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312 results for “goat”
Supplementary Material to article "Molecular Diversity of Mycobacterium avium subsp. paratuberculosis in Four Dairy Goat Herds from Thuringia (Germany)"
<p>These data (supplementary material) belong to the publication "Molecular Diversity of <i>Mycobacterium avium</i> subsp. <i>paratuberculosis</i> in Four Dairy Goat Herds from Thuringia (Germany)". The study determined the diversity of <i>Mycobacterium avium</i> subsp. <i>paratuberculosis</i> (MAP) isolated from four goat herds affected by paratuberculosis in Thuringia (Germany), as well as the detailed distribution of MAP genotypes among the animals and their environment in one herd (herd 1). A combination of three methods was used to genotype isolates from fecal samples of infected goats, from various intestinal and other tissues of clinically affected goats, and from environmental samples. The six MAP-C genotypes identified could be assigned to five different phylogenetic subgroups. The results suggest individual infection strains within each herd. In herd 1, one predominant strain was found, and two strains occurred sporadically. The identified genotypes were not goat specific.</p>
A genome-wide study of ruminants reveals two endogenous retrovirus families still active in goats
<p>Additional file - A genome-wide study of ruminants reveals two endogenous retrovirus families still active in goats </p>
Decomposition and lignin content of wooden dowels deployed in a saltmarsh for 3, 5, or 7 years at Goat Island, North Inlet, Georgetown SC, 2017-2024.
Wooden dowels were used to examine organic matter decomposition in saltmarsh soils. Dowels were inserted to a depth of 45cm in control and fertilized marsh plots. They were harvested after 3, 5 or 7 years and sectioned into 5-cm segments. Weight loss of the dowel segments was used to quantify the fate of labile organic matter, while change in lignin content was used to quantify the fate of refractory organic matter.
Potential and realized distribution at 30m for Goat willow (Salix caprea) in Europe for 2000 - 2020
<p>Probability and uncertainty maps showing the potential and realized distribution for the goat willow (<em>Salix caprea, L.</em>) for Europe from the dataset prepared by <a href="http://doi.org/10.5281/zenodo.5818021">Bonannella et al. (2022)</a> and predicted using Ensemble Machine Learning (EML). Potential distribution map cover the period 2018 - 2020; realized distribution cover the period 2000 - 2020, split in the following time periods:</p> <ul> <li>2000 - 2002,</li> <li>2002 - 2006,</li> <li>2006 - 2010,</li> <li>2010 - 2014,</li> <li>2014 - 2018,</li> <li>2018 - 2020.</li> </ul> <p>Files are named according to the following naming convention, e.g:</p> <ul> <li>veg_salix.caprea_anv.eml_md_30m_0..0cm_2000..2002_eumap_epsg3035_v0.3</li> </ul> <p>with the following fields:</p> <ul> <li>theme: e.g. <strong>veg</strong>,</li> <li>species code: e.g. <strong>salix.caprea</strong>,</li> <li>species distribution type: e.g. <strong>anv</strong> (= actual natural vegetation),</li> <li>species estimation method: e.g. <strong>eml</strong>,</li> <li>species estimation type: e.g. <strong>md</strong> ( = model deviation),</li> <li>resolution in meters e.g. <strong>30m</strong>,</li> <li>reference depths (vertical dimension): e.g. <strong>0..0cm</strong>,</li> <li>reference period begin end: e.g. <strong>2000..2002</strong>,</li> <li>reference area: e.g. <strong>eumap</strong>,</li> <li>coordinate system: e.g. <strong>epsg3035</strong>,</li> <li>data set version: e.g. <strong>v0.3</strong>.</li> </ul> <p>For each species is then easy to identify probability and uncertainty distribution maps:</p> <ul> <li>veg_salix.caprea_<strong>anv</strong>.eml_<strong>md</strong>: model uncertainty for realized distribution</li> <li>veg_salix.caprea_<strong>anv</strong>.eml_<strong>p</strong>: probability for realized distribution</li> <li>veg_salix.caprea_<strong>pnv</strong>.eml_<strong>md</strong>: model uncertainty for potential distribution</li> <li>veg_salix.caprea_<strong>pnv</strong>.eml_<strong>p</strong>: probability for potential distribution</li> </ul> <p>Files are provided as <a href="https://gdal.org/drivers/raster/cog.html">Cloud Optimized GeoTIFFs</a> and projected in the Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035). Styling files are provided in both <em>SLD</em> and <em>QML</em> format.</p> <p>If you would like to know more about the creation of the maps and the modeling:</p> <ul> <li><strong>watch</strong> the talk at Open Data Science Workshop 2021 (<a href="https://doi.org/10.5446/55256">TIB AV-PORTAL</a>)</li> <li><strong>access </strong>the repository with our R/Python scripts and follow the instructions (<a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/tree/master/veg_mapping">GitLab</a>)</li> <li><strong>access </strong>the repository with the training dataset (<a href="https://doi.org/10.5281/zenodo.5818021">Zenodo</a>)</li> <li><strong>read </strong>the tutorial with executable code on our <a href="https://opengeohub.github.io/spatial-prediction-eml/spatiotemporal-ml.html#spatiotemporal-distribution-of-fagus-sylvatica">GitBook</a></li> </ul> <p>A publication describing, in detail, all processing steps, accuracy assessment and general analysis of species distribution maps is available on <a href="https://doi.org/10.7717/peerj.13728">PeerJ</a>. To suggest any improvement/fix use <a href="https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues">https://gitlab.com/geoharmonizer_inea/spatial-layers/-/issues</a></p> <p> </p>
Supplementary material for journal article "Challenge dose titration in a Mycobacterium bovis infection model in goats"
<p>Supplementary Figure and Table to Journal article. Figure shows daily rectal temperature of each animal after inoculation. Table 1 shows number and volume of pulmonary lesions for each animal as detected by computed tomography imaging. Table 2 gives details about scoring used at clinical examination.</p>
Sevilleta Field Station Meteorological Network (SevMET): High frequency measurements from the Goat Draw Meteorological Station (GTDR), Sevilleta National Wildlife Refuge, NM, USA, 2024-ongoing.
