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.
196
datasets available to search
ShareScore release 0.9.0
Dataset results
196 results for “Spatial map”
Supplementary material 1 from: Prodanov B, Dimitrov L, Kotsev I, Bekova R, Lambev T (2023) Spatial distribution of sand dunes along the Bulgarian Black Sea coast: inventory, UAS mapping and new discoveries. Nature Conservation 54: 81-120. https://doi.org/10.3897/natureconservation.54.105507
List of identified beach-dune systems along the Bulgarian Black Sea coast
Appendix B of Berg, Afruni et al. 2024, entitled:' Mapping the spatial extent of HI-rich absorbers using MgII absorption along gravitational arcs', accepted for publication on A&A
<p>J1527 velocity profiles</p>
Appendix C of Berg, Afruni et al. 2024, entitled:' Mapping the spatial extent of HI-rich absorbers using MgII absorption along gravitational arcs', accepted for publication on A&A
<p>J0033 velocity profiles</p>
Beagle kelp maps from: One of the least disturbed marine coastal ecosystems on Earth: Spatial and temporal persistence of Darwin's sub-Antarctic giant kelp forests
<p>Aim: Marine habitats and their dynamics are difficult to systematically monitor, particularly those in remote locations. This is the case with the sub-Antarctic ecosystem of the giant kelp <i>Macrocystis pyrifera</i>, which was already noted by Charles Darwin in his accounts on the <i>Voyage of the Beagle</i> and recorded on the nautical charts made during that expedition. We combined these and other nautical charts from the 19th and early 20th centuries with surveys conducted in the 1970s and 1980s and satellite detection algorithms from 1984 to 2019, to analyse kelp distribution through time and the factors that correlate with it.</p> <p>Location: Marine ecoregions of Channels and Fjords of Southern Chile, Falkland Islands (Malvinas), and the island of South Georgia.</p> <p>Taxon: <i>Macrocystis pyrifera</i>.</p> <p>Methods: We characterised 309 giant kelp forests by their coastal geospatial attributes. Statistically significant variables were included in a conditional inference tree to predict kelp forest size. Sea surface temperature (SST) records were analysed to confirm temperature ranges over the last four decades. Nautical charts, historical surveys, aerial photogrammetry, unmanned aerial vehicle (UAV) surveys and satellite imagery were overlaid to assess spatial distribution of kelp forest canopies, spanning the period 1829–2020.</p> <p>Results: Considering the extensive natural and human caused changes over the last two centuries, this diverse kelp ecosystem is remarkably persistent. We found that the ocean currents and wave exposure, combined with the geomorphological settings of the coastline are the most critical factors predicting the extent of the kelp forests.</p> <p>Main conclusions: We have described the long-term ecological persistence of the kelp forests in this vastly under-studied region that offers a conceptual biogeographical model supporting the global importance proposed by Charles Darwin 200 years ago (Darwin, 1845). In the current context of global change, the need for conservation of this persistent and well-preserved marine ecosystem has never been more important.</p>
Supplementary material 4 from: Nedkov S, Zhiyanski M, Dimitrov S, Borisova B, Popov A, Ihtimanski I, Yaneva R, Nikolov P, Bratanova-Doncheva S (2017) Mapping and assessment of urban ecosystem condition and services using integrated index of spatial structure. One Ecosystem 2: e14499. https://doi.org/10.3897/oneeco.2.e14499
Contains descriptions of urban ecosystem subtypes and their relation to EUNIS habitat classess
Supplementary material 3 from: Nedkov S, Zhiyanski M, Dimitrov S, Borisova B, Popov A, Ihtimanski I, Yaneva R, Nikolov P, Bratanova-Doncheva S (2017) Mapping and assessment of urban ecosystem condition and services using integrated index of spatial structure. One Ecosystem 2: e14499. https://doi.org/10.3897/oneeco.2.e14499
Map of urban ecosystem condition representing an example of map sheets that cover the whole country
Spatial probability maps of the main morphological patterns of the inferior frontal sulcus in fsaverage space
Open the record for dataset details and reuse information.
