The zip file contains all data and scripts used for the material presented in the manuscript by Kosnik et al. This dataset is associated with the following publication: Kosnik, M., J. Strickland, S. Marvel, D. Wallis, K. Wallace, A. Richard, D. Reif, and T. Shafer. Concentration-Response Evaluation of ToxCast Compounds for Multivariate Activity Patterns of Neural Network Function. Archives of Toxicology. Springer, New York, NY, USA, 94: 469-484, (2020).

The attached zip file contains all of the source data, intermediate outputs and R-scripts used to calculate the tipping points reported in the Frank et al manuscript. This dataset is associated with the following publication: Christopher, F., J. Brown, K. Wallace, J. Wambaugh, I. Shah, and T. Shafer. Defining toxicological tipping points in neuronal network development. TOXICOLOGY AND APPLIED PHARMACOLOGY. Academic Press Incorporated, Orlando, FL, USA, 354: 81-93, (2018).

The attached zip file contains all of the source data, intermediate outputs and R-scripts used to calculate the tipping points reported in the Frank et al manuscript. This dataset is associated with the following publication: Christopher, F., J. Brown, K. Wallace, J. Wambaugh, I. Shah, and T. Shafer. Defining toxicological tipping points in neuronal network development. TOXICOLOGY AND APPLIED PHARMACOLOGY. Academic Press Incorporated, Orlando, FL, USA, 354: 81-93, (2018).

This file contains the data used to generate hit calls from neural activity recordings on microelectrode array (MEA) plates treated with ToxCast compounds at a single concentration. This dataset is associated with the following publication: Strickland, J., M. Martin, A. Richard, K. Houck, and T. Shafer. Screening the ToxCast phase II libraries for alterations in network function using cortical neurons grown on multi-well microelectrode array (mwMEA) plates. Archives of Toxicology. Springer, New York, NY, USA, 92(1): 487-500, (2018).

This file contains the data used to generate hit calls from neural activity recordings on microelectrode array (MEA) plates treated with ToxCast compounds at a single concentration. This dataset is associated with the following publication: Strickland, J., M. Martin, A. Richard, K. Houck, and T. Shafer. Screening the ToxCast phase II libraries for alterations in network function using cortical neurons grown on multi-well microelectrode array (mwMEA) plates. Archives of Toxicology. Springer, New York, NY, USA, 92(1): 487-500, (2018).

the zip files contain outputs from the R-analysis for the nanosilver experiments. This dataset is associated with the following publication: Strickland, J., W. LeFew, J. Crooks, D. Hall, J. Ortenzio , K. Dreher , and T. Shafer. In vitro screening of silver nanoparticles and ionic silver using neural networks yields differential effects on spontaneous activity and pharmacological responses.. TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 355(11): 1-8, (2016).

the zip files contain outputs from the R-analysis for the nanosilver experiments. This dataset is associated with the following publication: Strickland, J., W. LeFew, J. Crooks, D. Hall, J. Ortenzio , K. Dreher , and T. Shafer. In vitro screening of silver nanoparticles and ionic silver using neural networks yields differential effects on spontaneous activity and pharmacological responses.. TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 355(11): 1-8, (2016).

The tcpl package provides a set of tools for processing and modeling high-throughput and high-content chemical screening data. This dataset is associated with the following publication: Filer, D.L., P. Kothiya, R.W. Setzer, R.S. Judson, and M.T. Martin. (BIOINFORMATICS) tcpl: The ToxCast Pipeline for High-Throughput Screening Data. BIOINFORMATICS. Oxford University Press, Cary, NC, USA, 1-3, (2016).

The US EPA Toxicity Forecasting (ToxCast) project has involved the generation of large amount of high throughput in vitro data (Over 4000 chemicals tested in between 100 and 700 assays). This data is generated in a consistent manner, and includes a wide variety of chemicals including industrial and consumer products, food additives, pesticides, and drugs. These chemicals were not chosen because they were expected to be active, resulting in a database containing a balance of positive and negative data points. As such this data is useful for computational model construction. This in vitro data has been used at the EPA and elsewhere in modelling approaches, including computational modelling for specific target binding as biological descriptors for toxicity prediction, and pharmacokinetic modelling of human dose responses. All analyses in the generation of the burst flag hit-call matrix and extraction of chemicals for the targets in this study (AR and GR) were performed using R v3.1.2. This dataset is associated with the following publication: Allen, T.E., M.D. Nelms, S.W. Edwards, J.M. Goodman, S. Gutsell, and P.J. Russell. In Silico Guidance for In Vitro Androgen and Glucocorticoid Receptor ToxCast Assays. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 54(12): 7461-7470, (2020).

The US EPA Toxicity Forecasting (ToxCast) project has involved the generation of large amount of high throughput in vitro data (Over 4000 chemicals tested in between 100 and 700 assays). This data is generated in a consistent manner, and includes a wide variety of chemicals including industrial and consumer products, food additives, pesticides, and drugs. These chemicals were not chosen because they were expected to be active, resulting in a database containing a balance of positive and negative data points. As such this data is useful for computational model construction. This in vitro data has been used at the EPA and elsewhere in modelling approaches, including computational modelling for specific target binding as biological descriptors for toxicity prediction, and pharmacokinetic modelling of human dose responses. All analyses in the generation of the burst flag hit-call matrix and extraction of chemicals for the targets in this study (AR and GR) were performed using R v3.1.2. This dataset is associated with the following publication: Allen, T.E., M.D. Nelms, S.W. Edwards, J.M. Goodman, S. Gutsell, and P.J. Russell. In Silico Guidance for In Vitro Androgen and Glucocorticoid Receptor ToxCast Assays. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 54(12): 7461-7470, (2020).