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1,977 results for “2007”
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P75 (2007): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2007. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P50 (2007): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P50 (median) values of corresponding predictors for the year 2007. The median values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P25 (2007): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P25 (percentile 25) values of corresponding predictors for the year 2007. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Modelled relative abundance of bird species in Britain and Ireland 2007-2011
<p>This data package describes the modelled relative (not absolute) abundance of Carrion Crow (<em>Corvus corone</em>), Magpie (<em>Pica pica</em>), Buzzard (<em>Buteo buteo</em>), Kestrel (<em>Falco tinnunculus</em>) and Red Kite (<em>Milvus milvus</em>) in Britain and Ireland.</p> <p>This was used to produce Bird Atlas 2007-2011 <a href="https://app.bto.org/mapstore/StoreServlet" target="_blank" rel="noopener">maps</a> of relative abundance at a tetrad (2x2km) resolution. </p> <p>Acknowledgement: These data originate from the Bird Atlas 2007–11 project which was run by the BTO in partnership with BirdWatch Ireland and the Scottish Ornithologists’ Club. We are grateful to the thousands of volunteers who undertook and organised the fieldwork for the atlas.</p> <p>Please refer to the metadata for a more detailed description, and for information on dataset usage.</p> <p>v1.3 update: added Red Kite (<em>Milvus milvus</em>) and put the species lookup back in.</p> <p><em>If you would like access to this data for another species, please get in touch with BTO via email: datarequests@bto.org</em></p> <p>........................................................................................</p> <p>BTO would also greatly appreciate if you could fill out <a href="https://forms.gle/DCc58VXpdmqnTmTk8" target="_blank" rel="noopener">this very short form</a> to tell us how you intend to use these data. Thanks in advance!</p>
Stuifzandkaart van Nederland 2007-2018
<p>Deze kaart geeft het landgebruik van zandverstuivingen in Nederland en de veranderingen daarin weer tussen 2007 en 2018. Drie typen landgebruik worden onderscheiden: kaal zand, open terrein, en bos. Als basis zijn de topografische kaarten 1:10000 gebruikt. Deze zijn deels gecorrigeerd aan de hand van luchtfoto’s.</p> <p>Voor een aantal natuurgebieden zijn dronefoto’s en automatische classificatie gebruikt. De deelkaarten van deze gebieden zijn in de kaart gemonteerd, wat soms tot extra details heeft geleid. In beide jaren is hetzelfde detailniveau gebruikt.</p> <p>Het gebruikte beeldmateriaal had in alle gevallen een resolutie van 25 cm. De resolutie van de kaart bedraagt ongeveer een meter. Bij het berekenen van verschillen tussen beide jaren is gewerkt met een tolerantie van 1 m en zijn vlakken kleiner dan 1 m2 samengevoegd met naburige vlakken. Infrastructuur, bebouwing en water zijn weggesneden uit het kaartbeeld.</p> <p>Vlakken zijn verder opgesplitst door het samenvoegen met provinciegrenzen, Natura2000-grenzen en eigendomsgrenzen van terreinbeheerders.</p> <p>In het bestand zijn solitaire bomen soms niet correct weergegeven, maar deze vormen maar een klein deel van het totale oppervlak bos en bomen. Soms zijn solitaire bomen in 2007 niet en in 2018 wel opgenomen, omdat ze in die tijd de vereiste minimale omvang hebben bereikt.</p> <p>Het GIS-bestand is topologisch niet helemaal correct. Sommige vlakken overlappen enkele centimeters, maar dit maakt voor het algehele beeld niet uit.</p>
Milan (ITALY) - Urban Agriculture spatial dataset (years 2007 and 2014)
