AndreaBozzo/ceres-open-data-index huggingface dataset:
Salivary antibody study in recreational water users in Racine, WI

This data set contains sociodemographic data on households and individuals who  participated in the study, behavioral data (recreational water contacts during the index beach visit and prior to it), and symptoms data from a follow-up survey as well as data on salivary antibody responses to selected waterborne pathogens. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: The datasets generated and analyzed during the current study are not publicly available due Personally Identifiable Information (PII) on study participants but are available from the corresponding author on reasonable request. EPA cannot publicly release PII regarding living individuals according to the Privacy Act and the Freedom of Information Act (FOIA). Restricted access may be granted to authorized persons by contacting the party listed. Format: This study was conducted in one town. The study datasets includes personal identifiers of study participants, such as sex, age, and race, as well as behavioral (recreational water contacts) and gastrointestinal symptoms data, and data on antibody responses to waterborne pathogens. Demographic data can be used to identify study participants residing in the specific community. 

This dataset is associated with the following publication:
Egorov, A., R. Converse, S. Griffin, R. Bonasso, L. Wickersham, E. Klein, J. Kobylanski, R. Ritter, J. Styles, H. Ward, E. Sams, E. Hudgens, A. Dufour, and T. Wade. Recreational water exposure and waterborne infections in a prospective salivary antibody study at a Lake Michigan beach.   Scientific Reports. Nature Publishing Group, London,  UK, 11: 20540, (2021).

Tags: data-set-analysis, health-effects, norovirus, salivary-antibody-assay, statistics, surface-water
Organization: U.S. Environmental Protection Agency
U.S. Electric Utility Companies and Rates: Look-up by Zipcode (2021)

This dataset, compiled by NREL using data from [ABB, the Velocity Suite](http://energymarketintel.com/) and the [U.S. Energy Information Administration dataset 861](http://www.eia.gov/electricity/data/eia861/), provides average residential, commercial and industrial electricity rates with likely zip codes for both investor owned utilities (IOU) and non-investor owned utilities. Note: the files include average rates for each utility (not average rates per zip code), but not the detailed rate structure data found in the [OpenEI U.S. Utility Rate Database](https://openei.org/apps/USURDB/).

Tags: electric-rates, rate-lookup, rates, us-utilities, utility-companies, utility-rates, zip-code, zipcode
Organization: Department of Energy
PNNL Distribution System State Estimator Docker Image

This is the docker image for Pacific Northwest National Laboratory's (PNNL) distribution system state estimator (DSSE) used for the demo of OEDI-SI platform. To support the operation of modern distribution systems, operators require real-time visibility into system states. Due to a lack of measurements and unbalanced operation, the state estimation in distribution systems is challenging. This submission is related to an OEDI-SI use case which demonstrates the application of an extended Kalman filter-based state estimator on an IEEE-123 bus system. The state estimator uses the measurements to generate voltage estimates for the system.

Tags: distribution-system, distribution-system-state-estimator, docker-program, dsse, energy, grid, helics, ieee-123-bus, kalman-filter, oedi-si, pnnl, power, power-flow, simulation, state-estimator
Organization: Department of Energy
Sample IEEE123 Bus system for OEDI SI

Time series load and PV data from an IEEE123 bus system. An example electrical system, named the OEDI SI feeder, is used to test the workflow in a co-simulation. The system used is the IEEE123 test system, which is a well studied test system (see link below to IEEE PES Test Feeder), but some modifications were made to it to add some solar power modules and measurements on the system. 

The aim of this project is to create an easy-to-use platform where various types of analytics can be performed on a wide range of electrical grid datasets. The aim is to establish an open-source library of algorithms that universities, national labs and other developers can contribute to which can be used on both open-source and proprietary grid data to improve the analysis of electrical distribution systems for the grid modeling community. OEDI Systems Integration (SI) is a grid algorithms and data analytics API created to standardize how data is sent between different modules that are run as part of a co-simulation. 

The readme file included in the S3 bucket provides information about the directory structure and how to use the algorithms. The sensors.json file is used to define the measurement locations.

