AndreaBozzo/ceres-open-data-index huggingface dataset:
Data from: Dataset on characterization of recombinant interleukin-23α, IL-12p40 and IL-23 complex protein, which activates JAK-STAT signaling pathway in chicken cell lines using immunocytochemical staining

<p>The data herein is related to the research article entitled "Functional analyses of the interaction of chicken interleukin 23 subunit p19 with IL-12 subunit p40 to form the IL-23 complex", where it is demonstrated that the chicken interleukin (IL)-23α, IL-12p40, and IL-23 complex regulates Th1, Th17, and Treg cytokine production through heterodimer receptors as well as a homodimer receptor consisting of IL-12Rβ1 and IL-23R, and activates the JAK/STAT signaling pathways. Here,  the effects of the recombinant chicken IL-23α, IL-12p40, and IL-23 complex protein on cell proliferation and nitric oxide (NO) production in chicken macrophage (HD11) and CU91 T cell lines are evaluated. In addition, the expression of IL-6, IL-17A, and interferon-γ mRNA were upregulated <em>in vivo</em> and <em>in vitro</em>. Moreover, treatment with the chicken IL-23α, IL-12p40, and IL-23 complex activated phosphorylation of tyrosine and serine residues in JAK2, STAT1, TYK2, and SOCS1 in chicken cell lines. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Dataset on characterization of recombinant interleukin-23α, IL-12p40 and IL-23 complex protein, which activates JAK-STAT signaling pathway in chicken cell lines using immunocytochemical staining.</p> <p>File Name: Web Page, url: <a href="https://www.sciencedirect.com/science/article/pii/S2352340917307035">https://www.sciencedirect.com/science/article/pii/S2352340917307035</a> </p><p><em>Data in Brief</em> article providing the nucleotide and amino acid sequences of the chicken IL-23α coding region. The data provide information about the effect of chicken IL-23α, IL-12p40, and IL-23 complex protein on cell proliferation and production of reactive oxygen species in the form of NO in both cell lines. </p></li></ul><p></p>

Tags: ars, cytokine-production, data-gov, jak-stat-signaling-pathway
Organization: Department of Agriculture
NAL Agricultural Thesaurus and Glossary

<p>The NAL Agricultural Thesaurus (NALT) was first released by the National Agricultural Library in 2002, with in-depth coverage of agriculture, biology, and related disciplines. It contains over 135,000 terms, including 63,000 cross references, and is arranged into <a href="https://agclass.nal.usda.gov/dne/search_sc.shtml">17 subject categories</a> which are used to browse the Thesaurus in a specific discipline or subject area. NALT is updated annually each January.  The subject scope of agriculture is broadly defined in the thesaurus, and includes terminology in the supporting biological, physical, and social sciences. Biological nomenclature comprises a majority of the terms in the thesaurus and is located in the "Taxonomic Classification of Organisms" Subject Category. Political geography is mainly described at the country level. Published since 2007, the Glossary is a collection of definitions of agricultural terms developed in conjunction with the creation of the NAL Agricultural Thesaurus. The 2018 edition of the glossary contains 5,618 terms ranging across agriculture and its many ancillary subjects. Most definitions were composed by the NAL Thesaurus Staff. Those definitions taken from government sources are indicated in the "Definition Source" field and are included in the bibliography. In 2010, the thesaurus was made available as Linked Open Data. Linked Open Data translates information into a form both readable and understandable by computers. This translation makes it possible for different information resources, such as Web pages, datasets and research articles, to be interconnected, creating meaningful relationships that make it easier to locate related content. In May 2007, Spanish language versions of its NAL Agricultural Thesaurus (NALT) and Glossary of Agricultural Terms were published under the Spanish language names "Tesauro Agrícola" and "Glosario". The Thesaurus and Glossary are produced cooperatively by the USDA National Agricultural Library and the Inter-American Institute for Cooperation on Agriculture (IICA), as well as other Latin American agricultural institutions belonging to the Agriculture Information and Documentation Service of the Americas (SIDALC). The Thesaurus and Glossary can be downloaded in XML, RDF-SKOS, PDF, MARC, and DOC formats. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: NAL Thesaurus and Glossary Home.</p> <p>File Name: Web Page, url: <a href="https://agclass.nal.usda.gov/agt.shtml">https://agclass.nal.usda.gov/agt.shtml</a> </p><p>Website for the NAL Thesaurus (NALT) and Glossary.</p></li></ul><p></p>

