language is richer than what has been captured by the FIP, which, in turn, calls an update of the FIP.


[1] M. D. Wilkinson et al., ‘The FAIR Guiding Principles for scientific data management and stewardship’, Sci Data, vol. 3, no. 1, p. 160018, Dec. 2016, doi: 10.1038/sdata.2016.18.
[2] E. Schultes, B. Magagna, K. M. Hettne, R. Pergl, M. Suchánek, and T. Kuhn, ‘Reusable FAIR Implementation Profiles as Accelerators of FAIR Convergence’, in Advances in Conceptual Modeling, G. Grossmann and S. Ram, Eds., in Lecture Notes in Computer Science. Cham: Springer International Publishing, 2020, pp. 138–147. doi: 10.1007/978-3-030-65847-2_13.
[3] S. Wang, A. Maineri, N. K. Singh, and T. Kuhn, ‘FAIR Implementation Profiles for Social Science’, in Proceeding of 17th International Conference on Metadata and Semantics Research, 2023.