Dataset for "Nicolas Chantel I., Linakis Matthew W., Minto Melyssa S., Mansouri Kamel, Clewell Rebecca A., Yoon Miyoung, Wambaugh John F., Patlewicz Grace, McMullen Patrick D., Andersen Melvin E., Clewell III Harvey J, Estimating provisional margins of exposure for data-poor chemicals using high-throughput computational methods, Frontiers in Pharmacology, 13, 2022, https://www.frontiersin.org/articles/10.3389/fphar.2022.980747,10.3389/fphar.2022.980747"

Links to data for "The Chemical and Products Database v4.0, an updated resource supporting chemical exposure evaluations". This dataset is associated with the following publication: Handa, S., K. Isaacs, J. Wall, A. Larger, S. Burns, L. Koval, K. Baron-Furuyama, C. Elonen, D. Lyons, K. Dionisio, M.B. Horton, and K. Phillips. The Chemical and Products Database v4.0, an updated resource supporting chemical exposure evaluations. Scientific Data. Springer Nature, LONDON, UK, 12: 950, (2025).

N/A. This dataset is associated with the following publication: Groff, L., J. Grossman, A. Kruve, J. Minucci, C. Lowe, J. McCord, D. Kapraun, K. Phillips, S. Purucker, A. Chao, C. Ring, A. Williams, and J. Sobus. Uncertainty estimation strategies for quantitative non-targeted analysis. Analytical and Bioanalytical Chemistry. Springer, New York, NY, USA, 414(17): 4919-4933, (2022).

N/A. This dataset is associated with the following publication: Groff, L., J. Grossman, A. Kruve, J. Minucci, C. Lowe, J. McCord, D. Kapraun, K. Phillips, S. Purucker, A. Chao, C. Ring, A. Williams, and J. Sobus. Uncertainty estimation strategies for quantitative non-targeted analysis. Analytical and Bioanalytical Chemistry. Springer, New York, NY, USA, 414(17): 4919-4933, (2022).

Datasets for "A harmonized chemical monitoring database for support of exposure assessments". This dataset is associated with the following publication: Isaacs, K., J. Wall, A. Williams, K. Hobbie, J. Sobus, E. Ulrich, D. Lyons, K. Dionisio, A. Williams, C. Grulke, C. Foster, J. McCoy, and C. Bevington. A harmonized chemical monitoring database for support of exposure assessments. Scientific Data. Springer Nature, New York, NY, 9: 314, (2022).

A publised mansucript describing a quantitative adverse outcome pathway (qAOP) and its relevance to risk assessment. This dataset is not publicly accessible because: This work describes computational modeling, not acquisition of laboratory data. It can be accessed through the following means: The mansucript is published in Environmental Science and Technology. Format: This ScienceHub entry is associated with the published manuscript: Quantitative Adverse Outcome Pathways and Their Application to Predictive Toxicology Rory B. Conolly,*,† Gerald T. Ankley,‡ WanYun Cheng,† Michael L. Mayo,§ David H. Miller,∥ Edward J. Perkins,§ Daniel L. Villeneuve,‡ and Karen H. Watanabe⊥ †U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Integrated Systems Toxicology Division, Research Triangle Park, North Carolina 27709, United States ‡U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, Duluth, Minnesota 55804, United States §Environmental Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, Mississippi 39180, United States ∥U.S. Environmental Protection Agency, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, Grosse Isle, Michigan 48138, United States ⊥School of Mathematical and Natural Sciences, Arizona State University, West Campus, Glendale, Arizona 85306, United States DOI: 10.1021/acs.est.6b06230 Environ. Sci. Technol. 2017, 51, 4661−4672. This dataset is associated with the following publication: Conolly, R., G. Ankley, W. Cheng, M. Mayo, D. Miller, E. Perkins, D. Villeneuve, and K. Watanabe. Quantitative Adverse Outcome Pathways and Their Application to Predictive Toxicology. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 51(8): 4661-4672, (2017).

Data for "John K. Colbourne, Joseph R. Shaw, Elena Sostare, Claudia Rivetti, Romain Derelle, Rosemary Barnett, Bruno Campos, Carlie LaLone, Mark R. Viant, Geoff Hodges, Toxicity by descent: A comparative approach for chemical hazard assessment, Environmental Advances, Volume 9, 2022, 100287, ISSN 2666-7657, https://doi.org/10.1016/j.envadv.2022.100287". This dataset is associated with the following publication: Colbourne, J., J. Shaw, E. Sostare, C. Rivetti, R. Derelle, R. Barnett, B. Campos, C. Lalone, M. Viant, and G. Hodges. Toxicity by descent: a comparative approach for chemical hazard assessment. Environmental Advances. Elsevier B.V., Amsterdam, NETHERLANDS, 9: 100287, (2022).

Data for "John K. Colbourne, Joseph R. Shaw, Elena Sostare, Claudia Rivetti, Romain Derelle, Rosemary Barnett, Bruno Campos, Carlie LaLone, Mark R. Viant, Geoff Hodges, Toxicity by descent: A comparative approach for chemical hazard assessment, Environmental Advances, Volume 9, 2022, 100287, ISSN 2666-7657, https://doi.org/10.1016/j.envadv.2022.100287". This dataset is associated with the following publication: Colbourne, J., J. Shaw, E. Sostare, C. Rivetti, R. Derelle, R. Barnett, B. Campos, C. Lalone, M. Viant, and G. Hodges. Toxicity by descent: a comparative approach for chemical hazard assessment. Environmental Advances. Elsevier B.V., Amsterdam, NETHERLANDS, 9: 100287, (2022).

We created a consensus, meta-model using the Systematic Empirical Evaluation of Models framework in which the predictors of exposure were combined by pathway and weighted according to predictive ability for chemical intake rates inferred from human biomonitoring data for 114 chemicals. This dataset is associated with the following publication: Ring, C., J. Arnot, D. Bennett, P. Egeghy, P. Fantke, L. Huang, K. Isaacs, O. Jolliet, K. Phillips, P. Price, H. Shin, J. Westgate, R. Setzer, and J. Wambaugh. Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 53(2): 719-732, (2019).

We created a consensus, meta-model using the Systematic Empirical Evaluation of Models framework in which the predictors of exposure were combined by pathway and weighted according to predictive ability for chemical intake rates inferred from human biomonitoring data for 114 chemicals. This dataset is associated with the following publication: Ring, C., J. Arnot, D. Bennett, P. Egeghy, P. Fantke, L. Huang, K. Isaacs, O. Jolliet, K. Phillips, P. Price, H. Shin, J. Westgate, R. Setzer, and J. Wambaugh. Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 53(2): 719-732, (2019).