Tags: 123-bus, algorithms, distributed-solar, energy, gadal, grid-algorithms-and-data-analytics-library, ieee-123-bus, ieee-pes-test-feeders, oedi, oedi-si, power, pv, pv-data, qsts, quasi-static-time-series, systems-integration, test-system, time-series
Organization: Department of Energy
U.S. Electric Utility Companies and Rates: Look-up by Zipcode (2020)

This dataset, compiled by NREL using data from [ABB, the Velocity Suite](http://energymarketintel.com/) and the [U.S. Energy Information Administration dataset 861](http://www.eia.gov/electricity/data/eia861/), provides average residential, commercial and industrial electricity rates with likely zip codes for both investor owned utilities (IOU) and non-investor owned utilities. Note: the files include average rates for each utility (not average rates per zip code), but not the detailed rate structure data found in the [OpenEI U.S. Utility Rate Database](https://openei.org/apps/USURDB/).

Tags: electric-rates, rate-lookup, rates, us-utilities, utility-companies, utility-rates, zip-code, zipcode
Organization: Department of Energy
New York State Energy Research and Development Authority Exterior Building Solutions To Insulation

TO4 8.2.2: Space Cond. for Deep Energy Retrofits

The purpose of this project for the New York State Energy Research and Development Authority (NYSERDA) is to find new or improved strategies for exterior building solutions in regard to insulation that will provide extensive energy-savings benefits for homeowners. Deep energy retrofits (DER) is one of the solutions. In the past, DERs have been expensive and hard for the average homeowner to budget. The DER solutions that IBACOS and GreenHomes America (GHA) are proposing will be market friendly, affordable, and easily repeatable. The insulation strategies that will be used on nine test homes include the following: 

- Rigid foam insulation
- Four homes will have approximate center-of-wall R-values of R-28, including furring strips for siding, with additional systems approach work to supply the complete integrated DER solution.
- Spray foam insulation
- Four homes will have approximate center-of-wall R-values of R-30, with additional systems approach work to supply the complete integrated DER solution.
- HPwES upgrade
- One home will include criteria from the Home Performance with ENERGY STAR (HPwES) program, including dense pack walls and window upgrades for this retrofit strategy. 

In addition to the insulation, the strategies will include treatment where needed in the attic, mechanical systems, ventilation and basement band joist, walls, and floors. 




Tags: acca-manual-rs, air-leakage, airflow, beopt, building-america, building-shell, buildingamerica, cold, comfort, deep-energy-retrofit, energy-star, exterior-wall-insulation, new-construction, residential, single-family-detached, space-conditioning, whole-home
Organization: Department of Energy
Transportation Secure Data Center

The Transportation Secure Data Center (TSDC) provides free access to detailed transportation data from a variety of travel surveys and studies conducted across the nation. Data include global positioning system (GPS) readings for millions of miles of travel, along with vehicle characteristics and survey participant demographics. NREL screens the initial data for quality control, translates each data set into a consistent format, and interprets the data for spatial analysis. NREL's processing routines add information on vehicle fuel economy and road grades and join data points to the road network. 

Tags: characteristics, data, data-center, database, energy, fuel-economy, gps, metropolitan-travel-survey-archive, mtsa, preprocessed-data, processed-data, raw-data, road-grades, road-network, secure-data, spatial-analysis, survey, transportation, travel, travel-mode, travel-survey, tsdc, u-s, united-states, us, vehicle, vehicle-choice
Organization: Department of Energy
U.S. Electric Utility Companies and Rates: Look-up by Zipcode (2019)

This dataset, compiled by NREL using data from [ABB, the Velocity Suite](http://energymarketintel.com/) and the [U.S. Energy Information Administration dataset 861](http://www.eia.gov/electricity/data/eia861/), provides average residential, commercial and industrial electricity rates with likely zip codes for both investor owned utilities (IOU) and non-investor owned utilities. Note: the files include average rates for each utility (not average rates per zip code), but not the detailed rate structure data found in the [OpenEI U.S. Utility Rate Database](https://openei.org/apps/USURDB/).

