AndreaBozzo/ceres-open-data-index huggingface dataset:
Science Tree Canopy Cover (TCC) Puerto Rico USVI

<div style='text-align:Left;'><div><div><p><span>The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2023-5 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 1985 through 2023. The NCLD data are processed to mask TCC from non-treed features such as water and non-tree crops, and to reduce interannual noise and smooth the NLCD time series. TCC pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here. Data Download and Methods Documents:  - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/ </span></p><p></div></div></div></p>

Tags: 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, basemaps, continuous, digital-spatial-data, earthcover, environment, gis, imagery, national-geospatial-data-asset, ngda, percent-tree-canopy, pr, prusvi, puerto-rico, remote-sensing, tcc, tree-canopy-cover, virgin-islands
Organization: Department of Agriculture
Science Tree Canopy Cover (TCC) Southeast Alaska

<div style='text-align:Left;'><div><div><p><span>The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2023-5 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 1985 through 2023. The NCLD data are processed to mask TCC from non-treed features such as water and non-tree crops, and to reduce interannual noise and smooth the NLCD time series. TCC pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here. Data Download and Methods Documents:  - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/ </span></p></div></div></div>

Tags: 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, ak, alaska, basemaps, continuous, digital-spatial-data, earthcover, environment, gis, imagery, national-geospatial-data-asset, ngda, percent-tree-canopy, remote-sensing, seak, tcc, tree-canopy-cover
Organization: Department of Agriculture
Data from: Sensitivity of soil nutrient pools, but stability of microbial processes, under reduced rainfall and altered grazing management in northern mixed-grass prairie

<p dir="ltr">Nutrient cycling is a key ecosystem service provided by soils, which may be impacted by global change-induced droughts and alterations to grazing pressure. While the belowground abiotic and biotic responses to drought are increasingly studied, linkages among plant, animal, and microbial responses to drought remain poorly understood. Here we used an innovative experimental approach to enable understanding the relative importance of rainfall reduction, bovine grazing, and their interplay on soil nutrient pools and processes during and after treatments. Specifically, we experimentally imposed a two-year drought of varying intensity (five levels) at two northern mixed-grass prairie sites in Montana and Wyoming. Crossed with this drought treatment, we also imposed a gradient of bovine grazing pressure during the two drought years and three years of recovery following the drought. We found that rainfall reductions at both sites resulted in reductions in soil available P and micronutrients during treatment application. Conversely, rainfall reductions caused both immediate and persistent increases in soil NO<sub>3</sub><sup>-</sup>. Soil nutrients were generally unaffected by grazing treatments. In contrast, biotic soil properties including the activities of six extracellular enzymes and bacterial and fungal community compositions were relatively resistant to rainfall reduction treatments. However, grazing treatments appeared to have a greater effect on extracellular enzyme activity potentials and soil microbial community composition. Overall, our results highlight the relative stability of belowground processes in semi-arid rangelands in the face of drought and land management strategies.</p><p dir="ltr">The article utilizing this dataset is at <a href="https://doi.org/10.1016/j.soilbio.2025.110071" rel="noreferrer noopener" target="_blank">https://doi.org/10.1016/j.soilbio.2025.110071</a>.</p>

Tags: cattle, drought, extracellular-enzyme-activity-eea, fort-keogh, grassland, micronutrients, nitrogen, northern-mixed-grass-prairie-soil, phosphorus, soil-compaction, soil-microbial-community, thunder-basin-ecoregion
Organization: Department of Agriculture
Data from: Grazing and Cover Crop Effects on Soil Health During Transition to Organic Production

