AndreaBozzo/ceres-open-data-index huggingface dataset:
Land FASAB

<div style='text-align:Left;'><p style='margin:0 0 0 0;'>In 2021, the Federal Accounting Standards Advisory Board (FASAB) initiated a tracking mechanism that requires all federal agencies that own or manage land to categorize the acres of those lands into three broad categories of predominant land use and produce an annual report. In this context the USDA Forest Service has considered the definitions of each category and classified all acres of National Forest System (NFS) lands into 1. Conservation/Preservation, 2. Commercial, 3. Operational designations. Those designations are additionally separated by whether the lands have been identified as Stewardship lands, or if the lands were acquired by the federal government for General Plant, Property &amp;amp;amp; Equipment purposes. In response, the Washington Office Lands and Realty Management staff determined the appropriate data sources for this annual report and developed classification rules and a geoprocessing methodology to overlay and extract a seamless and complete NFS acreage total by the required categorizations. The acres of land in each category are dynamic, as the status of surface ownership may change from year to year based on administrative and congressional designations, purchases, dispositions, or exchanges. The data used to generate the report are updated weekly, and this translates into continuously refreshed reporting and mapping products. Presented here is the spatial representation of the rule-based land category designations of NFS lands.The FASAB land categories are defined as follows:Conservation/ Preservation: land or land rights that are predominantly used for conservation or preservation purposes.Conservation: protection and proper use of natural resources.Preservation: the protection of buildings, objects, and landscapes.Commercial: land and permanent land rights that are predominately used to generate inflows of resources derived from the land itself or activities that nonfederal third parties perform on the land, usually through special use permits, right-of-way grants, and leases.Operational: land that is used for general or administrative purposes.The Land_FASAB dataset covers National Forest System Lands including federally owned units of forest, range, and related land consisting of national forests, purchase units, national grasslands, land utilization project areas, experimental forest areas, experimental range areas, designated experimental areas, other land areas, water areas, and interests in lands that are administered by the U.S. Department of Agriculture (USDA) Forest Service or designated for administration through the Forest Service. Additional information on this FASAB and the geoprocessing used to produce this dataset can be found here: https://www.fs.usda.gov/sites/default/files/Land-FASAB-dataset-FAQ.pdf<br /></p><p style='margin:0 0 0 0;'><br /></p><p style='margin:0 0 0 0;'><a href='https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=FASAB' target='_blank' rel='nofollow ugc noopener noreferrer'>Metadata and Downloads</a><br /></p></div>

Tags: open-data, planningcadastre
Organization: Department of Agriculture
Terrestrial Condition Assessment (TCA) Wildfire Hazard Potential High (Map Service)

<span style='color:rgb(0, 0, 0); font-family:Verdana, Arial, Helvetica, sans-serif; font-size:14.4px;'>The 2023 Wildland Hazard Potential (WHP) represents areas of significant fuel buildup. The WHP is a raster geospatial product produced by the USFS Fire Modeling Institute in the Fire, Fuel, and Smoke Program. Higher values have a higher probability of high-intensity fire, with torching, crowning, and other forms of extreme fire behavior. Data used represent areas classified as “high” or “very high” risk. Wildfire hazard potential, version 2023, 4th Edition. https://www.firelab.org/project/wildfire-hazard-potential (October 2, 2023).</span>

Tags: fire, gtac, open-data, risk, tca, usfs
Organization: Department of Agriculture
Terrestrial Condition Assessment (TCA) Vegetation Departure (Map Service)

<span style='font-family:&quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size:16px;'>Departure is calculated as: Departure = 100 ? Similaritywhere Similarity = a??? (min(Ai,Bi)) over all classes i,...nThe vegetation departure value represents the weighted average (areal extent) of the departure from each Landfire Biophysical Settings (BpS) within each landscape ecosystem. [citation] Swaty, R.; Blankenship, K.; Hall, K.R.; Smith, J.; Dettenmaier, M.; Hagen, S. Assessing Ecosystem Condition: Use and Customization of the Vegetation Departure Metric. Land 2022, 11, 28. https://doi.org/10.3390/land11010028.Vegetation departure within a TCA landscape ecosystem describes the degree to which the observed proportion (percent, 0-100%) of successional classes (i.e. A through E) derived from Landfire Succession Classes (SClass) 2.2.0 dataset, is departed from the proportions one would expect of the reference condition contained in the Landfire Biophysical Settings (BpS) 2.2.0 dataset at each cell location.</span>