Tags: electric-rates, rate-lookup, rates, us-utilities, utility-companies, utility-rates, zip-code, zipcode
Organization: Department of Energy
Dataset for Evaluation of Extreme Weather Impacts on Utility-Scale Photovoltaic Plant Performance in the United States

This dataset is a fusion of three data types (operations and maintenance tickets, weather data, and production data) that was used to support machine learning analysis and evaluation of drivers for low performance at photovoltaic (PV) sites during compound, extreme weather events.  After being processed with machine learning, the data was used in the "Evaluation of Extreme Weather Impacts on Utility-scale Photovoltaic Plant Performance in the United States" manuscript. Additional details are captured in the associated manuscript.

Tags: ai, analysis, artificial-intelligence, climate-region, data, data-fusion, energy, extreme-weather, flood, flooding, humidity-zone, hurricanes, lightning, machine-learning, maintenance, photovoltaic, power, production, pv, rain, snow, solar, storm, temperature-zone, weather, wind, wind-speed
Organization: Department of Energy
Distributed Generation Market Demand (dGen) model

The Distributed Generation Market Demand (dGen) model simulates customer adoption of distributed energy resources (DERs) for residential, commercial, and industrial entities in the United States or other countries through 2050. The dGen model can be used for identifying the sectors, locations, and customers for whom adopting DERs would have a high economic value, for generating forecasts as an input to estimate distribution hosting capacity analysis, integrated resource planning, and load forecasting, and for understanding the economic or policy conditions in which DER adoption becomes viable, and for illustrating sensitivity to market and policy changes such as retail electricity rate structures, net energy metering, and technology costs. 

Tags: aws, bass-diffusion, csp, database, database-of-state-incentives-for-renewables-efficiency, der, dgen, diffusion, distributed, distributed-energy-resource, distributed-generation-market-demand, economics, energy, forecast, geosptatial, github, market, model, modeling, solar, wind
Organization: Department of Energy
City and County Commercial Building Inventories

The Commercial Building Inventories provide modeled data on commercial building type, vintage, and area for each U.S. city and county. Please note this data is modeled and more precise data may be available through county assessors or other sources. Commercial building stock data is estimated using CoStar Realty Information, Inc. building stock data.  

This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and builds on Cities-LEAP energy modeling, available at the "EERE Cities-LEAP Page" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.

Tags: building-area, building-size-class, building-type, building-use, buildings, cities-leap, city, commercial, commercial-buildings, county, efficiency, energy, example, inventory, modeled-data, planning, size, united-states, us, year-built
Organization: Department of Energy
Wind Turbine Gearbox Condition Monitoring Vibration Analysis Benchmarking Datasets

Wind turbine condition monitoring (CM) can potentially help the wind industry reduce turbine downtime and operation and maintenance (O&M) cost. NREL CM research has investigated various condition-monitoring techniques such as acoustic emission (AE specifically stress wave), vibration, electrical signature, lubricant and debris monitoring based on the Gearbox Reliability Collaborative dynamometer and field tests, and other test turbines and resources accessible by NREL. During the past several years, NREL CM research has shown that there are very few validation and verification efforts on commercial wind turbine CM systems. One of the reasons might be limited benchmarking datasets accessible by stakeholders. To fill this gap, NREL executed a data collection effort. The targeted users of these datasets include those investigating vibration-based wind turbine CM research, evaluating commercially available vibration-based CM systems, or testing prototyped vibration-based CM systems.

NREL collected data from a healthy and a damaged gearbox of the same design tested by the GRC. Vibration data were collected by accelerometers along with high-speed shaft RPM signals during the dynamometer testing. The healthy gearbox was only tested in the dynamometer. The damaged gearbox was first tested in the dynamometer and later sent to a wind farm close to NREL for field testing. In the field test, it experienced two loss-of-oil events that damaged its internal bearings and gear elements. The gearbox was brought back to NREL and it was retested in the dynamometer with CM systems deployed under controlled loading conditions that would not cause catastrophic failure of the gearbox.

The objective of releasing these datasets to the public along with information about the real damage that occurred to the damaged gearbox is to provide the wind industry with some benchmarking datasets. These datasets will benefit research, development, validation, verification, and advancement of vibration-based wind condition-monitoring techniques.

By accessing this data you acknowledge the terms outlined in the "License Information" document.

