Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
7
datasets available to search
ShareScore release 0.9.0
Dataset results
7 results for “administrative boundaries”
Shapefile of administrative boundaries in Glasgow, UK, around 1920
<p>This dataset consists of a shapefile of administrative boundaries (municipal wards) in Glasgow around 1920, based on 'Map of the City of Glasgow shewing Parliamentary Divisions as fixed in 1918, and Municipal Wards as fixedin 1920'. The map is held in the Glasgow City Archives, reference DTC/13/98.</p> <p>Shapefile construction was undertaken as described in the related article:</p> <p>Angelopoulos, K., Stewart, G. and Mancy, R. <em>Local infectious disease experience influences vaccine refusal rates: a natural experiment. Proceedings of the Royal Society B: Biological Sciences. DOI: 10.1098/rspb.2022.1986.</em></p> <p>The attributes which are included in the shapefile are Ward_Num (municipal ward number) and Ward_Name (municipal ward name). Full details of the wards numbers and ward names are given in the Report of the Medical Officer of Health for the City of Glasgow for 1921, which can be accessed at<em> https://wellcomecollection.org/works/jxgvafxr/items. </em></p>
Shapefiles of administrative boundaries, Subway and main rivers in Glasgow, UK, around 1910
<p>This collection consists of ESRI shapefiles for Glasgow around 1910:</p> <ul> <li>sanitary district boundaries in 1903 (Sanitary_Districts.shp, etc.)</li> <li>municipal ward boundaries in 1912 (Wards_1912.shp, etc.)</li> <li>registration district boundaries within the area of the City of Glasgow in 1913 (Registration_Districts.shp, etc.)</li> <li>routes of main rivers (River Clyde and River Kelvin) around 1915 (Rivers.shp, etc.)</li> <li>route of the Glasgow Subway around 1915 (Subway.shp, etc.)</li> </ul> <p>For details of shapefile construction, please see the descriptions in the following article:</p> <p>Angelopoulos, K., Stewart, G. and Mancy, R. <em>Local infectious disease experience influences vaccine refusal rates: a natural experiment. Proceedings of the Royal Society B: Biological Sciences. DOI: 10.1098/rspb.2022.1986.</em></p> <p>Details of construction and references to original map sources are provided in the second paragraph of the section "Geographic conversion" in the online supplementary materials of the above reference. Further information about the boundaries is provided in the caption of Figure S1 of the supplementary materials. Additional contextual information is provided in both the main text and supplementary materials.</p>
Pan-EU Landmask: 10m Resolution Geospatial Land Coverage with Administrative Boundary details on country and regional level
<p><strong>Pan-EU Land Mask Summary</strong></p> <p>Considering the land mask for pan-EU, we will closely match the data coverage of <a href="https://land.copernicus.eu/pan-european">https://land.copernicus.eu/pan-european</a> i.e. the official selection of countries listed here: <a href="https://land.copernicus.eu/portal_vocabularies/geotags/eea39">https://lanEEA39d.copernicus.eu/portal_vocabularies/geotags/eea39</a>.</p> <p>There are a total of three landmask files available, each of which is aligned with the standard spatial/temporal resolution and sizes of <a href="https://ai4soilheath.eu">AI4SoilHealth</a> Data Cube specifications, which is: Xmin = 900,000, Ymin = 899,000, Xmax = 7,401,000, Ymax = 5,501,000, with Coordinate reference system of epsg:3035. Additionally, these files include a corresponding look-up table that provides explanations for the values present in the raster data. The scripts used to generate these masks can be found <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/tree/main/paneu_landmask">here</a>.</p> <p>The masks are:</p> <ol> <li> <p>Landmask</p> </li> <li> <p>ISO-code country mask</p> </li> <li> <p>NUTS3 mask</p> </li> </ol> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, the files here are named according to the standard OpenLandMap file-naming convention. The OpenLandMap file-naming convention works with 10 fields that basically define the most important properties of the data, this way users can search files, prepare data analysis etc, without even needing to access or open files. The 10 fields include:</p> <ol> <li> <p>Generic variable name: country.code</p> </li> <li> <p>Variable procedure combination i.e. method standard (standard abbreviation): iso.3166</p> </li> <li> <p>Position in the probability distribution / variable type: c</p> </li> <li> <p>Spatial support (usually horizontal block) in m or km: 30m</p> </li> <li> <p>Depth reference or depth interval e.g. below ("b"), above ("a") ground or at surface ("s"): s</p> </li> <li> <p>Time reference begin time (YYYYMMDD): 20210101</p> </li> <li> <p>Time reference end time: 20211231</p> </li> <li> <p>Bounding box (2 letters max): eu </p> </li> <li> <p>EPSG code: epsg.3035</p> </li> <li> <p>Version code i.e. creation date: v20230722</p> </li> </ol> <p>An example of a file-name based on the description above:</p> <p><em>country.code_iso.3166_c_100m_s_20210101_20211231_eu_epsg.3035_v20230722</em></p> <p><strong>Landmask</strong></p> <p>The basic principle to create the land mask is to include as much as land as possible, to avoid missing any land pixels and ensure precise differentiation between land, ocean and inland water bodies.