<p dir="ltr">Incorporation of grazed cover crops in cropping systems can improve soil. However, information concerning how cover crops and livestock grazing interact during transitions to organic crop production is limited. An experiment was conducted to quantify soil responses to cover crops and grazing during a transition to organic crop production on a Tally sandy loam near Mandan, ND USA. Main-plot factors included grazing vs. no grazing, while split plot factors included six cropping treatments (soil-building cover crop mix, pollinator cover crop mix, weed suppression cover crop mix, multipurpose cover crop mix, annual crop rotation, and perennial forage biculture). Treatments were replicated four times. Soil samples were collected from 0-10 and 10-30 cm depths at the beginning and end of a 3-year organic transition period. Soil samples were evaluated for soil bulk density, soil pH, nitrate-N, available phosphorus, potassium, total soil nitrogen, soil organic carbon, and wet aggregate stability. Soil pH was estimated from a 1:1 soil-water mixture. Soil nitrate-N was determined from 1:10 soil-KCl (2 M) extracts using cadmium reduction followed by a modified Griess-Ilosvay method. Plant-available soil P was estimated by bicarbonate extraction. Exchangeable K was estimated by atomic absorption spectrometry. Total soil carbon and nitrogen were determined by dry combustion. Wet aggregate stability was analyzed for the 0-10 cm soil depth using the 1-2 mm aggregate fraction. Values for soil properties were incorporated into a soil quality index using the Soil Management Assessment Framework. Data may be used to understand soil responses to grazed and ungrazed cover crops under rainfed conditions in a semiarid continental climate. Related USDA soil types include Baggs, Baxton, Belain, Bitterroot, Chincap, Hopley, Mott, Panguitch, Relan, Vebar, and Victor.</p>

Tags: aggregate-stability, northern-plains, organic-farming, sandy-soil, semiarid, soil-organic-carbon, soil-quality-index
Organization: Department of Agriculture
Data and code from: Assessing varied maize germplasm lines for resistance to fall armyworm and corn earworm with agronomic quality consideration

<p dir="ltr">This dataset includes the raw data and R statistical software code needed to reproduce all statistical model outputs from the linked manuscript.</p><p dir="ltr">In this study, observations of fall armyworm (FAW; <i>Spodoptera frugiperda</i>) and corn earworm (CEW; <i>Helicoverpa zea</i>) damage, along with agronomic quality assessments, were made on different maize genotypes in 2022 and 2023. The goal was to characterize how resistance to FAW and CEW varies across genotypes and how this resistance correlates with agronomic quality. In the trial, three replicate plots of each genotype were planted each year. This dataset includes all the observations of FAW leaf damage, plant aspect, seed set, ear aspect, and ear rot (categorical scores) and CEW ear damage (continuous measurement) on each genotype. The R script includes the Bayesian generalized linear mixed models fit to the data, using the software Stan interfaced with R via the cmdstanr and brms packages. Cumulative logistic mixed models were fit to the categorical score data, and a hurdle Gamma model was fit to the ear damage data. The model posterior estimates were used to make comparisons between the genotype mean damage ratings. Pre-fit model objects are also included in this dataset.</p><h2>Included files</h2><ul><li><b>combined_2022_2023_data.xlsx</b>: All raw data, in two spreadsheet tabs. The first tab includes leaf damage and ear damage data from 2022 and 2023. Up to 20 plants from each single-genotype plot were scored for leaf damage on a categorical 1-9 scale in each year. Up to 10 plants per plot were measured for ear damage, a continuous variable in mm units. Genotypes are identified with a numerical ID (entry column) and a name (genotype column). The second tab includes four different variables scored in 2022 on a categorical 1-9 scale, with a single value per plot: plant aspect, ear aspect, seed set, and ear rot.</li><li><b>FAW_damage_rating_analysis</b>.<b>R</b>: R script to import the data, fit Bayesian generalized linear mixed models, generate posterior estimates for genotype means and pairwise differences, generate comparison letter summaries, and produce figures and tables.</li><li><b>get_comp_letters.R</b>: R script defining custom function to generate comparison letter summaries from Bayesian model output.</li><li><b>earrot_bfit.rds, ed_bfit.rds, ld_bfit.rds, plantaspect_bfit.rds, seedset_bfit.rds, earaspect_bfit.rds</b>: R objects allowing the user to reload the pre-fit statistical models so that the outputs can be generated without refitting the models.</li></ul><p><br></p>

Tags: agronomic-trait, corn-earworm, ear-damage, fall-armyworm, helicoverpa-zea, leaf-damage, maize, maize-lines, rating-score, spodoptera-frugiperda, zea-mays
Organization: Department of Agriculture
Data from: Conditions to terminate reproductive diapause of a univoltine insect: Ceratapion basicorne (Coleoptera: Apionidae), a biological control agent of yellow starthistle

