The experimental data were taken from Simon et al., who compiled potency data for effects related to neurotoxicity from four experimental datasets, Stenberg et al. [18] and Wigestrand et al. The measures of potency were EC50 (µM) or IC50 values for all the effects except Stenberg data, which were expressed as a percentage of the control uptake for different concentrations measured. This dataset is associated with the following publication: Pradeep, P., L. Carlson, R. Judson, G. Lehmann, and G. Patlewicz. Integrating data gap filling techniques: A case study predicting TEFs for neurotoxicity TEQs to facilitate the hazard assessment of polychlorinated biphenyls. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 101: 12-23, (2019).

data supporting manuscript figures. This dataset is associated with the following publication: Moser , V.C., P. Phillips , J. Hedge , and K. Mcdaniel. Neurotoxicological and thyroid evaluations of rats developmentally exposed to tris(1,3-dichloro-2-propyl)phosphate (TDICPP) and tris(2-chloro-2-ethyl)phosphate(TCEP). NEUROTOXICOLOGY AND TERATOLOGY. Elsevier Science Ltd, New York, NY, USA, 52: 236-247, (2015).

data supporting manuscript figures. This dataset is associated with the following publication: Moser , V.C., P. Phillips , J. Hedge , and K. Mcdaniel. Neurotoxicological and thyroid evaluations of rats developmentally exposed to tris(1,3-dichloro-2-propyl)phosphate (TDICPP) and tris(2-chloro-2-ethyl)phosphate(TCEP). NEUROTOXICOLOGY AND TERATOLOGY. Elsevier Science Ltd, New York, NY, USA, 52: 236-247, (2015).

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"

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"

Table of contents Table S1 DNT inventory List of sources and associated data collected for the development of the Bayesian hierarchical model. Table S2 Data collection It contains the compiled raw data. Table S3 Final dataset It contains data after curation agreed to be included in the Bayesian hierarchical model. Table S4 List of encoders Machine readable format used to encode the data. Table S5 Machine readable The final dataset translated into a machine readable file using the encoders listed above. Table S6 Results for DNT Based on the cut-offs derived from the results themselves, three classes of DNT are proposed (low, medium and high predicted probability). Results are summarised accordingly. This dataset is associated with the following publication: Spinu, N., M. Cronin, J. Lao, A. Bal-Price, I. Campia, S. Enoch, J. Madden, L. Lagares, M. Novic, D. Pamies, S. Scholz, D. Villeneuve, and A. Worth. Probabilistic modelling of developmental neurotoxicity based on a simplified adverse outcome pathway network. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 21: 100206, (2022).

These data are the individual parameter and well-level data that were support the conclusions in Brown et al. Note: the parameters CVtime and CVnetwork were not used. This dataset is associated with the following publication: Brown, J., D. Hall, C. Frank, K. Wallace, W. Mundy, and T. Shafer. Editor's highlight: Evaluation of a Microelectrode Array-based Assay for Neural Network Ontogeny using Training Set Chemicals. TOXICOLOGICAL SCIENCES. Society of Toxicology, 154(1): 126-139, (2016).

These data are the individual parameter and well-level data that were support the conclusions in Brown et al. Note: the parameters CVtime and CVnetwork were not used. This dataset is associated with the following publication: Brown, J., D. Hall, C. Frank, K. Wallace, W. Mundy, and T. Shafer. Editor's highlight: Evaluation of a Microelectrode Array-based Assay for Neural Network Ontogeny using Training Set Chemicals. TOXICOLOGICAL SCIENCES. Society of Toxicology, 154(1): 126-139, (2016).

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).

Contains values from pbpk models for each study on n-butanol effects. This dataset is associated with the following publication: Segal, D., A. Bale, L. Phillips, A. Sasso, P. Schlosser, C. Starkey, and S. Makris. Issues in Assessing the Health Risks of n-Butanol. JOURNAL OF APPLIED TOXICOLOGY. John Wiley & Sons, Ltd., Indianapolis, IN, USA, 40(1): 72-86, (2020).