Find research datasets worth reusing

opencc-by-4.0Oct 2024View details →

Global Pasture Watch - Annual grassland class and extent maps at 30-m spatial resolution (2000—2024)

<h2>Sub-dataset: Dominant grassland class, 2006-2008</h2> <h2>Description</h2> Global annual grassland class and extent for 2000—2024 produced by <a href="https://doi.org/10.1038/s41597-024-04139-6">Parente et al. (2024)</a> within the scope of the <a href="https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring/">Global Pasture Watch initiative</a>. The mapped grassland extent includes any land cover type, which contains at least 30% of dry or wet low vegetation, dominated by grasses and forbs (less than 3 meters) and a: <ul> <li>maximum of 50% tree canopy cover (greater than 5 meters),</li> <li>maximum of 70% of other woody vegetation (scrubs and open shrubland), and</li> <li>maximum of 50% active cropland cover in mosaic landscapes of cropland & other vegetation.</li> </ul> The grassland extent is classified into two classes: <ul> <li>Cultivated grassland: Areas where grasses and other forage plants have been intentionally planted and managed, as well as areas of native grassland-type vegetation where they clearly exhibit active and 'heavy' management for specific human-directed uses, such as directed grazing of livestock.</li> <li>Natural/semi-natural grassland: Relatively undisturbed native grasslands/short-height vegetation, such as steppes and tundra, as well as areas that have experienced varying degrees of human activity in the past, which may contain a mix of native and introduced species due to historical land use and natural processes. In general, they exhibit natural-looking patterns of varied vegetation and clearly ordered hydrological relationships throughout the landscape.</li> <li>Open shrubland (v2-beta): Land on which the vegetation is dominated by low-growing woody plants, characterized by a sparse distribution of shrubs and dominated by woody perennials. Typically covers 50—75% of the area, with significant open ground (with or without herbaceous understory) between them, where shrub canopies are less than 10 meters in diameter, and tree cover is below 10%, meaning they do not form a continuous or semi-continuous canopy.</li> </ul> The dataset is organized in 69 global mosaics (25 years for each time series) in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 degrees, including: <ul> <li>Probabilities of cultivated grassland (values range from 0–100),</li> <li>Probabilities of natural/semi-natural grassland (values range from 0–100), and</li> <li>Probabilities of open shrubland (values range from 0–100), and</li> <li>Dominant class (0-other land cover, 1-cultivated grassland and 2-natural/semi-natural grassland, 3-open shrubland).</li> </ul> All raster files are in unsigned <code>8-bit integer format</code> and use <code>255</code> as no-data value (pixels ignored by prediction), following an specific naming convention: <ol> <li>Project name: Global Pasture Watch (<code>gpw</code>)</li> <li>Class name: cultivated grassland (<code>cultiv.grassland</code>), natural/semi-natural grassland (<code>nat.semi.grassland</code>), open shrubland (<code>open.shrubland</code>)  and dominant grassland (<code>grassland</code>)</li> <li>Procedure combination: Random Forest (<code>rf</code>), median filter (med.filt) and balanced threshold (<code>bthr</code>).</li> <li>Variable type: probability (<code>p</code>) and factor class (<code>c</code>)</li> <li>Spatial resolution: 30m</li> <li>Begin of time reference: date of first Landsat composite used by the modeling (<code>20240101</code>)</li> <li>End of time reference: date of last Landsat composite used by the modeling (<code>20241231</code>)</li> <li>Spatial extent: global (<code>go</code>)</li> <li>Coordinate system: World Geodetic System 1984, used in GPS (<code>epsg.4326</code>)</li> <li>Version: v2</li> </ol> <h3>Related resources</h3> <ul> <li>Maps of dominant grassland: <a href="http://doi.org/10.5281/zenodo.15646181">2000-2002</a><a href="http://doi.org/10.5281/zenodo.15644486"> 2003-2005</a><a href="http://doi.org/10.5281/zenodo.15644623"> 2006-2008</a><a href="http://doi.org/10.5281/zenodo.15647042"> 2009-2011</a><a href="http://doi.org/10.5281/zenodo.15647681"> 2012-2014</a><a href="http://doi.org/10.5281/zenodo.15648306"> 2015-2017</a><a href="http://doi.org/10.5281/zenodo.15648551"> 2018-2020</a><a href="http://doi.org/10.5281/zenodo.15648751"> 2021-2023</a> <a href="http://doi.org/10.5281/zenodo.15649332">2024</a></li> <li>Probability maps of cultivated grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li>Probability maps of natural/semi-natural grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li> <div>Grassland reference samples based on VHR imagery (2000–2024): <a href="https://doi.org/10.5281/zenodo.15631655">GeoPackage files</a></div> </li> <li>Global machine learning models (Random Forest): <a href="https://doi.org/10.5281/zenodo.13952806">Parquet and joblib python files</a></li> <li>Reference sampling design derived by FSCV: <a href="https://doi.org/10.5281/zenodo.11391517">GeoPackage and raster files</a></li> <li>Harmonized reference samples based on existing LULC dataset: <a href="https://doi.org/10.5281/zenodo.13951976">GeoPackage and raster files</a></li> <li>Source code for reproducibility: <a href="https://doi.org/10.5281/zenodo.13952867">GitHub release</a> </li> <li>Mapping feedback tool: <a href="https://geo-wiki.org">GeoWiki</a></li> <li>Data catalogues: <a href="https://stac.openlandmap.org/gpw_ggc-30m/collection.json?.language=en">OpenLandMap STAC</a> <a href="https://global-pasture-watch.projects.earthengine.app/view/ggc-30m">Google Earth Engine</a></li> </ul> Support For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in <a href="https://github.com/wri/global-pasture-watch">https://github.com/wri/global-pasture-watch</a>

