AndreaBozzo/ceres-open-data-index huggingface dataset:
Solar PV Oil and Gas Pipeline Setbacks: Ordinances (2022) and Extrapolated Trends

This dataset represents solar energy setback requirements from oil and gas pipelines. A setback requirement is a minimum distance from a pipeline that an energy project may be developed. As of April 2022, no ordinances were discovered for any counties. Such ordinances are likely to arise as regulations continue to expand. Therefore, this dataset applies a 30-meter setback, sourced from trends in other infrastructure.  A TIF data file and a PNG map of the data are provided, showing areas where solar energy is prohibited or permitted across the contiguous United States.

For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843. 


Tags: county-ordinance, data, energy, gas, infrastructure, oil, photovoltaic, pipeline, png, power, pv, regulation, regulatory, regulatory-constraints, setback, setbacks, siting-lab, sitinglab, solar, solar-energy, solar-power, solar-pv, tif
Organization: Department of Energy
Wind Turbine Oil and Gas Pipeline Setbacks: Ordinances (2022) and Extrapolated Trends, 115 Hub Height 170 Rotor Diameter

This dataset represents wind energy setback requirements from oil and gas pipelines. A setback requirement is a minimum distance from a pipeline that an energy project may be developed. As of April 2022, no ordinances were discovered for any counties. Such ordinances are likely to arise as regulations continue to expand. Therefore, this dataset applies a median setback equivalent to 1.1 times the turbine tip-height, sourced from trends in other infrastructure. A TIF data file and a PNG map of the data are provided, showing areas where wind energy is prohibited or permitted across the contiguous United States. The turbine parameters used were a hub-height of 115 meters and a rotor diameter of 170 meters, as obtained from the Annual Technology Baseline (ATB) 2022. 

For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843. 

Tags: county-ordinance, data, gas, infrastructure, land-based-wind, oil, pipelines, png, regulation, regulatory, regulatory-constraints, setback, setbacks, siting-lab, sitinglab, tif, turbine, wind, wind-energy, wind-power
Organization: Department of Energy
Wind Energy: Bans or Moratoriums (2022)

This dataset identifies counties and municipalities that had a wind energy ban or moratorium as of April 2022. A TIF data file and a PNG map of the data are provided, showing areas where wind energy bans or moratoriums exist across the contiguous United States.

For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843. 

Tags: bans, bans-or-moratoriums, data, energy, land-based-wind, moratoriums, png, power, regulatory, regulatory-constraints, siting-lab, sitinglab, tif, turbine, wind, wind-energy
Organization: Department of Energy
Next Generation Weather Radar (NEXRAD) Radar Line-of-Sight

The Next Generation Weather Radar (NEXRAD) system is a network of doppler radar operated jointly by the National Weather Service (NWS), the Federal Aviation Administration (FAA), and the U.S. Air Force. This dataset represents a line-of-sight for each radar station. Radar line-of-sight can become saturated with wind energy due to interference.

For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843.

Tags: airspace-defense, data, infrastructure, land-based-wind, nexrad, next-generation-radar, png, radar-line-of-sight, regulatory-constraints, sitinglab, tif, wind, wind-energy
Organization: Department of Energy
REFIT Data for ScienceHub-12.16.2021

This dataset contains data for a manuscript describing facemask's fitted filtration efficiency and material performance after repeated washing and wearing. 

This dataset is associated with the following publication:
Chen, H., J. Samet, H. Tong, A. Abzhanova, A. Rappold, and S. Prince. Can Disposable Masks Be Worn More Than Once?.   ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY. Elsevier Science Ltd, New York, NY, USA, 242: 113908, (2022).

Tags: covid-19, disposable-facemask, durability, filtration-efficiency, personal-protection-equipment-ppe, plastic-waste, reusability
Organization: U.S. Environmental Protection Agency
Next Generation Weather Radar (NEXRAD) Setback (4-km)

The Next Generation Weather Radar (NEXRAD) system is a network of doppler radar operated jointly by the National Weather Service (NWS), the Federal Aviation Administration (FAA), and the U.S. Air Force. This dataset represents 4-kilometer setback requirements typically applied to wind energy development. A setback requirement is a minimum distance away that an energy project may be developed.