Please contract Shawn Sheng (NREL) if you have any questions on the data or would like to collaborate on publications based on the datasets.

Tags: benchmark, condition-monitoring, damage-analysis, dynamo, dynamometer, energy, failure-analysis, failure-testing, gearbox, performance, testing, vibration-analysis, wind-energy, wind-turbine
Organization: Department of Energy
Study of Legionella pneumophila treatment with copper in drinking water by single cell-ICP-MS: Metadata entry

The paper introduces a new method for quickly analyzing how copper interacts with waterborne pathogens. The technique uses a single-cell inductively coupled plasma mass spectrometer (ICP-MS) to measure L. hemophilia cell concentration and observe the absorption of copper. Portions of this dataset are inaccessible because: Data is included in the manuscript. They can be accessed through the following means: The manuscript includes a comprehensive presentation of information through well-structured tables and figures. Format: Data include ICP-MS analysis of single particles and single cell analysis. 

This dataset is associated with the following publication:
Xu, L., A. Singler, A. Chernatynskaya, L. Rasmussen, J. Lu, E. Sahle-Demessie, D. Westenberg, H. Yang, and H. Shi. Study of Legionella pneumophila treatment with copper in drinking water by single cell-ICP-MS.   Analytical and Bioanalytical Chemistry. Springer, New York, NY, USA, 416(2): 419-430, (2024).

Tags: copper, legionella-pneumophila, single-cell-icp-ms
Organization: U.S. Environmental Protection Agency
University of Miami Coupled Model (UMCM) for Hurricanes Ike and Sandy

The University of Miami Coupled Model (UMCM) is a coupled model that integrates atmospheric, wave, and ocean components to produce wind, wave, and current data. Atmospheric data is produced using the [Weather Research and Forecasting model](https://www.mmm.ucar.edu/weather-research-and-forecasting-model) (WRF), Wave data is produced using the [University of Miami Wave Model](https://umwm.org/) (UMWM), While ocean current data is produced using the [HYbrid Coordinate Ocean Model](https://www.hycom.org/) (HYCOM).

The time resolution for each model run is as follows:
Hurricane Ike
  -> 1 sample/hour from 9/8/2008 12:00:00 UTC to 9/12/2008 6:00:00 UTC
  -> 1 sample/10 minutes from 9/12/2008 6:00:00 UTC to 9/13/2008 9:00:00 UTC
Hurricane Sandy
  -> 1 sample/10 minutes from 10/28/2012 00:10:00 UTC to 10/31/2012 00:00:00 UTC

Tags: atmospheric, blade-pitch-control, boundary-value-problem, coupled-model, current, directional-wave-spectra, energy, hurricane, hurricanes, hycom, hydrodynamic, ike, loading, ocean, offshore-wind, power, reaction-forces, sandy, simulation, swell, tower-bending, turbine, umcm, umwm, university-of-miami-coupled-model, wave, wave-age-criterion, wave-theory, wind, wind-seas
Organization: Department of Energy
U.S. Electric Utility Companies and Rates: Look-up by Zipcode (Feb 2011)

This dataset, compiled by NREL using data from [ABB, the Velocity Suite](http://energymarketintel.com/) and the [U.S. Energy Information Administration dataset 861](http://www.eia.gov/electricity/data/eia861/), provides average residential, commercial and industrial electricity rates by zip code for both investor owned utilities (IOU) and non-investor owned utilities. Note: the file includes average rates for each utility, but not the detailed rate structure data found in the [OpenEI U.S. Utility Rate Database](http://en.openei.org/wiki/Utility_Rate_Database).  A more recent version of this data is also available through the [NREL Utility Rate API](http://developer.nrel.gov/docs/electricity/utility-rates-v3/) with more search options. This data was released by NREL/Ventyx in February 2011.

