AndreaBozzo/ceres-open-data-index huggingface dataset:
Tree Canopy Cover (TCC) Science Standard Error (SE) 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 v2021-4 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 Science data are the initial annual model outputs that consist of two images: percent tree canopy cover (TCC) and standard error. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset, and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the years 2008 through 2021 are available. The Science data were produced using a random forests regression algorithm. For standard error data, the initial standard error estimates that ranged from 0 to approximately 45 were multiplied by 100 to maintain data precision (e.g., 45 = 4500). Therefore, standard error estimates pixel values range from 0 to approximately 4500. The value 65534 represents the non-processing area mask where no cloud or cloud shadow-free data are available to produce an output, and 65535 represents the background value. The Science data are accessible for multiple user communities, through multiple channels and platforms. For information on the NLCD TCC data and processing steps see the NLCD metadata. Information on the Science data and processing steps are included here. </span><span>Data Download and Methods Documents: </span><span> - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/ </span></p></div></div></div>

Tags: 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 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
Northern Plains High Resolution Land Cover

<div style='text-align:Left;'><div><div><p><span>This image service contains high-resolution land cover data for the states of Nebraska, South Dakota, and North Dakota. These data are a digital representation of land cover derived from 1-meter aerial imagery from the USDA National Agriculture Imagery Program (NAIP.) The year of NAIP used for each state was 2014.</span></p><p><span>Data are intended for use in rural areas and therefore do not include land cover in cities and towns. Land cover classes (tree cover, other land cover, or water) were mapped using an object-based image analysis approach and supervised classification. </span></p><p><span>These data are designed for conducting geospatial analyses and for producing cartographic products. In particular, these data are intended to depict the location of tree cover in the county. The mapping procedures were developed specifically for agricultural landscapes that are dominated by annual crops, rangeland, and pasture and where tree cover is often found in narrow configurations, such as windbreaks and riparian corridors. Because much of the tree cover in agricultural areas of the United States occurs in windbreaks and narrow riparian corridors, many geospatial datasets derived from coarser-resolution satellite data (such as Landsat), do not capture these landscape features. This dataset is intended to address this particular data gap. </span></p><p><span /></p><p><span>These data can be downloaded by county at the Forest Service Research Data Archive. </span></p><p><span /></p><p><span>Nebraska: https://www.fs.usda.gov/rds/archive/catalog/RDS-2019-0038 </span></p><p><span /></p><p><span>South Dakota: https://www.fs.usda.gov/rds/archive/catalog/RDS-2022-0068 </span></p><p><span /></p><p><span>North Dakota: https://www.fs.usda.gov/rds/archive/catalog/RDS-2022-0067 </span></p><p><span /></p><p><span>A Kansas dataset was also developed using the same methods and is located at: </span></p><p><span /></p><p><span>Kansas data download: https://www.fs.usda.gov/rds/archive/catalog/RDS-2019-0052 </span></p><p><span /></p><p><span>Kansas map service: https://data-usfs.hub.arcgis.com/documents/high-resolution-tree-cover-of-kansas-2015-map-service/explore</span></p></div></div></div>

Tags: 2014, __, agroforestry, analysis, cover, dakota, imagerybasemapsearthcover, inventory, land, management, monitoring, naip, natural, nebraska, north, northern-plains, resource, riparian, south, tree, use, water, windbreaks
Organization: Department of Agriculture
<b>Data From: Bacterial Communities of House Flies from Beef and Dairy Cattle Operations Are Diverse and Contain Pathogens of Medical and Veterinary Importance</b>