For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843. 

Tags: airspace-defense, characterization, data, infrastructure, land-based-wind, nexrad, next-generation-weather-radar, png, radar, regulatory-constraints, setbacks, siting-lab, sitinglab, tif, wind, wind-energy
Organization: Department of Energy
Airport and Heliport Setbacks

This dataset represents a first-order quantification of airport and heliport setback requirements based on the Federal Aviation Administration (FAA) 14 CFR Part 77.9. It defines the following FAA notice criteria that we use to create runway buffers. Runways longer than 3,200 feet receive a 20,000 foot buffer, runways less than 3,200 feet long receive a buffer of 10,000 feet, and heliports receive a buffer of 5,000 feet. A setback requirement is a minimum distance from an airport or heliport that an energy project may be developed.

For further details and citation, please refer to the publication linked below: Lopez, Anthony, Pavlo Pinchuk, Michael Gleason, Wesley Cole, Trieu Mai, Travis Williams, Owen Roberts, Marie Rivers, Mike Bannister, Sophie-Min Thomson, Gabe Zuckerman, and Brian Sergi. 2024. Solar Photovoltaics and Land-Based Wind Technical Potential and Supply Curves for the Contiguous United States: 2023 Edition. Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-87843. 

Tags: airport, airspace-defense, data, faa, federal-aviation-administration, heliport, infrastructure, land-based-wind, png, regulation, regulatory-constraints, setback, setbacks, siting-lab, sitinglab, tif, turbine, wind, wind-energy, wind-power
Organization: Department of Energy
Number of Veterans Insured by VGLI by State 2013-11-30

This report provides the number of Veterans insured under the Veterans' Group Life Insurance (VGLI) program by state as of January 31, 2013, February 28, 2013, and November 30, 2013.  This report also provides the VGLI coverage amount by state as of January 31, 2013 and November 30, 2013.

Tags: 2013, insurance, life-insurance-policies, vgli, vgli-2013
Organization: Department of Veterans Affairs
VA Pension Recipients as a Percentage of Veteran Population by State for Fiscal Years: 2019, 2020, 2021, and 2023

This table provides state-level estimates of the percentage of Veterans who were VA pension recipients at the end of the fiscal years 2019, 2020, 2021, and 2023.  Percents are rounded to the nearest tenth.  Percents for fiscal year (FY) 2022 are not available by state.

Prepared by the National Center for Veterans Analysis and Statistics.

Sources: Department of Veterans Affairs, Office of Enterprise Integration, Veteran Population Projection Model (VetPop) 2020, Veteran Object FY 2023 data, United States Veterans Eligibility Trends & Statistics (USVETS) 2019, 2020, and 2021 data; Veterans Benefits Administration, VETSNET FY 2019, FY 2020, FY 2021, and FY 2023 pension data.

Tags: fy-19, fy-20, fy-2019, fy-2020, fy-2021, fy-2023, fy-21, fy-23, fy19, fy20, fy2019, fy2020, fy2021, fy2023, fy21, fy23, pension, pension-recipients, pension-recipients-as-a-percentage, state, state-level
Organization: Department of Veterans Affairs
Temp_air_preterm_NC_JUN2024

Dataset is birth registry data from NC for 2006 - 2015, linked to air pollution and temperature exposures. 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: Health data can be requested through the North Carolina Department of Human Health Services. Air pollution data can be accessed through the EPA RSIG portal. Temperature data can be requested from the CDC. Format: SAS, R, and csv files. 

This dataset is associated with the following publication:
Krajewski, A., B. Alman, A. Vaidyanathan, T. Luben, and K. Rappazzo. Effect of extreme heat exposure on the associations between weekly gestational exposure to fine particulate matter and preterm birth in a North Carolina birth cohort. Presented at Society of Epidemiologic Research (SER) and Society of Pediatric and Perinatal Epidemiologic Research (SPER), Austin, TX, USA, 06/16/2024 - 06/21/2024.