Tags: ars, controlled-vocabulary, data-gov, nal-ksd, nal-thesaurus-and-glossary, nalt, online-database, skos, tesauro-agricola
Organization: Department of Agriculture
Code from: Fast and robust curve skeletonization for real-world elongated objects

<p>This record contains C++ code and a Docker release for performing curve skeletonization of objects, which have a voxel representation.   Curve skeletonization is used to convert a three-dimensional digital object or shape to locally one-dimensional parts; in other words, to reduce the shape information to a more easily processed form.  Our algorithm does so for objects whose surface may be noisy, which is a common occurrence when working with data acquired under real-world conditions. This record also includes a test dataset for verifying that the code is running correctly and as examples of how to convert from different file types.  This code is a companion to the paper, “Fast and robust curve skeletonization for real-world elongated objects,” by Amy Tabb and Henry Medeiros, published as part of the IEEE WACV 2018 conference. A free, identical version is available on arXiv:1702:07619[cs.CV] (<a href="https://arxiv.org/pdf/1702.07619.pdf">https://arxiv.org/pdf/1702.07619.pdf</a>).</p>
<p>Open source code can be found at:
<a href="https://github.com/amy-tabb/CurveSkel-Tabb-Medeiros">https://github.com/amy-tabb/CurveSkel-Tabb-Medeiros</a></p>
<p>Docker container release can be found at:
<a href="https://hub.docker.com/r/amytabb/curveskel-tabb-medeiros-docker/">https://hub.docker.com/r/amytabb/curveskel-tabb-medeiros-docker/</a></p>
<p>We also include the results of using our code on one of the examples so that you can be sure that the code worked correctly. This folder is named 'A_Result' and can be found in the resource titled 'examples.zip'.  A README is included in this record as well as in the examples folder.  Both the GitHub and Docker repositories contain the test data to confirm that your data is formatted appropriately.</p>
<p>Problems/Comments/Bugs should be addressed to amy.tabb@ars.usda.gov</p>
<p>Note: Docker link to Curve Skeleton repository resource was added on 7/30/2018 </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Examples of data formats needed to run the code.</p> <p>File Name: examples.zip</p><p>Resource Description: This directory contains a three-dimensional object in voxel representation, in directory "OurFileFormat".  "ConversionFromImageSequence" contains files to demonstrate how to convert from the image sequence format common with ImageJ/Fiji to our format using our code.  Finally, "A_Result" shows the results from running our code.  A README is also included.</p><p>Resource Software Recommended: Link to Github code,url: <a href="https://github.com/amy-tabb/CurveSkel-Tabb-Medeiros">https://github.com/amy-tabb/CurveSkel-Tabb-Medeiros</a> </p></li><br><li><p>Resource Title: README for the example directory.</p> <p>File Name: README_2017.txt</p></li></ul><p></p>

Tags: 3-d-conversion, ars, c_, computer-code, data-gov, np305
Organization: Department of Agriculture
Data from: Yield and profit comparison of diversified versus conventional crop rotation systems in South Dakota

