Open Science in regulatory environmental risk assessment. (18th May 2021) - Brock, Theo C. M.; Elliott, Kevin C.; Gladbach, Anja; Moermond, Caroline; Romeis, Jörg; Seiler, Thomas‐Benjamin; Solomon, Keith; Peter Dohmen, G.
Abstract: A possible way to alleviate the public skepticism toward regulatory science is to increase transparency by making all data and value judgments used in regulatory decision making accessible for public interpretation, ideally early on in the process, and following the concepts of Open Science. This paper discusses the opportunities and challenges in strengthening Open Science initiatives in regulatory environmental risk assessment (ERA). In this discussion paper, we argue that the benefits associated with Open Science in regulatory ERA far outweigh its perceived risks. All stakeholders involved in regulatory ERA (e.g., governmental regulatory authorities, private sector, academia, and nongovernmental organizations), as well as professional organizations like the Society of Environmental Toxicology and Chemistry, can play a key role in supporting the Open Science initiative, by promoting the use of recommended reporting criteria for reliability and relevance of data and tools used in ERA, and by developing a communication strategy for both professionals and nonprofessionals to transparently explain the socioeconomic value judgments and scientific principles underlying regulatory ERA. Integr Environ Assess Manag 2021;17:1229–1242. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC) KEY POINTS: Open Science is important to increase transparency and trust in regulatory Environmental Risk Assessment (ERA). Open Science requires that data, tools, and value judgments used in decision making are made accessible for public interpretation. Benefits associated with Open Science outweigh its perceived risks. Open Science in regulatory ERA is supported by promoting the use of reporting criteria for reliability and relevance of data and tools.