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13,117 results for “Maps”
Ouagadougou land use map at street block level
<p>This datatset contains a land use classification of Ouagadougou (Burkina Faso) at the street block level. It was created following the methodology presented in [1].</p> <p>Description of the files:</p> <ul> <li>"Ouagadougou_landuse.gpkg" : GeoPackage with two layers: (1) layer of the street blocks extracted from OpenStreetMap using [2] with classification results in the attribute table. (2) layer with manual correction made by GEORGANOS Stefanos (sgeorganos@ulb.ac.be).</li> <li>"Ouagadougou_landuse_style_QGIS.zip" : Files for style for rendering in QGIS.</li> </ul> <p>Attribute table content:</p> <ul> <li>"CAT", "GID" : ID of the street block</li> <li>"PROB_ACS" : Probability to belong to class ACS</li> <li>"PROB_BARE" : Probability to belong to class BARE</li> <li>"PROB_PLAN" : Probability to belong to class PLAN</li> <li>"PROB_UNPLA" : Probability to belong to class UNPLAN</li> <li>"PROB_VEG" : Probability to belong to class VEG</li> <li>"FIRST_LABE" : Class with the highest classification probability</li> <li>"SEC_LABEL" : Class with the second highest classification probability</li> <li>"FIRST_PROB" : Value of the highest classification probability</li> <li>"SEC_PROB" : Value of the second highest classification probability</li> <li>"UNCERTAIN" : Difference between "FIRST_PROB" and "SEC_PROB"</li> <li>"BUILT_PERC" : Percentage of the street blocks covered by built-up (from land cover map)</li> <li>"MAP_LABEL" : Final classification label with uncertainty and different density classes. Depending on the layer, the label is with or without manual corrections</li> </ul> <p>Legend classes label:</p> <ul> <li>"VEG" : Vegetation</li> <li>"BARE" : Bare soils</li> <li>"ACS" : Non-residential built-up (administrative, commercial, services, etc.)</li> <li>"PLAN" : Planned residential built-up</li> <li>"PLAN_LD" : Planned residential low density built-up</li> <li>"UNPLAN" : Unplanned residential built-up</li> <li>"UNPLAN_LD" : Unplanned residential low density built-up</li> <li>"UNCERT" : Uncertain classification</li> <li>"WET" : Wetlands</li> <li>"AGRI" : Agricultural land</li> </ul> <p>References:</p> <p>[1] Grippa, Tais, 2018, "Mapping urban land use at street block level using OpenStreetMap, remote sensing data and spatial metrics", <em>ISPRS Int. J. Geo-Inf.</em> <strong>2018</strong>, <em>7</em>(7), 246. <a href="https://doi.org/10.3390/ijgi7070246">https://doi.org/10.3390/ijgi7070246</a> </p> <p>[2] Grippa, Tais. 2018. “Osm Street Blocks Extraction.” Zenodo. <a href="https://doi.org/10.5281/zenodo.1290637">https://doi.org/10.5281/zenodo.1290637</a>.</p> <p>Funding: </p> <p>This dataset was produced in the frame of two research project : MAUPP (<a href="http://maupp.ulb.ac.be">http://maupp.ulb.ac.be</a>) and REACT (<a href="http://react.ulb.be">http://react.ulb.be</a>), funded by the Belgian Federal Science Policy Office (<a href="http://eo.belspo.be/About/Stereo3.aspx">BELSPO</a>).</p>
Unpacking the concept of "educators' data literacy in Higher Education" - Systematic Review of the literature and Keyword Map
<p>As algorithmic decision-making and data collection become pervasive within higher education, how can educators make sense of the systems that shape life and learning in the 21st century? Through a systematic review of the literature, the paper investigates the gaps in the literature, which prevent the formulation of potential pathways and principles on which educators’ data literacy can - and should - be developed and fostered. The analysis of 137 papers through the methods of classification under relevant categories, and key words mapping, showed that there is little attention on HE teachers, and most approaches to educators’ data literacy address management and technical abilities for data processing, with less concern on critical, ethical and personal approaches to datafication in education.</p> <p>The present dataset shows the full list of articles analysed.