<p>The data in this dataset is a spatial inventory of <strong>urban agriculture</strong> (UA) carried out in the city of Milan (Italy). UA areas where identified with a multi-step and iterative procedure by using different web-mapping tools, especially multitemporal Google Earth images, and ancillary data such as Google Street View and Bing Maps.</p> <p><strong>License</strong></p> <p>Creative Commons CC-BY</p> <p><strong>Disclaimer</strong></p> <p>Despite our best efforts to validate the data, some information may be incorrect.</p> <p><strong>Description of the dataset</strong></p> <p><em><strong>Typologies of UA</strong></em></p> <ul> <li><strong>Residential garden: </strong>Private parcel near single houses (e.g. backyard), villas, buildings, industrial and commercial activities, generally managed by property owners. Cultivation is diversified ranging from leafy vegetables to herbs and fruit trees. Production is intended for self-consumption and/or for hobby purposes.</li> <li><strong>Community garden: </strong>A large area subdivided into multipleplots managed individually (i.e. allotment) or collectively by a group of people. Crop production is intended for self-consumption. Land is assigned by the Municipality; several cases of land cultivated without authorization are also common.</li> <li><strong>Urban farm: </strong>Parcel managed by professional farmers with an intensive and an advanced cropping system. The cultivation can be specialized or oriented to high diversity vegetables. The production is intended for market. The mapping procedure focus on arable crops, horticulture, vineyard, olive groves and orchard.</li> <li><strong>Institutional garden: </strong>Parcel managed by institutions or organizations like schools, religious center, prisons and non-profit organizations. The production is generally intended for self-consumption and less frequently for trade. Several gardens in this category are intended for social purposes (e.g. recreation,education, etc.).</li> <li><strong>Illegal garden: </strong>Parcel isolated, cultivated without authorization organized and managed individually or by a few people. Localization occurs on unused or abandoned areas owned by public bodies or private subjects. The production is intended for self-consumption.</li> <li><strong>Nurseries: </strong>A large area subdivided into multiple plots managed for growing ornamental plants and flowers.</li> </ul> <p><em><strong>Land use typologies</strong></em></p> <ul> <li><strong>Horticulture: </strong>annual crops generally seed sown in spring or summer (tomatoes, lettuce, zucchini, cucumbers, peppers).</li> <li><strong>Vineyard: </strong>grape vines grown in order to produce wine or table grape.</li> <li><strong>Olive groves: </strong>olive trees grown in order to produce olive oil or table olives.</li> <li><strong>Orchards: </strong>mixed trees such as orange, stone fruit, pome fruit, olive trees.</li> <li><strong>Mixed crops: </strong>an area grown with a mix of horticulture crops and fruit trees, not divisible.</li> <li><strong>Nurseries: </strong>ornamental plants, trees, flowers.</li> </ul> <p><strong>Credit</strong></p> <p>Pulighe G., Lupia F. (2019) <em>Multitemporal Geospatial Evaluation of Urban Agriculture and (Non)-Sustainable Food Self-Provisioning in Milan, Italy. </em><strong>Sustainability </strong>2019, <em>11</em>(7), 1846</p> <p>https://www.mdpi.com/2071-1050/11/7/1846</p>
Forest stages in OAL-Austria (2007)
<p>Forest stages in the upslope contributing area of OAL-Austria, derived from airborne laserscanning data</p> <p>Further details can be found in D4.5 of the OPERANDUM project.</p>
Field-Level California Crop Maps 2007-2021
<p>Field-Level California Crop Maps 2007-2021</p> <p><strong>Abstract</strong></p> <p>Technological advances in satellite image processing have made crop maps readily available over the last decade. Because of the diversity and complexity of crop production in California, however, reliable crop maps for the state are still scant. To fill this gap, we created field-level crop maps of California (hereinafter, Field-Level California Crop Map (FLCCM)) for 2007-2021. We leverage highly accurate ground-truth labels that exist in 2014, 2016, and 2018 to train our crop classifier using probability random forests. We then feed to our classifier the data for predictors that are available from 2007 to 2021. We release three types of crop predictions, and their corresponding accuracy measures in three formats (.csv, .shp, .rds). Our training algorithm can be applied to other settings in which field-level ground-truth data are scarce but fine-resolution pixel-level data are relatively more abundant. </p> <p> </p> <p><strong>Disclamer: </strong>The dataset is in the process of being peer-reviewed. </p>
Wind data (2007-2017) in florentine and chianti areas to support tree's damages reporting.
