This data set contains R code for a hypothetical exposure model described in the manuscript "A quantitative source-to-outcome case study to demonstrate the integration of human health and ecological endpoints using the Aggregate Exposure Pathway and Adverse Outcome Pathway frameworks". Additionally, this data set contains an Excel file that provides the range of parameters used in Monte Carlo simulations to generate iterations of the exposure network. This dataset is associated with the following publication: Hines, D., R. Conolly, and A. Jarabek. A quantitative source-to-outcome case study to demonstrate the integration of human health and ecological endpoints using the Aggregate Exposure Pathway and Adverse Outcome Pathway frameworks. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 53(8): 11002-11012, (2019).

This data set contains R code for a hypothetical exposure model described in the manuscript "A quantitative source-to-outcome case study to demonstrate the integration of human health and ecological endpoints using the Aggregate Exposure Pathway and Adverse Outcome Pathway frameworks". Additionally, this data set contains an Excel file that provides the range of parameters used in Monte Carlo simulations to generate iterations of the exposure network. This dataset is associated with the following publication: Hines, D., R. Conolly, and A. Jarabek. A quantitative source-to-outcome case study to demonstrate the integration of human health and ecological endpoints using the Aggregate Exposure Pathway and Adverse Outcome Pathway frameworks. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 53(8): 11002-11012, (2019).

The data used in this analysis was obtained from published literature and available through the high-throughput toxicokinetic (HTTK) R package. The dataset consists of 1486 chemicals that span a variety of use classes including pharmaceuticals, food-use chemicals, pesticides and industrial chemicals of which 1139 chemicals had experimental human in vitro fraction unbound data and 642 chemicals that had experimental human in vitro intrinsic clearance data. Structures were curated and obtained from the DSSTox database. The distribution of experimental values for fraction unbound and intrinsic clearance is shown in Supplementary Figure S1. Since the data were non-normally distributed they were appropriately transformed before any analysis was conducted. The details of the transformation and the transformed data distribution are presented in the results section and Supplementary Figures S2 and S3. A complete list of chemicals with CAS registry numbers (CASRN), DSSTox generic substance IDs (DTXSIDs), structure and experimental data for both parameters are included as supplemental data (1.ChemicalListData.csv and 1.ChemicalList-QSARready.sdf). This dataset is associated with the following publication: Pradeep, P., G. Patlewicz, R. Pearce, J. Wambaugh, B. Wetmore, and R. Judson. Using Chemical Structure Information to Develop Predictive Models for In Vitro Toxicokinetic Parameters to Inform High-throughput Risk-assessment. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 16: 100136, (2020).

Dataset for "Incorporating human exposure information in a weight of evidence approach to inform design of repeated dose animal studies". This dataset is associated with the following publication: Lowe, K., J. Dawson, K. Phillips, J. Minucci, J. Wambaugh, H. Qian, T. Ramanarayanan, P. Egeghy, B. Ingle, R. Brunner, E. Mendez, M. Embry, and C. Tan. Incorporating human exposure information in a weight of evidence approach to inform design of repeated dose animal studies. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 127: 105073, (2021).

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).

Data for "Breen, M., Wambaugh, J.F., Bernstein, A. et al. Simulating toxicokinetic variability to identify susceptible and highly exposed populations. J Expo Sci Environ Epidemiol 32, 855–863 (2022). https://doi.org/10.1038/s41370-022-00491-0". This dataset is associated with the following publication: Breen, M., J. Wambaugh, A. Bernstein, M. Sfeir, and C. Ring. Simulating toxicokinetic variability to identify susceptible and highly exposed populations. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 32: 855-863, (2022).

