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

Céline KerfantJavier Ruiz-PérezJuan José García-GraneroCarla LancelottiMarco MadellaEmma Karoune
Published in: Scientific data (2023)
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.
Keyphrases
  • electronic health record
  • big data
  • randomized controlled trial
  • systematic review
  • healthcare
  • primary care
  • machine learning
  • quality improvement
  • wastewater treatment
  • meta analyses
  • case control