AndreaBozzo/ceres-open-data-index huggingface dataset:
4.04 Solid Waste Landfill Diversion (summary)

<p>This page provides data for the Solid Waste Landfill Diversion performance measure.<br />&nbsp;</p><p>The performance measure dashboard is available at&nbsp;<a target='_blank' href='https://sustainable-growth-and-development-tempegov.hub.arcgis.com/pages/solid-waste-diversion' rel='nofollow ugc noopener noreferrer'>4.04 Solid Waste Landfill Diversion</a>.<br />&nbsp;</p><p><strong>Additional Information</strong><br />&nbsp;</p><p>Source:<br />Contact:<br />Contact E-Mail:<br />Data Source Type: Excel / CSV<br />Preparation Method:<br />Publish Frequency: Annual<br />Publish Method: Manual<br /><a target='_blank' href='https://tempe.gitbook.io/data-dictionary/environment-and-sustainability/4.04-solid-waste-diversion' rel='nofollow ugc noopener noreferrer'>Data Dictionary</a><br />&nbsp;</p>

Tags: solid-waste, solid-waste-diversion-pm-4-04, sustainability, sustainable-growth-and-development
Organization: City of Tempe
3.22 Graffiti Survey by Roads (detail)

<div>The Engineering and Transportation Department operates a robust graffiti abatement program. Its strategic approach includes proactive abatement through directed patrols across the City.</div><div>&nbsp;</div><div>Tempe performs a yearly survey to identify the number of tags within the City of Tempe. The survey is conducted along several roads from city limit to city limit by the City of Tempe Transportation Maintenance. The average number of tags per mile is calculated to assess graffiti abatement impact and efficacy.<br />&nbsp;</div><div>&nbsp;</div><div>This page provides data for the Graffiti Removal performance measure.&nbsp;<br />&nbsp;</div><div>&nbsp;</div><div>Annual graffiti survey results showing graffiti tags counted per mile of surveyed road segments.<br />&nbsp;</div><div>&nbsp;</div><div>The performance measure dashboard is available at&nbsp;<a target='_blank' href='https://quality-of-life-tempegov.hub.arcgis.com/pages/graffiti-removal' rel='nofollow ugc noopener noreferrer'>3.22&nbsp; Graffiti Removal</a>.<br />&nbsp;</div><div>&nbsp;</div><div><strong>Additional Information</strong><br />&nbsp;</div><div>Source:&nbsp;&nbsp;<br />&nbsp;</div><div>Contact: Isaac A. Chavira<br />&nbsp;</div><div>Contact E-Mail: Isaac_Chavira@tempe.gov<br />&nbsp;</div><div>Data Source Type: Excel<br />&nbsp;</div><div>Preparation Method:&nbsp;<br />&nbsp;</div><div>Publish Frequency: Annual<br />&nbsp;</div><div>Publish Method:&nbsp; Manual<br />&nbsp;</div><div><a target='_blank' href='https://tempe.gitbook.io/data-dictionary/3.22-graffiti-removal' rel='nofollow ugc noopener noreferrer'>Data Dictionary</a><br />&nbsp;</div>

Tags: engineering-and-transportation, field-operations, graffiti, graffiti-removal-pm-3-22, graffiti-survey, quality-of-life
Organization: City of Tempe
1.22 PQI Average Citywide (summary)