<p dir="ltr">Adult house flies (<i>Musca domestica</i> L.) are important reservoirs and mechanical vectors of bacteria in livestock operations. House fly bacterial communities are influenced by their local environment, yet a comprehensive understanding of bacterial diversity, pathogen prevalence, and bacterial source is not fully understood. We characterized bacterial communities from adult female house flies and associated manure samples from beef and dairy cattle farms in Kansas, Oklahoma, and Texas over four months (July-October). Bacterial community composition in flies and manure reflected the local environment, and house flies shared the majority (≥99%) of bacterial taxa with manure. The variability of bacterial diversity was greater among individual fly (species richness range: 48 - 1747) samples than manure (species richness range: 345 - 1162). Temporal variability of fly bacterial diversity was observed within each farm type. Bacterial taxa of veterinary and medical importance such as <i>Corynebacterium</i>, <i>Turicibacter</i>, <i>Staphylococcus</i>, <i>Streptococcus</i>, and <i>Acinetobacter</i> were highly prevalent in flies, constituting core bacterial communities. The prevalence of bacterial taxa associated with bovine keratoconjunctivitis (IBK) and bovine respiratory disease (BRD) was higher in flies than in manure and prevalence varied monthly. This study underscores the crucial role house flies play as carriers of cattle pathogens, contributing to their dissemination among animals and to off-site locations, where they pose a threat to surrounding communities and agricultural operations.</p><p dir="ltr">The raw Illumina MiSeq sequence data for this project can be found here: <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1009094" target="_blank">https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1009094</a></p><p dir="ltr">Resources in this dataset:</p><ul><li>Resource Title: Metadata for Dairy farm associated house fly and manure bacterial communities; File Name: Metadata_ Microbiome_House Flies and manure from Beef and Dairy Cattle Operations.xlsx Resource; Description: This spreadsheet links the raw sequence reads on NCBI Accession No. PRJNA1009094 data.</li></ul><p></p>

Tags: bacterial-community, bovine-respiratory-disease-pathogens, cattle-manure, house-fly, infectious-bovine-keratoconjunctivitis
Organization: Department of Agriculture
Single-cell RNA sequencing data and resources from blood and milk immune cells of Holstein cattle with chronic mastitis caused by experimental <i>Staphylococcus aureus</i> infection

<p dir="ltr">This online resource provides supplementary items used to analyze data in the work, "Single-cell RNA sequencing characterization of Holstein cattle blood and milk immune cells during a chronic <i>Staphylococcus aureus</i> mastitis infection", by Wiarda et al. Cells were collected from milk and blood of three cattle with chronic mastitis infections caused by experimental <i>Staphylococcus aureus </i>challenge. Isolated cells were processed for single-cell RNA sequencing, resulting in a dataset of 35,338 cells distributed across 62 cells clusters. Cell clusters were classified as granulocytes, monocyte/macrophage/conventional dendritic cells, B cells/antibody-secreting cells, T cells/innate lymphoid cells, plasmacytoid dendritic cells, and non-immune cells. A data subset consisting of 30 granulocyte clusters was also created. Data objects of total cell and granulocyte datasets are included here (.h5seurat files), as well as results of pairwise differential gene expression of all cell clusters (resulting in over 4.3 million differentially expressed genes), and a data object containing cell neighborhoods used for differential abundance testing.</p>

Tags: ars, blood, bovine, cattle, cell, cow, dairy, data-gov, differential-gene-expression, gene, granulocyte, holstein, immune, leukocyte, mastitis, milk, national-animal-disease-center, neutrophil, np103, np108, rna-seq, scrna-seq, single-cell, staphylococcus-aureus, usda
Organization: Department of Agriculture
Data From: Two Novel Species of <i>Pestalotiopsis</i> Fungi on <i>Picea</i> and <i>Tsuga </i>from Temperate Forests in the United States

