AndreaBozzo/ceres-open-data-index huggingface dataset:
Watt_HTS_Uncertainty_Quantification_2017_data

In this work, we introduce a new method for uncertainty quantification in ToxCast data. We explore how unavoidable uncertainties in the data result in uncertainties in concentration-response parameters such as potency and efficacy. These uncertainties are then extended throughout to the analysis and interpretation of results for risk assessment. By quantifying these uncertainties through the analysis stages we increase the confidence in the data interpretation and allow for a more robust risk assessment. We also flag chemicals and assays for manual inspection, removal, and retesting so that data quality and model outputs can be further improved. 

This dataset is associated with the following publication:
Watt, E., and R. Judson. Uncertainty Quantification in ToxCast High Throughput Screening.   PLoS Computational Biology. Public Library of Science, San Francisco, CA, USA, 13(7): 1-23, (2018).

Tags: actor, bootstrap-resampling, high-throughput-screening, toxcast, uncertainty-quantification
Organization: U.S. Environmental Protection Agency
Zipped NetCDF data for Precipitation partitioning in Multi-Scale Atmospheric Simulations: Impacts of Stability Restoration Methods

Data for all figures in NetCDF format zipped files. 

This dataset is associated with the following publication:
He, J., and K. Alapaty. Precipitation Partitioning in Multiscale Atmospheric Simulations: Impacts of Stability Restoration Methods.   JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 123(18): 10,185-10,201, (2018).

Tags: convective-parameterization, convective-timescale, deep-convection, measurements, precipitation
Organization: U.S. Environmental Protection Agency
Estimated floodplain map for the conterminous United States

Understanding the relationship between flood inundation and floodplains is critical for ecosystem and community health and well-being, as well as targeting floodplain and riparian restoration. Many communities in the United States, particularly those in rural areas, lack inundation maps due to the high cost of flood modeling. Only 60% of the conterminous United States has Flood Insurance Rate Maps (FIRMs) through the U.S. Federal Emergency Management Agency (FEMA). This EnviroAtlas dataset provides an estimate of the 100-year floodplain for the conterminous United States at 30-meter resolution to fill the gaps in the FIRM. The model hit rate for the CONUS was 0.79 compared to the FIRM, indicating that the model captured 79% of the 100-year floodplain identified by FEMA. This product provides complete coverage for the CONUS by identifying floodplains in areas without FIRMs, while also identifying floodplains in tributaries sometimes excluded by FEMA. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets) or journal article (https://doi.org/10.1016/j.scitotenv.2018.07.353). This dataset is useful for evaluating the potential value of ecosystem services provided by floodplains. The overall goal of EnviroAtlas is to employ and develop the best available science to map indicators of ecosystem services production, demand, and drivers for the nation. These data are not meant to replace or supplement FEMA Flood Insurance Rate Maps. 

This dataset is associated with the following publication:
Woznicki, S., J. Baynes, S. Panlasigui, M. Mehaffey, and A. Neale. Development of a spatially complete floodplain map of the conterminous United States using random forest.   SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM,  NETHERLANDS, 647: 942-953, (2018).

Tags: ecosystem-services, enviroatlas, floodplain, gis, machine-learning, random-forest
Organization: U.S. Environmental Protection Agency
Assessing the Social and Environmental Costs of Institutions Nitrogen Footprints

This dataset allowed estimates the damage costs associated with the institutional nitrogen (N) footprint and explores how this information could be used to create more sustainable institutions. Potential damages associated with the release of nitrogen oxides (NOx), ammonia (NH3), and nitrous oxide (N2O) to air and release of nitrogen to water were estimated
using existing values and a cost per unit of nitrogen approach. These damage cost values were then applied to two universities. Annual potential damage costs to human health, agriculture, and natural ecosystems associated with the N footprint of institutions were $11.0 million (2014) at the University of Virginia (UVA) and $3.04 million at the University of New Hampshire (UNH). Costs associated with the release of nitrogen oxides to human health, in particular the use of coal-derived energy, were the largest component of damage at UVA. At UNH the energy N footprint is much lower because of a landfill cogeneration source, and thus the majority of damages were associated with food production. Annual damages associated with release of nitrogen from food production were very similar
at the two universities ($1.80 million vs. $1.66 million at UVA and UNH, respectively). These damages also have implications for the extent and scale at which the damages are felt. For example, impacts to human health from energy and transportation are generally larger near the power plants and roads, while impacts from food production can be distant from the campus. Making this information available to institutions and communities can improve their understanding of the damages associated with the different nitrogen forms and sources, and inform decisions about
nitrogen reduction strategies. 