opencc-by-4.0Oct 2024View details →

Global Pasture Watch - Annual grassland class and extent maps at 30-m spatial resolution (2000—2024)

<h2>Sub-dataset: Dominant grassland class, 2003-2005</h2> <h2>Description</h2> Global annual grassland class and extent for 2000—2024 produced by <a href="https://doi.org/10.1038/s41597-024-04139-6">Parente et al. (2024)</a> within the scope of the <a href="https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring/">Global Pasture Watch initiative</a>. The mapped grassland extent includes any land cover type, which contains at least 30% of dry or wet low vegetation, dominated by grasses and forbs (less than 3 meters) and a: <ul> <li>maximum of 50% tree canopy cover (greater than 5 meters),</li> <li>maximum of 70% of other woody vegetation (scrubs and open shrubland), and</li> <li>maximum of 50% active cropland cover in mosaic landscapes of cropland & other vegetation.</li> </ul> The grassland extent is classified into two classes: <ul> <li>Cultivated grassland: Areas where grasses and other forage plants have been intentionally planted and managed, as well as areas of native grassland-type vegetation where they clearly exhibit active and 'heavy' management for specific human-directed uses, such as directed grazing of livestock.</li> <li>Natural/semi-natural grassland: Relatively undisturbed native grasslands/short-height vegetation, such as steppes and tundra, as well as areas that have experienced varying degrees of human activity in the past, which may contain a mix of native and introduced species due to historical land use and natural processes. In general, they exhibit natural-looking patterns of varied vegetation and clearly ordered hydrological relationships throughout the landscape.</li> <li>Open shrubland (v2-beta): Land on which the vegetation is dominated by low-growing woody plants, characterized by a sparse distribution of shrubs and dominated by woody perennials. Typically covers 50—75% of the area, with significant open ground (with or without herbaceous understory) between them, where shrub canopies are less than 10 meters in diameter, and tree cover is below 10%, meaning they do not form a continuous or semi-continuous canopy.</li> </ul> The dataset is organized in 69 global mosaics (25 years for each time series) in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 degrees, including: <ul> <li>Probabilities of cultivated grassland (values range from 0–100),</li> <li>Probabilities of natural/semi-natural grassland (values range from 0–100), and</li> <li>Probabilities of open shrubland (values range from 0–100), and</li> <li>Dominant class (0-other land cover, 1-cultivated grassland and 2-natural/semi-natural grassland, 3-open shrubland).