</p> <p>Two reference datasets are used, </p> <ol> <li> <p><a href="https://esa-worldcover.org/en">WorldCover</a>, 10 m resolution.</p> </li> <li> <p><a href="https://www.mapsforeurope.org/datasets/euro-global-map">EuroGlobalMap</a>, with shapefiles of administrative boundaries, inland water bodies, ocean and landmask.</p> </li> </ol> <p>When generating the land mask, the two reference datasets in a way that:</p> <ul> <li> <p>If either of the two reference datasets identifies a pixel as land, it is considered a land pixel in our mask. </p> </li> <li> <p>Regarding ocean and inland water bodies, a pixel is classified as a water pixel only when both reference datasets confirm its identification as water.</p> </li> </ul> <p>The landmask consists of 4 values:</p> <ul> <li> <p>10: not in the pan-EU area, i.e. out of mapping scope</p> </li> <li> <p>1: land</p> </li> <li> <p>2: inland water</p> </li> <li> <p>3: ocean</p> </li> </ul> <p>This landmask is available in 10m, 30m, 100m, 250m, and 1km resolution formats respectively. The coarse resolution landmasks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “min” in GDAL. This “min” method allows taking the minimum values from the contributing pixels, to keep as much land as possible.</p> <p><strong>ISO-3166 country code mask</strong></p> <p>This ISO-3166 country code mask is created from <a href="https://www.mapsforeurope.org/datasets/euro-global-map">EuroGlobalMap</a> country shapefile. This mask is available in 10m, 30m and 100m resolution. In this raster file, each country is assigned a unique value, which allows for the interpretation and analysis of data associated with a specific country.</p> <p>The values are assigned to each country according to iso-3166 country code, which can be found in the corresponding look-up table. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “mode” in GDAL.</p> <p><strong>NUTS-3 mask</strong></p> <p>The nuts-3 code mask is created from the European NUTS3 shapefile. In this raster file, each unique NUT3 level area is assigned a unique value, which allows for the interpretation and analysis of data associated with specific NUTS3 regions.</p> <p>The values of pixels and its associated meanings can be found in the corresponding look-up table. This nut-3 code mask is available in 10m, 30m and 100m resolution formats. The coarse resolution masks (>10 m) are generated by resampling from the 10m resolution base map using resampling method “mode” in GDAL.</p> <p>It should be noted that the ISO-code country mask covers a more extensive area compared to the NUTS3 mask. This broader coverage includes countries like Ukraine and others beyond the NUTS3 mask, while NUTS mask shows more details about regional administrative boundaries.</p>
Gridded Population of the World, Version 3 (GPWv3): Subnational Administrative Boundaries
The Gridded Population of the World, Version 3 (GPWv3): Subnational Administrative Boundaries are the basis of the population data products in GPWv3. Due to copyright restrictions, only maps of the subnational administrative boundaries are available, the underlying data cannot be released. GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).
Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): National Administrative Boundaries
The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1): National Administrative Boundaries are derived from the land area grid to show the outlines of pixels (cells) that contain administrative Units in GRUMPv1 on a per-country/territory basis. They are derived from the pixels as polygons and thus have rectilinear boundaries at a large scale. The polygons that outline the countries and territories are not official representations; rather they represent the area covered by the statistical data as provided. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT).
Gridded Population of the World, Version 3 (GPWv3): National Administrative Boundaries
The Gridded Population of the World, Version 3 (GPWv3): National Administrative Boundaries are derived from the land area grid to show the outlines of pixels (cells) that contain administrative Units in GPWv3 on a per-country/territory basis. The National Boundaries data are derived from the pixels as polygons and thus have rectilinear boundaries at large scale. Note that the polygons that outline the countries and territories are not official representations; rather, they represent the area covered by the statistical data as provided. The national/territorial boundaries are designed for cartographic use with the GPWv3 population raster data sets. GPWv3 is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with Centro Internacional de Agricultura Tropical (CIAT).
National Administrative Boundary of China
<p>This dataset is the country boundary of China. It is drived from National Catalogue Service for Geographic Information, which belong to National Geomatics Center of China.</p> <p>The dataset include three parts: the boundary at country-level (PAC), prefecture-level (level2) and province-level (level1). They were identified as PAC, level2, level1 in the corresponding shapfiles.</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.