<p>The data are the number and proportion of female weevils (<em>Ceratapion basicorne</em>) that oviposited after exposure to three different environmental hibernation conditions for three different durations (4, 8 and 11 weeks).  The conditions were Greenhouse [ambient temperature and photoperiod], glass door Refrigerator [5°C and ambient photoperiod], and incubator [5°C and 24 h dark]). </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Data from: Conditions to terminate reproductive diapause of a univoltine insect: Ceratapion basicorne (Coleoptera: Apionidae), a biological control agent of yellow starthistle.</p> <p>File Name: Ceba_diapause.csv</p><p>Resource Description: The data are the number and proportion of female weevils (Ceratapion basicorne) that oviposited after exposure to three different environmental hibernation conditions for three different durations (4, 8 and 11 weeks).  The conditions were Greenhouse [ambient temperature and photoperiod], glass door Refrigerator [5°C and ambient photoperiod], and incubator [5°C and 24 h dark]).</p><p>Resource Software Recommended: Microsoft Excel,url: <a href="https://www.microsoft.com/microsoft-365/excel">https://www.microsoft.com/microsoft-365/excel</a> </p></li><br><li><p>Resource Title: Meta data for: Conditions to terminate reproductive diapause of a univoltine insect: Ceratapion basicorne (Coleoptera: Apionidae), a biological control agent of yellow starthistle.</p> <p>File Name: Ceba_diapause_meta.csv</p><p>Resource Description: Description of the meaning of the variables.</p><p>Resource Software Recommended: Microsoft Excel,url: <a href="https://www.microsoft.com/microsoft-365/excel">https://www.microsoft.com/microsoft-365/excel</a> </p></li></ul><p></p>

Tags: ars, data-gov, diapause, hibernation, insect, np304, oviposition, weevil
Organization: Department of Agriculture
Image dataset of plant-parasitic nematodes associated with cool-season turfgrass for machine learning and deep learning classification algorithms

<p dir="ltr">The dataset contains micrographs of <i>Hoplolaimus</i>, <i>Helicotylenchus</i>, <i>Meloidogyne</i>, <i>Mesocriconema,</i> <i>Pratylenchus</i>, <i>Trichodorus</i>, and <i>Tylenchorhynchus </i>nematodes. The data were collected using 10×, 20× objectives with a Zeiss Observer Z1 fitted with a Zeiss Axiocam 503 color camera and with 10× and 40× objectives using an Olympus BX51 fitted with a DP74 Olympus camera. Data collected with the Zeiss microscope setup were processed with Zen blue version 2.6 and data collected with the Olympus microscope were processed with CellSens version 4.1. Individual images in the dataset were extracted from videos recorded with Zen blue and CellSens software.</p><p dir="ltr">Micrographs are grouped by three objectives (10×, 20×, 40×) for seven nematode genera (<i>Hoplolaimus, Helicotylenchus, Meloidogyne, Mesocriconema, Pratylenchus, Trichodorus, Tylenchorhynchus</i>). </p><p dir="ltr">Number of images:</p><p dir="ltr"><i>Helicotylenchus</i></p><p>827</p><p dir="ltr"><br></p><p dir="ltr"><i>Hoplolaimus</i></p><p>995</p><p dir="ltr"><br></p><p dir="ltr"><i>Mesocriconema</i></p><p>527</p><p dir="ltr"><br></p><p dir="ltr"><i>Meloidogyne</i></p><p>433</p><p dir="ltr"><br></p><p dir="ltr"><i>Pratylenchus</i></p><p>666</p><p dir="ltr"><br></p><p dir="ltr"><i>Trichodorus</i></p><p>507</p><p dir="ltr"><br></p><p dir="ltr"><i>Tylenchorhynchus</i></p><p>1425</p><p dir="ltr"><br></p>

Tags: convolutional-neural-network, deep-learning, machine-learning, micrograph, nematode, turfgrass
Organization: Department of Agriculture
Data and Images from: Numerical Signature Dataset of Thoracic and Elytral Fragments from Curculionidae and Tenebrionidae Beetles for AI-Based Species Identification