<p>This resource represents the annual citywide average PQI.</p><p>This resource is used in the indicators found in the Safe and Secure Communities dashboard.</p><p>Tempe’s roadways are an important means of transportation for residents, the workforce, students, and visitors. Tempe measures the quality and condition of its roadways using a Pavement Quality Index (PQI). This measure, rated from a low of 0 to a high of 100, is used by the City to plan for maintenance and repairs, and to allocate resources in the most efficient way possible.</p><p>This measure is created using pavement quality data maintained in the RoadMatrix Pavement Management Program. About every three years, the City surveys pavement, such as the smoothness of roadways and any signs of distress in the pavement surface. This data is then used to calculate the PQI, which determines roadway maintenance prioritization schedules as well as the most optimal road treatment options (such as placing a filler material in the cracks and treating the entire pavement surface, milling and replacing the top layer of the asphalt pavement, reconstructing the street section)</p><p>This page provides data for the performance measure related to PQI.&nbsp; To access geospatial data regarding PQI, please visit https://data.tempe.gov/dataset/pavement-quality-index-segments</p><p>The performance measure dashboard is available at&nbsp;<a target='_blank' href='https://safe-and-secure-communities-tempegov.hub.arcgis.com/pages/pavement-quality-index' rel='nofollow ugc noopener noreferrer'>1.22 Pavement Quality Index</a></p><p>&nbsp;</p><p><strong>Additional Information</strong></p><p>Source: Stantec/Road Matrix</p><p>Contact (author): Isaac Chavira</p><p>Contact E-Mail (author): isaac_chavira@tempe.gov</p><p>Contact (maintainer): Sue Taaffe</p><p>Contact E-Mail (maintainer): sue_taaffe@tempe.gov</p><p>Data Source Type: CSV</p><p>Preparation Method: Extracted from Roadmatrix and joined to the GIS network</p><p>Publish Frequency: Annual (Average PQI)/Quarterly (Segment PQI)</p><p>Publish Method: Manual</p><p><a target='_blank' href='https://tempe.gitbook.io/data-dictionary/1.22-pqi-average-citywide' rel='nofollow ugc noopener noreferrer'>Data Dictionary</a><br />&nbsp;</p>

Tags: engineering-and-transportation, pavement-quality-index-pm-1-22, safe-and-secure-communities, transit, transportation
Organization: City of Tempe
Penobscot Estuary (Maine) Monitoring

NOAA's NEFSC collects fisheries data from the Penobscot Estuary using several types of fishing gear.  The data is used to determine species presence, relative numbers, and migration timing for diadromous and resident fish species.

Tags: continuous, doc/noaa/nmfs/nefsc, estuarine, fish, national marine fisheries service, noaa, northeast fisheries science center, northeast salmon team (nest), penbscot river estuary, survey, u.s. department of commerce
Organization: National Oceanic and Atmospheric Administration, Department of Commerce
Habitat Mapping Camera (HABCAM)

This dataset entails imagery collected using the HabCam towed underwater vehicle and annotated data on objects or habitats in the images and notes on image qualities. Most HabCam images and data are collected as part of the NEFSC sea scallop survey but the vehicle is occasionally used for other surveys. For each survey, a subset of images are selected and manually annotated for the presence of target species. The PostgreSQL database contains basic image metadata (date, time, latitude, longitude, depth, etc.) as well as the data from the manual annotations. Eventually, other software-derived data products will be added including bottom rugosity and substrate composition.

Tags: doc/noaa/nmfs/nefsc, national marine fisheries service, noaa, northeast fisheries science center, population dynamics branch, u.s. department of commerce
Organization: National Oceanic and Atmospheric Administration, Department of Commerce
Atlantic Salmon Scale Measurements

Scales are collected annually from smolt trapping operations in Maine as wellas other sampling opportunities (e.g. marine surveys, fishery sampling etc.).  Scale samples are imaged and age, origin, and measurement data are collected as needed for specific growth-related research.

Tags: age, atlantic salmon, continuous, doc/noaa/nmfs/nefsc, growth, maine, national marine fisheries service, noaa, northeast fisheries science center, northeast salmon team (nest), rearing origin, scale measurements, scales, u.s. department of commerce, west greenland
Organization: National Oceanic and Atmospheric Administration, Department of Commerce
Biofilm Dataset

The dataset includes the accession numbers to the RNA sequences and qPCR data for pathogens and validation of some important RNA sequences. 

This dataset is associated with the following publication:
Lu, J., I. Struewing, and N. Ashbolt. Meta-Transcriptomic Response to Copper Corrosion in Drinking Water Biofilms.   Microorganisms. MDPI, Basel,  SWITZERLAND, 13(7): 1528, (2025).

