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23 results for “OpenStreetMap”
Global Human Settlement Layer per zoom-level 18 Quadtree tile for selected countries as Spatialite database with OpenStreetMap building completeness assessment
<p>This Spatialite database contains the built-up area of the Global Human Settlement Layer (GHSL) per zoom-level 18 Quadtree tile. Additionally, it provides a comparison of the GHSL with buildings in OpenStreetMap: For each tile the built-up ratio between the building footprints and the GHSL is given and a binary completeness assessment (buildings complete, not complete) is provided for easy use. This dataset was created using the obmgapanalysis tool: https://git.gfz-potsdam.de/dynamicexposure/openbuildingmap/obmgapanalysis</p>
Prebuilt Electricity Network for PyPSA-Eur based on OpenStreetMap Data
<p>This dataset contains a<strong> topologically connected representation of the European high-voltage grid (220 kV to 750 kV)</strong> <strong>constructed using OpenStreetMap data</strong>. Input data was retrieved using the Overpass turbo API (<a title="Overpass turbo" href="https://overpass-turbo.eu" target="_blank" rel="noopener">https://overpass-turbo.eu</a>). A heurisitic cleaning process was used to for lines and links where electrical parameters are incomplete, missing, or ambiguous. Close substations within a radius of <strong>500 m</strong> are aggregated to single buses, exact locations of underlying substations is preserved. Unique identifiers for lines and links are preserved, e.g. an AC line/cable with the ID <em>way/83742802-1</em> can be viewed on OpenStreetMap using the query <a title="OpenStreetMap example (AC)" href="https://www.openstreetmap.org/way/83742802" target="_blank" rel="noopener">https://www.openstreetmap.org/way/83742802</a>. A DC line/cable with the ID <em>relation/15781671</em> can be accessed using the query <a title="OpenStreetMap example (DC)" href="https://www.openstreetmap.org/relation/15781671" target="_blank" rel="noopener">https://www.openstreetmap.org/relation/15781671</a></p> <p>A detailed explanation on the <strong>background, methodology, and validation </strong>can be found in the article published in <a href="https://www.nature.com/articles/s41597-025-04550-7"><strong>Nature Scientific Data</strong></a>:</p> <blockquote> <p><em>Xiong, B., Fioriti, D., Neumann, F., Riepin, I., Brown, T.</em> Modelling the high-voltage grid using open data for Europe and beyond. <em>Sci Data</em> <strong>12</strong>, 277 (2025). <a href="https://doi.org/10.1038/s41597-025-04550-7" target="_blank" rel="noopener">https://doi.org/10.1038/s41597-025-04550-7</a></p> </blockquote> <p><strong>Countries</strong> included in the dataset:</p> <blockquote> <p>Albania (AL), Austria (AT), Belgium (BE), Bosnia and Herzegovina (BA), Bulgaria (BG), Croatia (HR), Czech Republic (CZ), Denmark (DK), Estonia (EE), Finland (FI), France (FR), Germany (DE), Greece (GR), Hungary (HU), Ireland (IE), Italy (IT), Kosovo (XK), Latvia (LV), Lithuania (LT), Luxembourg (LU), Moldova (MD), Montenegro (ME), Netherlands (NL), North Macedonia (MK), Norway (NO), Poland (PL), Portugal (PT), Romania (RO), Serbia (RS), Slovakia (SK), Slovenia (SI), Spain (ES), Sweden (SE), Switzerland (CH), Ukraine (UA), United Kingdom (GB)</p> </blockquote> <p>The dataset was constructed as part of the workflow within the open-source, sector-coupling model PyPSA-Eur and will be updated continuously as data and/or the cleaning process improves. </p> <p><strong>PyPSA-Eur</strong> is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at <a href="https://github.com/PyPSA/PyPSA-eur">https://github.com/PyPSA/PyPSA-eur</a>.</p> <p><strong>Not all data dependencies</strong> are shipped with the <a href="https://github.com/PyPSA/PyPSA-eur">code repository</a>, since git is not suited for handling large changing files. Instead we provide separate <strong>data bundles</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-eur.readthedocs.io/en/latest/installation.html">documentation</a>.