Tags: air-pollution, birth-outcomes, childrens-environmental-health, climate, particulate-matter-pm
Organization: U.S. Environmental Protection Agency
2002-2017 Anthropogenic Emissions Data for AQ Model US

The US EPA developed a set of modeled meteorology, emissions, air quality and pollutant deposition spanning the years 2002 through 2019.  Modeled datasets cover the Conterminous US (CONUS) at a 12km horizontal grid spacing (12US1) and the Northern Hemisphere at a 108km (108NHEMI) using WRFv4.1.1 for meteorology and CMAQv5.3.2 for air quality modeling. New hemispheric and North American emissions inventories were developed using, to the extent possible, consistent input data and methods across all years, including emissions from mobile, fire, and oil and gas sources. Collectively these model outputs represent 100s of TB of data.  We have selected a subset of the model input and output datasets that we hope will be most useful to the air quality research community.  These datasets include:
- Emissions inventory files for the CONUS for 2002-2019 suitable for input into the Sparse Matrix Operator Kernel Emissions (SMOKE) emission processor
- Emissions trends data with annual total emissions, summed by pollutant and emissions source category
- CMAQ-ready emissions, initial conditions and boundary condition input files for the 12US1 domain for 2002-2019
- CMAQ-ready meteorology files for the 12US1 domain for 2002-2019. 
- Matched meteorology model output with surface observations for 2002-2019
- Daily average CMAQ output for the 12US1 domain for 2002-2019 for 14 pollutants
- Daily average 3D CMAQ output for 44 layers for the 108NHEMI domain for 2002–2019
- Annual total wet and dry deposition for the 12US1 domain for 2002-2019
- Hourly surface and 3D modeled meteorology, deposition and air concentrations for the 12US1 and 108NHEMI domains for 2002-2019. 

This dataset is associated with the following publication:
Foley, K., G. Pouliot, A. Eyth, M. Aldridge, C. Allen, K. Appel, J. Bash, M. Beardsley, J. Beidler, J. Choi, C. Farkas, R. Gilliam, J. Godfrey, B. Henderson, C. Hogrefe, S. Koplitz, R. Mason, R. Mathur, C. Misenis, N. Possiel, H. Pye, L. Reynolds, M. Roark, S. Roberts, D. Schwede, K. Seltzer, D. Sonntag, K. Talgo, C. Toro, J. Vukovich, J. Xing, and E. Adams. 2002-2017 Anthropogenic Emissions Data for Air Quality Modeling over the United States.   Data in Brief. Elsevier B.V., Amsterdam,  NETHERLANDS, 47: N/A, (2023).

Tags: air-emissions, air-trends, cmaq, deposition, moves, nei, o3, pm2-5, wrf
Organization: U.S. Environmental Protection Agency
United States Environmental Protection Agency Emissions Data on the HTAP_v3 grid for the years 2002-2017

This dataset contains the EPA submission to the HTAP_v3 emission mosaic for the years 2002-2017 on the HTAP_v3 grid (0.1 degree by 0.1 degree). These data were based  on the same inventories used in EPA's Air Quality Time Series (EQUATES) (https://www.epa.gov/cmaq/equates).  Data for the years 2000,2001, and 2018 were not provided directly but were extrapolated by the HTAP_v3 mosaic team outside of EPA.  For PM2.5 and PM10, EC and OC, separate files for fugitive dust are also provided since the base inventory was adjusted for meteorology or land use.  

Tags: air-quality-modeling, emissions, equates, global, mosaic
Organization: U.S. Environmental Protection Agency
Sensitivity of Air Quality to Vehicle Ammonia Emissions in the United States

These data are described in the associated publication and consist of a 2017 model simulation of EPA’s Community Multiscale Air Quality (CMAQ) model version 5.3.2 with updated mobile NH3 emission factors for diesel and gasoline vehicles. CMAQ model results are evaluated against AMoN and AQS monitoring networks and CrIS satellite retrievals. 