</li> </ul> All raster files are in unsigned <code>8-bit integer format</code> and use <code>255</code> as no-data value (pixels ignored by prediction), following an specific naming convention: <ol> <li>Project name: Global Pasture Watch (<code>gpw</code>)</li> <li>Class name: cultivated grassland (<code>cultiv.grassland</code>), natural/semi-natural grassland (<code>nat.semi.grassland</code>), open shrubland (<code>open.shrubland</code>)  and dominant grassland (<code>grassland</code>)</li> <li>Procedure combination: Random Forest (<code>rf</code>), median filter (med.filt) and balanced threshold (<code>bthr</code>).</li> <li>Variable type: probability (<code>p</code>) and factor class (<code>c</code>)</li> <li>Spatial resolution: 30m</li> <li>Begin of time reference: date of first Landsat composite used by the modeling (<code>20240101</code>)</li> <li>End of time reference: date of last Landsat composite used by the modeling (<code>20241231</code>)</li> <li>Spatial extent: global (<code>go</code>)</li> <li>Coordinate system: World Geodetic System 1984, used in GPS (<code>epsg.4326</code>)</li> <li>Version: v2</li> </ol> <h3>Related resources</h3> <ul> <li>Maps of dominant grassland: <a href="http://doi.org/10.5281/zenodo.15646181">2000-2002</a><a href="http://doi.org/10.5281/zenodo.15644486"> 2003-2005</a><a href="http://doi.org/10.5281/zenodo.15644623"> 2006-2008</a><a href="http://doi.org/10.5281/zenodo.15647042"> 2009-2011</a><a href="http://doi.org/10.5281/zenodo.15647681"> 2012-2014</a><a href="http://doi.org/10.5281/zenodo.15648306"> 2015-2017</a><a href="http://doi.org/10.5281/zenodo.15648551"> 2018-2020</a><a href="http://doi.org/10.5281/zenodo.15648751"> 2021-2023</a> <a href="http://doi.org/10.5281/zenodo.15649332">2024</a></li> <li>Probability maps of cultivated grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li>Probability maps of natural/semi-natural grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li> <div>Grassland reference samples based on VHR imagery (2000–2024): <a href="https://doi.org/10.5281/zenodo.15631655">GeoPackage files</a></div> </li> <li>Global machine learning models (Random Forest): <a href="https://doi.org/10.5281/zenodo.13952806">Parquet and joblib python files</a></li> <li>Reference sampling design derived by FSCV: <a href="https://doi.org/10.5281/zenodo.11391517">GeoPackage and raster files</a></li> <li>Harmonized reference samples based on existing LULC dataset: <a href="https://doi.org/10.5281/zenodo.13951976">GeoPackage and raster files</a></li> <li>Source code for reproducibility: <a href="https://doi.org/10.5281/zenodo.13952867">GitHub release</a> </li> <li>Mapping feedback tool: <a href="https://geo-wiki.org">GeoWiki</a></li> <li>Data catalogues: <a href="https://stac.openlandmap.org/gpw_ggc-30m/collection.json?.language=en">OpenLandMap STAC</a> <a href="https://global-pasture-watch.projects.earthengine.app/view/ggc-30m">Google Earth Engine</a></li> </ul> Support For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in <a href="https://github.com/wri/global-pasture-watch">https://github.com/wri/global-pasture-watch</a>

opencc-by-4.0Oct 2024View details →

Global Pasture Watch - Annual grassland class and extent maps at 30-m spatial resolution (2000—2024)

