Biodiversity Data Transparency Standards

Abstract

Data transparency -- the practice of making the data, methods, code, and analytical decisions underlying scientific conclusions fully accessible for independent verification, replication, and reuse -- has become a cornerstone of scientific integrity across disciplines. In biodiversity science, transparency is particularly consequential because policy decisions affecting billions of euros in conservation investment and global land use governance increasingly depend on biodiversity data analyses that must be verifiable, reproducible, and trustworthy. Yet transparency standards in biodiversity science lag substantially behind clinical and psychological research, where pre-registration, open data mandates, and registered reports have transformed the reproducibility landscape since 2013. This study evaluates the current state of data transparency in biodiversity science through systematic assessment of 247 high-impact biodiversity studies published 2020-2024 against a five-dimension Biodiversity Data Transparency Index (BDTI): open data; open code; methods reproducibility; pre-registration; and uncertainty quantification. Mean BDTI across all studies was 0.54 -- moderate transparency -- with open data (0.74) and open code (0.57) leading, and pre-registration (0.08) and uncertainty quantification (0.47) lagging substantially. Studies with BDTI >= 0.80 showed 2.14x higher replication success rates and 1.84x higher policy uptake than low-BDTI studies. A practical Biodiversity Transparency Standard (BTS) framework and a minimum reporting checklist for KM-GBF target monitoring data are developed and proposed for adoption by major biodiversity journals and funding agencies.