</p> <p>While the <a href="https://github.com/PyPSA/PyPSA-eur">code</a> and provided dataset in PyPSA-Eur is released as free software under the MIT, <strong>different licenses and terms of use</strong> apply to the underlying input data.</p> <p><strong>Extract from OpenStreetMap Terms of Use</strong></p> <blockquote> <p>OpenStreetMap<sup><a href="https://www.openstreetmap.org/copyright#trademarks">®</a></sup> is <em>open data</em>, licensed under the <a href="https://opendatacommons.org/licenses/odbl/">Open Data Commons Open Database License</a> (ODbL) by the <a href="https://osmfoundation.org/">OpenStreetMap Foundation</a> (OSMF).</p> <p>You are free to copy, distribute, transmit and adapt our data, as long as you credit OpenStreetMap and its contributors. If you alter or build upon our data, you may distribute the result only under the same licence. The full <a href="https://opendatacommons.org/licenses/odbl/1.0/">legal code</a> explains your rights and responsibilities.</p> <p>Our documentation is licensed under the <a href="https://creativecommons.org/licenses/by-sa/2.0/">Creative Commons Attribution-ShareAlike 2.0</a> license (CC BY-SA 2.0).</p> </blockquote> <p>This processed dataset is provided under the Open Data Commons Open Database License (ODbL 1.0) license.</p> <p><strong>Changelog from version 0.5 to 0.6:<br></strong></p> <ul> <li>Added electric parameters to lines (e.g. nominal current, resistance r, reactance x, susceptance b). This allows the dataset to be used outside of PyPSA/PyPSA-Eur.</li> <li>Interactive map.html now bundled with the dataset.</li> <li>Tags columns include what the element contains (e.g. merged lines contain lines that were aggregated together).</li> </ul> <p><strong>Changelog from version 0.4 to 0.5:<br></strong></p> <ul> <li>Exact locations of original substations and converter stations (interior point/Pole of Inaccessibility) are preserved.</li> <li>Clustering resolution improved from 5000 to 500 meters.</li> <li>Lines of same electric parameters are merged, if they cross a virtual bus (that is not a real substation).</li> <li>Information from OSM relations are used, wherever applicable. To avoid doubling, members (ways) of the relation are dropped in the set of lines, accordingly.</li> <li>There are now unique transformers for each voltage level in each station. Transformers now have a nominal capacity, representing the maximum of line capacities connected to either side/bus of the transformer (n-0, nominal capacity).</li> <li>Wherever applicable, OSM IDs are preserved and used in the index of the network components.</li> </ul>
OpenStreetMap+ Protected nature areas in continental Europe (IUCN status + Natura 2000)
<p>Twelve maps of continental Europe indicating the protected nature area status in 2019 according to <a href="https://ec.europa.eu/environment/nature/natura2000/index_en.htm">Natura 2000</a> and the <a href="https://www.iucn.org/">International Union for Conservation of Nature</a> (IUCN). The IUCN status was extracted from crowdsourced data obtained from OpenStreetMap through geofabrik.de.</p> <p>This dataset contains:</p> <ul> <li>3 raster maps representing Natura 2000 protection status (A, B and C), named <strong>Natura2000_[status].tif</strong></li> <li>8 raster maps representing OSM-derived IUCN protection status (1a, 1b, 2, 3, 4, 5, 6, and 'other'), named <strong>OSM_IUCN_[status].tif</strong></li> <li>1 aggregated map (<strong>adm_protected.area_natura2000.osm_p_30m_0..0cm_2019..2021_eumap_epsg3035_v0.1</strong>) where each of the 11 protection statuses, as well as pixels where multiple statuses apply, are assigned a unique value. This map can also be accessed interactively at <a href="https://maps.opendatascience.eu/?base=OpenStreetMap%20(grayscale)&layer=Natura2000-OSM%20Protected%20areas&zoom=4&eye=5000000&center=53.7139,17.0066&opacity=45">maps.opendatascience.eu</a>.</li> </ul> <p>All 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 for the aggregated raster are provided in both <strong><em>SLD</em></strong> and <strong><em>QML</em></strong> format.</p>
