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Preparing for the Next Generation of Material Environmental Health and Safety (EHS) Concerns: Guidelines for Future Data Curation Collaborations

Environmental Science & Technology Letters 2025 1 citation ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Jaleesia D. Amos, Anastasios G. Papadiamantis, Camille de Garidel-Thoron, Zhao Zhang, Yuan Tian, Gregory V. Lowry, Iseult Lynch, Jérôme Rose, Armand Masion, Lee A. Walker, Mélanie Auffan, Claus Svendsen, Christine Ogilvie Hendren, Mark R. Wiesner

Summary

This paper presents guidelines for international data curation collaborations in the field of environmental health and safety, addressing emerging contaminants including micro- and nanoplastics. The study outlines nine actionable guidelines structured in scalable phases to help researchers create trustworthy, standardized data for future analyses of advanced materials and environmental contaminants.

Emergence of a new class of nanoscaled contaminants, such as micro- and nanoplastics, as well as nanotechnology’s current transition into advanced materials have broadened the boundaries of the field. These field-expanding topics exemplify the core role of collaboration in creating “good” and trustable data for future analyses. It is imperative that the environmental health and safety community recalls previous efforts to integrate data surrounding engineered nanomaterials to serve as guidance for the next generation of materials. We therefore describe an international collaboration focused at the early stages in the informatics process, the data curation level. Our experiences are described in nine guidelines that can be adopted by future collaborations. These guidelines were written to be actionable and structured within scalable phases. Guidance is also provided on the necessary personnel roles that should be incorporated into funding plans for developing and using advanced materials. Current shifts in the field demand community consensus to define data that are qualified as “good” and “trustable”, which require collaboration at the early stages of the informatics and data generation processes.

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