Dataset for Karmaus et al. journal article 'Evaluation of Variability across Rat Acute Oral Systemic Toxicity Studies', published in Toxicological Sciences in July 2022 ( https://doi.org/10.1093/toxsci/kfac042). The Excel data file is from the following link in the article: The compiled data allowed for comprehensive characterization of the variability and performance for the rat acute oral toxicity study. The data set is available for download via the National Toxicology Program’s Integrated Chemical Environment (https://ice.ntp.niehs.nih.gov/). This dataset is associated with the following publication: Karmaus, A., K. Mansouri, K. To, B. Blake, J. Fitzpatrick, J. Strickland, G. Patlewicz, D. Allen, W. Casey, and N. Kleinstreuer. Evaluation of Variability Across Rat Acute Oral Systemic Toxicity Studies. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 188(1): 34-47, (2022).

NNDSS - TABLE 1CC. Rabies, Animal to Rabies, Human - 2020. In this Table, provisional cases* of notifiable diseases are displayed for United States, U.S. territories, and Non-U.S. residents. Note: This table contains provisional cases of national notifiable diseases from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data from the 50 states, New York City, the District of Columbia and the U.S. territories are collated and published weekly on the NNDSS Data and Statistics web page (https://wwwn.cdc.gov/nndss/data-and-statistics.html). Cases reported by state health departments to CDC for weekly publication are provisional because of the time needed to complete case follow-up. Therefore, numbers presented in later weeks may reflect changes made to these counts as additional information becomes available. The national surveillance case definitions used to define a case are available on the NNDSS web site at https://wwwn.cdc.gov/nndss/. Information about the weekly provisional data and guides to interpreting data are available at: https://wwwn.cdc.gov/nndss/infectious-tables.html. Footnotes: U: Unavailable — The reporting jurisdiction was unable to send the data to CDC or CDC was unable to process the data. -: No reported cases — The reporting jurisdiction did not submit any cases to CDC. N: Not reportable — The disease or condition was not reportable by law, statute, or regulation in the reporting jurisdiction. NN: Not nationally notifiable — This condition was not designated as being nationally notifiable. NP: Nationally notifiable but not published. NC: Not calculated — There is insufficient data available to support the calculation of this statistic. Cum: Cumulative year-to-date counts. Max: Maximum — Maximum case count during the previous 52 weeks. * Case counts for reporting years 2019 and 2020 are provisional and subject to change. Cases are assigned to the reporting jurisdiction submitting the case to NNDSS, if the case's country of usual residence is the U.S., a U.S. territory, unknown, or null (i.e. country not reported); otherwise, the case is assigned to the 'Non-U.S. Residents' category. Country of usual residence is currently not reported by all jurisdictions or for all conditions. For further information on interpretation of these data, see https://wwwn.cdc.gov/nndss/document/Users_guide_WONDER_tables_cleared_final.pdf. †Previous 52 week maximum and cumulative YTD are determined from periods of time when the condition was reportable in the jurisdiction (i.e., may be less than 52 weeks of data or incomplete YTD data).

NASA's Rodent Research (RR) project is playing a critical role in advancing biomedical research on the physiological effects of space environments. Due to the limited resources for conducting biological experiments aboard the International Space Station (ISS), it is imperative to use crew time efficiently while maximizing high-quality science return. NASA's GeneLab project has as its primary objectives to 1) further increase the value of these experiments using a multi-omics, systems biology-based approach, and 2) disseminate these data without restrictions to the scientific community. The current investigation assessed viability of RNA, DNA, and protein extracted from archived RR-1 tissue samples for epigenomic, transcriptomic, and proteomic assays. During the first RR spaceflight experiment, a variety of tissue types were harvested from subjects, snap-frozen or RNAlater-preserved, and then stored at least a year at -80C after return to Earth. They were then prioritized for this investigation based on likelihood of significant scientific value for spaceflight research. All tissues were made available to GeneLab through the bio-specimen sharing program managed by the Ames Life Science Data Archive and included mouse adrenal glands, quadriceps, gastrocnemius, tibialis anterior, extensor digitorum longus, soleus, eye, and kidney. We report here protocols for and results of these tissue extractions, and thus, the feasibility and value of these kinds of omics analyses. In addition to providing additional opportunities for investigation of spaceflight effects on the mouse transcriptome and proteome in new kinds of tissues, our results may also be of value to program managers for the prioritization of ISS crew time for rodent research activities.