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).

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).

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).

In vitro and in vivo data for the estrogen receptor. The in vivo data is for binding, agonism, and antagonism. The in vivo data is from mouse uterotropic assay data. The following columns are provided in each data set: molecular id, SMILES structure, class (1=active, 0 = inactive), and set (T=training, P=prediction set). This dataset is associated with the following publication: Martin , T. Prediction of in vitro and in vivo oestrogen receptor activity using hierarchical clustering. SAR AND QSAR IN ENVIRONMENTAL RESEARCH. Taylor & Francis, Inc., Philadelphia, PA, USA, 27(1): 17-30, (2016).

In vitro and in vivo data for the estrogen receptor. The in vivo data is for binding, agonism, and antagonism. The in vivo data is from mouse uterotropic assay data. The following columns are provided in each data set: molecular id, SMILES structure, class (1=active, 0 = inactive), and set (T=training, P=prediction set). This dataset is associated with the following publication: Martin , T. Prediction of in vitro and in vivo oestrogen receptor activity using hierarchical clustering. SAR AND QSAR IN ENVIRONMENTAL RESEARCH. Taylor & Francis, Inc., Philadelphia, PA, USA, 27(1): 17-30, (2016).

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).

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).

Compares results of bioassay with hormones measured using analytical chemistry. This dataset is associated with the following publication: Conley, J., H. Mash , N. Evans , K. Schenck , L. Rosenblum, S. Glassmeyer , E.T. Furlong, D.W. Kolpin, and V. Wilson. Comparison of in vitro estrogenic activity and estrogen concentrations in source and treated waters from 25 U.S. drinking water treatment plants. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, N/A, (2016).

Background Recent evidence suggests that estrogens exert effects in different tissues throughout the body, and that the estrogen receptor β (ERβ) may be important for the action of estrogen (17-β-estradiol) on the skeleton. The cellular localization of ERβ in the human intervertebral disc, however, has not yet been explored. Methods Human disc tissue and cultured human disc cells were used for immunocytochemical localization of ERβ. mRNA was isolated from cultured human disc cells, and RT-PCR amplification of ERβ was employed to document molecular expression of this receptor. Cultured human disc cells were tested to determine if 17-β-estradiol stimulated cell proliferation. Results In this report data are presented which provide evidence for ERβ gene expression in human intervertebral disc cells in vivo and in vitro. Culture of annulus cells in the presence of 10-7 M 17-β-estradiol significantly increased cell proliferation. Conclusions These data provide new insight into the biology of cells in the annulus of the intervertebral disc.

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).