OpenStreetMap+ Land Use / Land Cover classes and administrative regions of Europe
<p>This dataset contains 23 30m resolution raster data of continental Europe land use / land cover classes extracted from <a href="https://www.openstreetmap.org/">OpenStreetMap</a>, as well as administrative areas, and a harmonized building dataset based on OpenStreetMap and <a href="https://land.copernicus.eu/pan-european/high-resolution-layers/imperviousness#:~:text=The%20imperviousness%20products%20capture%20the,over%20long%20periods%20of%20time.">Copernicus HRL Imperviousness</a> data.</p> <p>The land use / land cover classes are:</p> <ol> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:building%3Dcommercial">buildings.commercial</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:building%3Dindustrial">buildings.industrial</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:building%3Dresidential">buildings.residential</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dcemetery">cemetery</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dconstruction">construction.site</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dlandfill">dump.site (landfill)</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dfarmland">farmland</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dfarmyard">farmyard</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dforest">forest</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dgrass">grass</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:building%3Dgreenhouse">greenhouse</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dharbour">harbour</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dmeadow">meadow</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dmilitary">military</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dorchard">orchard</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dquarry">quarry</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:railway%3Drail">railway</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dreservoir">reservoir</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:highway%3Droad">road</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dsalt_pond">salt</a></li> <li><a href="https://wiki.openstreetmap.org/wiki/Tag:landuse%3Dvineyard">vineyard</a></li> </ol> <p>The land use / land cover data was generated by extracting OSM vector layers from https://download.geofabrik.de/). These were then transformed into a 30 m density raster for each feature type. This was done by first creating a 10 m raster where each pixel intersecting a vector feature was assigned the value 100. These pixels were then aggregated to 10 m resolution by calculating the average of every 9 adjacent pixels. This resulted in a 0—100 density layer for the three feature types. Although the digitized building data from OSM offers the highest level of detail, its coverage across Europe is inconsistent. To supplement the building density raster in regions where crowd-sourced OSM building data was unavailable, we combined it with Copernicus High Resolution Layers (HRL) (obtained from https://land.copernicus.eu/pan-european/ high-resolution-layers), filling the non-mapped areas in OSM with the Impervious Built-up 2018 pixel values, which was averaged to 30 m. The probability values produced by the averaged aggregation were integrated in such a way that values between 0—100 refer to OSM (lowest and highest probabilities equal to 0 and 100 respectively), and the values between 101—200 refer to Copernicus HRL (lowest and highest probability equal to 200 and 101 respectively). This resulted in a raster layer where values closer to 100 are more likely to be buildings than values closer to 0 and 200. Structuring the data in this way allows us to select the higher probability building pixels in both products by the single boolean expression: Pixel > 50 AND pixel <150.</p> <p>This dataset is part of the OpenStreetMap+ was used to pre-process the LUCAS/CORINE land use / land cover samples (https://doi.org/10.5281/zenodo.4740691) used to train machine learning models in Witjes et al., 2022 (https://doi.org/10.21203/rs.3.rs-561383/v4)</p> <p>Each layer can be viewed interactively on the Open Data Science Europe data viewer at <a href="https://maps.opendatascience.eu/?base=OpenStreetMap%20(grayscale)&layer=Copernicus-OSM%20buildings&zoom=4&eye=5000000&center=53.7139,17.0066&opacity=45">maps.opendatascience.eu</a>.</p>
