AndreaBozzo/ceres-open-data-index huggingface dataset:
Validating Prison Security Classification Instruments in Hawaii, 1984-1985

The purpose of this study was to develop and validate a
 reliable and accurate method for measuring the effectiveness of
 offender classification systems to improve the management of
 correctional facilities. In the early 1980s, the state of Hawaii began
 classifying its prisoners with a newly developed Federal Bureau of
 Prisons classification instrument. This study was designed to develop
 a method to evaluate this form. Two prediction models were used. The
 first, initial classification, used the sum of four variables to
 arrive at a security score, which was taken to be predictive of
 violence. The second, reclassification, used the sum of seven
 different variables to obtain a custody total, which was then used as
 a major determinant of reclassification. Two groups of inmates were
 used: inmates who had committed infractions and inmates with no
 reported infractions. Research variables include (a) initial
 classification: offense (severity), expected length of incarceration
 (sentence), type of prior commitments, and history of violence, and
 (b) reclassification: percentage of time served, involvement with
 drugs/alcohol, mental/psychological stability, most serious
 disciplinary report, frequency of disciplinary reports, responsibility
 that the inmate demonstrated, and family/community ties. In addition,
 the collection supplies information on race and sex of inmates,
 sentence limitation, history of escapes or attempts, previous
 infractions, entry, reclassification, and termination dates (month and
 year), and custody level. There are demographic variables for sex and
race. The unit of observation is the inmate.

Tags: corrections-management, criminal-histories, criminality-prediction, imprisonment, inmate-classification, prison-inmates, prison-security, program-evaluation
Organization: Department of Justice
Military Bases

The Military Bases dataset was last updated on November 11, 2025 and are defined by Fiscal Year 2024 data, from the Office of the Assistant Secretary of Defense for Energy, Installations, and Environment and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The dataset depicts the authoritative locations of the most commonly known Department of Defense (DoD) sites, installations, ranges, and training areas world-wide. These sites encompass land which is federally owned or otherwise managed. This dataset was created from source data provided by the four Military Service Component headquarters and was compiled by the Defense Installation Spatial Data Infrastructure (DISDI) Program within the Office of the Assistant Secretary of Defense for Energy, Installations, and Environment. Only sites reported in the BSR or released in a map supplementing the Foreign Investment Risk Review Modernization Act of 2018 (FIRRMA) Real Estate Regulation (31 CFR Part 802) were considered for inclusion. This list does not necessarily represent a comprehensive collection of all Department of Defense facilities. For inventory purposes, installations are comprised of sites, where a site is defined as a specific geographic location of federally owned or managed land and is assigned to military installation. DoD installations are commonly referred to as a base, camp, post, station, yard, center, homeport facility for any ship, or other activity under the jurisdiction, custody, control of the DoD.

While every attempt has been made to provide the best available data quality, this data set is intended for use at mapping scales between 1:50,000 and 1:3,000,000. For this reason, boundaries in this data set may not perfectly align with DoD site boundaries depicted in other federal data sources. Maps produced at a scale of 1:50,000 or smaller which otherwise comply with National Map Accuracy Standards, will remain compliant when this data is incorporated. Boundary data is most suitable for larger scale maps; point locations are better suited for mapping scales between 1:250,000 and 1:3,000,000.

If a site is part of a Joint Base (effective/designated on 1 October, 2010) as established under the 2005 Base Realignment and Closure process, it is attributed with the name of the Joint Base. All sites comprising a Joint Base are also attributed to the responsible DoD Component, which is not necessarily the pre-2005 Component responsible for the site. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529039

Tags: administrative, air, army, atlas, base, boundary, brac, database, defense, disdi, dod, federal, firrma, force, installations, intelligence, joint, lands, marine corps, military, national, national guard, national transportation atlas database, navy, ntad, polygon, ranges, report, sites, space, structure, training areas, transportation, usa, vector, whs
Organization: Department of Transportation
Adjusted Urban Areas

