Meta-analysts must lead by example and embrace all FAIR principles

Abstract

Meta-analyses rely on comprehensive reporting of results and data in primary studies. They should therefore also strive for exemplary reporting and follow FAIR principles (Findable, Accessible, Interoperable, Reusable) for data sharing. Yet, despite growing calls for open, reliable, and transparent science, the quality, reporting and FAIRness of meta-analyses in ecology, evolution, and environmental sciences remain poorly characterised. Here, we evaluate features of methodological quality, reporting and data and code sharing practices of 81 meta-analyses published between 2016 and 2020 in ecology, evolution, and environmental sciences. Whilst we found data sharing was relatively common (68%), code sharing was rare (15%). We found low uptake of reporting guidelines (22%), a small proportion of unweighted approaches (17%) and no risk of bias assessments (0%). The FAIRness of data requires urgent attention. Most openly available meta-analytic datasets were Findable and Accessible, but less often Interoperable or Reusable. Low Interoperability and Reusability prevent verification and may explain why data reuse remains uncommon, and ultimately contributes to research waste. We found no evidence of higher FAIRness scores when studies reported having used guidelines, nor that FAIRness associated with citation rates, suggesting a lack of incentives for adopting FAIR principles. Lastly, we provide recommendations for readily implementable methodological improvements applicable to meta-analyses in any field.

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BibTeX RIS

BibTeX

@misc{dimri2026metaanalysts,
  title = {{Meta-analysts must lead by example and embrace all FAIR principles}},
  author = {Shreya Dimri and Marit Praetz and Vedant Dixit and Heikel Balti and Antica Culina and Ryan Field and Alicia J. Foxx and Matthew Grainger and Edward Richard Ivimey-Cook and Matt Lloyd Jones and Malgorzata Lagisz and Nicholas Patrick Moran and Nerea Piñeiro-Juncal and Pietro Pollo and Gabe Winter and Yefeng Yang and Chengsheng Zheng and Alfredo Sánchez-Tójar},
  howpublished = {EcoEvoRxiv},
  year = {2026},
  month = sep,
  doi = {10.32942/x2139c},
  url = {https://ecoevorxiv.org/repository/view/14796/},
  note = {CC BY-NC 4.0},
}

RIS

TY  - UNPB
TI  - Meta-analysts must lead by example and embrace all FAIR principles
AU  - Shreya Dimri
AU  - Marit Praetz
AU  - Vedant Dixit
AU  - Heikel Balti
AU  - Antica Culina
AU  - Ryan Field
AU  - Alicia J. Foxx
AU  - Matthew Grainger
AU  - Edward Richard Ivimey-Cook
AU  - Matt Lloyd Jones
AU  - Malgorzata Lagisz
AU  - Nicholas Patrick Moran
AU  - Nerea Piñeiro-Juncal
AU  - Pietro Pollo
AU  - Gabe Winter
AU  - Yefeng Yang
AU  - Chengsheng Zheng
AU  - Alfredo Sánchez-Tójar
PY  - 2026
DA  - 2026/09/05
PB  - EcoEvoRxiv
DO  - 10.32942/x2139c
UR  - https://ecoevorxiv.org/repository/view/14796/
AB  - Meta-analyses rely on comprehensive reporting of results and data in primary studies. They should therefore also strive for exemplary reporting and follow FAIR principles (Findable, Accessible, Interoperable, Reusable) for data sharing. Yet, despite growing calls for open, reliable, and transparent science, the quality, reporting and FAIRness of meta-analyses in ecology, evolution, and environmental sciences remain poorly characterised. Here, we evaluate features of methodological quality, reporting and data and code sharing practices of 81 meta-analyses published between 2016 and 2020 in ecology, evolution, and environmental sciences. Whilst we found data sharing was relatively common (68%), code sharing was rare (15%). We found low uptake of reporting guidelines (22%), a small proportion of unweighted approaches (17%) and no risk of bias assessments (0%). The FAIRness of data requires urgent attention. Most openly available meta-analytic datasets were Findable and Accessible, but less often Interoperable or Reusable. Low Interoperability and Reusability prevent verification and may explain why data reuse remains uncommon, and ultimately contributes to research waste. We found no evidence of higher FAIRness scores when studies reported having used guidelines, nor that FAIRness associated with citation rates, suggesting a lack of incentives for adopting FAIR principles. Lastly, we provide recommendations for readily implementable methodological improvements applicable to meta-analyses in any field.
ER  -

Abstract

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