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1,425 results for “Agriculture”
Agriculture - General: Animal Production and Health 4
<p>This dataset contains the digitized treatments in Plazi based on the original journal article Yus-Ramos, Rafael, Ventura, Daniel, Bensusan, Keith, Coello-García, Pedro, György, Zoltán, Stojanova, Anelia (2014): Alien seed beetles (Coleoptera: Chrysomelidae: Bruchinae) in Europe. Zootaxa 3826 (3): 401-448, DOI: <a href="http://dx.doi.org/10.11646/">http://dx.doi.org/10.11646/</a>...</p> <p>Stojanova A (2013). Alien seed beetles (Coleoptera: Chrysomelidae: Bruchinae) in Europe. Plazi.org taxonomic treatments database. Checklist dataset <a href="https://doi.org/10.11646/zoot">https://doi.org/10.11646/zoot</a>... accessed via GBIF.org</p>
Agriculture - General
<p>CABI (Centre for Agriculture and Biosciences International). CABI Bioscience Nematode Collection. Occurrence dataset <a href="https://doi.org/10.15468/bdayzp">https://doi.org/10.15468/bdayzp</a> accessed via GBIF.org</p>
Agriculture - General 2
<p>CABI (Centre for Agriculture and Biosciences International). CABI Bioscience Genetic Resource Collection. Occurrence dataset <a href="https://doi.org/10.15468/yr757j">https://doi.org/10.15468/yr757j</a> accessed via GBIF.org</p>
Agriculture - Forestry 5
<p>Over the last 5 years several REDD+ pilot projects were implemented in Tanzania to generate information on forest carbon stocks to contribute to the REDD+ strategy. The generated information constitutes a good source of important biodiversity data that can be used in conservation and sustainable development. Lyimo P, Munishi P, Gideon H (2018). Tree species Occurrence Data of Coastal Miombo in Western Tanzania.. Version 1.6. Sokoine University of Agriculture, Department of Ecosystems and Conservation. Occurrence dataset <a href="https://doi.org/10.15468/nyhbez">https://doi.org/10.15468/nyhbez</a> accessed via GBIF.org</p>
Agriculture - General 3
<p>CABI (Centre for Agriculture and Biosciences International). CABI Bioscience Fungus Collection. Occurrence dataset <a href="https://doi.org/10.15468/c9vdlf">https://doi.org/10.15468/c9vdlf</a> accessed via GBIF.org</p>
Agriculture - Forestry 3
<p>Miombo is the vernacular word for Brachystegia, a genus of tree comprising a large number of tree species together with Julbernadia species in woodlands. Miombo woodland is classified in the tropical and subtropical grasslands, savannas, and shrublands biome. The biome includes four woodland savanna ecoregions characterized by the predominant presence of miombo species, with a range of climates from humid to semi-arid, and tropical to subtropical or even temperate. Lyimo P, Munishi P (2018). A Checklist of Tree Species in the Miombo Woodlands of Western Tanzania. Version 1.1. Sokoine University of Agriculture, Department of Ecosystems and Conservation. Checklist dataset <a href="https://doi.org/10.15468/zofuvf">https://doi.org/10.15468/zofuvf</a> accessed via GBIF.org</p>
Agriculture - General: Animal Production and Health 8
<p>1992, invertebrate fauna inventory on peat swamps in The Netherlands using pyramidtraps Siepel H, Dimmers W (2016). Alterra (NL) - Entomofauna inventory in peat swamps. Version 1.1. Alterra, Wageningen UR. Occurrence dataset <a href="https://doi.org/10.15468/ibom6z">https://doi.org/10.15468/ibom6z</a> accessed via GBIF.org</p>
Agriculture - General: Animal Production and Health, Natural Resources and Environment 5
<p>2001 till 2002 and 2004, inventory of entomofauna in forestwalls banks, diches, road verges in The Netherlands using pitfalls and sweeping net</p> <p>Jagers op Akkerhuis G, Dimmers W (2016). Alterra (NL) - Comparison of entomofauna in four different habitats. Version 1.1. Alterra, Wageningen UR. Occurrence dataset <a href="https://doi.org/10.15468/mkoqqh">https://doi.org/10.15468/mkoqqh</a> accessed via GBIF.org</p>
Agriculture - General: Animal Production and Health, Natural Resources and Environment 6
<p>2007, 2011 and 2012, microarthropod fauna inventory in a nature restauration experiment (re-introduction) on a calcareous grassland and three reverence sites in the province of Limburg using pF-cores</p> <p>Smits N, Dimmers W (2016). Alterra (NL) - Microarthropods inventory in calcareous grasslands. Version 1.1. Alterra, Wageningen UR. Occurrence dataset <a href="https://doi.org/10.15468/28jocn">https://doi.org/10.15468/28jocn</a> accessed via GBIF.org</p>