This dataset is associated with the following publication:
Compton, J., A. Leach, E. Castner, and J. Galloway. Assessing the Social and Environmental Costs of Institutional Nitrogen Footprints.   Sustainability: The Journal of Record. Mary Ann Liebert, Inc., New Rochelle, NY, USA, 10(2): 114-122, (2017).

Tags: atmospheric-deposition, benefit-cost-analysis, ecosystem-services, fertilizer, n-fixation, nitrogen, nutrients, point-sources
Organization: U.S. Environmental Protection Agency
Additional benefits of federal air quality rules: model estimates of controllable biogenic secondary organic aerosol

Dataset is a link to the publically available CMAQ v5.1 model code. The following update was also implemented: 
https://github.com/USEPA/CMAQ/blob/5.2/CCTM/docs/Release_Notes/AH3OPJ_IEPOX_update.md. 

This dataset is associated with the following publication:
Carlton, A., H. Pye, K. Baker, and C. Hennigan. Additional Benefits of Federal Air-Quality Rules: Model Estimates of Controllable Biogenic Secondary Organic Aerosol.   ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 52(16): 9254–9265, (2018).

Tags: biogenic-voc, cmaq, pm2-5, pm2-5-air-quality-modeling, semivolatile-organic-compounds-svocs, soa
Organization: U.S. Environmental Protection Agency
Model codes and run scripts developed for the addition of Four-Dimensional Data Assimilation (FDDA) to the Model for Prediction Across Scales - Atmosphere (MPAS-A)

Web link to a Zenodo repository containing Model codes, run scripts, the computational mesh definition file, and user instructions for the addition of Four-Dimensional Data Assimilation (FDDA) to the Model for Prediction Across Scales - Atmosphere (MPAS-A). 

This dataset is associated with the following publication:
Bullock, R., H. Foroutan, R. Gilliam, and J. Herwehe. Adding four-dimensional data assimilation by analysis nudging to the Model for Prediction Across Scales – Atmosphere (version 4.0).   Geoscientific Model Development. Copernicus Publications, Katlenburg-Lindau,  GERMANY, 11: 2897-2922, (2018).

Tags: air-quality, data-assimilation, global, meteorological-model
Organization: U.S. Environmental Protection Agency
Simmons_DeGroot_Metabolism_mRNA_transfection_ApplInVitroTox_Data

The US EPA’s ToxCast program is designed to assess chemical perturbations of molecular and cellular endpoints using a variety of high-throughput screening (HTS) assays. However, existing HTS assays have limited or no xenobiotic metabolism which could lead to false positive (chemical is detoxified in vivo) as well as false negative results (chemical is bioactivated in vivo) and thus potential mischaracterization of chemical hazard. We have addressed this challenge by introducing the ten most prevalent human liver cytochrome P450 (CYP) enzymes into a human cell line (HEK293T) with low endogenous metabolic capacity. The CYP enzymes were introduced via transfection of modified mRNAs as singlets or as a mixture in relative proportions expressed in the liver. Initial experiments using luminogenic CYP450 substrates demonstrate that cell models express metabolic enzymes from the transfected mRNAs and activities are significantly increased when co-transfected with a CYP accessory protein, P450 oxidoreductase (POR). Transfected HEK293T cells demonstrate the ability to produce predicted metabolites following treatment with well-studied CYP substrates, with metabolite formation occurring through 18 hours post-treatment. As a demonstration of how this method can be used to retrofit existing HTS assays, a proof-of-concept screen for cytotoxicity in HEK293T cells was conducted using 56 test compounds. The results demonstrate that the xenobiotic metabolism conferred by transfection of CYP-encoding mRNAs shifts the dose-response relationship for certain test chemicals such as aflatoxin B1 (bioactivation) and fenazaquin (detoxification). Overall, transfection of CYP-encoding mRNAs is an effective and portable solution for retrofitting metabolic competence to existing cell-based HTS assays. 