<p>Wind data of several weather station to support tree damages investigations.</p> <p><strong>Firenze Peretola</strong> Areoporto LIRQ ENAV LAT 43.809722 LON 11.203 ELEV 44</p> <p><strong>Sesto Polo Scientifico</strong> LAMMA-CNR LAT 43.8189 LON 11.2021 ELEV 40</p> <p><strong>Sesto Case Passerini</strong> Codice CFR TOS01001225 LAT 43.82 LON 11.17 ELEV 33</p> <p><strong>Scandicci San Giusto</strong> CFR TOS01001215 LAT 43.76 LON 11.19 ELEV 42</p> <p><strong>Tavarnelle</strong> CFR TOS11000021 LAT 43.57 LON 11.16 ELEV 374</p> <p><strong>Greve in Chianti</strong> CFR TOS11000073 LAT 43.61 LON 11.30 ELEV 254</p> <p>Data sets gives annual and seasonal windplot roses. Wind data summaries by sectors of wind provenience ( Mean, Max,Median and Quantile95). Futher the 500th maximum records of gust are also extracted. Data are provided to support tree damages reporting.</p>
ERA5-Land selected indicators daily aggregates for Africa, 2007
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 2007.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
Australian waste account 2007-2021
<p>This dataset updates the previous release from 2019 to 2021, in line with the <a href="https://www.dcceew.gov.au/environment/protection/waste/national-waste-reports/2022">National Waste Database</a> update. The previous release is available here: https://zenodo.org/records/5646740. This release also incorporates LGA waste generation data where it is available (<a href="https://www.epa.nsw.gov.au/-/media/epa/corporate-site/resources/wastestrategy/23p4660-lg-warr-report-2021-22.pdf">NSW </a>and <a href="https://www.vic.gov.au/victorian-local-government-waste-data-dashboard">Victoria</a>).</p> <p>Updated notes: </p> <p>The National Waste Database (https://www.awe.gov.au/environment/protection/waste/national-waste-reports/2020) is a repository for Australia's solid waste data. This collection of waste data is useful however has some issues: The timeseries is not complete, as some years are missing. Allocation to industries is very coarse, there are only 3 waste generating entities: construction and demolition, commercial and industrial, and municipal (households). Further, not all reporting regions (States and Territories) provide data at the same resolution of material type.</p> <p>We have created an open source dataset in an attempt to solve some of these issues. Missing years are filled using linear interpolation. The regional resolution is disaggregated to SA2 regions using the ABS Business Register (https://www.abs.gov.au/Ausstats/abs@.nsf/0/49658AFA6CC395CECA2583A700121A41). Municipal (households) waste is split into SA2s from state totals using population. The ABS Waste Account is used to establish a relationship between waste types and generating sectors (https://www.abs.gov.au/statistics/environment/environmental-management/waste-account-australia-experimental-estimates/latest-release).</p> <p>The data is published as labelled flat files (.csv). The dataset dimensions are:</p> <p>- years: 2007 - 2021,</p> <p>- regions: 2310 SA2 (2016) ASGS regions,</p> <p>- entities: 116 generating entities; 115 SUPG (supply-use product group) industries + 1 households,</p> <p>- waste_types: 69 waste material types,</p> <p>- treatments: 5 waste treatment methods</p> <p>This dataset is made available under a Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/</p>
Trento 1936 - Building 2007
<u>Coordinates</u>: N/A <br><u>Length</u>: 25.71 m<br><u>Width</u>: 8.7 m<br><u>Height</u>: 7.05 m<br><u>Points</u>: 12 <br><u>Vertices</u>: 60 <br><u>Primitives</u>: 20 <br><br><u>Main Files:</u><br><table><tbody><tr><th>Filename</th><th>.glb</th><th>.xml</th><th>.obj</th></tr><tr><td><a href="https://zenodo.org/api/records/12694502/files/building_2007.obj/content">building_2007.obj</a></td><td></td><td></td><td><a href="https://zenodo.org/api/records/12694502/files/building_2007.obj/content">Link</a></td></tr><tr><td><a href="https://zenodo.org/api/records/12694502/files/building_2007.glb/content">building_2007.glb</a></td><td><a href="https://zenodo.org/api/records/12694502/files/building_2007.glb/content">Link</a></td><td></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12694502/files/11577884_metsmods.xml/content">11577884_metsmods.xml</a></td><td></td><td><a href="https://zenodo.org/api/records/12694502/files/11577884_metsmods.xml/content">Link</a></td><td></td></tr><tr><td><a href="https://zenodo.org/api/records/12694502/files/11577884_edm.xml/content">11577884_edm.xml</a></td><td></td><td><a href="https://zenodo.org/api/records/12694502/files/11577884_edm.xml/content">Link</a></td><td></td></tr></tbody></table><br><br><u>Thumbnails:</u><br><table><tbody><tr><th>Perspective</th><th>1000x1000</th><th>512x512</th><th>256x256</th><th>128x128</th></tr><tr><td>Perspective 1</td><td><a href="https://zenodo.org/api/records/12694502/files/building_2007_perspective_1.