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.
3,105
datasets available to search
ShareScore release 0.9.0
Dataset results
3,105 results for “Vegetation =”
Russian Arctic Vegetation Archive – a new database of plant community composition and environmental conditions
<p><span>The goal of the Russian Arctic Vegetation Archive is to unite and harmonize data of plot-based plant species and their abundance, vegetation structure and environmental variables from the Russian Arctic (including historical Soviet data). This database can be used to assess the status of the Russian Arctic vegetation and as a baseline to assess biodiversity changes in the future. The archive can be used for scientific studies as well as to inform nature protection and restoration efforts.</span></p>
500-year periodic vegetation and monsoonal climate oscillations during the last deglaciation in East Asia
<p>1 Core Xiaolongwan description</p> <p>2 Age-depth models</p> <p>3 Pollen counts</p> <p>4 Pollen concentration</p> <p>5 Pollen percentage</p> <p>6 HHT Betula</p> <p>7 HHT Boreal Conifer with Herb</p> <p>8 HHT Artemisia</p> <p>9 HHT Broadleaved Tree</p> <p>10 Long chain <em>n</em>-alkanes δ<sup>13</sup>C</p>
Vegetation sampling around Balma del Gai site (Moià, Barcelona, Spain); Spring 2023
<p>Vegetation sampling raw data conducted during May 2023, aimed to assess the diversity and abundance of vegetation in the vicinity of the Balma del Gai site, located in the Moià plateau, Spain. Various transects were conducted within the study area to systematically record and analyze the presence and frequency of different plant taxa.</p>
Currier et al 2023 - AMS - Vegetation Represenation
<pre>Necessary files to run VIC (besides the model forcing data) for Currier et al. 2023: "Vegetation Representation Influences Projected Streamflow Changes in the Colorado River Basin". </pre>
Fig. 8. Peruvian vegetation types. A. Amazonian forest, Pasco Region. B. Northwest Peruvian montane forest, Piura Region. C. Dry forest, Piura Region. D in The genus Begonia (Begoniaceae) in Peru
Fig. 8. Peruvian vegetation types. A. Amazonian forest, Pasco Region. B. Northwest Peruvian montane forest, Piura Region. C. Dry forest, Piura Region. D. Lomas, Lima Region. All photographs taken by P.W. Moonlight.
FIG. 4 in Lichen community assemblages and functional traits as indicators of vegetation types in central Mexico, based on herbarium specimens
FIG. 4. — Community weighted mean proportions of functional traits for each vegetation type: A, growth forms; B, substrate; C, reproductive structure types; D, photobionts.
FIG. 3 in Lichen community assemblages and functional traits as indicators of vegetation types in central Mexico, based on herbarium specimens
FIG. 3. — Ordination and clustering of lichen communities from the different vegetation types: A, non-parametric multidimensional scaling (NDMS); B, cluster analysis dendrogram. The sites Cerro el Capulín and Cerro Juan el Grande show different affinities in both analyses (part of the xerophytic shrubland in the NDMS, but more similar to the Quercus L. forest in the dendrogram, labeled as the Xerophytic-Quercus group in the latter).
FIG. 5 in Lichen community assemblages and functional traits as indicators of vegetation types in central Mexico, based on herbarium specimens
FIG. 5. — Non-metric Multidimensional Scaling analysis of lichen community functional traits and environmental variables in three vegetation types. * (p <0.05) and ** (p <0.005) represent the significance of functional traits related to the Xerophytic shrubland (X), Subtropical shrubland (S), and Quercus L. forest (Q).
FIG. 1 in Lichen community assemblages and functional traits as indicators of vegetation types in central Mexico, based on herbarium specimens
FIG. 1. — Amplitude and mean values of alpha diversity (species richness) per vegetation type. Symbols: black dot, outlier; white dots, mean values.
FIG. 6 in Lichen community assemblages and functional traits as indicators of vegetation types in central Mexico, based on herbarium specimens
FIG. 6. — Correlation of lichen community functional traits and environmental variables in three vegetation types.