<p dir="ltr">Diversified crop rotations with no-till management are considered fundamental to sustainable agroecosystems; however, associated uncertainty in economic returns may hinder farmers’ decisions to adopt these practices. The research objectives were to evaluate data from a long-term crop rotation experiment to compare economic performance (gross revenue, net revenue, and production cost) among various low-input diversified versus conventional crop rotations. The experiment was initiated in the fall of 2000 with winter wheat (Triticum aestivum L.) planting and planting of remaining crops in the spring of 2001 near Brookings, SD. Economic analysis was performed from data collected during the fifth complete 4-year crop rotational cycle (2017–2020) of six crop rotations: (1) 4-year corn (Zea mays L.)-soybean [Glycine max L. (Merr.)]-spring wheat-sunflower (Helianthus annus L.) (CSSwSf), (2) 4 year corn-soybean-spring wheat-pea (Pisum sativum L.) (CSSwP), (3) 4-year corn-pea-winter wheat-soybean (CPWwS), (4) 4-year corn-oat (Avena sativa L.)-winter wheat-soybean (COWwS), (5) 2-year corn-soybean (CS), and (6) continuous corn (CCC, treatment initiated in 2017). Corn yield in CSSwP rotation, where corn followed peas, was higher (p < 0.05) by 20%, 25%, 45%, and 89%, compared to CPWwS, CSSwSf, CS, and CCC rotations, respectively. Similarly, soybean yield following winter wheat was significantly higher by 16%–38% in COWwS and 13%–38% in CPWwS compared to CSSwP, CSSwSf, and CS. Overall, diversified crop rotations improved both corn and soybean yield and net revenue compared to 2-year CS and monoculture CCC rotations. Moreover, 4-year diversified systems, specifically COWwS, CPWwS, and CSSwP, demonstrated economic resilience by maintaining stable production costs.</p>

Tags: crop-rotation, economic-performance, economic-return, gross-revenue, long-term-research, net-return, production-cost, soil-health
Organization: Department of Agriculture
Forest Health Protection Insect and Disease Survey

The applications available on this portal access a myriad of state, county and local level forest insect and disease conditions data. In addition it offers a window into near real time forest disturbance information collected from space. Data input applications are restricted to cooperators with specific training and expertise. If you need access to an application not listed in this portal please <a href='https://www.fs.usda.gov/foresthealth/contact-us/index.shtml' target='_blank' rel='nofollow ugc noopener noreferrer'>contact us</a>.

Tags: fhp, forest-health-protection, insect-and-disease, open-data
Organization: Department of Agriculture
National Insect and Disease Risk Maps - 2012

FHAAST has completed the <a href='https://www.fs.usda.gov/foresthealth/applied-sciences/mapping-reporting/national-risk-maps.shtml' target='_blank' rel='nofollow ugc noopener noreferrer'>2013 - 2027 National Insect and Disease Risk Map (2012 NIDRM)</a>; a nationwide strategic assessment and database of the potential hazard for tree mortality due to major forest insects and diseases. The goal of NIDRM is to summarize landscape-level patterns of potential insect and disease activity, consistent with the philosophy that science-based, transparent methods should be used to allocate pest-management resources across geographic regions and individual pest distributions. In other words: prioritize investment for areas where both hazard is significant and effective treatment can be efficiently implemented.

Tags: 2012, 2018, fhp, forest-health, nidrm, open-data
Organization: Department of Agriculture
Forest Service Digital Maps

<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 Forest Service National Maps experience page is designed to distribute and deliver maps to the Forest Service and public. Maps cover Forest Service lands. Map series include National; Regional; Admin; Forest; Ranger District and 24K or better known as FSTopo, and our historical product FSTopo Legacy.</span>

Tags: forest-service, fs-base-map, fsbasemap, fstopo, national-forest, open-data, ranger-districts, usda
Organization: Department of Agriculture
Wildfire Crisis Strategy Landscapes (Feature Layer)

<p>The <b>Infrastructure Investment and Jobs Act</b> and the <b>Inflation Reduction Act </b>include significant funding to execute fuels mitigation projects. Regions submitted proposed project boundaries designed to address community exposure to wildfire. The Executive Leadership Team of the Forest Service selected &quot;Landscapes&quot; for initial investment in fiscal years 2022 and 2023. Additional landscapes may be selected for future action. This dataset documents the official boundary of the landscapes selected for fuels treatment activities in the Wildfire Crisis Strategy. The public-facing version of these boundaries is called Wildfire Crisis Strategy Landscapes. This Dataset is current from June 9, 2023 to Present.</p><p><a href='https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=wildfire+crisis+strategy' target='_blank' rel='nofollow ugc noopener noreferrer'>Metadata and Downloads</a><br /></p>