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694502/files/building_2007_perspective_1_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694502/files/building_2007_perspective_1_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694502/files/building_2007_perspective_1_128x128.png/content">Link</a></td></tr><tr><td>Perspective 2</td><td><a href="https://zenodo.org/api/records/12694502/files/building_2007_perspective_2.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694502/files/building_2007_perspective_2_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694502/files/building_2007_perspective_2_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694502/files/building_2007_perspective_2_128x128.png/content">Link</a></td></tr><tr><td>Perspective 3</td><td><a href="https://zenodo.org/api/records/12694502/files/building_2007_perspective_3.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694502/files/building_2007_perspective_3_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694502/files/building_2007_perspective_3_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694502/files/building_2007_perspective_3_128x128.png/content">Link</a></td></tr><tr><td>Perspective 4</td><td><a href="https://zenodo.org/api/records/12694502/files/building_2007_perspective_4.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694502/files/building_2007_perspective_4_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694502/files/building_2007_perspective_4_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694502/files/building_2007_perspective_4_128x128.png/content">Link</a></td></tr><tr><td>Perspective Top</td><td><a href="https://zenodo.org/api/records/12694502/files/building_2007_perspective_top.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694502/files/building_2007_perspective_top_512x512.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694502/files/building_2007_perspective_top_256x256.png/content">Link</a></td><td><a href="https://zenodo.org/api/records/12694502/files/building_2007_perspective_top_128x128.png/content">Link</a></td></tr></tbody></table><br><br><br><u>Changelog</u>: <br> - v<a href="https://doi.org/10.5281/zenodo.12549091">0.0.2</a>: Thumbnails added, Description updated with Link Tables.<br> - v<a href="https://doi.org/10.5281/zenodo.12694502">0.0.3</a>: Added XMLs for Europeana Data Model (EDM) and MetsMods.<br>
CLDF dataset derived from Allen's "Bai Dialect Survey" from 2007
<p>Cite the source of the dataset as:</p> <blockquote> <p>Allen, Bryan (2007): Bai Dialect Survey. Dallas: SIL International.</p> </blockquote>
Territorial land use data relative to Pesa Basin ( Tuscany) and its recent changes ( 2016- 2007)
<p>Data and elaborations are provided by IBIMET CNR , Accademia Georgofili and <a href="http://www.cbmv.it/">Consorzio di Bonifica 3 Medio Valdarno. </a></p>
Audiovisual Vignettes of Sea Ice Ridging in the Beaufort Sea in 2007
<p>This presents footage demonstrating the scales of sea ice motion involved in creating ridges under varied degrees of compression and shear. Sound heard in these vignettes is associated with frictional dissipation of kinetic energy during vertical ice displacement. Images shown were recorded during April 2-15, 2007 UTC, as part of the field campaign: Sea Ice Experiment - Dynamic Nature of the Arctic (SEDNA). Footage and photographs presented in this vignette were taken by Andrew Roberts with the assistance of Jennifer Hutchings and Cathleen Geiger. Funding for SEDNA was provided by the National Science Foundation, grant number OPP ARC 0612527. An overview of the SEDNA field campaign is given in: Hutchings, J. K. et al. (2008), Role of Ice Dynamics in the Sea Ice Mass Balance, <em>Eos Trans. AGU</em>, <em>89</em>(50), doi:10.1029/2008EO500003. </p> <p>[Version 2 includes minor corrections and additions to text in Version 1]</p>