</p> <p>The dataset, and ods file, is composed by the following sheets:</p> <ol> <li>Codebook</li> <li>List of articles extracted from SCOPUS</li> <li>List of articles extracted from WOS</li> <li>List of articles extracted from ERIC</li> <li>List of articles extracted from DOAJ</li> <li>Interrater Agreement</li> <li>PRISMA workflow</li> <li>Analysis - First Level (classification of 137 articles selected)</li> <li>Analysis - Second Level (List of articles relating faculty development)</li> <li>Supplementary tables (counting articles in relation to the categories of analysis).</li> </ol> <p>As for the Keywords' Map, a second file .csv displays the text over which basis was performed the keyword maps analysis. A .txt file shows notes relating the analysis procedures using the software VOS-Viewer <a href="http://www.vosviewer.com/">http://www.vosviewer.com/</a></p> <p> </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>
Redistribution of the map with the ecoregions of Flanders (version 2002)
<p>This is a redistribution of the data source '<a href="http://www.geopunt.be/catalogus/datasetfolder/5/58279e22-b9c0-4bcc-a774-95eb76208e7e">Ecodistricten en Ecoregio's</a>', originally published by the Research Institute for Nature and Forest (INBO) and distributed by 'Informatie Vlaanderen' under a CC-BY compatible license. It is redistributed for reproducible, analytical workflows on Flemish Natura 2000 habitats and regionally important biotopes.</p> <p>A first version of the ecodistricts of the Flemish region was made by the Institute of Nature Conservation and included in the ‘Flemish Structure Plan, part Open Space (1993)’ (Structuurplan Vlaanderen, deelfacet Open Ruimte). The former division in ecodistricts was partly based on the partition in ‘traditional landscapes’. Ecodistricts were defined as spatial entities, homogeneous with respect to abiotic characteristics which are slowly changing in time, as there are geology-lithology, soil, geomorphology-relief, (geo)hydrology. However, a well defined hierarchy linked to a policy level as well as to an ecological level was missing. Moreover, the internal heterogeneity in certain ecodistricts was relatively high and no unambiguous method was used to define the boundaries of the ecodistricts. For the delimitation of the new ecodistricts in Flanders in 2002 the following abiotic components were considered (in hierarchical order): climatology, geology, relief, geomorphology, geohydrology, hydrology of surface water, soil. Ecodistricts were defined on the basis of their homogeneity for most of these abiotic components. On a higher hierarchical level, ecodistricts were grouped into ecoregions, based on the similarity of their geological and geomorphological properties. The 36 ecodistricts were grouped into 12 ecoregions. The data source was produced as part of action 134 of the Flemish environmental policy plan for the period 1997-2001 and is managed by the Research Institute for Nature and Forest.</p> <p>The dataset is a shapefile of geospatial polygons that describe the ecoregions of the Flemish region, identical to the shapefile <code>hb_ecoreg_inbo</code> in the original data source.</p>
Redistribution of the shapefile of the digital soil map of the Flemish Region (status 2017-06-20)
<p>This is a redistribution of the shapefile of the '<a href="https://www.dov.vlaanderen.be/geonetwork/srv/dut/catalog.search#/metadata/5c129f2d-4498-4bc3-8860-01cb2d513f8f">Digitale bodemkaart van het Vlaams Gewest: bodemtypes, substraten, fasen en varianten van het moedermateriaal en de profielontwikkeling</a>' (digital soil map of the Flemish Region: soil types, substrates, phases, variants of the parent material, and profile development), originally published by 'Databank Ondergrond Vlaanderen’ (Subsurface Database of Flemish Region, DOV) under a CC-BY compatible license. Its shapefile has been redistributed as the <code>soilmap</code> data source, used in reproducible, analytical workflows on Flemish Natura 2000 habitats and regionally important biotopes. These workflows rely on a stable, clean (datafile-only) and uniform representation of each data source version, represented by a Zenodo DOI.