This dataset is associated with the following publication:
DeGroot, D., A. Swank, R. Thomas, M. Strynar, M. Lee, P. Carmichael, and S. Simmons. mRNA transfection retrofits cell-based assays with xenobiotic metabolism.   JOURNAL OF PHARMACOLOGICAL & TOXICOLOGICAL METHODS. Elsevier Science Ltd, New York, NY, USA, 92: 77-94, (2018).

Tags: biotransformation, cell-based-assay, high-throughput-screening, metabolism, toxcast
Organization: U.S. Environmental Protection Agency
Changes in gene expression in Arabidopsis in response to nano CeO2 and nano TiO2

- Changes in tissue transcriptomes and productivity of Arabidopsis thaliana were investigated during exposure of plants to two widely used engineered metal oxide nanoparticles, titanium dioxide (nano-titanium) and cerium dioxide (nano-cerium). Microarray analyses confirmed that exposure to either nanoparticle altered the transcriptomes of rosette leaves and roots, with comparatively larger numbers of differentially expressed genes (DEGs) found under nano-titania exposure. Nano-titania induced more DEGs in rosette leaves, whereas roots had more DEGs under nano-ceria exposure.  MapMan analyses indicated that while nano-titania up-regulated overall metabolism metabolism in both tissues, metabolic processes under nano-ceria remained mostly unchanged. Gene enrichment analysis indicated that both nanoparticles mainly enriched ontology groups such as responses to stress (abiotic and biotic), and defense responses (pathogens), and responses to endogenous stimuli (hormones). Nano-titania specifically induced genes associated with photosynthesis, whereas nano-ceria induced expression of genes related to activating transcription factors, most notably those belonging to the ethylene responsive element binding protein family.  Interestingly, there were also increased numbers of rosette leaves and plant biomass under nano-ceria exposure, but not under nano-titania. Other transcriptomic responses did not clearly relate to responses observed at the organism level. This may be due to functional and genomic redundancy in Arabidopsis, which may mask expression of morphological changes, despite discernable responses at the transcriptome level. Additionally, transcriptomic changes often relate with transgenerational phenotypic development, hence it may be productive to direct further experimental work to integrate high-throughput genomic results with longer-term changes in subsequent generations. 

This dataset is associated with the following publication:
Tumburu, L., C. Andersen, P.T. Rygiewicz, and J. Reichman. Molecular and physiological responses to titanium dioxide and cerium oxide nanoparticles in arabidopsis.   ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 36(1): 71-82, (2017).

Tags: ammonium-nitrate, engineered-nanomaterials-enms, intergenerational-effects, isotope, isotopic-discrimination, nitrogen
Organization: U.S. Environmental Protection Agency
Data contributed by EPA/ORD/NERL/CED researchers to the manuscript "Influence of anthropogenic emissions and boundary conditions on multi-model simulations of major air pollutants over Europe and North America in the framework of AQMEII3"

This dataset contains the data contributed by EPA/ORD/NERL/CED researchers to the manuscript "Influence of anthropogenic emissions and boundary conditions on multi-model simulations of major air pollutants over Europe and North America in the framework of AQMEII3" led by Dr. Ulas Im of Aarhus University in Denmark. 

This dataset is associated with the following publication:
Im, U., J. Christensen, C. Geels, K. Hansen, J. Brandt, E. Solazzo, U. Alyuz, A. Balzarini, R. Baro, R. Bellasio, R. Bianconi, J. Bieser, A. Colette, G. Curci, A. Farrow, J. Flemming, A. Fraser, P. Jimenez-Guerrero, N. Kitwiroon, P. Liu, U. Nopmongcol, L. Palacios-Peña, G. Pirovano, L. Pozolli, M. Prank, R. Rose, R. Sokhi, P. Tuccella, A. Unal, M. Garcia Vivanco, G. Yarwood, C. Hogrefe, and S. Galmarini. Influence of anthropogenic emissions and boundary conditions on multi-model simulations of major air pollutants over Europe and North America in the framework of AQMEII3.   Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau,  GERMANY, 18: 8929-8952, (2018).

