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).

The increasing number of chemicals for which SHEDS probabilistic exposure assessment has been performed over the years. This dataset is associated with the following publication: Egeghy , P., L. Sheldon, K. Isaacs , H. Ozkaynak, M. Goldsmith, J. Wambaugh , R. Judson , and T. Buckley. Computational Exposure Science: An Emerging Discipline to Support 21st-Century Risk Assessment. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 124(6): 697–702, (2016).

The increasing number of chemicals for which SHEDS probabilistic exposure assessment has been performed over the years. This dataset is associated with the following publication: Egeghy , P., L. Sheldon, K. Isaacs , H. Ozkaynak, M. Goldsmith, J. Wambaugh , R. Judson , and T. Buckley. Computational Exposure Science: An Emerging Discipline to Support 21st-Century Risk Assessment. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 124(6): 697–702, (2016).

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"

The dataset that was evaluated in this approach was taken from Wambaugh et al [29] who filtered the Tox21 library to reflect substances with similar uses to those in NHANES. The zip file contains the supplementary information being provided for the re-analysis performed in this dataset. There was no specific code as such developed for the analysis aside from using KNIME to help combine different outputs from different tools including Leadscope in order to arrive at the counts reflected in Table 2 of the manuscript. Instead of this very laborious approach, we re-did the analysis using Toxtree alone and streamlined the processing of the outcomes with R. This is documented in the supplementary information file. List of files: SMARTS Toxtree schemes use to identify carbamates, OPs and steroids Carbamates.tml OPs.tml Steroids.tml R code used to manipulate the various outputs derived from processing the associated sdf through the Kroes, specific Toxtree schemes and Cramer scheme within Toxtree TTC_HTTK.R R data file HTTK_TTC_070218.RData sdf file used in the analysis HTTK_7K_mod_kekule.sdf. This dataset is associated with the following publication: Patlewicz, G., J. Wambaugh, S. Felter, T. Simon, and R. Becker. Utilizing Threshold of Toxicological Concern (TTC) with High Throughput Exposure Predictions (HTE) as a Risk-Based Prioritization Approach for thousands of chemicals. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 7: 58-67, (2018).

The dataset that was evaluated in this approach was taken from Wambaugh et al [29] who filtered the Tox21 library to reflect substances with similar uses to those in NHANES. The zip file contains the supplementary information being provided for the re-analysis performed in this dataset. There was no specific code as such developed for the analysis aside from using KNIME to help combine different outputs from different tools including Leadscope in order to arrive at the counts reflected in Table 2 of the manuscript. Instead of this very laborious approach, we re-did the analysis using Toxtree alone and streamlined the processing of the outcomes with R. This is documented in the supplementary information file. List of files: SMARTS Toxtree schemes use to identify carbamates, OPs and steroids Carbamates.tml OPs.tml Steroids.tml R code used to manipulate the various outputs derived from processing the associated sdf through the Kroes, specific Toxtree schemes and Cramer scheme within Toxtree TTC_HTTK.R R data file HTTK_TTC_070218.RData sdf file used in the analysis HTTK_7K_mod_kekule.sdf. This dataset is associated with the following publication: Patlewicz, G., J. Wambaugh, S. Felter, T. Simon, and R. Becker. Utilizing Threshold of Toxicological Concern (TTC) with High Throughput Exposure Predictions (HTE) as a Risk-Based Prioritization Approach for thousands of chemicals. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 7: 58-67, (2018).

Data for the publication Isaacs, K.K., Egeghy, P., Dionisio, K.L. et al. The chemical landscape of high-throughput new approach methodologies for exposure. J Expo Sci Environ Epidemiol (2022). https://doi.org/10.1038/s41370-022-00496-9. This dataset is associated with the following publication: Isaacs, K., P. Egeghy, K. Dionisio, K. Phillips, A. Zidek, C. Ring, J. Sobus, E. Ulrich, B. Wetmore, A. Williams, and J. Wambaugh. The chemical landscape of high-throughput new approach methodologies for exposure. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 32: 820-832, (2022).

Data for the publication Isaacs, K.K., Egeghy, P., Dionisio, K.L. et al. The chemical landscape of high-throughput new approach methodologies for exposure. J Expo Sci Environ Epidemiol (2022). https://doi.org/10.1038/s41370-022-00496-9. This dataset is associated with the following publication: Isaacs, K., P. Egeghy, K. Dionisio, K. Phillips, A. Zidek, C. Ring, J. Sobus, E. Ulrich, B. Wetmore, A. Williams, and J. Wambaugh. The chemical landscape of high-throughput new approach methodologies for exposure. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 32: 820-832, (2022).

Data for publication Turley et al. "Applying the RISK21 approach to assess predictivity of new approach methodologies in toxicity testing and exposure assessment: a case study on food contact chemicals". Includes food concentration predictions from the model of Biryol et al. (2017) and SHEDS-HT exposure predictions. This dataset is associated with the following publication: Turley, A., K. Isaacs, B. Wetmore, A. Karmaus, M. Embry, and M. Krishan. Incorporating new approach methodologies in toxicity testing and exposure assessment for tiered risk assessment using the RISK21 approach: Case studies on food contact chemicals. FOOD AND CHEMICAL TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 134: 110819, (2019).

Data for publication Turley et al. "Applying the RISK21 approach to assess predictivity of new approach methodologies in toxicity testing and exposure assessment: a case study on food contact chemicals". Includes food concentration predictions from the model of Biryol et al. (2017) and SHEDS-HT exposure predictions. This dataset is associated with the following publication: Turley, A., K. Isaacs, B. Wetmore, A. Karmaus, M. Embry, and M. Krishan. Incorporating new approach methodologies in toxicity testing and exposure assessment for tiered risk assessment using the RISK21 approach: Case studies on food contact chemicals. FOOD AND CHEMICAL TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 134: 110819, (2019).