<h2>Sub-dataset: Dominant grassland class, 2000-2002</h2> <h2>Description</h2> Global annual grassland class and extent for 2000—2024 produced by <a href="https://doi.org/10.1038/s41597-024-04139-6">Parente et al. (2024)</a> within the scope of the <a href="https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring/">Global Pasture Watch initiative</a>. The mapped grassland extent includes any land cover type, which contains at least 30% of dry or wet low vegetation, dominated by grasses and forbs (less than 3 meters) and a: <ul> <li>maximum of 50% tree canopy cover (greater than 5 meters),</li> <li>maximum of 70% of other woody vegetation (scrubs and open shrubland), and</li> <li>maximum of 50% active cropland cover in mosaic landscapes of cropland & other vegetation.</li> </ul> The grassland extent is classified into two classes: <ul> <li>Cultivated grassland: Areas where grasses and other forage plants have been intentionally planted and managed, as well as areas of native grassland-type vegetation where they clearly exhibit active and 'heavy' management for specific human-directed uses, such as directed grazing of livestock.</li> <li>Natural/semi-natural grassland: Relatively undisturbed native grasslands/short-height vegetation, such as steppes and tundra, as well as areas that have experienced varying degrees of human activity in the past, which may contain a mix of native and introduced species due to historical land use and natural processes. In general, they exhibit natural-looking patterns of varied vegetation and clearly ordered hydrological relationships throughout the landscape.</li> <li>Open shrubland (v2-beta): Land on which the vegetation is dominated by low-growing woody plants, characterized by a sparse distribution of shrubs and dominated by woody perennials. Typically covers 50—75% of the area, with significant open ground (with or without herbaceous understory) between them, where shrub canopies are less than 10 meters in diameter, and tree cover is below 10%, meaning they do not form a continuous or semi-continuous canopy.</li> </ul> The dataset is organized in 69 global mosaics (25 years for each time series) in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 degrees, including: <ul> <li>Probabilities of cultivated grassland (values range from 0–100),</li> <li>Probabilities of natural/semi-natural grassland (values range from 0–100), and</li> <li>Probabilities of open shrubland (values range from 0–100), and</li> <li>Dominant class (0-other land cover, 1-cultivated grassland and 2-natural/semi-natural grassland, 3-open shrubland).</li> </ul> All raster files are in unsigned <code>8-bit integer format</code> and use <code>255</code> as no-data value (pixels ignored by prediction), following an specific naming convention: <ol> <li>Project name: Global Pasture Watch (<code>gpw</code>)</li> <li>Class name: cultivated grassland (<code>cultiv.grassland</code>), natural/semi-natural grassland (<code>nat.semi.grassland</code>), open shrubland (<code>open.shrubland</code>)  and dominant grassland (<code>grassland</code>)</li> <li>Procedure combination: Random Forest (<code>rf</code>), median filter (med.filt) and balanced threshold (<code>bthr</code>).</li> <li>Variable type: probability (<code>p</code>) and factor class (<code>c</code>)</li> <li>Spatial resolution: 30m</li> <li>Begin of time reference: date of first Landsat composite used by the modeling (<code>20240101</code>)</li> <li>End of time reference: date of last Landsat composite used by the modeling (<code>20241231</code>)</li> <li>Spatial extent: global (<code>go</code>)</li> <li>Coordinate system: World Geodetic System 1984, used in GPS (<code>epsg.4326</code>)</li> <li>Version: v2</li> </ol> <h3>Related resources</h3> <ul> <li>Maps of dominant grassland: <a href="http://doi.org/10.5281/zenodo.15646181">2000-2002</a><a href="http://doi.org/10.5281/zenodo.15644486"> 2003-2005</a><a href="http://doi.org/10.5281/zenodo.15644623"> 2006-2008</a><a href="http://doi.org/10.5281/zenodo.15647042"> 2009-2011</a><a href="http://doi.org/10.5281/zenodo.15647681"> 2012-2014</a><a href="http://doi.org/10.5281/zenodo.15648306"> 2015-2017</a><a href="http://doi.org/10.5281/zenodo.15648551"> 2018-2020</a><a href="http://doi.org/10.5281/zenodo.15648751"> 2021-2023</a> <a href="http://doi.org/10.5281/zenodo.15649332">2024</a></li> <li>Probability maps of cultivated grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li>Probability maps of natural/semi-natural grassland: <a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li> <div>Grassland reference samples based on VHR imagery (2000–2024): <a href="https://doi.org/10.5281/zenodo.15631655">GeoPackage files</a></div> </li> <li>Global machine learning models (Random Forest): <a href="https://doi.org/10.5281/zenodo.13952806">Parquet and joblib python files</a></li> <li>Reference sampling design derived by FSCV: <a href="https://doi.org/10.5281/zenodo.11391517">GeoPackage and raster files</a></li> <li>Harmonized reference samples based on existing LULC dataset: <a href="https://doi.org/10.5281/zenodo.13951976">GeoPackage and raster files</a></li> <li>Source code for reproducibility: <a href="https://doi.org/10.5281/zenodo.13952867">GitHub release</a> </li> <li>Mapping feedback tool: <a href="https://geo-wiki.org">GeoWiki</a></li> <li>Data catalogues: <a href="https://stac.openlandmap.org/gpw_ggc-30m/collection.json?.language=en">OpenLandMap STAC</a> <a href="https://global-pasture-watch.projects.earthengine.app/view/ggc-30m">Google Earth Engine</a></li> </ul> Support For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in <a href="https://github.com/wri/global-pasture-watch">https://github.com/wri/global-pasture-watch</a>