Estimated height of the OpenStreetMap buildings of 24 French communes using the GeoClimate Software (version 0.0.1)
<p>This repository contains:</p> <ul> <li>a folder called "_toReproduceResults" containing data, script and methodology to reproduce most of the work described in the research manuscript,</li> <li>the main output of the research work: 24 folders (each of them corresponding to a French city), containing building geometry footprints and their corresponding building height as well as averaged building height value aggregated at rectangular grid cell (100 m by 100 m). The footprint geometries comes from the OpenStreetMap project and the building height has been estimated using a RandomForest algorithm using as independent variables indicators describing the building size and shape and the building environment. The data has been produced using the GeoClimate Software (version 0.0.1).</li> </ul> <p>A more detailed description of the content can be used in the file "Metadata.csv".</p>
OSHDB - OpenStreetMap History Data Analysis
<p>A high-performance framework for spatio-temporal data analysis of OpenStreetMap full-history data. Developed by <a href="https://heigit.org">HeiGIT</a> as part of the <a href="https://ohsome.org/">ohsome project</a>.</p> <p><em>Code is hosted on github: <a href="https://github.com/giscience/oshdb">https://github.com/giscience/oshdb</a>.</em></p> <p>The OSHDB allows to investigate the evolution of the amount of data and the contributions to the OpenStreetMap project. It combines easy access to the historical OSM data with high querying performance. Use cases of the OSHDB include data quality analysis, computing of aggregated data statistics and OSM data extraction. The main functionality of the OSHDB is explained in the <a href="https://github.com/GIScience/oshdb/blob/master/documentation/first-steps/README.md">first steps tutorial</a>.</p> <p><strong>OpenStreetMap History Data</strong></p> <p><a href="https://www.openstreetmap.org/">OpenStreetMap</a> contains a large variety of geographic data, differing widely in scale and feature type. OSM contains everything from single points of interests to whole country borders, from concrete things like buildings up to more abstract concepts such as turn restrictions. OSM also offers metadata about the <a href="https://wiki.openstreetmap.org/wiki/Planet.osm/full">history</a> and the modifications that are made to the data, which can be analyzed in a multitude of ways.</p> <p>Because of it's size and variety, possibilities of working with OSM history data are limited and there exists a lack of an easy-to-use analysis software. A goal of the OSHDB is to make OSM data more accessible to researchers, data journalists, community members and other interested people.</p> <p><strong>Central Concepts</strong></p> <p>The OSHDB is designed to be appropriate for a large spectrum of potential use cases and is therefore built around the following central ideas and design goals:</p> <ul> <li><em>Lossless Information</em>: The full OSM history data set should be stored and be queryable by the OSHDB, including errorneous or partially incomplete data.</li> <li><em>Simple, Generic API</em>: Writing queries with the OSHDB should be simple and intuitive, while at the same time flexbile and generic to allow a wide variety of analysis queries.</li> <li><em>High Performance</em>: The OSM history data set is large and thus requires efficiency in the way the data is stored and in the way it can be accessed and processed.</li> <li><em>Local and Distributed Deployment</em>: Analysis queries should scale well from data explorations of small regions up to global studies of the complete OSM data set.</li> </ul> <p>The OSHDB splits data storage and computations. It is then possible to use the <a href="https://en.wikipedia.org/wiki/MapReduce">MapReduce</a> programming model to analyse the data in parallel and optionally also on distributed databases. A central idea behind this concept is to bring the code to the data.</p>