Tags: bil, boundaries, fuels-treatment, iija, ira, open-data, wildfire, wildfire-exposure
Organization: Department of Agriculture
Climate Shield Cutthroat Trout (0% Brook Trout), 2080 (Feature Layer)

This feature class represents the end-of-century (2070-2099) scenario for cutthroat trout, derived from the Climate Shield fish distribution models. These models provide stream-specific probabilistic predictions about the occurrence of juvenile bull trout and cutthroat trout in association with three different scenarios for climate change and brook trout invasions. These datasets indicate all potential cold-water habitats less than 11 degrees Celsius. The attribute fields CT_0BRK - CT_100BRK indicate the probabilities of cutthroat trout occurrence within a cold-water habitat based on the prevalence of brook trout at 0%, 25%, 50%, 75%, or 100% of the sites within a habitat. The probabilities were predicted using the Climate Shield native trout models developed from known species occurrence in greater than 500 cold-water streams. The stream centerlines were based on the National Hydrography Dataset (NHD) but were modified for purposes of modeling and cross-walking to other datasets.

Tags: biota, climate-change, climate-shield, climatologymeteorologyatmosphere, crowd-sourcing, cutthroat-trout, ectotherm, environment, geostatistics, health, inlandwaters, invasive-species, models, open-data, refugia, salmonid, species-distribution, stream-temperature, trout, usfs
Organization: Department of Agriculture
Fireshed Registry: Project Area (Feature Layer)

<p>The Fireshed Registry is a geospatial dashboard and decision tool built to organize information about wildfire transmission to buildings and monitor progress towards risk reduction for communities from management investments. The concept behind the Fireshed Registry is to identify and map the source of risk rather than what is at risk across all lands in the United States. While the Fireshed Registry was organized around mapping the source of fire risk to communities, the framework does not preclude the assessment of other resource management priorities and trends such as water, fish and aquatic or wildlife habitat, or recreation. The Fireshed Registry is also a multi-scale decision tool for quantifying, prioritizing, and geospatially displaying wildfire transmission to buildings in adjacent or nearby communities. Fireshed areas in the Fireshed Registry are approximately 250,000 acre accounting units that are delineated based on a smoothed building exposure map of the United States. These boundaries were created by dividing up the landscape into regular-sized units that represent similar source levels of community exposure to wildfire risk. Subfiresheds are approximately 25,000 acre accounting units nested within firesheds. Firesheds for the Conterminous U.S., Alaska, and Hawaii were generated in separate research efforts and are published in incremental versions in the Research Data Archive. They are combined here for ease of use.<br></p>

Tags: fire, fire-effects-on-environment, forest-management, geoscientificinformation, natural-resource-management-use, open-data, united-states, wildfire, wildfire-exposure, wildfire-management, wildfire-transmission, wildland-urban-interface
Organization: Department of Agriculture
Healthy Forest Restoration Act Activities (Feature Layer)

The Healthy Forest Restoration Act feature class depicts National Forest System (NFS) Lands within 38 States designated under section 602 and 603 of the Healthy Forest Restoration Act. Designated areas were selected based on a set of eligibility criteria regarding forest health and do not include any areas coinciding with Wilderness and Wilderness Study Areas. The data is comprised of selected HUC-6 units or other areas of similar size and scope clipped to Proclaimed National Forest System lands. Non-Forest Service land ownership areas (inholdings) are also removed. In some cases, entire National Forests were designated. Some state designations' methodologies may differ from the national standard. <div><br /></div><div><a href='https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=healthy+forest+restoration+act' target='_blank' rel='nofollow ugc noopener noreferrer'>Metadata and Downloads</a><br /><div><br /></div><div>Please note that this data is current as of the last refresh date, and changes to designated areas will be republished and archived on a weekly basis.</div></div>

Tags: designations, environment, farm-bill, healthy-forest-restoration-act, insect-and-disease, national-forest-system-lands, open-data, section-602-and-603
Organization: Department of Agriculture
Productivity of U.S. Rangelands, Annual Data lbs/acre (Map Service)

