Developing a FAIR data policy for chemical risk assessment and exploring future topics and stakeholder interests with large language models and data sciences.
10 affiliations
- 1TNO, Risk Analysis for Prevention, Innovation and Development, Princetonlaan 6, 3584 CB Utrecht, The Netherlands.
- 2TNO, Microbiology and Systems Biology, Sylviusweg 71, 2333 BE Leiden, The Netherlands.
- 3Universidade de Aveiro, Centre for Environmental and Marine Studies, Department of Biology, Applied Ecology and Ecotoxicology Laboratory, 3810-193 Aveiro, Portugal.
- 4University of Birmingham, School of Geography, Earth and Environmental Sciences, Edgbaston, Birmingham, B15 2TT UK.
- 5GO FAIR Foundation, Poortgebouw Noord, Rijnsburgerweg 10, 2333 AA Leiden, The Netherlands.
- 6Karolinska Institutet, Institute of Environmental Medicine, Nobels Väg 13, 17177 Stockholm, Sweden.
- 7UBA-German Environment Agency (Umweltbundesamt), Wörlitzer Platz 1, D-06844 Dessau-Roßlau, Germany.
- 8KWR Water Research Institute, Groningenhaven 7, 3433 PE Nieuwegein, The Netherlands.
- 9Flemish Institute for Technological Research (VITO), Environmental Intelligence Unit, Boeretang 200, 2400 Mol, Belgium.
- 10TNO, Research Information Support, Anna van Buerenplein 1, 2595 DA Den Haag, The Netherlands.
Abstract
Background
The EU Partnership for the Assessment of Risk from Chemicals (PARC) develops novel methods for human health and environmental risk assessment (RA) for regulatory application. Large amounts of environmental monitoring, in vitro, in silico and other types of data are generated. To facilitate data reuse and information exchange within and between research and regulatory stakeholders, it is important to document and manage data. In 2016, the FAIR (Findable, Accessible, Interoperable and Reusable) data principles were introduced to foster reuse of data. To adopt FAIR within PARC, a PARC FAIR data policy (PFDP) was developed.Results
To determine PFDP topics, expert panel discussions were held, followed by review of 37 stakeholder documents issued by authorities in RA and environmental, health, (open) data sciences. This resulted in a taxonomy of 57 topics ("narrower term(s)" (NT(s), together with 15 "broader terms"), forming the basis of the PFDP. These included, aside FAIR, aspects specifically relevant for RA, such as GDPR, sensitive data, transparency and data valuation. Large Language Models (LLMs) were used to contrive novel terms for PFDP revisions. Seventy-eight novel terms were discovered by LLMs, compared to 57 NTs already identified by experts. Based on a frequency analysis within stakeholder documents, of all 135 terms (57 + 78) -further categorized under eight ChatGPT-derived "FAIR categories"- and subsequent cluster analyses (PCA, K-means), differences between stakeholders were identified. Organizations concerned with environmental and human chemical RA (e.g. European Chemicals Agency, European Food Safety Authority) could be distinguished from those focused on technical FAIR data issues and those involved in publicly funded research and open science.Conclusions
To foster environmental and human health research data for research purposes and adoption of research data in regulatory applications, a taxonomy of terms and FAIR data policy was developed. A frequency analysis of the taxonomy (augmented with LLM-derived) terms in stakeholder documents revealed differences between stakeholders, which may help identify trends e.g. the adoption of FAIR principles within and across sciences and regulatory domains for (NG) RA. The PFDP is designed to support data driven RA improvement within PARC but can equally contribute to FAIR data in other environmental RA research and regulatory communities.Supplementary information
The online version contains supplementary material available at https://doi.org/10.1186/s12302-026-01472-y.
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BibTeX
@article{stierum2026developing,
title = {{Developing a FAIR data policy for chemical risk assessment and exploring future topics and stakeholder interests with large language models and data sciences.}},
author = {Rob Stierum and Gino Kalkman and Sabina Bijlsma and Jildau Bouwman and Susana Loureiro and Iseult Lynch and Barbara Magagna and Penny Nymark and Peter C von der Ohe and Tessa E Pronk and Erik Schultes and Karin Slater and Jan Theunis and Fred van de Brug},
journal = {Environmental sciences Europe},
year = {2026},
month = jul,
doi = {10.1186/s12302-026-01472-y},
url = {https://europepmc.org/article/MED/42923205},
note = {CC BY},
}
RIS
TY - JOUR
TI - Developing a FAIR data policy for chemical risk assessment and exploring future topics and stakeholder interests with large language models and data sciences.
AU - Rob Stierum
AU - Gino Kalkman
AU - Sabina Bijlsma
AU - Jildau Bouwman
AU - Susana Loureiro
AU - Iseult Lynch
AU - Barbara Magagna
AU - Penny Nymark
AU - Peter C von der Ohe
AU - Tessa E Pronk
AU - Erik Schultes
AU - Karin Slater
AU - Jan Theunis
AU - Fred van de Brug
PY - 2026
DA - 2026/07/01
JO - Environmental sciences Europe
DO - 10.1186/s12302-026-01472-y
UR - https://europepmc.org/article/MED/42923205
AB - Background The EU Partnership for the Assessment of Risk from Chemicals (PARC) develops novel methods for human health and environmental risk assessment (RA) for regulatory application. Large amounts of environmental monitoring, in vitro, in silico and other types of data are generated. To facilitate data reuse and information exchange within and between research and regulatory stakeholders, it is important to document and manage data. In 2016, the FAIR (Findable, Accessible, Interoperable and Reusable) data principles were introduced to foster reuse of data. To adopt FAIR within PARC, a PARC FAIR data policy (PFDP) was developed.Results To determine PFDP topics, expert panel discussions were held, followed by review of 37 stakeholder documents issued by authorities in RA and environmental, health, (open) data sciences. This resulted in a taxonomy of 57 topics ("narrower term(s)" (NT(s), together with 15 "broader terms"), forming the basis of the PFDP. These included, aside FAIR, aspects specifically relevant for RA, such as GDPR, sensitive data, transparency and data valuation. Large Language Models (LLMs) were used to contrive novel terms for PFDP revisions. Seventy-eight novel terms were discovered by LLMs, compared to 57 NTs already identified by experts. Based on a frequency analysis within stakeholder documents, of all 135 terms (57 + 78) -further categorized under eight ChatGPT-derived "FAIR categories"- and subsequent cluster analyses (PCA, K-means), differences between stakeholders were identified. Organizations concerned with environmental and human chemical RA (e.g. European Chemicals Agency, European Food Safety Authority) could be distinguished from those focused on technical FAIR data issues and those involved in publicly funded research and open science.Conclusions To foster environmental and human health research data for research purposes and adoption of research data in regulatory applications, a taxonomy of terms and FAIR data policy was developed. A frequency analysis of the taxonomy (augmented with LLM-derived) terms in stakeholder documents revealed differences between stakeholders, which may help identify trends e.g. the adoption of FAIR principles within and across sciences and regulatory domains for (NG) RA. The PFDP is designed to support data driven RA improvement within PARC but can equally contribute to FAIR data in other environmental RA research and regulatory communities.Supplementary information The online version contains supplementary material available at https://doi.org/10.1186/s12302-026-01472-y.
SN - 2190-4707
SN - 2190-4715
ER -