<p dir="ltr">This dataset presents curated and annotated 256×256 pixel image fragments of thoracic and elytral regions from six economically significant species within the beetle families Curculionidae and Tenebrionidae. These anatomical fragments were extracted from high-resolution trap images to support numerical signature generation for species-level classification. The dataset enables researchers to develop and validate image-based machine learning pipelines for pest identification in stored product environments. All samples are standardized and include metadata indicating species, anatomical region, and imaging parameters. This work contributes to advancing computational entomology, post-harvest pest management, and real-time automated monitoring systems.</p><p dir="ltr">Insect pest detection in post-harvest storage environments is crucial for mitigating economic losses and ensuring food security. Traditional identification approaches are labor-intensive and rely on full-body morphological features. This dataset introduces a fragment-based classification framework using annotated thoracic and elytral sections of stored-product beetles. Species included are:</p><ul><li><i>Sitophilus zeamais</i></li><li><i>Sitophilus oryzae</i></li><li><i>Sitophilus granarius</i></li><li><i>Tribolium castaneum</i></li><li><i>Latheticus oryzae</i></li><li><i>Tribolium confusum</i></li></ul><p dir="ltr">Folder abbreviations for the insects are <i>Sitophilus zeamais</i> (mw); <i>Sitophilus oryzae</i> (rw); <i>Sitophilus granarius</i> (ww); <i>Tribolium castaneum</i> (tc); <i>Latheticus oryzae</i> (lo); and <i>Tribolium confusum</i> (tco).</p><p dir="ltr">Key numerical signature descriptors were computed for each fragment:</p><ul><li><b>Skewness</b> – Asymmetry of pixel intensity distribution</li><li><b>Kurtosis</b> – Sharpness of contour distributions</li><li><b>Entropy</b> – Texture complexity</li><li><b>Standard Deviation</b> – Pixel intensity variation</li></ul><p></p>

Tags: computer-vision, image-identification, insect-fragment, numerical-signature, stored-product-insects
Organization: Department of Agriculture
Data and code from: <i>Tribolium cas</i><i>taneum </i>and <i>T. confusum</i> are preferentially attracted to decomposing wood, their putative historical host

<p dir="ltr"><i>Tribolium castaneum</i> and <i>T. confusum</i> are important stored grain pests, yet little is known about their life history outside the stored grain environment. Like related species, the historical habitat of <i>T. castaneum</i> and <i>T. confusum</i> is thought to be rotting logs, but current affinity and use of logs as habitat is unknown. Attraction to, survival on, and reproduction on three different species of decomposing wood was assessed for <i>T. castaneum</i> and <i>T. confusum</i>. Results indicate that both species exhibit a surprisingly high olfactory response to wood over wheat flour. Male and female beetles were equally attracted to wood, but female <i>T. castaneum</i> had reduced survivorship and did not oviposit on a decomposing wood substrate. These results suggest that attraction to decomposing wood is conserved across <i>Triobolium</i> species, yet wood itself does not fulfill critical food or reproductive substrate requirements. Flour, although less attractive than wood, does elicit a slight olfactory response, with significant reproductive capacity, which may contribute to <i>Tribiolium’s</i> ability to utilize this novel anthropogenic resource. This study contributes to an important yet little explored area of inquiry into the role natural microhabitats may play in supporting these economically important species and the origin of stored grain associations.</p><p dir="ltr">Datasets detail overall preference between wood and flour in a choice assay; reproductive output in flour versus wood; and sex specific differences in a choice assay for wood or flour. Analyses were performed in R statistical software; code is provided here for each analysis step and graphing.</p>

Tags: cereal-grain-food-product, decomposing-wood, diet-preferences, flour-beetles, post-harvest-insect-pests, source-code
Organization: Department of Agriculture
Data from: Modeling Grow Tube Microclimate Effects on Vine Cold Hardiness: Identifying Beneficial vs. Detrimental Thermal Modifications During Dormancy