Tags: gtac, open-data, tca, teui
Organization: Department of Agriculture
Terrestrial Condition Assessment (TCA) Uncharacteristic Fire Severity Moderate  (Map Service)

<span style='font-family:&quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size:16px;'>The LANDFIRE Percent Mixed-Severity Fire (PMS) raster dataset (LF US_120_PMS) was combined with the Monitoring Trends in Burn Severity (MTBS) data (1984-2017) to identify areas that have experienced unnaturally severe wildfires in the recent past (1984-2017). Areas mapped are greater than 50% Mixed-Severity Fire and a high severity fire MTBS mapped fire at the same location.</span>

Tags: fire, gtac, open-data, tca, usfs
Organization: Department of Agriculture
Terrestrial Condition Assessment (TCA) Alaska Wildfire Potential High (Map Service)

<span style='font-family:&quot;Avenir Next W01&quot;, &quot;Avenir Next W00&quot;, &quot;Avenir Next&quot;, Avenir, &quot;Helvetica Neue&quot;, sans-serif; font-size:16px;'>The percent area of a landscape analysis unit where the Wildland Fire Hazard 2020 class is High.</span>

Tags: gtac, open-data, tca, usfs, wildfire
Organization: Department of Agriculture
Monitoring Trends in Burn Severity Fire Occurrence Locations (Feature Layer)

The Monitoring Trends in Burn Severity MTBS project assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (includes wildfire, wildland fire use, and prescribed fire) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico from the beginning of the Landsat Thematic Mapper archive to the present. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer is a vector point of the location of all currently inventoried and mappable fires occurring between calendar year 1984 and the current MTBS release for CONUS, Alaska, Hawaii and Puerto Rico. Please visit https://mtbs.gov/announcements to determine the current release. Fires omitted from this mapped inventory are those where suitable satellite imagery was not available or fires were not discernable from available imagery. The point location represents the geographic centroid for the _BURN_AREA_BOUNDARY polygon(s) associated with each fire. <a href='https://data.fs.usda.gov/geodata/edw/edw_resources/meta/S_USA.MTBS_FIRE_OCCURRENCE_PT.xml' target='_blank' rel='nofollow ugc noopener noreferrer'>Metadata</a>

Tags: fire-and-aviation, fire-location, fire-occurrence, geospatial, land-use-land-cover-theme, landsat, monitoring-trends-in-burn-severity, mtbs, national-geospatial-data-asset, ngda, open-data, wildland-fire
Organization: Department of Agriculture
Terrestrial Condition Assessment (TCA) Wildfire Hazard Potential Moderate (Map Service)

<span style='color:rgb(0, 0, 0); font-family:Verdana, Arial, Helvetica, sans-serif; font-size:14.4px;'>The 2023 Wildland Hazard Potential (WHP) represents areas of significant fuel buildup. The WHP is a raster geospatial product produced by the USFS Fire Modeling Institute in the Fire, Fuel, and Smoke Program. Higher values have a higher probability of high-intensity fire, with torching, crowning, and other forms of extreme fire behavior. Data used represent areas classified as “high” or “very high” risk. The LANDFIRE Fire Regime Groups (FRG) raster dataset (LC16_BPS_200) was used to identify moderate fire regimes (Regime groups I and II). Fire regime groups represent expected historical fire regimes based on interactions between vegetation dynamics, fire spread, fire effects, and spatial context. The definitions of the regimes are outlined in the Interagency Fire Regime Condition Class Guidebook. The data for this indicator represent areas identified as having “moderate”, “high”, or “very high” wildland hazard potential within fire regime groups I and II. Wildfire hazard potential, version 2023, 4th Edition. https://www.firelab.org/project/wildfire-hazard-potential (October 2, 2023)</span>

Tags: fire, gtac, hazard-potential, open-data, tca, usfs
Organization: Department of Agriculture
Automated Lands Program Parent