Tags: biofilm, copper, drinking-water, microbial-organism, pvc, transcriptomic-analysis, tubing
Organization: U.S. Environmental Protection Agency
Subchronic Toxicities of Four Per- and Polyfluoroalkyl Substances (PFASs) by Oral Exposure in Sprague–Dawley Rats

Study reports for "Subchronic Toxicities of Four Per- and Polyfluoroalkyl Substances (PFASs) by Oral Exposure in Sprague–Dawley Rats". 

This dataset is associated with the following publication:
Kenyon, E., M. Devito, G. Patlewicz, L. Adams, R. Thomas, J. Ambroso, X. Yang, J. Blake, B. Upadhyay, J. Furr, and M. Hughes. Subchronic Toxicities of Four Per- and Polyfluoroalkyl Substances (PFASs) by Oral Exposure in Sprague–Dawley Rats.   Toxics. MDPI, Basel,  SWITZERLAND, 13(7): 524, (2025).

Tags: methyl-heptafluoropropyl-ketone, pfas, subchronic-oral-toxicity
Organization: U.S. Environmental Protection Agency
Thematic accuracy assessment of the National Land Cover Database (NLCD2021) for the conterminous United States

The U.S. Geological Survey (USGS), in partnership with several federal agencies, has now developed and released seven National Land Cover Database (NLCD) products: NLCD 1992, 2001, 2006, 2011, 2016, 2019, and 2021. Beginning with the 2016 release, land cover products were created for two-to-three-year intervals between 2001 and the most recent year. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. NLCD continues to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database. The NLCD 2021 release is update based, so the Land Cover and Impervious Surface products released in 2019 are unchanged and used directly with NLCD 2021 for change analysis though the NLCD timespan. Science products and the change index are updated and will need to be reacquired to contain the additional 2021 change. These new products use a streamlined compositing process for assembling and preprocessing Landsat imagery and geospatial ancillary datasets; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a theme-based post-classification protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and a scripted operational system. The overall accuracy of the 2019 Level I land cover was 91%. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2021 operational mapping (see https://doi.org/10.1080/15481603.2023.2181143 for the latest accuracy assessment publication). Questions about the NLCD 2021 land cover product can be directed to the NLCD 2021 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.

Tags: satellite-imaging, statistics, uncertainty, urban-growth
Organization: U.S. Environmental Protection Agency
UNC COVID Healthcare Workers

This is a study on healthcare workers at the University of North Carolina Hospital system conducted during the COVID-19 pandemic in 2020-2021. This includes responses to survey questions on occupation, living situation, mental health, physical health, prior COVID-19 infection, and vaccination status. As the data are identifiable, we cannot release them publicly. 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: These data are owned by the University of North Carolina at Chapel Hill. Contact Dr. Emily Ciccone  ciccone@med.unc.edu with inquiries. Format: This dataset includes data on healthcare workers, including questionnaire responses and data from wearable tracking devices. These data are sensitive and participants are potentially identifiable. 

Tags: covid-19, healthcare, mental-health, perceived-stress
Organization: U.S. Environmental Protection Agency
Utility-Scale Solar, 2025 Edition: Analysis of Empirical Plant-level Data from U.S. Ground-mounted PV, PV+battery, and CSP Plants (exceeding 5 MWAC)

Lawrence Berkeley National Laboratory compiled and synthesized empirical data on the U.S. utility-scale solar sector. Utility-scale solar is defined here to include ground-mounted systems larger than 5MW-AC. Smaller projects are covered in Berkeley Lab's separate U.S. Distributed Solar and Storage annual data update. The focus is on utility-scale solar projects, though the "Wholesale Market Value" sections often contain data inclusive of all utility-scale solar installations. Data sources are diverse but include data from the Energy Information Administration (EIA), the Federal Energy Regulatory Commission (FERC), and state agencies. The latest update contains project-level data on 1,760 solar projects installed through 2024. 

The update includes the resulting data synthesis covering: 
(1)	Deployment and Technology Trends
(2)	Capital Costs (CapEx) and Operation & Maintenance (O&M) Costs
(3)	Performance (Capacity Factors)
(4)	Levelized Cost of Energy (LCOE) and Power Purchase Agreement (PPA) Prices
(5)	Wholesale Market Value and Net Value
(6)	PV+Battery Hybrid Plants (Deployment, CapEx, LCOE, PPAs)
(7)	Concentrating Solar Thermal Power (CSP) Plants
(8)	Capacity in Interconnection Queues

These data are made available in several formats, including a summary slide deck, a public data file with non-confidential data on 57 tabs and additional visualizations, and data visualizations.