<p dir="ltr">In a 2023 survey evaluating conifers with <i>Rosellinia </i>infections, five <i>Pestalotiopsis</i>-like fungal endophytes were isolated from plant samples obtained from Maine, New Hampshire, and Ohio by The Mycology & Nematology Genetic Diversity & Biology Laboratory at the United States Department of Agriculture. The two data sets provided herein contain species-specific base pair substitutions for the partial translation elongation factor 1-alpha gene (<i>TEF)</i>. In the alignments, the novel <i>Pestalotiopsis</i> fungi are compared to their most closely related species. The data set can be used as a diagnostic tool to differentiate between the closely related species. </p><p dir="ltr">DNA was extracted from fungal samples using the E.Z.N.A HP Plant & Fungal DNA Kit (OMEGA<sup>® </sup>Bio-Tek, Norcross, GA, USA) following manufacturer’s protocol. The <i>TEF </i>locus was amplified, and reactions were conducted in 25 μL volumes with 12.5 μL of KAPA2G Robust Hotstart<sup>® </sup>(Kapa Biosystems, Inc., Wilmington, MA, USA), 1.25<sup> </sup>μL of the forward and reverse primers at 10 μM, 1-1.75 μL of DNA at 10-20 ng, and 8 μL of molecular grade H<sub>2</sub>O. Amplifications were performed following the protocol described by Maharachchikumbura <i>et al.</i> (2014) in a C1000 Touch PCR Thermal Cycler (Bio-Rad, Hercules, CA). PCR products were analyzed through capillary electrophoresis with the QIAxcel Advanced System instrument and the QIAxcel ScreenGel software (Qiagen, Hilden, Germany). PCR products were then purified using ExoSAP-IT Cleanup (Affymetrix, Santa Clara, CA) following the manufacturer’s protocol. The BigDye™ 3.1 Terminator Cycle sequencing kit was used to sequence amplicons bi-directionally with the Applied Biosystems SeqStudio Genetic Analyzer (Thermo Fisher Scientific, Waltham, MA, USA).</p><p dir="ltr">Resources in this dataset:</p><ul><li>Resource Title: TEF <i>P. neohantoniensis</i> alignment, File Name: TEF_<i>P. neohantoniensis</i> alignment, Resource Description: Alignment of <i>TEF</i> DNA sequences for species closely related to <i>Pestalotiopsis neohantoniensis</i>.</li><li>Resource Title: TEF <i>P</i><i>. maineana</i> alignment, File Name: TEF_<i>P. maineana</i> alignment, Resource Description: Alignment of <i>TEF</i> DNA sequences for species closely related to <i>Pestalotiopsis maineana.</i></li></ul><p dir="ltr"><br></p><p dir="ltr"><br></p>

Tags: ascomycota, endophyte, pestalotiopsis, sporocadaceae
Organization: Department of Agriculture
Data from: Where Flowers Bloom, so do Downy Mildews: New Species and New Records of <i>Hyaloperonospora</i>, <i>Peronospora</i>, and <i>Plasmopara</i> Species on Ornamental and Wild Plants in the United States

<p dir="ltr">Downy mildews are biotrophic plant pathogens in the family Peronosporaceae (Peronosporales, Oomycota) of high value crops which can cause significant damage and economic losses. The floral and nursery crop industries are particularly susceptible to downy mildews, especially when annuals, perennials, and woody plants are growing in protected environments. In addition to affecting cultivated plants, downy mildews are a common occurrence on wild relatives, or non-cultivated plant hosts. The role these play, however, as pathogen reservoirs and on the emergence or re-emergence of downy mildew epidemics is still unknown. In this study, we report downy mildew disease observations on several annual, perennial and woody shrub plants in several states in the Eastern U.S. Based on morphological observations and phylogenetic analyses of two nuclear loci (Internal transcribed spacer 1 and 2, 28 ribosomal large subunit) and two mitochondrial loci (<i>cytochrome oxidase subunits 1 and 2</i>), we report <i>Peronospora</i> and <i>Plasmopara</i> occurrences on frost grape (Ohio), Lenten rose (North Carolina, Virginia), mock strawberry (Maryland), redbud (North Carolina), snapdragon (Virginia), viburnum (North Carolina), and Virginia creeper (Maryland, Ohio). The phylogenetic analysis indicated that the <i>Peronospora</i> isolates on snapdragon (<i>Antirrhinum majus</i>) from Virginia were clearly separated from other <i>P. antirrhini</i> isolates collected on <i>Misopates orontium</i>, indicating that they constitute a separate species here described as <i>Pe. ceperoae</i> (dataset containing SNPs for species identification here included)<i>.</i> The phylogenetic analyses also supported the combination of <i>Peronospora matthiolae</i>, a pathogen of <i>Matthiola incana</i> (stock), into <i>Hyaloperonospora</i>, based on specimens collected in Ohio. Collectively, our results suggest a wider geographic distribution for the downy mildew species reported, highlighting the need for continuous surveillance of these pathogens to prevent outbreaks and their introduction to naïve environments.</p>

