AndreaBozzo/ceres-open-data-index huggingface dataset:
Basin and Range Investigation for Developing Geothermal Energy: Exploration Data

This data package includes exploration material from the Basin & Range Investigation for Developing Geothermal Energy [in Hidden Systems] project (BRIDGE), which is part of a broader initiative to advance the exploration of hidden geothermal resources in the Basin & Range Province of the western U.S.

Data modalities include a helicopter-borne time-domain electromagnetic survey, magnetotellurics, 2-meter temperature measurements, ground-based gravity and legacy aeromagnetic surveys, geochemistry, geologic mapping, LiDAR analysis, 3D models, associated geospatial data, and a bibliography of existing data and references utilized in prospect characterization and conceptual modeling. Key files are in CSV, Geosoft, and Geotools formats. Please refer to READMEs for dataset-specific information. Where applicable, acquisition data and inversion models for a particular prospect or area of interest are organized separately.

This BRIDGE data package is the product of a collaboration led by Sandia National Laboratories with partners from Geologica Geothermal Group, Inc., the U.S. Navy Geothermal Program Office, and consultants Steven Sewell (Australis Geoscience Ltd) and William Cumming (Cumming Geoscience). The project's areas of interest (AOIs) are based off priority areas of interest in the southwestern portion of the Nevada Play Fairway map, distribution across tectonic provinces, accessibility, and the project team's extensive experience in the region. AOIs cover about a dozen basins that include unexplored prospects, partially explored prospects, and some developed analogue resources that provide validation cases. Many unexplored and partially explored prospects are on U.S. Department of Defense (DoD) land, though adjacent lands are included as well.

Tags: 2-meter, airborne-electromagnetics, basin, blind, blind-systems, clay-cap, conceptual-model, dixie-valley, energy, exploration, gabbs-valley, geochemistry, geologic-mapping, geophysics, geothermal, gravity, grover-point, hawthorne, hidden, hidden-systems, inversion, lee-allen, lidar-analysis, magnetotellurics, nevada, potential-fields, processed-data, range, raw-data, temperature, walker-lane
Organization: Department of Energy
Utah FORGE: EGS Reservoir Produced Fluids Geochemistry 2022-2024

This dataset contains geochemical analyses of produced fluids from the Utah FORGE site, specifically from wells 16A(78)-32, 16B(78)-32, and 58-32, collected during various stimulation, flowback, and circulation tests conducted between 2022 and 2024. The data contains element concentrations, pH, and dissolved gas compositions. Geochemical analyses for 2022 and 2023 were performed at the Brigham Young University geochemistry laboratory, while the 2024 results were obtained from Thermochem. Dry gas samples were collected using a mini-separator attached to the single-phase production line between the wellhead and separator, where fluid was flashed to atmospheric pressure. Gas concentrations were recalculated to a single phase reservoir liquid based on heat and mass balance expressions. Additional contextual information, including interpretations of geochemical trends and reservoir behavior, is available in the included report from Simmons et al. (2025), which was presented at the Stanford Geothermal Workshop in February 2025.

Tags: 16a78-32, 16b78-32, 58-32, brine-composition, chemical-geothermometry, circulation-tests, dissolved-gases, egs, energy, flowback-water, flowback-waters, fluid-chemistry, geochemistry, geothermal, mineral-dissolution, produced-fluids, reservoir-monitoring, stimulation, stimulation-tests, utah-forge
Organization: Department of Energy
National Emissions Inventory, U.S., 2014, EPA/OAR/OAQPS/AQAD

