We recently published a data paper about museum closures in the Journal of Open Humanities Data, and it has prompted me to reflect on why this form of academic output is worth taking seriously. It documents our process to carefully assemble museum data from fragmented sources, making several epistemic decisions in a context of high uncertainty.
Data papers are an undervalued type of output designed to share data according to FAIR principles (findable, accessible, interoperable and reusable). The twist compared to a traditional research paper is the sole focus on data collection, structuring, categorisation, and limitations, which are usually relegated to a couple of paragraphs or to an appendix. Data papers emphasise how creating data provides a scholarly contribution in its own right, giving credit for the intense cognitive labour involved and separating the process of data curation from interpretation and analysis.
👉 Reference: Liebenrood, M., Wright, G. A., Candlin, F., Poulovassilis, A., Ballatore, A., & Wood, P. T. (2026). A Dataset of Collections Dispersal Following Museum Closures in the UK During 2000–2025. Journal of Open Humanities Data, 12: 13, pp. 1–7. DOI: https://doi.org/10.5334/johd.460
Cultural Geo-Analytics Lab 