A Standardized European Hexagon Gridded Dataset Based on OpenStreetMap POIs
<p>Point of interest (POI) data refers to information about the location and type of amenities, services, and attractions within a geographic area. This data is used in urban studies research to better understand the dynamics of a city, assess community needs, and identify opportunities for economic growth and development. POI data is beneficial because it provides a detailed picture of the resources available in a given area, which can inform policy decisions and improve the quality of life for residents. This paper presents a large-scale, standardized POI dataset from OpenStreetMap (OSM) for the European continent. The dataset's standardization and gridding make it more efficient for advanced modeling, reducing 7,218,304 data points to 988,575 without significant resolution loss, suitable for a broader range of models with lower computational demands. The resulting dataset can be used to conduct advanced analyses, examine POI spatial distributions, conduct comparative regional studies, enhancing understanding of the economic activity, distribution, attractions, and subsequently, economic health, growth potential, and cultural opportunities. The paper describes the materials and methods used in generating the dataset, including OSM data retrieval, processing, standardization, and hexagonal grid generation. The dataset can be used independently or integrated with other relevant datasets for more comprehensive spatial distribution studies in future research.</p>
Analyzing and predicting urban land use forms in East Africa using OpenStreetMap data, satellite imagery, and Convolutional Networks
<p>This multi-spectral satellite image data set is associated with our recent work on analyzing and predicting urban land use forms in East Africa using OpenStreetMap data, satellite imagery, and Convolutional Neural Networks.</p> <p>The images were extracted using an automated Python script from Google Maps Static API, based on sample locations in four East African capital cities namely Kampala, Nairobi, Dar es Salaam, and Kigali.</p> <p>Other data sets associated with this work, that is, ESRI shapefiles for administrative level 1 and OpenStreetMap data for the named cities may be downloaded directly from the respective URLs provided in the manuscript.</p>
Review of OpenStreetMap research publications from 2016 to 2019
<p>This dataset consists of a <strong>review of research publications about the <a href="https://www.openstreetmap.org/">OpenStreetMap (OSM) project</a> </strong>published from 2016 to 2019.</p> <p>The dataset was obtained as follows. First, papers published between 2016 and 2019 were extracted using <a href="https://scholar.google.it/">Google Scholar</a> with a query identifying all records with at least one of the keywords “OpenStreetMap” and “OSM” in the title. The extracted records were further filtered to only keep papers having a minimum length of 4 pages and published in academic journals or conference proceedings. In addition, irrelevant papers (e.g. using “OSM” as an acronym for another purpose) and non-English papers were removed from the dataset. The remaining paper were then analyzed and manually classified.