A dataset for assessing phytolith data for implementation of the FAIR data principles

Phytolith research contributes to our understanding of plant-related studies such as plant use in archaeological contexts and past landscapes in palaeoecology. This multi-disciplinarity combined with the specificities of phytoliths themselves (multiplicity, redundancy, naming issues) produces a wide variety of methodologies. Combined with a lack of data sharing and transparency in published studies, it means data are hard to find and understand, and therefore difficult to reuse. This situation is challenging for phytolith researchers to collaborate from the same and different disciplines for improving methodologies and conducting meta-analyses. Implementing The FAIR Data principles (Findable, Accessible, Interoperable and Reusable) would improve transparency and accessibility for greater research data sustainability and reuse. This paper sets out the method used to conduct a FAIR assessment of existing phytolith data. We sampled and assessed 100 articles of phytolith research (2016–2020) in terms of the FAIR principles. The end goal of this project is to use the findings from this dataset to propose FAIR guidance for more sustainable publishing of data and research in phytolith studies.This project has received funding through EOSC-life from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 824087. The FAIR Phytoliths Project is based at two institutions: Universitat Pompeu Fabra and Historic England. We would like to thank both these institutions for their support and also financial commitment to this project. Thank you also for in-kind support from Texas A&M University and the Spanish National Research Council for time on this project by Javier Ruiz-Pérez and Juan José García-Granero. Ruiz-Pérez was supported by the National Science Foundation under award number DEB-1931232 to J. W. Veldman. García-Granero acknowledges funding from the Spanish Ministry of Science, Innovation and Universities (Grant No. IJC2018-035161-I). Carla Lancelotti acknowledges funding from the European Research Council (ERC-Stg 759800). Celine Kerfant is a recipient of Juan de la Cierva-Formación contract FJC2021-046496-I funded by MCIN/AEI/10.13039/501100011033 and by the European Union “Next Generation EU”/PRTR