opencc-by-4.0Jan 2022View details →

Model outputs from the study "A scalable framework for soil property mapping tested across a highly diverse tropical data-scarce region"

Model outputs from the study "A scalable framework for soil property mapping tested across a highly diverse tropical data-scarce region". The study is published as open access and can be found at the following link: <a href="https://www.sciencedirect.com/science/article/pii/S2950289625000326">https://www.sciencedirect.com/science/article/pii/S2950289625000326</a>   The file "SWAT_USERSOIL.csv" was included to facilitate the assimilation of the soil mapping data into the Soil & Water Assessment Tool (SWAT, https://swat.tamu.edu/) for hydrological modeling.    Regarding the raster files, please note: a) All values in these datasets have been multiplied by 10,000 to optimize file sizes. b) Files are named using the variable acronym, followed by the corresponding soil layer. For outputs derived from pedotransfer functions (PTFs), the PTF reference is appended after the variable acronym. c) Available data decrease with increasing soil layer number. This occurs because not all locations (grid cells) have the same soil depth or number of soil layers.   If you have any questions about the dataset or its use, please don't hesitate to contact us.

SeMRA Gene Mappings Database

Analyze the landscape of gene nomenclature resources, species-agnostic. See instructions for reproduction and usage in the attached README.md.

SeMRA Protein Complex Mappings Database

Analyze the landscape of protein complex nomenclature resources, species-agnostic. See instructions for reproduction and usage in the attached README.md.

SeMRA Anatomy Mappings Database

Supports the analysis of the landscape of anatomy nomenclature resources. See instructions for reproduction and usage in the attached README.md.

SeMRA Cell and Cell Line Mappings Database

Originally a reproduction of the EFO/Cellosaurus/DepMap/CCLE scenario posed in the Biomappings paper, this configuration imports several different cell and cell line resources and identifies mappings between them. See instructions for reproduction and usage in the attached README.md.

SeMRA Disease Mappings Database

Supports the analysis of the landscape of disease nomenclature resources. See instructions for reproduction and usage in the attached README.md.