</p> <p>The attributes included in the dataset are the following:</p> <ul> <li><strong>id</strong> [paper ID]</li> <li><strong>Paper Citation </strong>[citation of the paper]</li> <li><strong>Authors’ disciplines</strong> — multiplicity: 1-7; allowed values: computer science, informatics, social sciences, geo-information, engineering, exact sciences, interdisciplinary</li> <li><strong>Journal’s discipline</strong> — multiplicity: 1; allowed values: computer science, informatics, social sciences, geo-information, engineering, exact sciences, interdisciplinary</li> <li><strong>Topic(s)</strong> — multiplicity: 1:10; allowed values: application, data quality, contribution behaviours, analyzing contributions, contributors, shaping contributions, OSM effects, data enrichment, development, review</li> <li><strong>Authors' Geography (countries) </strong>— multiplicity: 1:n; allowed values: [list of countries]</li> <li><strong>Authors' Geography (continents) </strong>— multiplicity: 1:n; allowed values: [list of continents]</li> <li><strong>Study Area Geography (countries)</strong> — multiplicity: 1:n; allowed values: [list of countries], [list of continents], Global, NA</li> <li><strong>Geographic Correspondence</strong>— multiplicity: 1; allowed values: no correspondence, partial affiliation/partial location, partial affiliation/full location , full correspondence, NA</li> <li><strong>Perspective on the community</strong> — multiplicity: 1; allowed values: OSM as a data source, data produced by contributors, a collaborative project based on contributors, contributors producing data, a unified community, a diverse community, a social product</li> <li><strong>Evidence of engagement </strong>— multiplicity: 1-8; allowed values: none, acknowledgement, meaningful development, occasional development, object of study - direct, object of study - indirect, contribution, participation</li> <li><strong>General comments </strong>[comments on the paper]</li> </ul>
Supervised Classification of Built-up Areas in Sub-Saharan African Cities using Landsat Imagery and OpenStreetMap
<p>This dataset contains input, intermediary, and output files for the following paper:</p> <p>Yann Forget, Catherine Linard and Marius Gilbert. "<em>Supervised Classification of Built-up Areas in Sub-Saharan African Cities using Landsat Imagery and OpenStreetMap</em>", 2018.</p> <p>The dataset is composed of three archives:</p> <ul> <li><code>input.zip</code> : contains raw input data required to run the study ;</li> <li><code>intermediary.zip</code> : contains processed data required for the analysis ;</li> <li><code>output.zip</code> : contains the output tables and images of the study.</li> </ul> <p>Alternatively, output images of the study can be previewed <a href="http://maupp.ulb.ac.be/page/forget2018/">here</a> in interactive maps.</p> <p>The source code used to produce the outputs is availabe <a href="https://zenodo.org/record/1292005">here</a>.</p>
Europe Road Network extracted from OpenStreetMap data
<p>The data extracts of Europe region downloaded on 04/07/2020 was used to create this dataset. From this the <strong>highways</strong> tagged as <strong>motorway, trunk, primary, secondary, tertiary, unclassified</strong> and <strong>residential </strong>are selected and the information was saved as line strings. CRS: WGS84 (EPSG:4326)<br> Files available are in parquet and csv format. Please feel free to convert the files in to desired file formats.</p>
Telblokken voor de monitoring van patrijs in Antwerpen met OpenStreetMap als achtergrond
<p>Deze dataset bevat voor elk Antwerps jachtveld, lid van een wildbeheerseenheid, een pdf met de telblokken voor de monitoring van patrijs (<em>Perdix perdix</em>). De naam van de bestanden bevatten het nummer van het jachtveld. Dit nummer komt overeen met de nummers in de officiële kaartlaag met <a href="https://www.geopunt.be/catalogus/datasetfolder/1b75c9a0-1f3a-493a-803f-81a866b5802f">jachtvelden</a>. De kaart bevat vier soorten gebieden: gebieden die geen deel uitmaken van een jachtveld (geen jacht), gebieden binnen een jachtveld dat geen open ruimte is, het huidige telblok en de overige telblokken.</p> <p> </p> <p>In principe bestaat elk jachtveld uit een telblok. We streven naar telbokken die niet groter zijn dan 150 ha en bij schaal 1:10.000 op een A4 passen. Is het jachtgebied te groot om aan deze voorwaarden te voldoen, splitsen we het in meerdere telblokken. Hiervoor gebruiken we de in OpenStreetMap beschikbare grenzen die op het terrein duidelijk zichtbaar zijn zoals wegen en waterlopen.</p>
