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).

List of biomarker genes used to predict estrogen receptor activity in MCF-7 cells; list of microarray accession numbers used in the study. This dataset is associated with the following publication: Vanduyn, N., B. Chorley , R. Tice, R. Judson , and C. Corton. Moving Toward Integrating Gene Expression Profiling into High-throughput Testing:A Gene Expression Biomarker Accurately Predicts Estrogen Receptor α Modulation in a Microarray Compendium. TOXICOLOGICAL SCIENCES. Society of Toxicology, 151(1): 88-103, (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).

Table 1. Antibodies used for immunohistochemistry. Table 2. Incidence of uterine glandular epithelial lesions over time following neonatal estrogen exposure (GEN or DES) Table 3. Immunohistochemical expression of differentiation markers in the uterine epithelium following neonatal estrogen exposure (GEN or DES). This dataset is associated with the following publication: Suen, A., W. Jefferson, C. Williams, and C. Wood. Differentiation patterns of uterine carcinomas and precursor lesions induced by neonatal estrogen exposure in mice. TOXICOLOGIC PATHOLOGY. Society of Toxicology, RESTON, VA, 46(5): 574-596, (2018).

Data associated with the figures presented in this study are images, graphics, or tabulated data based on histopathologic analysis performed by a certified study pathologist or image analysis. Data for Tables 1 and 2 provide incidence, labeling scores, and p-values for statistical tests for uterine pathology and IHC expression by treatment group and timepoint or human endometrial tissue, as described in the Main Text file of the manuscript. Manual histopathologic data can be found in excel spreadsheets and are based on presence/absences (yes/no) of a pathologic finding and/or severity score as described in the manuscript (Fig. 1, 2, 3, 4). The image analysis data is based on the quantified area that is designated as “positive” for a particular immunohistochemical stain (Fig. 2, 4, and Suppl. Fig. S2). Supplementary Table 1 provides summary information on antibodies used for immunohistochemistry. Supplementary fig. S1 includes real time RT-PCR data standardized to a housekeeping gene and western blot data. This dataset is associated with the following publication: Suen, A., W. Jefferson, C. Wood, and C. Williams. SIX1 regulates aberrant endometrial epithelial cell differentiation and cancer trajectory. Molecular Cancer Research. American Association for Cancer Research, Inc., Philadelphia, PA, USA, 17(12): 2369-2382, (2019).

Microarray experiments examined in the study. This dataset is associated with the following publication: Rooney, J., N. Ryan, J. Liu, R. Houtman, R. van Beuningen, J. Hsieh, G. Chang, S. Chen, and J. Corton. A Gene Expression Biomarker Identifies Chemical Modulators of Estrogen Receptor α in an MCF-7 Microarray Compendium. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 34(2): 313-329, (2021).

GEOSite dataset for article 'In vitro transcriptomic analyses reveal pathway perturbations, estrogenic activities, and potencies of data-poor BPA alternative chemicals '. This dataset is associated with the following publication: Matteo, G., K. Leingartner, A. Rowan-Carroll, M. Meier, A. Williams, M. Beal, M. Gagne, R. Farmahin, S. Wickramasuriya, A.J. Reardon, T. Barton-Maclaren, J. Corton, C. Yauk, and E. Atlas. In vitro transcriptomic analyses reveal pathway perturbations, estrogenic activities, and potencies of data-poor BPA alternative chemicals. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 191(2): 266-275, (2023).

This is an original dataset generated at the U.S. EPA. Data was analyzed with the ToxCast Pipeline and deposited for release in invitroDB accessible via the U.S. EPA CompTox Chemicals Dashboard. Dataset is a zip file containing two Excel spreadsheets titled AIME-ERTA_384_Tables_All_Submission_v2 and README_AIME-ERTA_384_Manuscript_Sup_Data_v2. This dataset is associated with the following publication: Hopperstad, K., D. DeGroot, T. Zurlinden, C. Brinkman, R. Thomas, and C. Deisenroth. Chemical Screening in an Estrogen Receptor Transactivation Assay with Metabolic Competence. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 187(1): 112-126, (2022).

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).

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).