Production data were generated using the Normalized Difference Vegetation Index (NDVI) from the Thematic Mapper Suite from 1984 to 2021 at 250 m resolution. The NDVI is converted to production estimates using two regression formulas depending on the level of the NDVI; there is one equation for lower values (and thus lower production values) and one for higher values.<div><br /></div><div>This raster dataset yields estimates of annual production of rangeland vegetation and should be useful for understanding trends and variability in forage resources.<div><br /></div><div>The Rangeland Productivity data can be downloaded here:</div><div><br /></div><div><a href='https://data.fs.usda.gov/geodata/rastergateway/rangelands/index.php' target='_blank' rel='nofollow ugc noopener noreferrer'>https://data.fs.usda.gov/geodata/rastergateway/rangelands/index.php</a><br /></div></div><div><br /></div>

Tags: biota, climate, drought, forage, grazing, office-of-sustainability-and-climate, open-data, osc, range-management, rangeland-ecology, rangeland-productivity, rangelands, rpa-assessment, usda-forest-service, usfs
Organization: Department of Agriculture
Rapid Assessment of Vegetation Condition after Wildfire (RAVG) Basal Area Percent Change (Alaska) (Map Service)

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 (&quot;initial assessments&quot;). Late-season fires, however, may be deferred until the following spring or summer (&quot;extended assessments&quot;). National mosaics of each thematic product are prepared annually. Mosaics of extended assessments, if any, are provided separately from initial assessment mosaics. This map service includes annual raster mosaics of published BA-7 datasets for fires that burned during calendar years 2019 in Alaska. The associated burned area perimeters are available via the <a href='https://data.fs.usda.gov/geodata/edw/datasets.php' target='_blank' rel='nofollow ugc noopener noreferrer'>Enterprise Data Warehouse</a>.

Tags: basal-area, biota, burn-severity, canopy-cover, cbi, composite-burn-index, conus, differenced-normalized-burn-ratio, dnbr, landsat, nbr, normalized-burn-ratio, open-data, ravg, relative-differenced-normalized-burn-ratio, rnbr, united-states, vegetation-condition, wildfire, wildland-fire
Organization: Department of Agriculture
Terrestrial Condition Assessment (TCA) Wildfire Hazard Potential Fire Regime Groups 1 and 2 (Map Service)

The 2018 Wildland Hazard Potential (WHP) represents areas of uncharacteristic fuel buildup. The WFP 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  hazard. The LANDFIRE Fire Regime Groups (FRG) raster dataset (US_140FRG_10252017) was used to identify moderate fire regimes (Regimes 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  high  or  very high  wildland hazard potential and fire regime groups I and II.

Tags: fire, gtac, open-data, tca, usfs
Organization: Department of Agriculture
Terrestrial Condition Assessment (TCA) Feral Pig Density (Map Service)

Data are derived from generalized linear models and model selection techniques using 129 estimates of population density of wild pigs (Sus scrofa) from 5 continents. Models were used to determine the strength of association among a diverse set of biotic and abiotic factors associated with wild pig population dynamics. The models and associated factors were used to predict the potential population density of wild pigs at the 1 km resolution. Predictions were then compared with available population estimates for wild pigs on their native range in North America indicating the predicted densities are within observed values. See Lewis et al (2017) and Lewis et al (2019) for more information.<div><br /></div><div>Lewis, Jesse S., Matthew L. Farnsworth, Chris L. Burdett, David M. Theobald, Miranda Gray, and Ryan S. Miller. &quot;Biotic and abiotic factors predicting the global distribution and population density of an invasive large mammal.&quot; Scientific reports7 (2017): 44152.</div><div><br /></div><div>Lewis, Jesse S., Joseph L. Corn, John J. Mayer, Thomas R. Jordan, Matthew L. Farnsworth, Christopher L. Burdett, Kurt C. VerCauteren, Steven J. Sweeney, and Ryan S. Miller. &quot;Historical, current, and potential population size estimates of invasive wild pigs (Sus scrofa) in the United States.&quot; Biological Invasions21, no. 7 (2019): 2373-2384.</div>

Tags: feral-pig-density, gtac, mdz3, open-data, tca, usfs
Organization: Department of Agriculture