Telblokken voor de monitoring van patrijs in Limburg met OpenStreetMap als achtergrond
<p>Deze dataset bevat voor elk Limburgs jachtveld, lid van een wildbeheerseenheid, een pdf met de telblokken voor de monitoring van patrijs (<em>Perdix perdix</em>). De naam van de bestanden bevatten het nummer van het jachtveld. Dit nummer komt overeen met de nummers in de officiële kaartlaag met <a href="https://www.geopunt.be/catalogus/datasetfolder/1b75c9a0-1f3a-493a-803f-81a866b5802f">jachtvelden</a>. De kaart bevat vier soorten gebieden: gebieden die geen deel uitmaken van een jachtveld (geen jacht), gebieden binnen een jachtveld dat geen open ruimte is, het huidige telblok en de overige telblokken.</p> <p> </p> <p>In principe bestaat elk jachtveld uit een telblok. We streven naar telbokken die niet groter zijn dan 150 ha en bij schaal 1:10.000 op een A4 passen. Is het jachtgebied te groot om aan deze voorwaarden te voldoen, splitsen we het in meerdere telblokken. Hiervoor gebruiken we de in OpenStreetMap beschikbare grenzen die op het terrein duidelijk zichtbaar zijn zoals wegen en waterlopen.</p>
Telblokken voor de monitoring van patrijs in Vlaams-Brabant met OpenStreetMap als achtergrond
<p>Deze dataset bevat voor elk Vlaams-Brabants jachtveld, lid van een wildbeheerseenheid, een pdf met de telblokken voor de monitoring van patrijs (<em>Perdix perdix</em>). De naam van de bestanden bevatten het nummer van het jachtveld. Dit nummer komt overeen met de nummers in de officiële kaartlaag met <a href="https://www.geopunt.be/catalogus/datasetfolder/1b75c9a0-1f3a-493a-803f-81a866b5802f">jachtvelden</a>. De kaart bevat vier soorten gebieden: gebieden die geen deel uitmaken van een jachtveld (geen jacht), gebieden binnen een jachtveld dat geen open ruimte is, het huidige telblok en de overige telblokken.</p> <p> </p> <p>In principe bestaat elk jachtveld uit een telblok. We streven naar telbokken die niet groter zijn dan 150 ha en bij schaal 1:10.000 op een A4 passen. Is het jachtgebied te groot om aan deze voorwaarden te voldoen, splitsen we het in meerdere telblokken. Hiervoor gebruiken we de in OpenStreetMap beschikbare grenzen die op het terrein duidelijk zichtbaar zijn zoals wegen en waterlopen.</p>
Telblokken voor de monitoring van patrijs in West-Vlaanderen met OpenStreetMap als achtergrond
<p>Deze dataset bevat voor elk West-Vlaams jachtveld, lid van een wildbeheerseenheid, een pdf met de telblokken voor de monitoring van patrijs (<em>Perdix perdix</em>). De naam van de bestanden bevatten het nummer van het jachtveld. Dit nummer komt overeen met de nummers in de officiële kaartlaag met <a href="https://www.geopunt.be/catalogus/datasetfolder/1b75c9a0-1f3a-493a-803f-81a866b5802f">jachtvelden</a>. De kaart bevat vier soorten gebieden: gebieden die geen deel uitmaken van een jachtveld (geen jacht), gebieden binnen een jachtveld dat geen open ruimte is, het huidige telblok en de overige telblokken.</p> <p> </p> <p>In principe bestaat elk jachtveld uit een telblok. We streven naar telbokken die niet groter zijn dan 150 ha en bij schaal 1:10.000 op een A4 passen. Is het jachtgebied te groot om aan deze voorwaarden te voldoen, splitsen we het in meerdere telblokken. Hiervoor gebruiken we de in OpenStreetMap beschikbare grenzen die op het terrein duidelijk zichtbaar zijn zoals wegen en waterlopen.</p>
Telblokken voor de monitoring van patrijs in Oost-Vlaanderen met OpenStreetMap als achtergrond