FIG. 2 in Lichen community assemblages and functional traits as indicators of vegetation types in central Mexico, based on herbarium specimens
FIG. 2. — Beta diversity and shared species per vegetation type: A, pairwise comparisons of the different elements of beta diversity (Xerophytic, xerophytic shrubland; Quercus, Quercus forest; Subtropical, subtropical shrubland); B, Venn diagram showing exclusive and shared species among the vegetation types included in this study.
Measured data of global fractional vegetation cover from 2013-2021 and algorithm code for calculating remote sensing products
<p>These data come from "A new computationally efficient algorithm to generate global fractional vegetation cover from Sentinel-2 imagery at 10m resolution", these include:</p> <p>1. Measured data of global fractional vegetation cover from 2013-2021 </p> <p>2. Algorithm code for calculating fractional vegetation cover, these codes are written by JavaScript in GEE (Google Earth Engine).</p>
Dense vegetation hinders sediment transport towards saltmarsh interiors - Supporting data and source code (Part I: Pre-processing)
<p>This is Part I of the supporting data and source code for the paper entitled "Dense vegetation hinders sediment transport towards saltmarsh interiors", submitted to <em>Limnology and Oceanography Letters.</em> It contains all input and output files to generate the simulation grids.</p>
Figure 2 in Could Fidicina mannifera (Hemiptera: Cicadoidea: Fidicinini) promote a resource pulse in two Brazilian Cerrado vegetation classes?
Figure 2. Profile of lipids (mg g–1) in adult males and females of F. mannifera along emerging period, collected on the Agroecological Technology Center for Small Farmers (AGROTEC), Diorama, Goiás. Open diamonds demonstrate the amount of lipids present in females.
Figure 3 in Could Fidicina mannifera (Hemiptera: Cicadoidea: Fidicinini) promote a resource pulse in two Brazilian Cerrado vegetation classes?
Figure 3. Precipitation (mm) for the months sampled in 2013 in the areas of cerrado woodland (closed symbol) and gallery forest (open symbol) in the Agroecological Technology Center for Small Farmers (AGROTEC), Diorama, GO, Brazil.
Figure 1 in Could Fidicina mannifera (Hemiptera: Cicadoidea: Fidicinini) promote a resource pulse in two Brazilian Cerrado vegetation classes?
Figure 1. Proteins (mg g–1) in adult males and females of F. mannifera along the emerging period, collected on the Agroecological Technology Center for Small Farmers (AGROTEC), Diorama, Goiás. Open diamonds show the amount of protein present in females.
Vegetation Density Across NYC: Analysis of Land Cover Data (2017) within 200 meter Buffers of Points
<p><strong>Summary:</strong></p><p>This repository contains spatial data files representing the density of vegetation cover within a 200 meter radius of points on a grid across the land area of New York City (NYC), New York, USA based on 2017 six-inch resolution land cover data, as well as SQL code used to carry out the analysis. The 200 meter radius was selected based on a study led by researchers at the NYC Department of Health and Mental Hygiene, which found that for a given point in the city, cooling benefits of vegetation only begin to accrue once the vegetation cover within a 200 meter radius is at least 32% (Johnson et al. 2020). The grid spacing of 100 feet in north/south and east/west directions was intended to provide granular enough detail to offer useful insights at a local scale (e.g., within a neighborhood) while keeping the amount of data needed to be processed for this manageable. </p><p>The contained files were developed by the NY Cities Program of <a href="https://www.nature.org/newyork">The Nature Conservancy</a> and the <a href="https://nyc-eja.org/">NYC Environmental Justice Alliance</a> through the <a href="https://medium.com/gage-nyc/introducing-the-just-nature-nyc-partnership-513612e8c3b4">Just Nature NYC Partnership</a>. Additional context and interpretation of this work is available in a <a href="https://medium.com/gage-nyc/looking-at-cooling-benefits-of-plants-through-nyc-vegetation-data-ccdeb33cbe17">blog post</a>.