The Sevilleta Field Station Meteorological Network (SevMET) is a spatially distributed, long-term climate monitoring network established to enhance and expand climate monitoring across a variety of dryland ecosystems (e.g., grasslands, shrublands, woodlands) within the Sevilleta National Wildlife Refuge in central New Mexico. Ecosystem processes in drylands are strongly regulated by climatic drivers that are highly variable in space and time, both within and among years. Therefore, accurate measurement of environmental variables at high spatial and temporal resolution is fundamental to understanding biophysical processes in these ecosystems. SevMET consists of fifteen standardized research-grade weather stations located across multiple dryland ecosystem types (e.g., grasslands, shrublands, woodlands) representative of the southwestern US. Stations continuously measure a standard suite of meteorological variables at five-minute intervals, including air temperature, relative humidity, precipitation, photosynthetically active radiation, incoming shortwave radiation, wind speed and direction, dew point, vapor pressure, and, at a subset of stations, barometric pressure. Stations also measure a suite of soil parameters (bulk electrical conductivity, dielectric permittivity, temperature, and volumetric water content) at six depths (5, 10, 20, 30, 40, and 50 cm) below the ground surface using 1-2 integrated soil profilers. Additionally, phenocams at each station capture images at thirty-minute intervals during daylight hours. This data package contains high-frequency meteorological measurements from the Goat Draw Meteorological Station (GTDR). Phenocam images can be accessed through the PhenoCam Network at: https://phenocam.nau.edu/webcam/sites/sevmetgtdr/. These data complement and extend meteorological data recorded by an adjacent station (Met48), accessible at: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sev&identifier=1.
Marsh surface elevation data in fertilization plots of a Spartina alterniflora-dominated salt marsh at Goat Island, North Inlet, Georgetown, SC.
A Surface Elevation Table (SET) is used to measure changes in the elevation of the marsh platform in fertilization plots of a Spartina alterniflora-dominated marsh on the Goat Island, North Inlet, Georgetown, SC.
Goat-CNN: A Lightweight Convolutional Neural Network for Pose-Independent Body Condition Score Estimation in Goats
<p>Here we introduce the dataset utilized in our published paper entitled "<a href="https://www.sciencedirect.com/science/article/pii/S2666154324002114">Goat-CNN: A Lightweight Convolutional Neural Network for Pose-Independent Body Condition Score Estimation in Goats</a>".</p> <p>Contained within the "bcs" folder are all the videos collected for this study. Each video file is named with a format denoting its respective details. The first number signifies the sequence of collection, the second denotes the ear tag, and the final figure represents the body condition score (BCS) value.</p> <p>For example: "1_158734_2.50" indicates the first sampling of an animal with the ear tag "158734" and a BCS value of "2.50".</p> <p>Additionally, we provide two Python scripts in this repository. The first script, "Video2Frame.py", facilitates the splitting of videos into individual frames. The second script, "Frames2npy.py", converts these frames into two numpy-friendly files with the extension ".npy". These files contain both the images ("X_train_bcs300.npy") and their corresponding labels ("Y_train_bcs300.npy").</p> <p>Furthermore, for the convenience of swift experimentation, we have included the desired .npy files within the repository.</p> <p>To load these files into your Python environment, you can use the following code snippet:</p> <div> <div>th4figs = '/content/drive/MyDrive/compag_2023/'</div> <br> <div>path4images = "/content/drive/MyDrive/CodeRefarm/datasets/BCS/X_train_bcs300.npy"</div> <div>Xtrain = np.load(path4images)</div> <br> <div>path4labels = "/content/drive/MyDrive/CodeRefarm/datasets/BCS/Y_train_bcs300.npy"</div> <div>Ytrain = np.load(path4labels).astype(float)</div> <br> <div>print("X train : ", Xtrain.shape)</div> <div>print("Y train : ", Ytrain.shape)</div> <div> <div> <div> <div> <div> <div> <div> </div> </div> <div> </div> </div> </div> </div> </div> <div> <div> <div> <div> <div> <div> <div> <div> <pre>X train : (5332, 300, 300, 3) Y train : (5332,)<br> </pre> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
FIG. 12 in Management of feral goats Capra hircus Linnaeus, 1758 in insular southern Greece: implications for prehistory
FIG. 12. — Water and a little fodder provided to feral goats Capra hircus Linnaeus, 1758 on Agia Moni, Kythera, by their owner makes them more approachable and facilitates capture of kids for consumption. Photo credit: Valasia Isaakidou.