opencc-by-4.0Apr 2021View details →

Viabundus map of premodern European transport and mobility

Viabundus.eu is a freely accessible online street map of late medieval and early modern northern Europe (1350-1650). Originally conceived as the digitisation of Friedrich Bruns and Hugo Weczerka's Hansische Handelsstraßen (1962) atlas of land roads in the Hanseatic area, the Viabundus map moves beyond that. It includes among others: a database with information about settlements, towns, tolls, staple markets and other information relevant for the pre-modern traveller; a route calculator; a calendar of fairs; and additional land routes as well as water ways. Viabundus is a work in progress. Version 2, released on 25 April 2025, contains a rough digitisation of the land routes from Hansische Handelsstraßen, as well as a thoroughly researched road network for the current-day Netherlands, Denmark, Finland, the German states of Lower Saxony, Schleswig-Holstein, Thuringia, Saxony-Anhalt, Brandenburg, Mecklenburg-Vorpommern, Hesse and North Rhine-Westfalia, and parts of Poland (Pomerania, Royal Prussia, Greater Poland). The inclusion of other regions is currently being planned. Additions to the dataset will be released as new versions in the future. The project's homepage viabundus.eu contains a web map application to explore the data. To allow for more advanced spatial and historical analyses, the underlying dataset is available for download under the CC-BY-SA license. The dataset is designed as a network model and therefore consists of two main elements: 1) a relational database of nodes, i.e. geographical places, with historical information about settlements, towns, tolls, staple markets, fairs, bridges, ferries, harbours and shipping locks; 2) a database with edges, i.e. the geospatial representations of the land and water routes that connected these nodes. The entire database is available in CSV format (with geospatial geometry as WKT); the edges and the outlines of towns in the 16th century are also separately available as geojson and GML files. For more information about the structure of the dataset, theoretical considerations and sources, please consult the enclosed documentation file.

opencc-by-4.0Mar 2024View details →

HR land use maps part 2

High resolution land use maps raster layers (.tif) with a spatial resolution of 10 m created by RethinkAcion project. These datasets are in ETRS89 in line with those that will be uploaded to the platform of the project. This v2 includes metadata

opencc-by-4.0Oct 2023View details →

Map data of historical global estimates of soil respiration

The map data of global soil respiration converted to NetCDF format.All open access available estimates were collated.Shoji Hashimoto, Akihiko Ito, Kazuya Nishina (2023) "Divergent data-driven estimates of global soil respiration". Communications Earth & Environment, 4 Article number: 460<a href="https://doi.org/10.1038/s43247-023-01136-2 ">https://doi.org/10.1038/s43247-023-01136-2</a> Refer to Table 1 for the study ID and data source or the attributions of the NetCDF file.

opencc-by-4.0Aug 2023View details →

Phlorest phylogeny derived from Bouckaert et al. 2012 'Mapping the Origins and Expansion of the Indo-European Language Family'

Cite the source of the dataset as: <blockquote> Bouckaert RR, Lemey P, Dunn M, Greenhill SJ, Alekseyenko AV, Drummond AJ, Gray RD, Suchard MA & Atkinson QD. 2012. Mapping the Origins and Expansion of the Indo-European Language Family. Science, 337(6097), 957-960. </blockquote>

opencc-by-4.0Jun 2023View details →

Screened literature for mapping review of environmental problem shifting (EPS)

Final literature sample used in manuscript. (506 publications). Version 2 after revision. Columns from the Scopus API:<ul><li>DOI, ISSN, ISBN</li><li>Subjabbr: Abbreviation of Scopus subject area</li><li>Publication year</li><li>Pubname: The name of the journal</li><li>Authkeywords: The article keywords</li><li>Title: The document title</li></ul> Columns from our classification:<ul><li>Articletype: Classification into empirical, review and conceptual-theoretical.</li><li>Documenttype: The type of publication: journal article, book chapter, reports.</li><li>Counter: Marks the 13 studies that find no shifting or positive environmental impacts only.</li><li>Miti measures: The type of mitigation option.</li><li>Split measures: The subtype of mitigation. Only coded for CDR; Solar, hydro and wind power and Other.</li><li>Methods: The method(s) used in empirical papers</li><li>Impacts: The environmental impact.</li><li>Split impacts: The environmental impact specified for subtypes of mitigation.</li></ul>

Integrated disturbance mapping over the Tibetan Plateau based on multiple detection algorithms

