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).

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"

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).

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).

This data set contains dose-response data for perchlorate exposure in multiple species. These data were assembled from peer-reviewed studies. Species included in this dataset are: rats (Rattus sp.), meadow voles (Microtus sp.), rabbits (Oryctolagus cuniculus), the African clawed frog (Xenopus laevis), zebrafish (Danio rerio), mosquito fish (Gambusia holbrooki), the bobwhite quail (Colinus virginianus), earthworms (Eisenia foetida), mosquito larvae (Culex quinquefasciatus), the water flea (Daphnia magna), and the sand dollar (Peronella japonica). This dataset is associated with the following publication: Hines, D., S. Edwards, R. Conolly, and A. Jarabek. The Aggregate Exposure Pathway (AEP) and Adverse Outcome Pathway (AOP) frameworks facilitate the integration of human health and ecological endpoints for Cumulative Risk Assessment (CRA). ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 52(2): 839-849, (2018).

This dataset comprises one SigmaPlot 13 file containing measured survival data and survival data predicted from the model coefficients selected by the LASSO procedure. This dataset is associated with the following publication: Ross, J., B. George, M. Bruno, and Y. Ge. Chemical-agnostic hazard prediction: statistical inference of in vitro toxicity pathways from proteomics responses to chemical mixtures. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 2: 39-44, (2017).

This dataset comprises one SigmaPlot 13 file containing measured survival data and survival data predicted from the model coefficients selected by the LASSO procedure. This dataset is associated with the following publication: Ross, J., B. George, M. Bruno, and Y. Ge. Chemical-agnostic hazard prediction: statistical inference of in vitro toxicity pathways from proteomics responses to chemical mixtures. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 2: 39-44, (2017).

Data files for "Oliver, S.K., Corsi, S.R., Baldwin, A.K., Nott, M.A., Ankley, G.T., Blackwell, B.R., Villeneuve, D.L., Hladik, M.L., Kolpin, D.W., Loken, L., DeCicco, L.A., Meyer, M.T. and Loftin, K.A. (2023), Pesticide Prioritization by Potential Biological Effects in Tributaries of the Laurentian Great Lakes. Environ Toxicol Chem, 42: 367-384. https://doi.org/10.1002/etc.5522". This dataset is associated with the following publication: Oliver, S., S. Corsi, A. Baldwin, M. Nott, G. Ankley, B. Blackwell, D. Villeneuve, M. Hladik, D. Kolpin, L. Loken, L. DeCicco, M. Meyer, and K. Loftin. Pesticide Prioritization by Potential Biological Effects in Tributaries of the Laurentian Great Lakes. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 42(2): 367-384, (2023).

Data files for "Oliver, S.K., Corsi, S.R., Baldwin, A.K., Nott, M.A., Ankley, G.T., Blackwell, B.R., Villeneuve, D.L., Hladik, M.L., Kolpin, D.W., Loken, L., DeCicco, L.A., Meyer, M.T. and Loftin, K.A. (2023), Pesticide Prioritization by Potential Biological Effects in Tributaries of the Laurentian Great Lakes. Environ Toxicol Chem, 42: 367-384. https://doi.org/10.1002/etc.5522". This dataset is associated with the following publication: Oliver, S., S. Corsi, A. Baldwin, M. Nott, G. Ankley, B. Blackwell, D. Villeneuve, M. Hladik, D. Kolpin, L. Loken, L. DeCicco, M. Meyer, and K. Loftin. Pesticide Prioritization by Potential Biological Effects in Tributaries of the Laurentian Great Lakes. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 42(2): 367-384, (2023).