This web service contains layers that depict annual emissions for 2014 at the facility and county level for the following criterial pollutants: CO, Lead, NH3, NOx, PM10, PM25, SO2, and VOC. The National Emissions Inventory (NEI) is a comprehensive and detailed estimate of air emissions of criteria pollutants, criteria precursors, and hazardous air pollutants from air emissions sources. The NEI is released every three years based primarily upon data provided by State, Local, and Tribal air agencies for sources in their jurisdictions and supplemented by data developed by the US EPA. The NEI is built using the Emissions Inventory System (EIS) first to collect the data from State, Local, and Tribal air agencies and then to blend that data with other data sources. NEI point sources include emissions estimates for larger sources that are located at a fixed, stationary location. Point sources in the NEI include large industrial facilities and electric power plants, airports, and smaller industrial, non-industrial and commercial facilities. A small number of portable sources such as some asphalt or rock crushing operations are also included. Some states voluntarily also provide facilities such as dry cleaners, gas stations, and livestock facilities, which are otherwise included in the NEI as nonpoint sources. The emissions potential of each facility determines whether that facility should be reported as a point source, according to emissions thresholds set in the Air Emissions Reporting Rule (AERR). NEI Point Sources are all included in the EIS Point Data Category. NEI nonpoint sources include emissions estimates for sources which individually are too small in magnitude to report as point sources. These emissions sources are included in the NEI as a county total or tribal total (for participating tribes). Examples include residential heating, commercial combustion, asphalt paving, and commercial and consumer solvent use. NEI nonpoint sources are all included in the EIS Nonpoint Data Category.

Tags: air, facilities, united states
Organization: U.S. Environmental Protection Agency
AmeriCorps Participant Demographics Data

- This dataset provides comparisons of demographic group prevalence in AmeriCorps Member/Volunteers populations to that of the greater U.S. population. The odds ratio analysis was completed by the Office of the Chief Data Officer. 
- Population estimates were obtained from U.S. Census Bureau data reported in American Community Survey 5-Year tables DP05 (total U.S. populations) and S1701 (U.S. populations below poverty line), and socioeconomic status-related microdata maintained by IPUMS USA.
- See Attached Document 'AmeriCorps Demographic Analysis Procedure.pdf' for a full technical documentation of the analysis.

Tags: americorps, americorps-members, americorps-volunteer-demographics, demographic-analysis, volunteers
Organization: AmeriCorps
Wind Integration National Dataset (WIND) Toolkit

Wind resource data for North America was produced using the Weather Research and Forecasting Model (WRF). The WRF model was initialized with the European Centre for Medium Range Weather Forecasts Interim Reanalysis (ERA-Interm) data set with an initial grid spacing of 54 km.  Three internal nested domains were used to refine the spatial resolution to 18, 6, and finally 2 km.  The WRF model was run for years 2007 to 2014. While outputs were extracted from WRF at 5 minute time-steps, due to storage limitations instantaneous hourly time-step are provided for all variables while full 5 min resolution data is provided for wind speed and wind direction only.

The following variables were extracted from the WRF model data:
- Wind Speed at 10, 40, 60, 80, 100, 120, 140, 160, 200 m
- Wind Direction at 10, 40, 60, 80, 100, 120, 140, 160, 200 m
- Temperature at 2, 10, 40, 60, 80, 100, 120, 140, 160, 200 m
- Pressure at 0, 100, 200 m
- Surface Precipitation Rate
- Surface Relative Humidity
- Inverse Monin Obukhov Length

Tags: api, code, data, eastern-wind-integration-data-set, energy, era-interm, meterology, model, numerical-weather-model, power, python, toolkit, weather, weather-research-and-forecasting-model, western-wind-integration-data-set, wind, wind-data, wrf
Organization: Department of Energy
North and South Dakota High Resolution 50m Wind Resource

Annual average wind resource potential for North and South Dakota at a 50 meter height in a GIS shapefile and links to US national wind resource information tools. This data set was produced and validated by NREL using their WRAM model. 




Tags: 50m, energy, gis, north-dakota, nrel, resource, shapefile, south-dakota, wind, wind-data, wind-resource, wram
Organization: Department of Energy
Evaluating structure-based activity in a high-throughput assay for steroid biosynthesis

Dataset from Foster, M.J., et al., Evaluating structure-based activity in a high-throughput assay for steroid biosynthesis, Computational Toxicology, Vol 24, No. 100245, Nov 2022, DOI https://doi.org/10.1016/j.comtox.2022.100245

The work described herein was conducted in R software (version 4.0.2) and is available at GitHub (https://github.com/USEPA/CompTox-HTH295R-SAR) and the US EPA Clowder repository as well as in Supplemental File 1 as an html file including code and resultant output. 