The 2020 Adjusted Urban Areas dataset was compiled on December 31, 2020 from the Federal Highway Administration (FHWA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The 2020 Federal Highway Administration (FHWA) Adjusted Urban Areas layer delineates the boundaries of adjusted urban areas as defined by the FHWA, based on the 2020 U.S. Census urban area boundaries. This nationwide geospatial dataset represents urbanized areas that have been modified through coordination between FHWA Division Offices, State Departments of Transportation (DOTs), and Metropolitan Planning Organizations (MPOs), in accordance with FHWA's Urban Area Boundary Adjustment guidelines. These boundaries are used for transportation planning and programming, eligibility determinations, and analysis purposes related to Federal-aid highway programs. A data dictionary, or other source of attribute information, is accessible at https://www.transportation.gov/gis/nad/nad-schema

Tags: atlas, boundaries, database, national, national transportation atlas database, ntad, polygon, rural, transportation, urban, usa, vector
Organization: Department of Transportation
Rapid Assessment of Vegetation Condition after Wildfire (RAVG) Basal Area Percent Change (Image Service)

<div style='text-align:Left;'><div><div><p><span>The USDA Forest Service Rapid Assessment of Vegetation Condition after Wildfire (RAVG) program produces geospatial data and maps of post-fire vegetation condition using standardized change detection methods based on Landsat or similar multispectral satellite imagery. RAVG data products characterize vegetation condition within a fire perimeter, and include estimates of percent change in basal area (BA), percent change in canopy cover (CC), and a standardized composite burn index (CBI). Standard thematic products include 7-class percent change in basal area (BA-7), 5-class percent change in canopy cover (CC-5), and 4-class CBI (CBI-4). Contingent upon the availability of suitable imagery, RAVG products are prepared for all wildland fires reported within the conterminous United States (CONUS) that include at least 1000 acres of forested National Forest System (NFS) land (500 acres for Regions 8 and 9 as of 2016). Data for individual fires are typically made available within 45 days after fire containment ("initial assessments"). Late-season fires, however, may be deferred until the following spring or summer ("extended assessments"). National mosaics of each thematic product are prepared annually. The associated burned area perimeters are available via the Enterprise Data Warehouse (EDW, see https://data.fs.usda.gov/geodata/edw/datasets.php). Assessment type (initial or extended assessment) for each fire is included as an attribute in the perimeter dataset.</span></p></div></div></div>

Tags: basal-area, biota, burn-severity, canopy-cover, cbi, composite-burn-index, conus, differenced-normalized-burn-ratio, dnbr, landsat, nbr, normalized-burn-ratio, percent-change, ravg, relative-differenced-normalized-burn-ratio, rnbr, united-states, vegetation-condition, wildfire, wildland-fire
Organization: Department of Agriculture
Rapid Assessment of Vegetation Condition after Wildfire (RAVG) Canopy Cover Percent Change (Image Service)

<div style='text-align:Left;'><div><div><p><span>The USDA Forest Service Rapid Assessment of Vegetation Condition after Wildfire (RAVG) program produces geospatial data and maps of post-fire vegetation condition using standardized change detection methods based on Landsat or similar multispectral satellite imagery. RAVG data products characterize vegetation condition within a fire perimeter, and include estimates of percent change in basal area (BA), percent change in canopy cover (CC), and a standardized composite burn index (CBI). Standard thematic products include 7-class percent change in basal area (BA-7), 5-class percent change in canopy cover (CC-5), and 4-class CBI (CBI-4). Contingent upon the availability of suitable imagery, RAVG products are prepared for all wildland fires reported within the conterminous United States (CONUS) that include at least 1000 acres of forested National Forest System (NFS) land (500 acres for Regions 8 and 9 as of 2016). Data for individual fires are typically made available within 45 days after fire containment ("initial assessments"). Late-season fires, however, may be deferred until the following spring or summer ("extended assessments"). National mosaics of each thematic product are prepared annually. The associated burned area perimeters are available via the Enterprise Data Warehouse (EDW, see https://data.fs.usda.gov/geodata/edw/datasets.php). Assessment type (initial or extended assessment) for each fire is included as an attribute in the perimeter dataset.</span></p></div></div></div>

Tags: basal-area, biota, burn-severity, canopy-cover, cbi, composite-burn-index, conus, differenced-normalized-burn-ratio, dnbr, landsat, nbr, normalized-burn-ratio, percent-change, ravg, relative-differenced-normalized-burn-ratio, rnbr, united-states, vegetation-condition, wildfire, wildland-fire
Organization: Department of Agriculture
Rapid Assessment of Vegetation Condition after Wildfire (RAVG) Composite Burn Index (Image Service)