Tags: emission-sensitivity, ensemble-modeling, long-range-transport, model-evaluation
Organization: U.S. Environmental Protection Agency
Watford_Novel_application__NPMI_Biomedlit_genesets_usecase_breast_cancer

We present a novel use of normalized pointwise mutual information (NPMI) to mine biomedical literature for gene associations with biological concepts as represented by Medical Subject Headings (MeSH terms) in PubMed. 

This dataset is associated with the following publication:
Watford, S., R. Grashow, V. De La Rosa, R. Rudel, K. Paul-Friedman, and M. Martin. Novel application of normalized pointwise mutual information (NPMI) to mine biomedical literature for gene sets associated with disease: Use case in breast carcinogenesis.   Computational Toxicology. Elsevier B.V., Amsterdam,  NETHERLANDS, 7: 46-57, (2018).

Tags: actor, biomedical-literature, breast-carcinogenesis, chemical-exposures, data-integration, genes, literature-mining
Organization: U.S. Environmental Protection Agency
Fitzpatrick_Jeremy_Skin_Sensitization_Data

Allergic contact dermatitis (ACD) is estimated to constitute about 10-15% of all occupational diseases. Predictive testing to characterise substances for their skin sensitisation potential has historically been based on animal models such as the Local Lymph Node Assay (LLNA) and the Guinea Pig Maximisation Test (GPMT). In recent years, EU regulations, have provided a strong incentive to develop non-animal alternatives.  Significant progress has been made in developing and evaluating non-animal test methods. There have been efforts to develop and evaluate the utility of in silico models for skin sensitisation including local and global (Q)SARs as well as expert systems. In this study, we selected three different types of expert systems: VEGA (statistical), Derek Nexus (knowledge based), TIMES-SS (hybrid) and evaluated their performance using 2 large datasets of substances that had been assessed for their skin sensitisation potential in animal models. We considered a model to be successful at predicting skin sensitisation potential if it had at least the same balanced accuracy as the LLNA and the GPMT had in predicting the outcomes of one another, which ranged from 79% to 86% depending on the dataset.  We found that none of the expert systems evaluated was able to achieve such a high balanced accuracy in their global predictions, with balanced accuracies ranging from 56% to 65%.  However, for substances within the domain of TIMES-SS, balanced accuracies were found to be 79% and 82%, for the two datasets, in line with the animal data. The expert systems evaluated could be extended in light of the additional data collected as part of this study. The incorrect predictions offer new insights for how the existing alerts within these expert systems could be refined. These datasets also offer exciting opportunities for the development of new models. 

This dataset is associated with the following publication:
Fitzpatrick, J., D. Roberts, and G. Patlewicz. (SAR and QSAR in ENVIRONMENTAL RESEARCH) An evaluation of selected (Q)SARs/expert systems for predicting skin sensitisation potential.   SAR AND QSAR IN ENVIRONMENTAL RESEARCH. Taylor & Francis, Inc., Philadelphia, PA, USA, 29(6): 439-468, (2018).

Tags: dsstox
Organization: U.S. Environmental Protection Agency
Read_Across_Prediction_of_Estrogenicity_for_Hindered_Phenols_2017_Data

Read-across is an important data gap filling technique used within category and analog approaches for regulatory hazard identification and risk assessment. Although much technical guidance is available that describes how to develop category/analog approaches, practical principles to evaluate and substantiate analog validity (suitability) are still lacking. This case study uses hindered phenols as an example chemical class to determine: (1) the capability of three structure fingerprint/descriptor methods (PubChem, ToxPrints and MoSS MCSS) to identify analogs for read-across to predict Estrogen Receptor (ER) binding activity and, (2) the utility of data confidence measures, physicochemical properties, and chemical R-group properties as filters to improve ER binding predictions. The training dataset comprised 462 hindered phenols and 257 non- hindered phenols. For each chemical of interest (target), source analogs were identified from two datasets (hindered and non-hindered phenols) that had been characterized by a fingerprint/descriptor method and by two cut-offs: (1) minimum similarity distance (range: 0.1 - 0.9) and, (2) N closest analogs (range: 1 - 10). Analogs were then filtered using: (1) physicochemical properties of the phenol (termed global filtering) and, (2) physicochemical properties of the R-groups neighboring the active hydroxyl group (termed local filtering). A read-across prediction was made for each target chemical on the basis of a majority vote of the N closest analogs. The results demonstrate that: (1) concordance in ER activity increases with structural similarity, regardless of the structure fingerprint/descriptor method, (2) increased data confidence significantly improves read-across predictions, and (3) filtering analogs using global and local properties can help identify more suitable analogs. This case study illustrates that the quality of the underlying experimental data and use of endpoint relevant chemical descriptors to evaluate source analogs are critical to achieving robust read-across predictions. 