</p> <p>The Belgian soil map was drawn up by intensive soil mapping from the 1950s to 1970s (Dudal et al., 2005). The map is based on the Belgian soil classification system. It is a national system that was set up exclusively for Belgian soils. The digital soil map of the Flemish Region is documented by Van Ranst & Sys (2000). Each spatial polygon, accurately digitized from the soil map sheets (published at map scale 1:20000) at scale 1:5000, includes information on soil types, substrates, phases, variants of the parent material and profile development. If available, the general characteristics and photos of a representative soil profile and environment can be obtained for each soil type. Finally, for each location it is possible to call in a scan of the analog soil map sheet, the corresponding explanation booklet and the basic maps on a 1: 5000 scale. The digital soil map was updated in 2017 with information from several military domains, a uniform soil type was generated for the polder area, and several mistakes were corrected.</p> <p>The data source is owned by ‘Vlaams Planbureau voor Omgeving’ (Flemish Planning Bureau for Environment, Department of Environment of the Flemish government).</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>
Inundation maps of Donana for 23 dates within the period 2015/12/19 to 2017/08/20 and their accompanying INSPIRE metadata XML files
<p>Satellite-derived inundation maps offer an efficient solution for monitoring the spatial and temporal variability of the hydrological cycle of wetlands. This task is important for taking mitigation actions against factors (e.g. climate change and human pressures) threatening wetlands' functions and services.</p> <p>Inundation maps within the period 2015/12/19 to 2017/08/20 were generated for Donana based on the methodology presented in "Kordelas, G.A.; Manakos, I.; Aragonés, D.; Díaz-Delgado, R.; Bustamante, J. Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data. <em>Remote Sens.</em> <strong>2018</strong>, <em>10</em>, 910.".</p> <p>Each inundation map is named as " 'Date'_inundation_map_Donana_S2.tif ", and contains the following classes: Inundated Class, Non-inundated Class. In this map, Inundated and Non-inundated Classes are denoted with 0 and 1, respectively. 'Date' is in the form YYYY_MM_DD.</p>
Inundation maps of Danube Delta for 10 dates within the period 2016/10/05 to 2017/08/01 and their accompanying INSPIRE metadata XML files
<p>Satellite-derived inundation maps offer an efficient solution for monitoring the spatial and temporal variability of the hydrological cycle of wetlands. This task is important for taking mitigation actions against factors (e.g. climate change and human pressures) threatening wetlands' functions and services.</p> <p>Inundation maps within the period 2016/10/05 to 2017/08/01 were generated for Danube Delta based on the methodology presented in "Kordelas, G.A.; Manakos, I.; Aragonés, D.; Díaz-Delgado, R.; Bustamante, J. Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data. <em>Remote Sens.</em> <strong>2018</strong>, <em>10</em>, 910.".</p> <p>Each inundation map is named as " 'Date'_inundation_map_Danube_Delta_S2.tif ", and contains the following classes: Inundated Class, Non-inundated Class. In this map, Inundated and Non-inundated Classes are denoted with 0 and 1, respectively. The regions, which are manually denoted as affected by clouds, are denoted with 2. 'Date' is in the form YYYY_MM_DD.</p>
Inundation maps of Camargue for 47 dates within the period 2016/02/09 to 2018/06/19 and their accompanying INSPIRE metadata XML files
<p>Satellite-derived inundation maps offer an efficient solution for monitoring the spatial and temporal variability of the hydrological cycle of wetlands. This task is important for taking mitigation actions against factors (e.g. climate change and human pressures) threatening wetlands' functions and services.</p> <p>Inundation maps within the period 2016/02/09 to 2018/06/19 were generated for Camargue based on the methodology presented in "Kordelas, G.A.; Manakos, I.; Aragonés, D.; Díaz-Delgado, R.; Bustamante, J. Fast and Automatic Data-Driven Thresholding for Inundation Mapping with Sentinel-2 Data. <em>Remote Sens.</em> <strong>2018</strong>, <em>10</em>, 910.".</p> <p>Each inundation map is named as " 'Date'_inundation_map_Camargue_S2.tif ", and contains the following classes: Inundated Class, Non-inundated Class. In this map, Inundated and Non-inundated Classes are denoted with 0 and 1, respectively. 'Date' is in the form YYYY_MM_DD.</p>