Anastasia Mosquito Control District entomological monitoring 2007
<p>Mosquito surveillance from the Anastasia Mosquito Control District Vector Surveillance program to survey mosquito populations.</p>
INEGI Uso del Suelo y Vegetacion Land Cover Classifications for Mexico (1985, 1993, 2002, 2007, 2011), Harmonized with NLCD 2011 Legend
<p>We have taken the Uso del Suelo y Vegetacion land cover classification products for Mexico (courtesy of Mexico's Instituto Nacional de Estadistica y Geografia, or INEGI) for years 1985, 1993, 2002, 2007, and 2011 (INEGI, 2015); and harmonized their classes with the classes of the Multi-Resolution Land Characteristics Consortium (MRLC) National Land Cover Database (NLCD) (Homer et al., 2015). Details of processing, along with the processing scripts, are archived in GitHub in the <a href="https://github.com/tbohn/NLCD_INEGI/tree/v1.5">NLCD_INEGI</a> project (Bohn, 2019).</p> <p>This project contains the following g-zipped tar files:</p> <ul> <li>SERIE_I.tgz - land cover from 1985</li> <li>SERIE_II.tgz - land cover from 1993</li> <li>SERIE_III.tgz - land cover from 2002</li> <li>SERIE_IV.tgz - land cover from 2007</li> <li>SERIE_V.tgz - land cover from 2011</li> </ul> <p>On LINUX, the contents of these files can be extracted via "tar":</p> <p>tar -xvzf SERIE_I.tgz >& log.tar.txt</p> <p>On Windows, applications such as "7-zip" can extract the contents.</p> <p>Each of these .tgz files contain a folder with the same name but without the ".tgz". Within each of these folders are the following sub-folders:</p> <ul> <li>For SERIE_I to SERIE_IV: <ul> <li>metatiles/ - original land cover shapefiles, with Mexico divided into "metatiles" along UTM zones, as documented in <a href="https://github.com/tbohn/NLCD_INEGI/tree/v1.0/docs/Processing_of_INEGI_USOSV_dataset.docx">Processing_of_INEGI_USOSV_dataset.docx</a></li> <li>geo/ - shapefiles from "metatiles", reprojected into geographic</li> <li>entire/ - shapefiles from "geo" merged into a single file for the entire country</li> </ul> </li> <li>For SERIE_V: <ul> <li>entire/ - original land cover shapefile in Lambert Conical projection, covering all of Mexico</li> <li>geo/ - shapefile from "entire" reprojected into geographic</li> </ul> </li> <li>SERIE_I to SERIE_V: <ul> <li>cve_union/ - shapefiles covering all of Mexico, in geographic projection, with land cover reclassified to NLCD 2011 legend</li> <li>rasters/ - files from "cve_union", rasterized at 0.000350884 degree resolution</li> <li>ascii/ - raster files from "rasters", exported to ascii ESRI grid file format</li> </ul> </li> </ul> <p>Output files (in the "ascii" folders) are ESRI ascii raster grid files, in geographic projection, with cellsize = 0.000350884 degrees.</p>
FADN data on the support under the CAP delimited for LAU2 (NUTS2) regions in the EU Member States for the 2007-2013 programming period
<p> </p> <p>Ready to use FADN dataset on the support under the CAP in 2007-2013 delimited for LAU2 (NUTS2) regions in the EU Member States.</p> <p>Investigation of the interaction between Cohesion and Rural Policies requires analysing comparable data. However, the CAP data are usually collected at the national level. The FADN database is the only data source for analysing the impact of agricultural policy instruments on the economic situation of farms. However, the regional breakdown of FADN data in some countries does not correspond to the NUTS2 breakdown for which cohesion policy is defined.</p> <p>The provided FADN data delimitation uses a methodology that takes into account the range of impact and features specific to a given region. Because the research shows a very strong relationship between the amount of support under the CAP and the number and size of farms on a given area, this criterion was used to delimit FADN data for particular LAU2 (NUTS2) regions, while maintaining the allocation to individual measures.