This dataset is associated with the following publication:
Foster, M., G. Patlewicz, I. Shah, D. Haggard, R. Judson, and K. Friedman. Evaluating structure-based activity in a high-throughput assay for steroid biosynthesis.   Computational Toxicology. Elsevier B.V., Amsterdam,  NETHERLANDS, 24: 100245, (2022).

Tags: chemotypes, in-silico-screening, read-across, steroidogenesis, structure-activity-relationships
Organization: U.S. Environmental Protection Agency
Data represents key inputs and modeled outputs in Willwerth et al. (2023). The effects of climate change on outdoor recreation participation in the U.S.: Projections for the 21st century.

Data represents key inputs and modeled outputs of the published journal article. Citation information for this dataset can be found in Data.gov's References section.

Tags: climate-change, future-projections, outdoor-recreation
Organization: U.S. Environmental Protection Agency
HAWC export and condensed data for analysis, with glossaries of fields

Two datasets are included: 1) A collection of datasets (“APROBA HAWC Export 8May2024.xlsx” with accompanying glossary) obtained from study reports available on the National Toxicology Program’s (NTP’s) website (https://ntp.niehs.nih.gov/publications/reports/tr?type=Technical%20Report); and 2) the NTP dataset and datasets collected from EPA’s Toxicity Reference Database (https://github.com/USEPA/CompTox-ToxRefDB), condensed for analysis (“data-ap-ow-tr-mdl-accepted.csv” with accompanying glossary). 

Tags: aroba, benchmark-dose, dose-response, hawc, hdmi, toxrefdb, uncertainty
Organization: U.S. Environmental Protection Agency
Metadata for Comparison of associations between proximity to major roads and all-cause mortality in individuals with varying severity of cardiovascular disease

The dataset is tabular data containing electronic health records from individuals with heart failure, a prior myocardial infarction, or represent a random sample of the UNC population. The data contains demographic data, dates of diagnoses, comorbidities, all-cause mortality dates, and proximity to major roadways (as a proxy for exposure to traffic related air pollution). This dataset is not publicly accessible because: This data contains PII in the form of electronic health records and cannot be uploaded to ScienceHub. It can be accessed through the following means: The data requires an approved IRB application to be accessed. With an approved IRB application it can be accessed by emailing ward-caviness.cavin@epa.gov. Format: The dataset is tabular data containing electronic health records from individuals with heart failure, a prior myocardial infarction, or represent a random sample of the UNC population. The data contains demographic data, dates of diagnoses, comorbidities, all-cause mortality dates, and proximity to major roadways. 

This dataset is associated with the following publication:
Raab, H., M. Breen, A. Weaver, J. Moyer, W. Cascio, D. Diazsanchez, and C. Ward-Caviness. Comparison of associations between proximity to major roads and all-cause mortality across a spectrum of cardiovascular diseases.   Environmental Epidemiology. Wolters Kluwer, Alphen aan den Rijn,  NETHERLANDS, 8(6): e351, (2024).

Tags: cardiovascular-disease, environmental-sensitivity, mortality, traffic
Organization: U.S. Environmental Protection Agency
Landfill Workface ES&T Publication

Datasets from Carbon Mapper portal finding 
 there is a need for wider availability of site specific
geospatial and time-resolved information (e.g., locations
of gas collection wells and time-series of well downtime) to
identify the methodological changes needed to better account
for work face emissions in current emissions models. 

This dataset is associated with the following publication:
Scarpelli, T.R., D.H. Cusworth, R.M. Duren, J. Kim, J. Heckler, G.P. Asner, E. Thoma, M.J. Krause, D. Heins, and S. Thorneloe. Investigating major sources of methane emissions at US landfills.   ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 58(49): 21545–21556, (2024).