Snow cover duration maps for Gran Paradiso
<p>Number of days for which a pixel is covered by snow. The snow cover duration (SCD) is generated from the 250 meter resolution snow cover maps realized with the algorithm described in Notarnicola, Claudia, et al. "Snow cover maps from MODIS images at 250 m resolution, Part 1: Algorithm description." Remote Sensing 5.1 (2013): 110-126.</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>
Monthly water maps of the Camargue from 2018
<p>Maps of water surface areas in the Camargue estimated by applying the Water in Wetland (WIW) logical rule to 35 Sentinel-2 scenes, from 12/24/2017 to 12/24/2018</p>
Monthly water maps of the Camargue from 2015-2017
<p>Maps of water surface areas in the Camargue estimated by applying the Water in Wetland (WIW) logical rule to 12 monthly scenes of Landsat 8 OLI from 02/10/2015 to 12/16/2017.</p>
Monthly water maps of the Camargue from 2001-2003
<p>Maps of water surface areas in the Camargue estimated by applying the Water in Wetland (WIW) logical rule to 12 monthly scenes of Landsat 7 ETM+ from 01/26/2001 to 04/06/2003.</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>
Snow cover duration maps for Bayerischer Wald
<p>Number of days for which a pixel is covered by snow. The snow cover duration (SCD) is generated from the 250 meter resolution snow cover maps realized with the algorithm described in Notarnicola, Claudia, et al. "Snow cover maps from MODIS images at 250 m resolution, Part 1: Algorithm description." Remote Sensing 5.1 (2013): 110-126.</p>
A map collection of the Paleolithic of the Swabian Jura
<p>This is a collection of maps for publications and presentations. The newest version and further information is available at <a href="https://github.com/sommergeo/swabian_jura">https://github.com/sommergeo/swabian_jura</a>. The project consits of</p> <ul> <li>The original dataset with a QGIS project file</li> <li>Maps with different extents for <ul> <li>Print</li> <li>Screen presentation</li> </ul> </li> <li>A first draft of a interactive web map</li> </ul> <p>All data and maps are provided and can be accessed and edited with Open Source tools, namely <a href="http://qgis.org/">QGIS</a>, <a href="http://inkscape.org/">Inkscape</a> and <a href="https://leafletjs.com">Leaflet</a>. Provided by <a href="https://www.sommergeo.com">C. Sommer</a>.</p>
Murgia Alta: land cover map (2018)
<p>A land cover map in "Murgia Alta" PA, for 2018, obtained by considering 4 multi-seasonal Sentinel-2 images. A Support Vector machine (SVM) classifier was used for a 13 classes problem. The images were atmospherically corrected.</p> <p>The map was produced at 10 meters spatial resolution and projected in WGS84/UTM33N.</p> <p>The Overall Accuracy (OA) of the map was: OA=97.37%±0.09%.</p> <p>The map has 14 values related as follows in LCCS-FAO taxonomy:</p> <p>Value 0 = Unclassified</p> <p>Value 1 = A11/A7.A9 (olive grooves)</p> <p>Value 2 = A11/A1.A7.A10 (orchards)</p> <p>Value 3 = A11/A2.A7.A10 (vineyards)</p> <p>Value 4 = A11/A3 (cultivated herbaceous)</p> <p>Value 5 = A12/A1.D1.E1 (natural broadleaved evergreen)</p> <p>Value 6 = A12/A1.D1.E2 (natural broadleaved deciduous)</p> <p>Value 7 = A12/A1.D2.E1 (natural needleleaved evergreen)</p> <p>Value 8 = A12/A2.A6 (natural grasslands)</p> <p>Value 9 = B15/A1 (artificial structures, buildings/roads)</p> <p>Value 10 = B15/A2.A6 (extraction sites)</p> <p>Value 11 = B27-B28/A1.A5 (artificial-natural water)</p> <p>Value 12 =BURNED AREAS </p> <p>Value 13 =Out of PA</p>
Gran Paradiso - Nivolet: binary map vegetated/not vegetated (2016)
<p>A vegetated/not vegetated binary map for the Nivolet area in Gran Paradiso (Italy) PA, detected by a thresholding on NDVI spectral index extracted from a Sentinel-2A image dated 13 August 2016, at 10 meters spatial resolution, projected in WGS84/UTM32N. <br> The map has binary values where value 1 indicates pixels of vegetation whereas value 0 indicates No vegetation pixels.<br> </p>
ScienceDex guides
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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)
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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.