Agriculture - General: Animal Production and Health, Natural Resources and Environment 4
<p>1993 till 2001, entomofauna inventory in cattle grazed versus non-grazed dune grassland using pitfalls</p> <p>van Wingerden W, Dimmers W (2016). Alterra (NL) - Entomofauna inventory in cattle grazed dune grassland. Version 2.1. Alterra, Wageningen UR. Occurrence dataset <a href="https://doi.org/10.15468/zp5oif">https://doi.org/10.15468/zp5oif</a> accessed via GBIF.org</p>
Agriculture - General: Animal Production and Health, Natural Resources and Environment 8
<p>2000-2003, microarthropod fauna inventory of arable land and grassland on sandy soil using pF-cores. Points of interest are biodiversity, nutrients and disease suppression</p> <p>Faber J, Dimmers W (2016). Alterra (NL) - Microarthropods inventory in grassland and arable land. Version 1.1. Alterra, Wageningen UR. Occurrence dataset <a href="https://doi.org/10.15468/fwvi7m">https://doi.org/10.15468/fwvi7m</a> accessed via GBIF.org</p>
Agriculture - General: biological diversity 2
<p>The records in this dataset are general records of different taxonomic groups submitted to the National Biodiversity Data Centre. This provides a temporary facility to store and make available data submitted to the Centre, until such time as subsets of the data can be added to a recognised national database. National Biodiversity Data Centre (2016). General Biodiversity Records from Ireland. Occurrence dataset <a href="https://doi.org/10.15468/w8q1jm">https://doi.org/10.15468/w8q1jm</a> accessed via GBIF.org</p>
Agriculture - Forestry 4
<p>The goal of the project was to compile metadata of biodiversity data generated from REDD+ Pilot Projects in Tanzania. Specifically to develop data collection tool to capture the required data, inventory of REDD+ data holders and the type of data they hold, convene IPT-training workshop for holders of biodiversity data from REDD+ project to enhance publishing. Lyimo P, Munishi P, Gideon H (2018). The Occurrence Data of Tree species for Coastal Forest of Tanzania.. Version 1.5. Sokoine University of Agriculture, Department of Ecosystems and Conservation. Occurrence dataset <a href="https://doi.org/10.15468/3v0exk">https://doi.org/10.15468/3v0exk</a> accessed via GBIF.org</p>
Agriculture - General: biological diversity 5
<p>Dataset of Invasive species - initial compilation of aquatic invasive species National Biodiversity Data Centre (2016). National Invasive Species Database. Occurrence dataset <a href="https://doi.org/10.15468/pkjqbk">https://doi.org/10.15468/pkjqbk</a> accessed via GBIF.org</p>
Global Soil Moisture Agricultural Drought Index (SMADI)
<p><br> This repository contains the global Soil Moisture Agricultural Drought Index (SMADI) spanning from June 2010 to December 2015 at fortnightly (bi-weekly) rate. The product is gridded in a 0.05deg Climate Modeling Grid.<br> <br> The data are provided in Matlab format (.mat). Each file is a 3600x7200 matrix size, where 3600 is the number of pixels in Latitude coordinates and 7200 to Longitude coordinates, both in the WGS84 system. The center-pixel coordinates are variables "Latitude" and "Longitude". No data values (NaN) correspond to pixels where the retrieval was not possible.<br> <br> <br> SMADI is computed using satellite time series of MODIS Normalized Difference Vegetation Index (NDVI, computed from daily reflectances MOD09CMG v.6), MODIS Land Surface Temperature (LST, product MOD11C1 v.6), and SMOS BEC L3 soil moisture data v2.0 (which corresponds to SMOS L2 v.620, average of ascending and descending orbits). <br> <br> The IGBP land cover (MODIS MOD12C1 product) is used prior to SMADI calculation to select only grassland and cropland/natural vegetation mosaic pixels as representative of the primary agro-ecosystems.</p>
ArcGIS Map Packages and GIS Data for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al. (2019)