This dataset is associated with the following publication:
Pradeep, P., K. Mansouri, G. Patlewicz, and R. Judson. (Computational Toxicology) A systematic evaluation of analogs and automated read-across prediction of estrogenicity: A case study using hindered phenols.   Computational Toxicology. Elsevier B.V., Amsterdam,  NETHERLANDS, 4: 22-30, (2017).

Tags: actor, analog-identification, estrogen-receptor-binding, read-across, toxcast
Organization: U.S. Environmental Protection Agency
2017_Prediction_of_H295R_steroidogenesis_Pathway_Perturbation

The objectives of this work were to: 1) develop an integrated analysis of chemical-mediated effects on steroidogenesis in the HT-H295R assay; and, 2) evaluate whether the HT-H295R assay predicts estrogen and androgen production specifically via comparison with the OECD-validated H295R assay. 

This dataset is associated with the following publication:
Haggard, D., A. Karmaus, M. Martin, R. Judson, W. Setzer, and K. Paul-Friedman. (Toxicological Sciences) High-throughput H295R steroidogenesis assay: utility as an alternative and a statistical approach to characterize effects on steroidogenesis.   TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA,  162(2): 509-534, (2018).

Tags: actor, edsp21, steroidogenesi, toxcast
Organization: U.S. Environmental Protection Agency
Sediment chemistry and toxicity of Dibit

Data consist of sediment toxicity and chemistry measurements and results. The data dictionary for each of the three spreadsheet files containing the data are provided as a separate tab within each spreadsheet. 

This dataset is associated with the following publication:
Barron, M., E. Moso, R. Conmy, P. Meyer, and D. Sundaravadivelu. Toxicity of sediment oiled with diluted bitumens to freshwater and estuarine amphipods.   MARINE POLLUTION BULLETIN. Elsevier Science Ltd, New York, NY, USA, 163: 7, (2021).

Tags: amphipod, dilbit, diluted-bitumen, sediment, toxicity
Organization: U.S. Environmental Protection Agency
Predict_Organ_Toxicity_ChemResTox_Data

We use a supervised machine learning strategy to systematically investigate the relative importance of study type, machine learning algorithm, and type of descriptor on predicting in vivo repeat-dose toxicity at the organ-level.   A total of 985 compounds were represented using chemical structural descriptors, ToxPrint chemotype descriptors, and bioactivity descriptors from ToxCast in vitro high-throughput screening assays.  Using ToxRefDB, a total of 35 target organ outcomes were identified that contained at least 100 chemicals (50 positive and 50 negative). Supervised machine learning was performed using Na&iuml;ve Bayes, k-nearest neighbor, random forest, classification and regression trees, and support vector classification approaches. Model performnce was assessed based on F1 scores using five-fold cross-validation with balanced bootstrap replicates.  Fixed effects modeling showed the variance in F1 scores was explained mostly by target organ outcome, followed by descriptor type, machine learning algorithm, and interactions between these three factors.  A combination of bioactivity and chemical structure or chemotype descriptors were the most predictive. Model performance improved with more chemicals (up to a maximum of 24%) and these gains were correlated (&rho;= 0.92) with the number of chemicals. 

This dataset is associated with the following publication:
Liu, J., G. Patlewicz, A. Williams, R. Thomas, and I. Shah. (Chemical Research in Toxicology) Predicting organ toxicity using in vitro bioactivity data and chemical structure.   CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 30: 2046−2059, (2017).

Tags: bioactivity, chemotypes, high-throughput-screening, high-throughput-toxicology, machine-learning, qsar, toxcast, toxrefdb
Organization: U.S. Environmental Protection Agency