Tags: downy-mildew-species, oomycota, ornamental-plant-pest, ornamental-plants, peronosporaceae
Organization: Department of Agriculture
Data and Code from: Smart vision-based monitoring system for AI-driven moth population estimation using camera-equipped trap imaging

<p dir="ltr">Real-time, image-based monitoring for stored product insect pests could increase timely treatments and protection for postharvest products throughout the supply chain. Artificial intelligence (AI) and machine learning can provide the models necessary for accurate identification and population-counting within a trap-based system. This study presents the development of a smart vision-based monitoring system for moth population estimation using sticky traps with automated camera imaging. The proposed system integrates advanced image processing techniques with a Convolutional Neural Network (CNN) to accurately detect and classify moths and non-moth insects captured on sticky traps. Sticky traps, widely employed in integrated pest management (IPM) systems, often require manual inspection, which is labor-intensive and prone to human error. To address this, the developed system automates the detection process, reducing reliance on manual counting while improving classification precision. The dataset, consisting of 1,739 high-resolution images, was divided into training and testing subsets with a 70–30% split. Each image was preprocessed and annotated with ground-truth labels for accurate performance evaluation. The model demonstrated a high overall classification accuracy of 95.8%, with precision, recall, and F1-scores consistently exceeding 90%. These results highlight the effectiveness of the CNN in managing complex scenarios such as insect overlap, varied environmental conditions, and trap orientations, offering a scalable and efficient solution for real-time insect population monitoring in storage environments. The findings suggest that the proposed system provides a reliable and automated alternative for pest management, significantly reducing labor and enhancing decision-making in storage facilities and postharvest agriculture. In addition, field validation demonstrated the system’s feasibility in real-world storage environments, offering an effective and scalable alternative to traditional inspection practices while minimizing labor and enhancing precision pest control decisions.</p><p dir="ltr">This dataset features a subset of the images captured every hour in a sticky trap baited with a Plodia interpunctella pheromone lure. The images were processed for classificaiton of Indian meal moths and for population counting over time. A read me on image file naming convention, meta data, and conversion code for MatLab are included in the data files.</p><p dir="ltr">This research used resources provided by the SCINet project and/or the AI Center of Excellence of the USDA Agricultural Research Service, ARS project numbers 0201-88888-003-000D and 0201-88888-002-000D.</p>

Tags: automated-trapping, camera-trap-models, computer-vision, indian-meal-moth-plodia-interpunctella, machine-learning-ml-models, population-counts, source-code, stored-products-pests
Organization: Department of Agriculture
Data from: Impacts of heat stress on the accuracy of an ear-tag accelerometer for monitoring rumination and eating behavior in dairy-beef cross cattle using an automated gold standard

<p dir="ltr">The objective of this study was to determine if a commercially available ear-tag sensor (CM; CowManager SensOor, Agis Automatisering BV) could accurately quantify eating and rumination time under heat stress conditions. Data obtained from CM sensors was compared with data collected using an automated gold standard (RW; Rumiwatch System; Itin+Hoch). Automated measurements were obtained from 2 experiments in which cattle were exposed to heat stress conditions. In the principal study (Experiment 1), 3428 h of data were collected from 9 Holstein × Angus steers (470.9 ± 23.9 kg) subjected to either thermoneutral (TN; 21.0°C; 64.0% humidity; temperature-humidity index [THI] = 67; 12- and 12-h light and dark cycle; n = 1714 h), or heat stress conditions (HS; cyclical daily temperatures to mimic diurnal patterns; 0800 – 2000 h: 33.6°C, 40.0% RH, THI: 83.5; 2000 – 0800 h: 23.2°C, 70.0% RH; THI: 70.3; n = 1714 h). Data (n = 719 h) from 6 Holstein x Angus steers (487.9 ± 9.1 kg) were obtained from a subsequent experiment (Experiment 2) to confirm consistency of ear-tag accelerometer performance under elevated THI (HS conditions as described above).</p>

Tags: bovine, cattle, precision-technologies, sensor-performance, temperature-humidity-index
Organization: Department of Agriculture
Code from: Using cameras for precise measurement of two-dimensional plant features