<p><strong>ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019)</strong></p> <p>**When using the GIS data included in these map packages, please cite all of the following:</p> <blockquote> <p>Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. <a href="http://doi.org/10.1371/journal.pone.0220457">http://doi.org/10.1371/journal.pone.0220457</a></p> <p>Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. <a href="https://doi.org/10.5281/zenodo.2572018">https://doi.org/10.5281/zenodo.2572018</a></p> </blockquote> <p><strong>OVERVIEW OF CONTENTS</strong></p> <p>This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are:</p> <ul> <li>Raw DEM and Soils data <ul> <li>Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the <a href="https://viewer.nationalmap.gov/basic/">National Elevation Dataset</a>) <ul> <li><strong>DEM_Individual_Tiles</strong>: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.</li> <li><strong>DEMs_Merged</strong>: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM. </li> </ul> </li> <li> Soils Data (Map services and data available from <a href="https://data.nal.usda.gov/dataset/natural-resources-conservation-service-web-soil-survey">Natural Resources Conservation Service Web Soil Survey</a>, U.S. Department of Agriculture) <ul> <li><strong>Animas-Dolores_Area_Soils</strong>: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).</li> <li><strong>Cortez_Area_Soils</strong>: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).</li> </ul> </li> </ul> </li> <li>ArcGIS Map Packages <ul> <li><strong>Goodman_Watershed_Full_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).</li> <li><strong>Goodman_Watershed_Mesa-Only_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.</li> <li><strong>Crow_Canyon_Watershed_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).</li> <li><strong>Coffey_Watershed_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).</li> </ul> </li> </ul> <p>For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties."</p> <p><strong>LICENSES</strong></p> <p>Code: <a href="http://opensource.org/licenses/MIT">MIT</a> year: 2019 <br> Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton</p> <p><strong>CONTACT</strong></p> <p><strong>Andrew Gillreath-Brown, PhD Candidate, RPA</strong><br> <a href="https://anthro.wsu.edu/">Department of Anthropology</a>, Washington State University<br> <a href="mailto:andrew.brown1234@gmail.com">andrew.brown1234@gmail.com</a> – Email<br> <a href="https://andrewgillreathbrown.wordpress.com/">andrewgillreathbrown.wordpress.com</a> – Web</p>
Map of 2016 agricultural crops in Camargue
<p>Agricultural landscape mapping (with focus on wheat and rice fields) based on a classification using random forest algorithm with field data and zonal statistics from Sentinel 1 and 2 as inputs.</p>
Map of 2018 agricultural crops in Camargue
<p>Agricultural landscape mapping (with focus on wheat and rice fields) based on a classification using random forest algorithm with field data and zonal statistics from Sentinel 1 and 2 as inputs.</p>
Map of 2017 agricultural crops in Camargue
<p>Agricultural landscape mapping (with focus on wheat and rice fields) based on a classification using random forest algorithm with field data and zonal statistics from Sentinel 1 and 2 as inputs.</p>
Data and Code for: Multiscale habitat mediates pest reduction by birds in an intensive agricultural region
<p>Associated data and analyses code for<em> </em>the publication:</p> <p><strong>Heath, Sacha K. and R. F. Long. 2019. Multiscale habitat mediates pest reduction by birds in an intensive agricultural region. Ecosphere 10(10):ecs2.2884. DOI: 10.1002/ecs2.2884</strong></p> <p>The home directory folder <em>Heath_Long_2019_data_code</em> contains a metadata.txt file describing entire contents and an Rstudio Project (<em>Heath_Long_2019_data_code</em>)<em> </em>comprised of four Rstudio Notebooks. Each notebook refers to data and output files from its associated folder(s):<br> <em>./appendix_s1_tabs2_tabs4.Rmd<br> ./pca_data/ <br> ./bird_analyses.Rmd<br> ./bird_data/<br> ./predation_analyses.Rmd<br> ./predation_data/<br> ./predation_data/predation_models/<br> ./predation_analyses_nocage.Rmd <br> ./predation_data/<br> . /predation_data/uncaged_predation_models/</em></p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.