FIG. 10 in Management of feral goats Capra hircus Linnaeus, 1758 in insular southern Greece: implications for prehistory
FIG. 10. — Feral goats Capra hircus Linnaeus, 1758 caught in a small trap with dry-stone entrance ramp, Mt Psiloritis, Crete. Photo credit: Valasia Isaakidou.
FIG. 11 in Management of feral goats Capra hircus Linnaeus, 1758 in insular southern Greece: implications for prehistory
FIG. 11. — Feral goats Capra hircus Linnaeus, 1758 drinking (more or less fresh) water on the shoreline at Avlemonas, Kythera: nos. 1-2 from rock pools above sea-level and no. 3 from the sea. Photo credit: Valasia Isaakidou.
FIG. 8 in Management of feral goats Capra hircus Linnaeus, 1758 in insular southern Greece: implications for prehistory
FIG. 8. — Kakia Langada gorge, Kythera: A, the inland origin of the gorge viewed from medieval Paliochora – drivers on the high ground to left and right ushered the goats (Capra hircus Linnaeus, 1758) down the gorge towards the sea; B, the mouth of the gorge – the goats were trapped on the storm beach between the sea, the steep walls of the gorge and the muddy pool in the bottom of the gorge. Photo credit: Valasia Isaakidou.
FIG. 9. — A in Management of feral goats Capra hircus Linnaeus, 1758 in insular southern Greece: implications for prehistory
FIG. 9. — A medium-sized (c. 50m2) purpose-built trap on Kythera with water trough, scattered remnants of hay, and dry-stone entrance ramp to right. Photo credit: Valasia Isaakidou.
FIG. 7 in Management of feral goats Capra hircus Linnaeus, 1758 in insular southern Greece: implications for prehistory
FIG. 7. — Trapped feral goats Capra hircus Linnaeus, 1758 on Crete with ears clipped (red circles) to mark ownership. Photo credit: Valasia Isaakidou.
FIG. 6 in Management of feral goats Capra hircus Linnaeus, 1758 in insular southern Greece: implications for prehistory
FIG. 6. — The fresh growth on evergreen oak (Quercus ilex L.) bushes (Kythera, spring 2018) is particularly sought out by feral goats Capra hircus Linnaeus, 1758. Photo credit: Valasia Isaakidou.
FIG. 5 in Management of feral goats Capra hircus Linnaeus, 1758 in insular southern Greece: implications for prehistory
FIG. 5. — On Kythera feral goats Capra hircus Linnaeus, 1758 initially occupied rocky and sparsely vegetated parts of the landscape but latterly, with the widespread abandonment of cultivation, have expanded their range to areas with richer forage. Photo credit: Valasia Isaakidou.
FIG. 4 in Management of feral goats Capra hircus Linnaeus, 1758 in insular southern Greece: implications for prehistory
FIG. 4. — The last resting place of an elderly feral goat Capra hircus Linnaeus, 1758 with a broken jaw in a rock-cut "cave" (previously used as a shelter for domestic goats and sheep) on eastern Kythera. Remains of feral goats, especially adult females and newborn kids, can also be found in many abandoned rural out-buildings. Photo credit: Valasia Isaakidou.
FIG. 2 in Management of feral goats Capra hircus Linnaeus, 1758 in insular southern Greece: implications for prehistory
FIG. 2. — Typical feral goat Capra hircus Linnaeus, 1758 habitat: cliffs and caves above Kato Zakros, eastern Crete. Photo credit: Valasia Isaakidou.
FIG. 3 in Management of feral goats Capra hircus Linnaeus, 1758 in insular southern Greece: implications for prehistory
FIG. 3. — Feral goats Capra hircus Linnaeus, 1758 browsing above Gonies on mid-slopes of Mt Psiloritis, central Crete. Photo credit: Valasia Isaakidou.
FIG. 1 in Management of feral goats Capra hircus Linnaeus, 1758 in insular southern Greece: implications for prehistory
FIG. 1. — Map of Greece showing location of modern feral goat Capra hircus Linnaeus, 1758 populations studied by observation or interview with owner:A, B, named locations on Crete: 1, Ano Zakros; 2, Kato Zakros; 3, Palaikastro; 4, Toplou monastery; 5, Xirolimni; 6, Kritsa; 7, Tzermiado; 8, Psychro; 9, Ag Georgios; 10, Martha; 11, Akhendrias;12, Anogia;13, Sisarkha;14, Gonies;15, Axos;C, named locations on Kythera:16, Avlemonas;17, Diakofti;18, Kakia Langada;other named locations: Antikythera, Proti; *, locations not mentioned by name in the text. Credits: Google Earth - Data SIO, NOAA, U.S. Navy, NGA, GEBCO Landsat / Copernicus (A-C).
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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