Tags: landfill
Organization: U.S. Environmental Protection Agency
Dictionary of Algorithms and Data Structures (DADS)

The Dictionary of Algorithms and Data Structures (DADS) is an online, publicly accessible dictionary of generally useful algorithms, data structures, algorithmic techniques, archetypal problems, and related definitions.  In addition to brief definitions, some entries have links to related entries, links to implementations, and additional information. DADS is meant to be a resource for the practicing programmer, although students and researchers may find it a useful starting point.  DADS has fundamental entries in areas such as theory, cryptography and compression, graphs, trees, and searching, for instance, Ackermann's function, quick sort, traveling salesman, big O notation, merge sort, AVL tree, hash table, and Byzantine generals. DADS also has index pages that list entries by area and by type.  Currently DADS does not include algorithms particular to business data processing, communications, operating systems or distributed algorithms, programming languages, AI, graphics, or numerical analysis.

Tags: algorithmic-techniques, algorithms, data-structures
Organization: National Institute of Standards and Technology
Performance data of a robotic system with a robotic hand and a robotic gripper completing a peg-in-hole assembly task

NIST is developing metrics and test methods to benchmark the performance of robotic systems when performing manufacturing tasks. The ability to perform simple insertions is critical for robotic systems in manufacturing. A simple peg-in-hole test was designed to measure a robotic system's capability for performing these simple insertions. The dataset captures the performance metrics of a robotic system outfitted with a robotic hand and a robotic gripper to study the effect of next-generation robotic hand technology versus conventional parallel gripper technologies.

Tags: assembly, manufacturing, robotic-hands, robotics
Organization: National Institute of Standards and Technology
NIST DART-MS Forensics Database (is-CID)

The NIST DART-MS Forensics Database is an evaluated collection of in-source collisionally-induced dissociation (is-CID) mass spectra of compounds of interest to the forensics community (e.g. seized drugs, cutting agents, etc.). The is-CID mass spectra were collected using Direct Analysis in Real-Time (DART) Mass Spectrometry (MS), either by NIST scientists or by contributing agencies noted per compound. The database is provided as a general-purpose structure data file (.SDF). For users on Windows operating systems,  the .SDF format library can be converted to NIST MS Search format using Lib2NIST and then explored using NIST MS Search v2.4 for general mass spectral analysis. These software tools can be downloaded at https://chemdata.nist.gov. The database is now (09-28-2021) also  provided in R data format (.RDS) for use with the R programming language. This database, also commonly referred to as a library, is one in a series of high-quality mass spectral libraries/databases produced by NIST (see NIST SRD 1a, https://dx.doi.org/10.18434/T4H594).

Tags: biosciences-and-health, chemical-identification, ion-fragmentation, mass-spectra, mass-spectrometry, nist-mass-spectral-libraries, security-and-forensics, standard-reference-data
Organization: National Institute of Standards and Technology
Suppression of filament defects in embedded 3D printing: images and videos of single filament extrusion

These images, videos, and tables show experimental data, where single lines of viscoelastic inks were extruded into moving viscoelastic support baths. Lines were printed at varying angles relative to the camera, such that videos and images captured the side of horizontal lines, cross-sections of horizontal lines, and the side of vertical lines. Metadata including pressure graphs, programmed speeds, toolpaths, and rheology data are also included.

Tags: 3d-printing, additive-manufacturing, herschel-bulkley, polymer, viscoelasticity
Organization: National Institute of Standards and Technology
Trojan Detection Software Challenge - image-classification-feb2021-train

Round 4 Train DatasetThe data being generated and disseminated is the training data used to construct trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform image classification. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 1008 adversarially trained, human level, image classification AI models using a variety of model architectures. The models were trained on synthetically created image data of non-real traffic signs superimposed on road background scenes. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.