<p>Images are used frequently in plant phenotyping to capture measurements. This chapter offers a repeatable method for capturing two-dimensional measurements of plant parts in field or laboratory settings using a variety of camera styles (cellular phone, DSLR), with the addition of a printed calibration pattern. The method is based on calibrating the camera using information available from the EXIF tags from the image, as well as visual information from the pattern. Code is provided to implement the method, as well as a dataset for testing.  We include steps to verify protocol correctness by imaging an artifact. The use of this protocol for two-dimensional plant phenotypoing will allow data capture from different cameras and environments, with comparison on the same physical scale. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Code repository: camera-as-scanner.</p> <p>File Name: Web Page, url: <a href="https://github.com/amy-tabb/camera-as-scanner">https://github.com/amy-tabb/camera-as-scanner</a> </p><p>Code for calibrating a camera using a pattern, EXIF tags, and then warping to physical dimensions times a scaling factor. Originally for use in two-dimensional plant phenotyping. </p></li><br><li><p>Resource Title: Code repository: aruco-pattern-write.</p> <p>File Name: Web Page, url: <a href="https://github.com/amy-tabb/aruco-pattern-write">https://github.com/amy-tabb/aruco-pattern-write</a> </p><p>Basic code for generating a gird of aruco patterns.</p></li><br><li><p>Resource Title: Code repository: camera-as-scanner-data.</p> <p>File Name: Web Page, url: <a href="https://github.com/amy-tabb/camera-as-scanner-data">https://github.com/amy-tabb/camera-as-scanner-data</a> </p><p>A minimal working example to verify that the installation of camera-as-scanner is working as expected.</p></li><br><li><p>Resource Title: Data and Code from: Using cameras for precise measurement of two-dimensional plant features: CASS (Zenodo).</p> <p>File Name: Web Page, url: <a href="http://doi.org/10.5281/zenodo.3677473">http://doi.org/10.5281/zenodo.3677473</a> </p></li></ul><p></p>

Tags: ars, camera-calibration, computer-vision, data-gov, np305, plant-phenotyping
Organization: Department of Agriculture
Data from: Assessing agronomic and physiological traits during reproductive developmental stages for breeding Upland drought tolerant cotton

<p dir="ltr">Developing new cultivars with drought resilience is difficult for cotton breeders/geneticists, because there is a lack of knowledge of which traits to target for improvement. Morphological and agronomic data was collected from two cultivars representing different production regions [far west – ‘PhytoGen 72’ (PHY72) and midsouth – ‘Stoneville 474’ (STV474)] of the US. These data were used to evaluate which traits are associated with drought resilience under semi environmental control (greenhouse). The dataset includes twenty eight traits that were evaluated for two irrigation regimes during the six critical reproductive stages of 50 % squaring, 100 % squaring, 50 % flowering, peak bloom and early boll setting, advanced flowering and boll setting, and initial boll cracking. The published article, based on these data, showed that traits such as leaf temperature (peak bloom and early boll setting), chlorophyll fluorescence yield (50 % squaring), number of leaves (peak bloom and early boll setting), flowers (50 % flowering) bolls (peak bloom and early boll setting and initial boll cracking), and plant height (50 % squaring and peak bloom and early boll setting) as single targets or in combination, could be used in selection strategies for breeding and genetically improving cotton for drought resiliency.</p>

Tags: biomass-production, chlorophyll-flourescence, drought-resillience, drought-tolerance, dry-matter-content, leaf-temperature, leaf-weight, number-of-bolls, number-of-flowers, number-of-nodes, plant-height, relative-water-content, reproductive-weight, total-water, turgid-weight, vegetative-weight
Organization: Department of Agriculture
Data from: Effects of a novel loss-of-function waxy endosperm allele on sorghum seed development and grain quality

<p dir="ltr">In grains such as sorghum, starch is an important source of carbohydrates and plays a key role in human food, livestock feed, and ethanol production for the brewing and biofuel industry. Starch has two main components, amylose and amylopectin. While the genetic changes resulting in waxy sorghum have been identified, their broader effects on seed development and grain quality remain poorly understood. To address this gap in knowledge, novel waxy sorghum lines were studied during seed development. This study provides additional information on waxy sorghum grain quality overall.</p>

Tags: amylose, grain, sorghum, starch
Organization: Department of Agriculture