This dataset is associated with the following publication:
Toro Vergara, C., D. Sonntag`, J. Bash, G. Burke, B. Murphy, K. Seltzer, H. Simon, M. Shephard, and K. Cady-Pereira. Sensitivity of Air Quality to Vehicle Ammonia Emissions in the United States.   ATMOSPHERIC ENVIRONMENT. Elsevier B.V., Amsterdam,  NETHERLANDS, 327: 120484, (2024).

Tags: ammonia, cmaq, national-emissions-inventory, nitrogen-and-co-pollutants, particulate-matter-pm
Organization: U.S. Environmental Protection Agency
ARPA-E Grid Optimization (GO) Competition Challenge 3

Synthetic Input Data and Team Results for the GO Competition Challenge 3 for Events 1 - 4 and the Sandbox, along with problem and format descriptions and code to validate data and solutions, are available here. Data for industry scenarios will not be made public.

The Grid Optimization (GO) Competition Challenge 3 focused on the security-constrained optimal power flow (SCOPF) problem. It is part of a continuing effort begun with Challenges 1 and 2, to successfully discover, develop, and test innovative and disruptive software solutions for critical energy challenges and to overcome existing barriers. The broader goal of the of the GO Competition is to accelerate the development of transformational and disruptive methods for solving problems related to the electric power grid and to provide a transparent, fair, and comprehensive evaluation of new solution methods. Challenge 3 used multiperiod dynamic markets, including advisory models for extreme weather events, day-ahead markets, and the real-time markets with an extended look-ahead.
 
In Event 4, whose submission window was August 31-September 4, 2023, 14 teams solved for the objective values of 669 scenarios (39 scenarios required solutions both with and without line switching being allowed). The 591 synthetic scenarios from 9 network models (3.6 GB) are available here. Ten teams were funded to participate and 7 won prizes totaling $2,400,000. The largest prize ($550,000) went to Mississippi State University. An additional $600,000 was awarded in Event 3 (6/15-16/2023). No prizes were awarded in Events 1 (1/25-27/2023) or 2 (4/13-14/2023).
 
For more information on the competition and challenge see the "GO Competition Challenge 3 Information" resource below. 

Challenge 1 and Challenge 2 information can be found in the resources linked below. 

Tags: acopf, arpa-e, challenge-3, competition, computational-science, energy, energy-model, go-competition, grid, grid-optimization, model, multiperiod, multiperiod-dynamic-markets, optimization, power, security-constrained-optimal-power-flow, unit-commitment
Organization: Department of Energy
ARPA-E Grid Optimization (GO) Competition Challenge 1

The ARPA-E Grid Optimization (GO) Competition Challenge 1, from 2018 to 2019, focused on the basic Security Constrained AC Optimal Power Flow problem (SCOPF) for a single time period. The Challenge utilized sets of unique datasets generated by the ARPA-E GRID DATA program. Each dataset consisted of a collection of power system network models of different sizes with associated operating scenarios (snapshots in time defining instantaneous power demand, renewable generation, generator and line availability, etc.). The datasets were of two types: Real-Time, which included starting-point information, and Online, which did not. Week-Ahead data is also provided for some cases but was not used in the Competition. Although most datasets were synthetic and generated by GRIDDATA, a few came from industry and were only used in the Final Event. All synthetic Input Data and Team Results for the GO Competition Challenge 1 for the Sandbox, Trial Events 1 to 3, and the Final Event along with problem, format, scoring and rules descriptions are available here. Data for industry scenarios will not be made public.

Challenge 1, a minimization problem, required two computational steps. Solver 1 or Code 1 solved the base SCOPF problem under a strict wall clock time limit, as would be the case in industry, and reported the base case operating point as output, which was used to compute the Objective Function value that was used as the scenario score. The feasibility of the solution was provided by the Solver 2 or Code 2, which solves the power flow problem for all contingencies based on the results from Solver 1. This is not normally done in industry, so the time limits were relaxed. In fact, there were no time limits for Trial Event 1. This proved to be a mistake, with some codes running for more than 90 hours, and a time limit of 2 seconds per contingency was imposed for all other events. Entrants were free to use their own Solver 2 or use an open-source version provided by the Competition.