</p><p> </p><p><i>References:</i></p><p>Johnson, S., Z. Ross, I. Kheirbek, and K. Ito. 2020. Characterization of intra-urban spatial variation in observed summer ambient temperature from the New York City Community Air Survey. <i>Urban Climate</i> 31:100583. <a href="https://doi.org/10.1016/j.uclim.2020.100583">https://doi.org/10.1016/j.uclim.2020.100583</a></p><p> </p><p><strong>Files in this Repository:</strong></p><p>Spatial Data (all data are in the New York State Plane Coordinate System - Long Island Zone, North American Datum 1983, <a href="https://epsg.io/2263">EPSG 2263</a>):</p><p>Points with unique identifiers (<i>fid</i>) and data on proportion tree canopy cover (<i>prop_canopy</i>), proportion grass/shrub cover (<i>prop_grassshrub</i>), and proportion total vegetation cover (<i>prop_veg</i>) within a 200 meter radius (same data made available in two commonly used formats, Esri File GeoDatabase and GeoPackage):</p><p><i>nyc_propveg2017_200mbuffer_100ftgrid_nowater.gdb.zip</i></p><p><i>nyc_propveg2017_200mbuffer_100ftgrid_nowater.gpkg</i> </p><p>Raster Data with the proportion total vegetation within a 200 meter radius of the center of each cell (pixel centers align with the spatial point data)</p><p><i>nyc_propveg2017_200mbuffer_100ftgrid_nowater.tif</i></p><p>Computer Code:</p><p>Code for generating the point data in PostgreSQL/PostGIS, assuming the data sources listed below are already in a PostGIS database.</p><p><i>nyc_point_buffer_vegetation_overlay.sql</i></p><p> </p><p><strong>Data Sources and Methods:</strong></p><p>We used two openly available datasets from the City of New York for this analysis:</p><p>Borough Boundaries (Clipped to Shoreline) for NYC, from the NYC Department of City Planning, available at <a href="https://www.nyc.gov/site/planning/data-maps/open-data/districts-download-metadata.page">https://www.nyc.gov/site/planning/data-maps/open-data/districts-download-metadata.page</a> </p><p>Six-inch resolution land cover data for New York City as of 2017, available at <a href="https://data.cityofnewyork.us/Environment/Land-Cover-Raster-Data-2017-6in-Resolution/he6d-2qns">https://data.cityofnewyork.us/Environment/Land-Cover-Raster-Data-2017-6in-Resolution/he6d-2qns</a> </p><p>All data were used in the New York State Plane Coordinate System, Long Island Zone (<a href="https://epsg.io/2263">EPSG 2263</a>). Land cover data were used in a polygonized form for these analyses.</p><p>The general steps for developing the data available in this repository were as follows:</p><p>Create a grid of points across the city, based on the full extent of the Borough Boundaries dataset, with points 100 feet from one another in east/west and north/south directions</p><p>Delete any points that do not overlap the areas in the Borough Boundaries dataset.</p><p>Create circles centered at each point, with a radius of 200 meters (656.168 feet) in line with the aforementioned paper (Johnson et al. 2020).</p><p>Overlay the circles with the land cover data, and calculate the proportion of the land cover that was grass/shrub and tree canopy land cover types. Note, because the land cover data consistently ended at the boundaries of NYC, for points within 200 meters of Nassau and Westchester Counties, the area with land cover data was smaller than the area of the circles.</p><p>Relate the results from the overlay analysis back to the associated points.</p><p>Create a raster data layer from the point data, with 100 foot by 100 foot resolution, where the center of each pixel is at the location of the respective points. Areas between the Borough Boundary polygons (open water of NY Harbor) are coded as "no data."</p><p>All steps except for the creation of the raster dataset were conducted in PostgreSQL/PostGIS, as documented in <i>nyc_point_buffer_vegetation_overlay.sql</i>. The conversion of the results to a raster dataset was done in QGIS (version 3.28), ultimately using the <a href="https://gdal.org/programs/gdal_rasterize.html">gdal_rasterize</a> function.</p>
Focal-TSMP: Deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation
<p>This is the preprocessed remote sensing dataset used in the paper<strong> "Focal-TSMP: Deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation"</strong>. It contains the preprocessed NOAA data along with the additional files necessary for the TSMP simulation.</p>
Data and model code for: Habitat use patterns suggest that climate-driven vegetation changes will negatively impact mammal communities in the Amazon (ACV)
Open the record for dataset details and reuse information.
Data from: Vegetation dynamics 1946–2018 in an old-growth conifer forest
Open the record for dataset details and reuse information.
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.