<div style="text-align:Left;"><div><div><p>This feature class includes States, Counties or Boroughs, Congressional Districts, Alaska Recording Districts, County Subdivisions, and Places boundaries that are derived from the latest official Census Bureau and Alaska Department of Natural Resources datasets. Features within Forest Service Administrative Forest boundaries may have been modified by the Forest Service for improved accuracy and spatial coincidence(vertical integration).</p></div></div></div>

Tags: alp-geopolitical-dataset, boundaries, land-status, usda-forest-service
Organization: Department of Agriculture
Congressional Districts FS revised 2020 Census

<div style="text-align:Left;"><div><div><p>This feature class includes States, Counties or Boroughs, Congressional Districts, Alaska Recording Districts, County Subdivisions, and Places boundaries that are derived from the latest official Census Bureau and Alaska Department of Natural Resources datasets. Features within Forest Service Administrative Forest boundaries may have been modified by the Forest Service for improved accuracy and spatial coincidence(vertical integration).</p></div></div></div>

Tags: alp-geopolitical-dataset, boundaries, land-status, usda-forest-service
Organization: Department of Agriculture
State FS revised 2020 Census

<div style="text-align:Left;"><div><div><p>This feature class includes States, Counties or Boroughs, Congressional Districts, Alaska Recording Districts, County Subdivisions, and Places boundaries that are derived from the latest official Census Bureau and Alaska Department of Natural Resources datasets. Features within Forest Service Administrative Forest boundaries may have been modified by the Forest Service for improved accuracy and spatial coincidence(vertical integration).</p></div></div></div>

Tags: alp-geopolitical-dataset, boundaries, land-status, usda-forest-service
Organization: Department of Agriculture
Geopolitical Units adjusted within Administrative Forest Boundaries

<div style="text-align:Left;"><div><div><p>This feature class includes States, Counties or Boroughs, Congressional Districts, Alaska Recording Districts, County Subdivisions, and Places boundaries that are derived from the latest official Census Bureau and Alaska Department of Natural Resources datasets. Features within Forest Service Administrative Forest boundaries may have been modified by the Forest Service for improved accuracy and spatial coincidence(vertical integration).</p></div></div></div>

Tags: alp-geopolitical-dataset, boundaries, land-status, usda-forest-service
Organization: Department of Agriculture
Geopolitical Units adjusted within Administrative Forest Boundaries: Congressional Districts FS revised 2020 Census (Feature Layer)

<p>The USDA Forest Service Rapid Assessment of Vegetation Condition after Wildfire (RAVG) program produces geospatial and related data representing post-fire vegetation condition by means of standardized change detection methods based on Landsat or similar multispectral satellite imagery. RAVG data products characterize the impact of disturbance (fire) on vegetation within a fire perimeter, and include estimates of percent change in live basal area (BA), percent change in canopy cover (CC), and the 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"). Annual national mosaics of each thematic product are prepared at the end of the fire season and updated, as needed, when additional fires from the given year are processed. The annual mosaics are available via the Raster Data Warehouse (RDW, see https://apps.fs.usda.gov/arcx/rest/services/RDW_Wildfire). A combined perimeter dataset, including the burn boundaries for all published Forest Service RAVG fires from 2012 to the present, is likewise updated as needed (at least annually). This current dataset is derived from the combined perimeter dataset and adds spatial information about land ownership (National Forest) and wilderness status, as well as the areal extent of forested land (pre-fire) that experience a modeled BA loss above 50 and 75 percent.</p>

Tags: basal-area, biota, burn-area-boundary, burn-severity, canopy-cover, cbi, composite-burn-index, conus, perimeter, ravg, united-states, vegetation-condition, wildfire, wildland-fire
Organization: Department of Agriculture
CottonGen CottonCyc Pathways Database