</p> <p>FADN data aggregated (averaged) to the level of FADN regions and economic size classes were used. Each FADN region has been assigned a corresponding NUTS2 region (or regions) according to the classification in 2010 in which the full census of the farm structure survey was carried out. The delimitation of FADN data to NUTS2 regions was based on weights constructed on the basis of Eurostat data on utilised agricultural area and number of holdings in 2010. In each economic size class, each FADN region consisted of the sum of the NUTS2 regions weighted by the utilised agricultural area. The result of each FADN variable was the sum of its values in each economic size class, weighted by the total number of holdings in each class.</p> <p>This database has served as a basis for two articles, one validating the assumptions of the NUTS2 (LAU) delimitation of the FADN regions and the other using the database to compare synergies and trade-offs between cohesion policy and the common agricultural policy.</p>
Maps related to the detection of abrupt changes in NDVI approximated phenological cycles of Donana marshes for 2007-2016
<p>Monitoring of abrupt changes among annual vegetation cycles of consequent years in Protected Areas is valuable for the recognition of patterns, which represent the reaction of the biomes to external factors, such as changes in the meteorological conditions (e.g. the precipitation regime), human intervention or extreme events (e.g. fire). It is an indicator of the primary production of the area and other relevant functions of the ecosystem. The BFAST, Breaks For Additive Seasonal and Trend, approach can be used for monitoring changes, since it is globally applicable and able to analyze each pixel individually without the need to set thresholds for detecting changes within time series. Thus, BFAST is applied for the detection of abrupt trend changes in NDVI time series in the case of Doñana marshes, as a proxy to phenological metrics per pixel.</p> <p>BFAST outputs are used to generate: (i) a raster with the time of all detected abrupt changes per pixel (filename: “All_break_times_2007_to_2016.tif”), (ii) a raster with the total number of detected abrupt changes per pixel (filename: “Marshes_maximum_number_of_breaks_2007_to_2016.tif”), (iv) a raster with the time for which the biggest change is detected per pixel has the (filename: “Marshes_maximum_break_time_2007_to_2016.tif”).</p> <p>The above files are accompanied by INSPIRE metadata XML files. Detailed information can be found in the “Readme.docx” included in the zip containing the dataset.</p>
Terrestrial water storage changes across the contiguous United States from GPS and GRACE, 2007–2017
<p>In this dataset, we provide terrestrial water storage anomalies (TWSA) from 2007-2017 at weekly time scales derived using Global Positioning System (GPS) displacements, further constrained by lower-resolution TWSA observations from the Gravity Recovery and Climate Experiment (GRACE).</p> <p>There are six fields in the HDF5 product provided here:</p> <ol> <li>'/cmwe', which provides terrestrial water storage in units of cm. of water equivalent.</li> <li>'/latitude', latitude at the center of each 0.5 degree grid cell</li> <li>/longitude', longitude at the center of each 0.5 degree grid cell</li> <li>'/time', time in days since January 1st, 2007. The resolution of our time series is weekly, and the first day in our record is January 3rd, 2007.</li> <li>'/signal_to_noise_ratio', the variance of the signal divided by variance of noise for each grid cell in the dataset. Please read the supplementary information document in the paper below for more details.</li> <li>'/uncertainty', 95% confidence interval for each grid cell in the dataset. Please read the supplementary information document in the paper below for more details.</li> </ol> <p>As a condition of using these data, we request that you acknowledge the authors of this data set by citing the following peer-reviewed publication. </p> <p>Adusumilli, S., Borsa, A. A., Fish, M. A., McMillan, H. K., & Silverii, F. (2019). A decade of water storage changes across the contiguous United States from GPS and Satellite Gravity. <em>Geophysical Research Letters</em>, 46, 13006-13015. <a href="https://doi.org/10.1029/2019GL085370">https://doi.org/10.1029/2019GL085370</a></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.