This dataset contains the mapped representation of vegetation disturbance across the Tibetan Plateau, rendered at a 30-meter spatial resolution. The dataset comprehensively illustrates the extent of vegetation disturbance on the Tibetan Plateau during the period spanning 1986 to 2020. Notably, the map employs a categorization system, with values assigned to distinct classes: 0 denotes areas of disturbed vegetation, 1 denotes regions characterized by undisturbed vegetation, and 2 denotes zones devoid of vegetation.

opencc-by-4.0Jun 2023View details →

Redistribution of the Natura 2000 habitat map of Flanders, partim habitat type 3260 (version 2023)

This is a redistribution of a subdataset of the data source '<a href="https://www.vlaanderen.be/datavindplaats/catalogus/biologische-waarderingskaart-en-natura-2000-habitatkaart-toestand-2023">Biologische Waarderingskaart en Natura 2000 Habitatkaart - Toestand 2023</a>', originally published by the Research Institute for Nature and Forest (INBO) and distributed by 'Digitaal Vlaanderen' under a CC-BY compatible license. It is redistributed for reproducible, analytical workflows on Flemish Natura 2000 habitats and regionally important biotopes.The subdataset is a shapefile of line segments of the Natura 2000 habitat type 3260 (Watercourses of plain to montane levels with the Ranunculion fluitantis and Callitricho-Batrachion vegetation) that correspond with its presence in watercourses in the Flemish Region, identical to the shapefile Hab3260 in the original data source.The data source is produced, owned and administered by the Research Institute for Nature and Forest (INBO, Department of Environment of the Flemish government).

Atrophy Pattern Maps of Frontotemporal Dementia variants (bvFTD, svPPA, pnfaPPA)

The files contain voxel-wise t-statistics maps contrasting deformation based morphometry (DBM) measurements of frontotemporal dementia (FTD) patients, divided according to subtype diagnosis, against matched normal controls. BV = behavioral-variant Frontotemporal Dementia SV = semantic-variant Primary Progressive Aphasia PNFA = non-fluent-variant Primary Progressive Aphasia   FTD map is based on NIFD data, available at: https://memory.ucsf.edu/research/studies/nifd For more information regarding the participants and method details, see: Dadar, Mahsa, et al. "White matter hyperintensities are associated with grey matter atrophy and cognitive decline in Alzheimer's disease and frontotemporal dementia." Neurobiology of aging 111 (2022): 54-63. Metz, Amelie, et al. "Brain Atrophy and White Matter Hyperintensities in Frontotemporal Dementia Variants and their Impact on Cognition" [Conference presentation] CCNA 2024 Partners Forum and Science Days (2024, March 19-21).

Data from: "Mapping opportunities and barriers for coexistence between people and pumas in the Argentine Dry Chaco"

Data from: "Mapping opportunities and barriers for coexistence between people and pumas in the Argentine Dry Chaco"====================================================Datasets include:  1. .csv file with puma occurrence data in 149 sites and analyzed covariates (pumadata.csv)2. .txt file with puma occupancy modelling scripts (pumascript.txt).3. .txt. file with interview data (intdataf.txt).4. .txt file with scripts for conflict risk modelling and mapping (confscript.txt).5. three zip files with .tiff of the three covariates used for conflict risk mapping (goat.zip, disturbedpr.zip,pue1km.zip).6. .csv file with protected area data: number of pixels for each landscape type within all protected areas,strict protected areas and multiple-use protected areas (PAchaco.csv). 7. Read me file with more information on datasets (README.txt)

Explore maps as you read a comic book

This repository contains three documents accompanying the article 'Explore maps as you read a comic book. The first document compiles several types of breaks encountered in various pan-scalar maps. Each type is also assigned a progression note after an analysis of their effects on the user. The next table illustrates an example of good generalisation for each of the hydrographic patterns observed in the article. This generalisation is based on three key concepts of progressivity, promoting a continuity of meaning, coherence, and rhythm. The final document represents a scale master inspired by the methodology of (Brewer and Buttenfield, 2007). In the article, we demonstrate how to adapt it to pan-scalar maps to better visualize sequences, as well as two types of rhythms: map cadence and abstraction cadence.