<p>The CottonGen CottonCyc Pathways Database, part of CottonGen, supports searching and browsing the following CottonCyc databases:</p>
<ul>
<li>Cyc pathways for JGI v2.0 <em>G. raimondii</em> D5 genome assembly</li>
</ul>
<p>This Cyc database was constructed using PathwayTools version 20.0 using the gene models from the JGI v2.0 D5 genome assembly of <em>Gossypium raimondii</em>. There has been no manual curation of this Cyc database. Pathway predictions were made using PathwayTools and in-silico v2.1 annotations as provided by JGI.</p>
<ul>
<li>Cyc pathways for CGP-BGI v1.0 <em>G. hirsutum</em> AD1 genome assembly</li>
</ul>
<p>This Cyc database was constructed using PathwayTools version 20.0 using the gene models from the CGP-BGI v1.0 AD1 genome assembly of <em>Gossypium hirsutum</em>. There has been no manual curation of this Cyc database. Pathway predictions were made using PathwayTools and in-silico v1.0 annotations as provided by CGP-BGI.</p>
<p>Search parameters include genes, proteins, RNAs, compounds, reactions, pathways, growth media, and BLAST search.  </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Website Pointer to CottonGen CottonCyc Pathways Database.</p> <p>File Name: Web Page, url: <a href="http://ptools.cottongen.org/">http://ptools.cottongen.org/</a> </p></li></ul><p></p>

Tags: ars, cmap, cotton-marker-database, cottondb, cottongen, data-gov, gbrowse, ncbi-blast, np301, online-database, tripal-database
Organization: Department of Agriculture
Agricultural Conservation Planning Framework (ACPF) Toolbox

<p>Spatial data on soils, land use, and topography combined with knowledge of conservation effectiveness can be used to reduce nutrient discharge from small watersheds. </p>
<p>The Agricultural Conservation Planning Framework (ACPF) Toolbox software includes tools to process the LiDAR-based digital elevation models for hydrologic analysis, which then allows a series of prioritization, riparian classification, and conservation-practice placement tools to be used. These toolsets identify agricultural fields most prone to deliver runoff directly to streams, map and classify riparian zones to inform whole-watershed riparian corridor management, and estimate the extent of tile drainage in the watershed. The software maps out suites of locations appropriate to install each of several types of conservation practices. These practice-placement opportunities are mapped for practices including controlled drainage, grassed waterways, water and sediment control basins, and nutrient removal wetlands. Rather than making any recommendations, ACPF provides an inventory of watershed assessment data and conservation placement opportunities across a watershed, in order to inform local watershed planning.</p>
<p>ACPF software runs as an extension to ArcGIS software (ver 10.2 or higher) and requires the customized ACPF spatial databases that include agricultural field boundaries and land uses, key soil survey information, and LiDAR based elevation data that are all tied to the Hydrologic Unit Code (HUC) 12 watershed scale.</p>
<div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title:  ACPF Watershed Planning Tool Informational Web Site .</p> <p>File Name: Web Page, url: <a href="http://northcentralwater.org/acpf/" target="_blank">http://northcentralwater.org/acpf/</a> </p><p> This related resource, called "A Framework to Facilitate Conservation Planning in Agricultural Watersheds Using Precision GIS-based Technologies," is a web page describing the watershed planning framework.  Conceptually, our framework is based on a “Conservation Pyramid” (Figure 1) that emphasizes soil conservation as the foundation to agricultural watershed management. Our planning framework (Figure 2) identifies locations where specific landscape attributes are favorable for installation of each type of practice, and includes methods to prioritize  locations according to susceptibility to runoff and erosion losses.</p></li></ul>

Tags: ars, conservation-planning, data-gov, np211, nutrient-reduction, online-database, water-quality, watershed-analysis
Organization: Department of Agriculture
Genomes To Fields (G2F) Inbred Ear Imaging Data 2017

<p>A subset of ~30 inbreds were evaluated in 2014 and 2015 to develop an image based ear phenotyping tool. The data is stored in <a href="http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Edgar_Spalding_G2F_Inbred_Ear_Imaging_June_2017">CyVerse</a>.</p>
<p>Data types in this directory tree are: dimension and width profile data collected from scanned images of ears, cobs, and kernels collected from the Genomes To Fields (G2F) project cooperators. G2F is an umbrella initiative to support translation of maize (<em>Zea mays</em>) genomic information for the benefit of growers, consumers and society. This public-private partnership is building on publicly funded corn genome sequencing projects to develop approaches to understand the functions of corn genes and specific alleles across environments. Ultimately this information will be used to enable accurate prediction of the phenotypes of corn plants in diverse environments. There are many dimensions to the over-arching goal of understanding genotype-by-environment (GxE) interactions, including which genes impact which traits and trait components, how genes interact among themselves (GxG), the relevance of specific genes under different growing conditions, and how these genes influence plant growth during various stages of development. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: CyVerse Genomes To Fields Inbred Ear Imaging 2017 dataset download.</p> <p>File Name: Web Page, url: <a href="http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Edgar_Spalding_G2F_Inbred_Ear_Imaging_June_2017">http://datacommons.cyverse.org/browse/iplant/home/shared/commons_repo/curated/Edgar_Spalding_G2F_Inbred_Ear_Imaging_June_2017</a> </p><p>Dataset (csv, tar.gz) and metadata (BibTex/Endnote) downloads. See _readme.txt for file contents.</p></li></ul><p></p>