Tags: battery, berkeley-lab, capacity, capacity-factors, capital-costs, csp, data, energy, generation, gound-mounted, hourly, hourly-generation, interconnection-queue, lcoe, market-analysis, market-value, operating-costs, photovoltaic, plant-level, plant-level-data, power, power-purchase-agreement, power-purchase-agreements, ppa, pv, solar, solar-energy, solar-power, utility-scale, utility-scale-solar, wholesale-market-value
Organization: Department of Energy
Chemical Features for: Examining Chemical Space Coverage of Solid Phase Extraction Methods in Environmental Water for Use with Non-Targeted Analysis Methods

A full list of non-targeted chemical features found in the study "Examining Chemical Space Coverage of Solid Phase Extraction Methods in Environmental Water for Use with Non-Targeted Analysis Methods." Including all chemical features detected by any of the four included solid phase extraction methods in February 2025 surface water across both ionization modes. Chemical identification candidates can be explored in GitHub with interactive scatterplots (https://alexchao32.github.io/SPE-Coverage-INTA-Visuals-ScatterPlot/) and bar chart visuals (https://alexchao32.github.io/SPE-Coverage-INTA-Visuals-BarCharts/). 

This dataset is associated with the following publication:
Brunelle, L., E. Stebel, A. Batt, A. Chao, and T. Ferland. Examining Chemical Space Coverage of Solid Phase Extraction Methods in Environmental Water for Use with LC-MS Non-Targeted Analysis Methods.   SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM,  NETHERLANDS, 1002: 180606, (2025).

Tags: emerging-contaminants, non-targeted-analysis, pfas, water-quality
Organization: U.S. Environmental Protection Agency
Air quality model data used in the analysis presented in manuscript "A Diagnostic Intercomparison of Modeled Ozone Dry Deposition Over North America and Europe Using AQMEII4 Regional-Scale Simulations"

This dataset contains the air quality model fields used for the analysis presented in manuscript “A Diagnostic Intercomparison of Modeled Ozone Dry Deposition Over North America and Europe Using AQMEII4 Regional-Scale Simulations” (Hogrefe et al., 2025, https://doi.org/10.5194/egusphere-2025-225). 

This dataset is associated with the following publication:
Hogrefe, C., S. Galmarini, P. Makar, I. Kioutsioukis, O. Clifton, U. Alyuz, J. Bash, R. Bellasio, R. Bianconi, T. Butler, P. Cheung, A. Hodzic, R. Kranenburg, A. Lupascu, K. Momoh, J.L. Perez-Camanyo, J. Pleim, Y. Ryu, R. San Jose, M. Schaap, D. Schwede, and R. Sokhi. A diagnostic intercomparison of modeled ozone dry deposition over North America and Europe using AQMEII4 regional-scale simulations.   Atmospheric Chemistry and Physics. Copernicus Publications, Katlenburg-Lindau,  GERMANY, 25(19): 12629–12656, (2025).

Tags: aqmeii4, land-use, model-evaluation, model-intercomparison, ozone-dry-deposition
Organization: U.S. Environmental Protection Agency
SWERA: Solar and Wind Energy Resource Assessment

The Solar and Wind Energy Resource Assessment (SWERA) Program was a collaboration of worldwide partners with a mission to provide information on renewable energy resources for countries and regions around the world. Funding and support for the SWERA Program concluded in 2011, at which time all program data and information were archived for long-term availability.

The program partners, which included the U.S. Department of Energy and NREL, are no longer supporting or maintaining the data.  The SWERA data have been archived for historical purposes.  However, it is suggested that users interested in global solar and wind resource data consult more recent sources.