<div style='text-align:Left;'><div><div><p><span>The USDA Forest Service Rapid Assessment of Vegetation Condition after Wildfire (RAVG) program produces geospatial data and maps of post-fire vegetation condition using standardized change detection methods based on Landsat or similar multispectral satellite imagery. RAVG data products characterize vegetation condition within a fire perimeter, and include estimates of percent change in basal area (BA), percent change in canopy cover (CC), and a standardized composite burn index (CBI). Standard thematic products include 7-class percent change in basal area (BA-7), 5-class percent change in canopy cover (CC-5), and 4-class CBI (CBI-4). Contingent upon the availability of suitable imagery, RAVG products are prepared for all wildland fires reported within the conterminous United States (CONUS) that include at least 1000 acres of forested National Forest System (NFS) land (500 acres for Regions 8 and 9 as of 2016). Data for individual fires are typically made available within 45 days after fire containment ("initial assessments"). Late-season fires, however, may be deferred until the following spring or summer ("extended assessments"). National mosaics of each thematic product are prepared annually. The associated burned area perimeters are available via the Enterprise Data Warehouse (EDW, see https://data.fs.usda.gov/geodata/edw/datasets.php). Assessment type (initial or extended assessment) for each fire is included as an attribute in the perimeter dataset.</span></p></div></div></div>

Tags: basal-area, biota, burn-severity, canopy-cover, cbi, composite-burn-index, conus, differenced-normalized-burn-ratio, dnbr, landsat, nbr, normalized-burn-ratio, ravg, relative-differenced-normalized-burn-ratio, rnbr, united-states, vegetation-condition, wildfire, wildland-fire
Organization: Department of Agriculture
Probabilistic Wildfire Risk Flame Length Probability 1 (Image Service)

<div style='text-align:Left;'><div><div><p><span>National data on burn probability (BP) and conditional flame-length probability (FLP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current (end of 2020) landscape conditions and fire management practices. The data presented here represent modeled BP and FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FILs correspond to flame-length classes as follows: FLP1 = &lt; 2 feet (ft); FLP2 = 2 &lt; 4 ft.; FLP3 = 4 &lt; 6 ft.; FLP4 = 6 &lt; 8 ft.; FLP5 = 8 &lt; 12 ft.; FLP6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment.</span></p><p><span>These data are a newer edition of the Short et al. (2016, 2020) data publications. This third edition is based on circa 2020 landscape data, which were the most current LANDFIRE products available at the time of production. The methods used to generate these data generally followed the same process used in previous editions, with improvements made at specific steps. The process steps outlined in the Data Quality, Lineage section of this metadata document are expanded from previous editions to more fully explain each step and provide additional details on methods for this edition. Beyond the newer input landscape data from LANDFIRE, we also used updated datasets for other inputs such as fire occurrence, observed gridded daily weather, and wind data from weather stations. To better capture recent climate conditions, we also shortened the time period of historical weather records used to inform the generation of simulated weather streams for simulation runs, using the most recent 15 years this time (2006-2020) rather than full record from 1972-2012 in the second edition. See the process steps described in the Data Quality, Lineage section for more details.</span></p></div></div></div>

Tags: alaska, burn-probability, conus, ecology, ecosystems, environment, fire, fire-intensity, flame-length, geoscientific-information, hawaii, united-states
Organization: Department of Agriculture
Probabilistic Wildfire Risk Flame Length Probability 2 (Image Service)