Tags: ars, data-gov, g2f, genomes-by-environment, genomes-to-fields, gxe, np301
Organization: Department of Agriculture
USDA Database for the Choline Content of Common Foods, Release 2 (2008)

<p>This database contains values for six choline metabolites: Betaine, Glycerophosphocholine, Phosphocholine, Phosphatidylcholine, Sphingomyelin, and Total choline</p>  
<p>This database was created through a collaborative effort between the USDA and the Department of Nutrition, University of North Carolina.</p> <div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: READ ME - Documentation: USDA Database for the Choline Content of Common  Foods .</p> <p>File Name: Choln02.pdf</p><p>Resource Description: Contains information about documentation, methods and procedures, data evaluation, format, and dissemination information. Also contains references and general information about choline compounds.
</p></li><br><li><p>Resource Title: Choline Content Release 2.</p> <p>File Name: Choln02.zip</p><p>Resource Description: .zip file with Food and Nutrient database tables for Choline from phosphocholine, Choline from phosphatidylcholine, Choline from glycerophoshocholine, Betaine, and Choline from sphingomyelin.</p></li></ul>

Tags: ars, choline, data-gov, food-composition, np107, nutrient-database, nutrition
Organization: Department of Agriculture
Data from: The data of change in macrophage gene expression which [is] induced by perilipin 1 overexpression

<p>The data presented are related to the research article entitled "<a href="https://www.sciencedirect.com/science/article/pii/S0021915018300194">Overexpression of Perilipin1 protects against atheroma progression in apolipoprotein E knockout mice</a>". This <em>Data in Brief</em> paper describes data that were obtained from perilipin 1 (PLIN1) transgenic mice (<em>Plin1Tg</em>) regarding atherosclerosis. The main aim of collecting the data was to clarify the role of PLIN1 in the pathophysiology of atherosclerosis. The data were collected from C57BL/6J mice, apolipoprotein E knockout mice (<em>ApoeKO</em>) and <em>Plin1Tg</em>/<em>ApoeKO</em>. The atherosclerotic lesion areas of aorta were 3.3 ± 1.2% in C57BL/6J mice, 14.2 ± 3.2% in <em>ApoeKO</em>, and 5.6 ± 1.9% in <em>Plin1Tg</em>/<em>ApoeKO</em>. Body weight, gonadal adipose mass and plasma triglyceride concentrations were comparable among the three groups [1]. Furthermore, PLIN1 overexpression did not affect the gene expressions related to cholesterol influx and efflux in macrophage.</p>
<p>Overexpression of PLIN1 in macrophages protected against atheroma progression. No major risk factors were altered in PLIN1 transgenic mice fed normal diet. Overexpression of PLIN1 did not affect the gene expressions related to cholesterol influx and efflux in macrophage. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: The data of change in macrophage gene expression which [is] induced by perilipin 1 overexpression.</p> <p>File Name: Web Page, url: <a href="https://www.sciencedirect.com/science/article/pii/S2352340918305304">https://www.sciencedirect.com/science/article/pii/S2352340918305304</a> </p><p>Thioglycollate-elicited peritoneal macrophages were isolated from C57BL/6J mice or perilipin 1 (PLIN1) transgenic mice (<em>Plin1Tg</em>). Total RNA was prepared and analyzed by reverse transcription polymerase chain reaction (RT-PCR). Expression of the PLIN1 transgene and endogenous PLIN1 and PLIN2 genes in the <em>Plin1Tg</em> macrophages was confirmed. </p></li></ul><p></p>

Tags: ars, data-gov, perilipin-1, plin1
Organization: Department of Agriculture