<p dir="ltr">This dataset contains high-frequency temperature data collected to evaluate the thermal performance of vineyard grow tubes during grapevine dormancy. The study investigates how different grow tube materials (plastic and paper) alter vineyard microclimates and affect cold hardiness dynamics across dormant, stable, and rewarming phases. Data were collected using sensor-based temperature monitoring systems installed in vineyard blocks at Washington State University’s Irrigated Agriculture Research and Extension Center (IAREC) in Prosser, WA, from late fall through early spring.<br></p><p><br></p><p dir="ltr">The dataset supports the manuscript titled <i>“Modeling Grow Tube Microclimate Effects on Vine Cold Hardiness: Identifying Beneficial vs. Detrimental Thermal Modifications During Dormancy.”</i> The analysis integrates temporal and thermal-phase-based metrics with cold hardiness modeling using the Ferguson et al. (2014) framework.</p><p dir="ltr"><b>Remark</b>: The dataset follows an open-source project structure consistent with the GitHub repository format used for code and figure organization.</p>

Tags: cold-hardiness-modeling, deacclimation-risk, grapevine-dormancy, linear-mixed-effects-model, thermal-microenvironment
Organization: Department of Agriculture
Bloom count data associated with Eastern Shore pollinator studies in 2018

<p dir="ltr">All data were collected from 20 farms located in the Eastern Shore region of Virginia and Maryland and Virginia Beach. Ten farms had wildflower meadows sown either in 2015 (N=1 farm) or 2016(N=9 farms) which were planted following NRCS guidelines for the creation of pollinator refuges. The bloom counts recorded in this dataset were taken weeks of May 7, 2018, and July 2, 2018 with a m<sup>2</sup> quadrat at 10-m intervals along a 50-m transect. Bloom transects were conducted within wildflower fields on farms with previously sown meadows (N=10 farms), or in unmanaged field edges at farms without wildflower meadows (N=10). Transects were initiated from random starting points by throwing the quadrat into the meadow or unmanaged field edge, proceeding toward the middle of the habitat. Blooming plants were identified to genus and species when possible. Blooms were quantified as the number of individual stems with open inflorescences. Composites and umbels were considered one flower: for example, multiple blooms on an unbranched stem (e.g., partridge pea, sunflower) were considered one flower while multiple blooming branches off of a stem (e.g., coreopsis) were individually counted. Bloom counts in the spreadsheet represent the total number summed across the 5-m<sup>2</sup> transects taken per farm per date, or the total number of blooms counted within the total 5-m<sup>2</sup> area surveyed at the farm on that date. Please see the following publications for more details regarding the study sites and research carried out at these farms with and without wildflower meadows:</p><p dir="ltr">-Angelella, G.M., & M.E. O'Rourke. 2017. Pollinator habitat establishment after organic and no-till seedbed preparation methods. HortScience 52(10): 1349-1355.</p><p dir="ltr">-McCullough, C., G. Angelella, & M. O'Rourke. 2020. Conservation wildflower plantings do not enhance on-farm abundance of <i>Amblyomma americanum</i> (Ixodida: Ixodidae). Insects 11(9): 617.</p><p dir="ltr">-McCullough, C.T., G.M. Angelella, & M.E. O'Rourke. 2021. Landscape context influences the bee conservation value of wildflower plantings. Environmental Entomology 50(4): 821-831.</p><p dir="ltr">-Angelella, G.M., McCullough, C.T., & O'Rourke, M.E. 2021. Honey bee hives decrease wild bee abundance, species richness, and fruit count on farms regardless of wildflower strips. Scientific Reports 11(1): 3202.</p><p dir="ltr">-McCullough, C.T., H. Grab, G. Angelella, S. Karpanty, J. Samtani, E.M. Olimpi, & M. O'Rourke. 2022. Diverse landscapes but not wildflower plantings increase marketable crop yield. Agriculture, Ecosystems & Environment 339: 108120.</p><p dir="ltr">-Angelella, G.M., C.T. McCullough, & M.E. O'Rourke. 2025. On-farm wildflower plantings generate opposing reproductive outcomes for solitary and bumble bee species. Environmental Entomology 54(3): 623-631.</p>

Tags: bloom-count, pollinator-conservation-planting, wildflower
Organization: Department of Agriculture