Here are a few suggested sources for more current and available renewable resource data:

Solar
- Global Solar Atlas (http://globalsolaratlas.info/)
- U.S. National Solar Radiation Database (https://nsrdb.nrel.gov/data-viewer)
- Renewable Energy Data Explorer (https://www.re-explorer.org/)
- IRENA Global Atlas for Renewable Energy (https://globalatlas.irena.org/workspace)
- NASA Surface Meteorology and Solar Energy: Global Datasets (https://power.larc.nasa.gov/)

Wind
- Global Wind Atlas (http://globalwindatlas.info/)
- IRENA Global Atlas for Renewable Energy (https://globalatlas.irena.org/workspace)
- WINDExchange: Wind Energy Maps and Data (https://windexchange.energy.gov/maps-data?height=80m)
- Renewable Energy Data Explorer (https://www.re-explorer.org/)
- USGS Wind Turbine Database (https://energy.usgs.gov/uswtdb/viewer/)
- NREL WIND Toolkit (https://www.nrel.gov/grid/wind-toolkit.html)

Climate
- NASA Goddard Institute for Space Studies (http://data.giss.nasa.gov/)
- NASA Surface Meteorology and Solar Energy: Global Datasets (https://power.larc.nasa.gov/)


Tags: archive, climate, energy, global, power, renewable-energy, resource-assessment, resources, solar, swera, wind
Organization: Department of Energy
Enhancing and Optimization of MADWEC Performance Through Numerical Simulations

This set of data was the result of the TEAMER project led by Adam Keester (Sandia) and Dr. Mohamed Shabara (NREL) on 'enhancing and optimization of maximal asymmetric drag wave energy converter (MADWEC) performance through numerical simulations' in support of research efforts at UMass Dartmouth on developing a wave energy conversion device (MADWEC). 

This project was conducted and structured around three key technical tasks:
(1) parameter search using frequency domain analysis for buoy dimensions and added mass
(2) Wave Analysis MIT (WAMIT) support to achieve the added mass predicted in Task 1 with new ballast geometry(s)
(3) modify existing WEC-Sim models that represent the highest fidelity model of the MADWEC.

This submission includes:
- Processed datasets corresponding to Tasks 1-3, and all corresponding subtasks
- RFTS award information, biweekly presentations, the testing plan with task and subtask descriptions, and final report
- The final TEAMER Report

This project is part of the TEAMER RFTS 10 (request for technical support) program.

Tags: energy, hydrokinetic, madwec, madwec-performance, marine, maximal-asymmetric-drag-wave-energy-converter, mhk, numerical-simulations, optomization, power, processed-data, rfts10, teamer, wamit, wave-energy-converter, wec-sim
Organization: Department of Energy
Urban Areas

The Urban Areas dataset was compiled on January 01, 2025 from the U.S. Department of Commerce, U.S. Census Bureau, Geography Division and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the urban footprint. There are 2,644 Urban Areas (UAs) in this data release with either a minimum population of 5,000 or a housing unit count of 2,000 units. Each urban area is identified by a five-character numeric census code that may contain leading zeros. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529088

Tags: areas, atlas, database, national, national transportation atlas database, ntad, polygon, transportation, u.s., ua, united states, urban, urbanized, urbanized areas, usa, vector
Organization: Department of Transportation
Core Based Statistical Areas

The Core Based Statistical Areas dataset was updated on September 22, 2025 from the U.S. Department of Commerce, U.S. Census Bureau, Geography Division and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Metropolitan and Micropolitan Statistical Areas are together termed Core Based Statistical Areas (CBSAs) and are defined by the Office of Management and Budget (OMB) and consist of the county or counties or equivalent entities associated with at least one urban core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core. Categories of CBSAs are: Metropolitan Statistical Areas, based on urban areas of 50,000 or more population; and Micropolitan Statistical Areas, based on urban areas of at least 10,000 population but less than 50,000 population. The CBSA boundaries are those defined by OMB based on the 2020 Census and published in 2023. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529014

Tags: area, atlas, based, boundaries, boundary, cbsa, core, database, metropolitan, metropolitan statistical areas, micropolitan, micropolitan statistical area, msa, nation, national, national transportation atlas database, ntad, planning, polygon, statistical, transportation, u.s., united states, usa, vector
Organization: Department of Transportation