Tags: trojan-detection-artificial-intelligence-ai-machine-learning-adversarial-machine-learning
Organization: National Institute of Standards and Technology
NexusLIMS: a Python Package for EM Experiment Metadata Management

This code repository contains the "back-end" of the Nexus Microscopy Facility Laboratory Information Management System (NexusLIMS), developed by the NIST Office of Data and Informatics. Its primary function is to build XML-formatted research experiment records by combining metadata from many different sources (reservation systems, the collected data files, a session logger, etc.). These records are structured according to the "Nexus Experiment" schema, meaning they can be loaded into a repository and used for structured data queries.

Tags: data-management, electron-microscopy, laboratory-information-management, materials-microscopy, open-science, reproducibility, scientific-data
Organization: National Institute of Standards and Technology
Simulated filament shapes in embedded 3D printing

In embedded 3D printing, a nozzle is embedded into a support bath and extrudes filaments or droplets into the bath. Using OpenFOAM, we simulated the extrusion of filaments and droplets into a moving bath. This dataset focuses on the effect of Newtonian viscosity and Herschel-Bulkley plateau (pre-yielded) viscosity on the extrusion of single lines. OpenFOAM is an open source computational fluid dynamics solver. This work used a combination of OpenFOAM v1912 and OpenFOAM 8 on desktop computers. OpenFOAM input files were generated using Python 3.7. Output files were analyzed using Paraview 5.8.0 and Python 3.7. Associated code can be found on Github: https://github.com/usnistgov/openfoamEmbedded3DP, doi:10.18434/mds2-2392 This data is described in: Friedrich, L., & Seppala, J.E. (2021) Simulated filament shapes in embedded 3D printing, submitted for publication See the file, mds2-2391-filelisting.csv, for a complete listing of the data files that available as part of this collection with URLs for downloading them.

Tags: 3d-printing, extrusion, herschel-bulkley, openfoam, rheology, support-bath, surface-tension
Organization: National Institute of Standards and Technology
Python tools for OpenFOAM simulations of filament shapes in embedded 3D printing

In embedded 3D printing, a nozzle is embedded into a support bath and extrudes filaments or droplets into the bath. Using OpenFOAM, we simulated the extrusion of filaments and droplets into a moving bath. OpenFOAM is an open source computational fluid dynamics solver. This repository contains the following Python tools: - Tools for generating input files for OpenFOAM v1912 or OpenFOAM v8 tailored to a nozzle extruding a filament into a static support bath. - Tools for monitoring the status of OpenFOAM simulations and aborting them if they are too slow. - Tools for moving output files between storage locations. (For example, it can automatically move all files to a server, but only necessary files to your hard drive) - Tools for generating images and tables from the 3D time series. - Tools for compiling images into videos. - Tools for analyzing, summarizing, and plotting data.

Tags: 3d-printing, extrusion, herschel-bulkley, openfoam, rheology, support-bath, surface-tension
Organization: National Institute of Standards and Technology
A Dynamic Plasticity Model for Rapidly-Heated 1045 Steel up to 1000 °C

This dataset contains raw mechanical data (strain, stress and strain rate curves and initial temperatures) for compression experiments performed on annealed 1045 steel using the NIST Kolsky Bar apparatus and a servohydraulic test machine that were used to generate fit coefficients for the plasticity model described in the NIST publication entitled: "A Dynamic Plasticity Model for Rapidly-Heated 1045 Steel up to 1000 °C" with authors S. P. Mates and S.-Y. Li.

Tags: carbon-steel, constitutive-model, high-strain-rate, high-temperature, machining
Organization: National Institute of Standards and Technology
Trojan Detection Software Challenge - nlp-sentiment-classification-apr2021-train part2

Round 6 Train Dataset part2This is the training data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform text sentiment classification on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 96 sentiment classification AI models using a small set of model architectures. The models were trained on text data drawn from product reviews. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.

Tags: trojan-detection-artificial-intelligence-ai-machine-learning-adversarial-machine-learning
Organization: National Institute of Standards and Technology