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

Shreya Dimri , Marit Praetz, Vedant Dixit, Heikel Balti , Antica Culina, Ryan Field, Alicia J. Foxx, Matthew Grainger, Edward Richard Ivimey-Cook , Matt Lloyd Jones , Malgorzata Lagisz , Nicholas Patrick Moran , Nerea Piñeiro-Juncal , Pietro Pollo, Gabe Winter, Yefeng Yang, Chengsheng Zheng, Alfredo Sánchez-Tójar 

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.