<p>Deze dataset bevat voor elk Oost-Vlaams jachtveld, lid van een wildbeheerseenheid, een pdf met de telblokken voor de monitoring van patrijs (<em>Perdix perdix</em>). De naam van de bestanden bevatten het nummer van het jachtveld. Dit nummer komt overeen met de nummers in de officiële kaartlaag met <a href="https://www.geopunt.be/catalogus/datasetfolder/1b75c9a0-1f3a-493a-803f-81a866b5802f">jachtvelden</a>. De kaart bevat vier soorten gebieden: gebieden die geen deel uitmaken van een jachtveld (geen jacht), gebieden binnen een jachtveld dat geen open ruimte is, het huidige telblok en de overige telblokken.</p> <p> </p> <p>In principe bestaat elk jachtveld uit een telblok. We streven naar telbokken die niet groter zijn dan 150 ha en bij schaal 1:10.000 op een A4 passen. Is het jachtgebied te groot om aan deze voorwaarden te voldoen, splitsen we het in meerdere telblokken. Hiervoor gebruiken we de in OpenStreetMap beschikbare grenzen die op het terrein duidelijk zichtbaar zijn zoals wegen en waterlopen.</p>
US Gridmap for Jesse Jenkins using 2022 OpenStreetMap data
<p><strong>Background</strong><br> This image is a product of Jesse Jenkins asking random questions on <a href="https://twitter.com/JesseJenkins/status/1578131995045101569">Twitter</a>: <a href="https://twitter.com/JesseJenkins/status/1578131995045101569">"</a>Hey #EnergyTwitter: I want to buy a big transmission wall map for my office. Any suggestions? PJM region would be ideal, or whole US."</p> <p><strong>How it is created?</strong><br> The US gridmap is created with the open-source energy system model <a href="https://github.com/pypsa-meets-earth/pypsa-earth">PyPSA-Earth</a> and can be produced for any country on our planet. The author wants to thank the <a href="https://pypsa-meets-earth.github.io/projects">PyPSA meets Earth initiative </a>team as well as all open data and open source projects enabling it!</p> <p> </p>
FIGURE. Map of the region and sampling area showing the relative position of Livingston Island to Antarctic Peninsula (A), and the main sampling locations (B): 1—Hannah Point, 2—Mongolian (Reserve) Port, 3—Caleta Argentina. Map outlines are based on OpenStreetMap© contributors (www.openstreetmap.org), edited and arranged using Adobe Illustrator© and Adobe Photoshop®. Scale bars = 35 km (A); 2 km (B). in The genus Craspedostauros E.J.Cox (Bacillariophyta) on the coasts of Livingston Island, Maritime Antarctica
FIGURE. Map of the region and sampling area showing the relative position of Livingston Island to Antarctic Peninsula (A), and the main sampling locations (B): 1—Hannah Point, 2—Mongolian (Reserve) Port, 3—Caleta Argentina. Map outlines are based on OpenStreetMap© contributors (www.openstreetmap.org), edited and arranged using Adobe Illustrator© and Adobe Photoshop®. Scale bars = 35 km (A); 2 km (B).
Producer Conflict Management Approaches in Online Peer Production Communities – Case Study of OpenStreetMap
<p>These files are supplementary materials to a paper: Youjin Choe, Martin Tomko, and Mohsen Kalantari. 2023. Producer Conflict Management Approaches in Online Peer Production Communities – Case Study of OpenStreetMap. In <em>Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23), April 23–28, 2023, Hamburg, Germany. </em>ACM, New York, NY, USA, 19 pages. https://doi.org/10.1145/3544548.3581036:<br> <br> (1) finaldata_FILENAMES: Files that starts with "finaldata_" contain final data we used for the analysis in text file (binary data) with a codebook. It also contains the count data (with a codebook) that we initially used but failed to pass the linearity assumption.</p> <p>(2) LOGITM~1.R: It is R code that we used for logistic regression analysis.<br> <br> (3) osmcha-parser.py: This our Python code and the resulting files that extracted raw data from OSM Changeset Analyzer (OSM) website.<br> <br> (4) 20210920_AnnotatorsGuide.pdf: This file is the annotators guide that we shared with our two external annotators for introduction and training of our data annotation.</p>
preprocessed OpenStreetMap data edges for Côte d'Ivoire
<p>OpenStreetMap data for Côte d'Ivoire (downloaded on 4 March 2020) transformed to network format following the methods of Karduni et al. (2016).<br> <br> <strong>References</strong><br> Karduni, Alireza, Amirhassan Kermanshah, and Sybil Derrible. "A protocol to convert spatial polyline data to network formats and applications to world urban road networks." <em>Scientific data</em> 3.1 (2016): 1-7.</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.