Containers, such as Docker, were considered to improve the portability of codes, but none that could reliably support a multi-node parallel computing environment, e.g., MPI, could be found.

For more information on the competition and challenge see the "GO Competition Challenge 1 Information" and "GO Competition Challenge 1 Additional Information" resources below.


Tags: acopf, arpa-e, competition, computational-science, data, energy, energy-model, go-competition, grid, grid-optimization, model, optimal-powerflow, optimization, power, security-constrained, synthetic-grid-data, unit-commitment
Organization: Department of Energy
Pesticide in Water Calculator amphibian exposures

Data sources for Pesticide in Water Calculator scenario simulations. 

This dataset is associated with the following publication:
Awkerman, J., T. Purucker, S. Raimondo, and L. Oliver. Long-term, landscape-level assessment of aquatic pesticide exposure to identify amphibian ontological traits affecting vulnerability.   Integrated Environmental Assessment and Management. Allen Press, Inc., Lawrence, KS, USA, 20(5): 1667-1676, (2024).

Tags: amphibian, ephemeral-wetland, pesticide-exposure, pesticide-in-water-calculator
Organization: U.S. Environmental Protection Agency
Citywide Mobility Survey - May 2020

In 2020, NYC DOT conducted a panel survey of respondents who completed the 2019 CMS. The three surveys allowed the agency to better understand how residents traveled and used public spaces during the stay-at-home period and reopening phases of the pandemic. This is the first panel survey that was fielded from May 4 – May 8, 2020.

Tags: city, citywidemobilitysurvey, mobility, new-york, survey, travel, zones
Organization: City of New York
Citywide Mobility Survey - July 2020

In 2020, NYC DOT conducted a panel survey of respondents who completed the 2019 CMS. The three surveys allowed the agency to better understand how residents traveled and used public spaces during the stay-at-home period and reopening phases of the pandemic. This is the second panel survey that was fielded from July 16 – July 31, 2020.

Tags: city, citywidemobilitysurvey, mobility, new-york, survey, travel, zones
Organization: City of New York
Open Storefronts Applications (Historical)

Program had ended.
The Open Storefronts program assists existing ground-floor storefront businesses who want to use outdoor areas on a temporary bases due to the COVID-19 crisis. Note: Geospatial data is based on the application address, geocoding error may be the result from an incorrect address submission. 

For real time business application updates, please visit NYC Open Storefront dashboard: http://nycopenstorefronts.info/

Tags: business, open, storefront, street
Organization: City of New York
Speculation Watch List

List of sales of select rent-regulated multiple dwellings where there may be potential for speculation. This list is promulgated pursuant to Local Law 7 of 2018 and 28 RCNY Chapter 52. This list is a subset of data provided in the LL7-2018 Qualified Transactions file. 
Please note that borough, block, and lot is the best geographic identifier for this data set, and Building Identification Number (BIN) is provided for convenience only. BIN as provided in this dataset may be inaccurate, as more than one BIN may be associated with borough, block, and lot.
This list is a subset of data provided in the<a href="https://data.cityofnewyork.us/Housing-Development/Local-Law-7-2018-Qualified-Transactions/8wi4-bsy4">LL7-2018 Qualified Transactions file</a>.

Tags: capitalization-rate, hpd, speculation-watch
Organization: City of New York
Local Law 7-2018 Qualified Transactions

List of sales of select rent-regulated multiple dwellings that are evaluated for the Speculation Watch List. This list is promulgated pursuant to Local Law 7 of 2018 and 28 RCNY Chapter 52. A subset of these data are provided as the Speculation Watch List.
Please note that borough, block, and lot is the best geographic identifier for this data set, and Building Identification Number (BIN) is provided for convenience only. BIN as provided in this dataset may be inaccurate, as more than one BIN may be associated with borough, block, and lot.
A subset of these data are provided as the <a href="https://data.cityofnewyork.us/Housing-Development/Speculation-Watch-List/adax-9mit">Speculation Watch List</a>.

Tags: capitalization-rate, hpd, speculation-watch
Organization: City of New York