<div style='text-align:Left;'><div><div><p><span>National data on burn probability (BP) and conditional flame-length probability (FLP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current (end of 2020) landscape conditions and fire management practices. The data presented here represent modeled BP and FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FILs correspond to flame-length classes as follows: FLP1 = &lt; 2 feet (ft); FLP2 = 2 &lt; 4 ft.; FLP3 = 4 &lt; 6 ft.; FLP4 = 6 &lt; 8 ft.; FLP5 = 8 &lt; 12 ft.; FLP6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment.</span></p><p><span>These data are a newer edition of the Short et al. (2016, 2020) data publications. This third edition is based on circa 2020 landscape data, which were the most current LANDFIRE products available at the time of production. The methods used to generate these data generally followed the same process used in previous editions, with improvements made at specific steps. The process steps outlined in the Data Quality, Lineage section of this metadata document are expanded from previous editions to more fully explain each step and provide additional details on methods for this edition. Beyond the newer input landscape data from LANDFIRE, we also used updated datasets for other inputs such as fire occurrence, observed gridded daily weather, and wind data from weather stations. To better capture recent climate conditions, we also shortened the time period of historical weather records used to inform the generation of simulated weather streams for simulation runs, using the most recent 15 years this time (2006-2020) rather than full record from 1972-2012 in the second edition. See the process steps described in the Data Quality, Lineage section for more details.</span></p></div></div></div>

Tags: alaska, burn-probability, conus, ecology, ecosystems, environment, fire, fire-intensity, flame-length, geoscientific-information, hawaii, united-states
Organization: Department of Agriculture
Probabilistic Wildfire Risk Flame Length Probability 3 (Image Service)

<div style='text-align:Left;'><div><div><p><span>National data on burn probability (BP) and conditional flame-length probability (FLP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current (end of 2020) landscape conditions and fire management practices. The data presented here represent modeled BP and FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FILs correspond to flame-length classes as follows: FLP1 = &lt; 2 feet (ft); FLP2 = 2 &lt; 4 ft.; FLP3 = 4 &lt; 6 ft.; FLP4 = 6 &lt; 8 ft.; FLP5 = 8 &lt; 12 ft.; FLP6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment.</span></p><p><span>These data are a newer edition of the Short et al. (2016, 2020) data publications. This third edition is based on circa 2020 landscape data, which were the most current LANDFIRE products available at the time of production. The methods used to generate these data generally followed the same process used in previous editions, with improvements made at specific steps. The process steps outlined in the Data Quality, Lineage section of this metadata document are expanded from previous editions to more fully explain each step and provide additional details on methods for this edition. Beyond the newer input landscape data from LANDFIRE, we also used updated datasets for other inputs such as fire occurrence, observed gridded daily weather, and wind data from weather stations. To better capture recent climate conditions, we also shortened the time period of historical weather records used to inform the generation of simulated weather streams for simulation runs, using the most recent 15 years this time (2006-2020) rather than full record from 1972-2012 in the second edition. See the process steps described in the Data Quality, Lineage section for more details.</span></p></div></div></div>

Tags: alaska, burn-probability, conus, ecology, ecosystems, environment, fire, fire-intensity, flame-length, geoscientific-information, hawaii, united-states
Organization: Department of Agriculture
Probabilistic Wildfire Risk Flame Length Probability 5 (Image Service)

<div style='text-align:Left;'><div><div><p><span>National data on burn probability (BP) and conditional flame-length probability (FLP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current (end of 2020) landscape conditions and fire management practices. The data presented here represent modeled BP and FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FILs correspond to flame-length classes as follows: FLP1 = &lt; 2 feet (ft); FLP2 = 2 &lt; 4 ft.; FLP3 = 4 &lt; 6 ft.; FLP4 = 6 &lt; 8 ft.; FLP5 = 8 &lt; 12 ft.; FLP6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment.</span></p><p><span>These data are a newer edition of the Short et al. (2016, 2020) data publications. This third edition is based on circa 2020 landscape data, which were the most current LANDFIRE products available at the time of production. The methods used to generate these data generally followed the same process used in previous editions, with improvements made at specific steps. The process steps outlined in the Data Quality, Lineage section of this metadata document are expanded from previous editions to more fully explain each step and provide additional details on methods for this edition. Beyond the newer input landscape data from LANDFIRE, we also used updated datasets for other inputs such as fire occurrence, observed gridded daily weather, and wind data from weather stations. To better capture recent climate conditions, we also shortened the time period of historical weather records used to inform the generation of simulated weather streams for simulation runs, using the most recent 15 years this time (2006-2020) rather than full record from 1972-2012 in the second edition. See the process steps described in the Data Quality, Lineage section for more details.</span></p></div></div></div>

Tags: alaska, burn-probability, conus, ecology, ecosystems, environment, fire, fire-intensity, flame-length, geoscientific-information, hawaii, united-states
Organization: Department of Agriculture