Tags: electric-rates, rate-lookup, rates, us-utilities, utility-companies, utility-rates, zipcode
Organization: Department of Energy
U.S. Electric Utility Companies and Rates: Look-up by Zipcode (2018)

This dataset, compiled by NREL using data from [ABB, the Velocity Suite](http://energymarketintel.com/) and the [U.S. Energy Information Administration dataset 861](http://www.eia.gov/electricity/data/eia861/), provides average residential, commercial and industrial electricity rates with likely zip codes for both investor owned utilities (IOU) and non-investor owned utilities. Note: the files include average rates for each utility (not average rates per zip code), but not the detailed rate structure data found in the [OpenEI U.S. Utility Rate Database](https://openei.org/apps/USURDB/).

Tags: electric-rates, rate-lookup, rates, us-utilities, utility-companies, utility-rates, zip-code, zipcode
Organization: Department of Energy
U.S. Electric Utility Companies and Rates: Look-up by Zipcode (2017)

This dataset, compiled by NREL using data from [ABB, the Velocity Suite](http://energymarketintel.com/) and the [U.S. Energy Information Administration dataset 861](http://www.eia.gov/electricity/data/eia861/), provides average residential, commercial and industrial electricity rates with likely zip codes for both investor owned utilities (IOU) and non-investor owned utilities. Note: the files include average rates for each utility (not per zip code), but not the detailed rate structure data found in the [OpenEI U.S. Utility Rate Database](https://openei.org/apps/USURDB/).

Tags: electric-rates, rate-lookup, rates, us-utilities, utility-companies, utility-rates, zip-code, zipcode
Organization: Department of Energy
U.S. Electric Utility Companies and Rates: Look-up by Zipcode (2016)

This dataset, compiled by NREL using data from [ABB, the Velocity Suite](http://energymarketintel.com/) and the [U.S. Energy Information Administration dataset 861](http://www.eia.gov/electricity/data/eia861/), provides average residential, commercial and industrial electricity rates by zip code for both investor owned utilities (IOU) and non-investor owned utilities. Note: the file includes average rates for each utility, but not the detailed rate structure data found in the [OpenEI U.S. Utility Rate Database](https://openei.org/apps/USURDB/).

Tags: electric-rates, rate-lookup, rates, us-utilities, utility-companies, utility-rates, zipcode
Organization: Department of Energy
U.S. Electric Utility Companies and Rates: Look-up by Zipcode (2015)

This dataset, compiled by NREL using data from [ABB, the Velocity Suite](http://energymarketintel.com/) and the [U.S. Energy Information Administration dataset 861](http://www.eia.gov/electricity/data/eia861/), provides average residential, commercial and industrial electricity rates by zip code for both investor owned utilities (IOU) and non-investor owned utilities. Note: the file includes average rates for each utility, but not the detailed rate structure data found in the [OpenEI U.S. Utility Rate Database](https://openei.org/apps/USURDB/).

Tags: electric-rates, rate-lookup, rates, us-utilities, utility-companies, utility-rates, zipcode
Organization: Department of Energy
U.S. Electric Utility Companies and Rates: Look-up by Zipcode (2014)

This dataset, compiled by NREL using data from [ABB, the Velocity Suite](http://energymarketintel.com/) and the [U.S. Energy Information Administration dataset 861](http://www.eia.gov/electricity/data/eia861/), provides average residential, commercial and industrial electricity rates by zip code for both investor owned utilities (IOU) and non-investor owned utilities. Note: the file includes average rates for each utility, but not the detailed rate structure data found in the [OpenEI U.S. Utility Rate Database](http://en.openei.org/apps/USURDB/).

Tags: electric-rates, rate-lookup, rates, us-utilities, utility-companies, utility-rates, zipcode
Organization: Department of Energy
U.S. Electric Utility Companies and Rates: Look-up by Zipcode (2013)

This dataset, compiled by NREL using data from [ABB, the Velocity Suite](http://energymarketintel.com/) and the [U.S. Energy Information Administration dataset 861](http://www.eia.gov/electricity/data/eia861/), provides average residential, commercial and industrial electricity rates by zip code for both investor owned utilities (IOU) and non-investor owned utilities. Note: the file includes average rates for each utility, but not the detailed rate structure data found in the [OpenEI U.S. Utility Rate Database](http://en.openei.org/apps/USURDB/).

Tags: electric-rates, rate-lookup, rates, us-utilities, utility-companies, utility-rates, zipcode
Organization: Department of Energy