Data are summarized in a two-dimensional data matrix that was developed for each substance for hazard characterization (Tables S1–S3). In the horizontal direction of the matrix, read-across of the target phenol to the source analogues was performed for the purpose of data-gap filling, whereas in the vertical direction, data from different streams (traditional and NAM) were compared and contrasted, to evaluate concordance of orthogonal approaches for evaluating potential estrogenicity. The greater the degree of agreement in orthogonal approaches for determining bioactivity, the greater the confidence one has in using the collective results of such NAMs in hazard characterization of the target phenol. This dataset is associated with the following publication: Webster, F., M. Gagne, G. Patlewicz, P. Pradeep, N. Trefiak, R. Judson, and T. Barton-Maclaren. Predicting estrogen receptor activation by a group of substituted phenols: An integrated approach to testing and assessment case study. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 106: 278-291, (2019).

GEO accession number of the microarray study. This dataset is associated with the following publication: Mesnage, R., A. Phedonos, M. Biserni, M. Arno, S. Balu, C. Corton, R. Ugarte, and M. Antoniou. Evaluation of estrogen receptor alpha activation by glyphosate-based herbicide constituents. FOOD AND CHEMICAL TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 108: 30-42, (2017).

Tables, Figures, and Supplemental Materials for doi: 10.1158/1541-7786.MCR-16-0084. This dataset is associated with the following publication: Suen, A., W. Jefferson, C. Wood, E. Padilla-Banks, V. Bae-Jump, and C. Williams. SIX1 Oncoprotein as a Biomarker in a Model of Hormonal Carcinogenesis and in Human Endometrial Cancer.. Molecular Cancer Research. American Association for Cancer Research, Inc., Philadelphia, PA, USA, 14(9): 849-858, (2016).

Data from a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) demonstrating using predictive computational models on high-throughput screening data to screen thousands of chemicals against the estrogen receptor. This dataset is associated with the following publication: Mansouri , K., A. Abdelaziz, A. Rybacka, A. Roncaglioni, A. Tropsha, A. Varnek, A. Zakharov, A. Worth, A. Richard , C. Grulke , D. Trisciuzzi, D. Fourches, D. Horvath, E. Benfenati , E. Muratov, E.B. Wedebye, F. Grisoni, G.F. Mangiatordi, G.M. Incisivo, H. Hong, H.W. Ng, I.V. Tetko, I. Balabin, J. Kancherla , J. Shen, J. Burton, M. Nicklaus, M. Cassotti, N.G. Nikolov, O. Nicolotti, P.L. Andersson, Q. Zang, R. Politi, R.D. Beger , R. Todeschini, R. Huang, S. Farag, S.A. Rosenberg, S. Slavov, X. Hu, and R. Judson. (Environmental Health Perspectives) CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 1-49, (2016).

the data set contains the figures and tables from the publication in addition to the means, standard errors of the mean and the sample sizes used in each group for every experiment. the data set also contains a description of the genes, their function and acronyms on the QPCR arrays used in the study. Finally, the dataset includes the histopathology reports on the uterine changes induced by the different chemicals and the criteria used by the pathologist to classify the estrogenic effects of the chemicals. This dataset is associated with the following publication: Conley, J., B. Hannas, V. Wilson, E. Gray, and J. Furr. A demonstration of the uncertainty in predicting the estrogenic activity of individual chemicals and mixtures from an in vitro estrogen receptor transcriptional activation assay (T47D-KBluc) to the in vivo uterotrophic assay using oral exposure. TOXICOLOGICAL SCIENCES. Society of Toxicology, 382-395, (2016).

Gene Expression Omnibus numbers only. This dataset is associated with the following publication: Mesnage, R., A. Phedonos, M. Arno, S. Balu, C. Corton, and M. Antoniou. Transcriptome profiling reveals bisphenol A alternatives activate estrogen receptor alpha in human breast cancer cells. TOXICOLOGICAL SCIENCES. Society of Toxicology, 158(2): 431-443, (2017).

this file has the metadata sheet and the data used for the figures and tables in the PFQ vs BPC manuscript. This dataset is associated with the following publication: Gray, E., J. Furr, J. Conley, C. Lambright, N. Evans, M. Cardon, V. Wilson, P. Foster, and P. Hartig. A Conflicted Tale of Two Novel AR Antagonists In vitro and In vivo: Pyrifluquinazon versus Bisphenol C.. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 632-643, (2019).

ToxCast bioactivity data and model predictions for the estrogen receptor (ER) and androgen receptor (AR) pathways were obtained from the inks provided. This dataset is associated with the following publication: Lizarraga, L., J. Dean, J. Kaiser, S. Wesselkamper, J. Lambert, and J. Zhao. A Case Study on the Application of An Expert-driven Read-Across Approach in Support of Quantitative Risk Assessment of p,p’-Dichlorodiphenyldichloroethane. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 103: 301-313, (2019).

METADATA OUTLINE SHEET 1 STUDY INFORMATION SHEET 2 RANDOM ASSISNMENT OF PREGNANT RATS TO TREATMENT GROUPS SHEER 3 MATERNAL WEIGHT AND WEIGHT GAIN DURING DOSING and fetal DATA RAW DATA RESULTS PREDICTIONS OF DOSE ADDITIVITY SHEET 4 TESTOSTERONE (T PROD) DATA RAW DATA SAS INPUT FILES TREATMENT EFFECTS SHEET 5 CUSTOM GENE (mRNA) SAS INPUT FILE WITH SAS STATEMENTS AND RQW DATA SHEET 6 RESULTS FROM STATISTICAL ANALYSIS OF CUSTOM ARRAY mRNA DATA CUSTOM ARRAY: LIST OF GENES AND GENE DESCRIPTIONS SHEET 7 PREDICTION MODELS OF TESTOSTERONE PRODUCTION REDUCTIONS AND REPRODUCTIVE EFFECTS OF IN UTERO PHTHALATE EXPOSURE SHEET 8 TREATMENT EFFECTS PREDICTED FROM THE TESTOSTERONE PREDICTION MODELS COMPARISON OF THE PREDICTED EFFECTS OF THE DBP+DINP MIXTURE WITH OBSERVED EFFECTS OF THE REFERENCE CHEMICAL (DBP) AT EQUIVALENT DOSES, ASSUMING DOSE ADDITIVITY. This dataset is associated with the following publication: Gray, L., C. Lambright, N. Evans, J. Ford, and J. Conley. Using Targeted Fetal Rat Testis Genomic and Endocrine Alterations to Predict the Effects of a Phthalate Mixture on the Male Reproductive Tract.. Current Research in Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 7: 100180, (2024).

This data includes metabolite predictions for in vitro inactive chemicals, predictions of those metabolite's estrogen receptor binding activity, in vitro and in silico information regarding parent compound binding activities, linking of metabolite structures and routes to parent compounds, and estimates of binding activity obtained from literature when possible. This dataset is associated with the following publication: Leonard, J., C. Stevens, K. Mansouri, D. Chang, H. Pudukodu, S. Smith, and C. Tan. A Workflow for Identifying Metabolically Active Chemicals to Complement in vitro Toxicity Screening. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 6: 71-83, (2018).