Maps of forest-smallholder homesteads in the Chaco at 10x10km² spatial resolution (1985-2015)
<p>The data contained in the three ZIP files represents the following information on smallholder homestead distribution and dynamics across the Gran Chaco ecoregion:<br> - presence of smallholder homesteads for target years in five-year intervals between 1985 and 2015 [% per grid cell]<br> - net loss of smallholder homesteads between five-year intervals between 1985 and 2015 [% per grid cell]<br> - net gain of smallholder homesteads between five-year intervals between 1985 and 2015 [% per grid cell]</p> <p>The original, digitized point data cannot be made publicly available because it could potentially increase the vulnerability of smallholders and/or results in (re-)identification of households. The raw data are maintained at the Conservation Biogeography Lab at the Geography Department of Humboldt-University Berlin (http://hu.berlin/biogeo).<br> Please contact Tobias Kuemmerle (tobias.kuemmerle@hu-berlin.de) for more information.</p>
HiPR-FISH Spatial Mapping of Cheese Rind Microbial Communities
<p>This dataset is associated with this HiPR-FISH Spatial Mapping of Cheese Rind Microbial Communities pub from Arcadia Science.</p> <p>HiPR-FISH spatial imaging was used to look at the distribution of microbes within five distinct microbial communities growing on the surface of aged cheeses. Probe design and imaging was performed by Kanvas Biosciences. </p> <p>This dataset includes the following:</p> <ul> <li>For each field of view (roughly 135µm x 135µm; 7 FOVs per each cheese specimen): <ul> <li>A fluorescence intensity image (*_spectral_max_projection.png/.tif).</li> <li>A pseudo-colored microbe-labeled image (*_identification.png/.tif).</li> <li>A data frame contains each identified microbe's identity, position, and size (*_cell_information.csv).</li> <li>A segmented mask for microbiota (*_segmentation.png/.tif)</li> <li>A spatial proximity graph for each species close to each other, showing the spatial enrichment over random distribution (*_spatialheatmap.png).</li> <li>A corresponding data frame used to generate the spatial proximity graph (*_absolute_spatial_association.csv) and dataframe for the average of 500 random shuffles of the taxa (*_randomized_spatial_association_matrix.csv). </li> </ul> </li> <li>For each cheese specimen: <ul> <li>A widefield image with FOVs located on the image (*_WF_overlay.png).</li> </ul> </li> <li>In general: <ul> <li>A png showing the color legend for each species. (ARC1_taxa_color_legend.png)</li> <li>A data frame showing the environmental location of each FOV in the cheese (RIND/CURD) and the location of each FOV relative to FOV 1. (ARC1_Cheese_Map.csv).</li> <li>A vignette showing an example of each cell and its false coloring according to its taxonomic identification (ARC1_detected_species_representative_cell_vignette.png).</li> <li>Sequences used as input in probe design (16S_18S_forKanvas.fasta).</li> <li>A CSV file containing the sequences that belong to each ASV (ARC1_sequences_to_ASVs.csv).</li> <li>Plots of log-transformed counts for each microbe detected across all FOVs, and broken down for each cheese (*detected_species_absolute_abundance.png).</li> <li>CSVs containing pairwise correlation of FOVs based on spatial association (ARC1_spatial_association_FOV_correlation.csv) and microbial abundance (ARC1_abundance_FOV_correlation.csv).</li> <li>Plots of spatial association matrices, aggregated for different cheeses and different locations (RIND vs CURD) (*samples_*loc_relative_spatial_association.png).</li> <li>CSV containing the principle component coordinates for each FOV (ARC1_abundance_FOV_PCA.csv, ARC1_spatial_association_FOV_PCA.csv).</li> <li>CSV containing the mean fold-change in number of edges between each ASV and the corresponding p-value when compared to the null state (random spatial association matrices) (ARC1_spatial_enrichment_significance.csv).</li> </ul> </li> </ul>
Data from: Mapping phosphorus hotspots in Sydney’s organic wastes: a spatially-explicit inventory to facilitate urban phosphorus recycling
Open the record for dataset details and reuse information.
Maps of forest-smallholder homesteads in the Chaco at 10x10km² spatial resolution (1985-2015)
Open the record for dataset details and reuse information.
Data from: Spatial detection of outlier loci with Moran eigenvector maps (MEM)
Open the record for dataset details and reuse information.
Beagle kelp maps from: One of the least disturbed marine coastal ecosystems on Earth: Spatial and temporal persistence of Darwin’s sub-Antarctic giant kelp forests
Open the record for dataset details and reuse information.
High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis
GEO Series GSE243280. Homo sapiens. 7 samples. Type: Other; Expression profiling by high throughput sequencing.
Spatial transcriptomic mapping of the mouse brain in chronic stress
GEO Series GSE263450. Mus musculus. 6 samples. Type: Other.
Spatiotemporal transcriptomic map of glial cell response in a mouse model of acute brain ischemia [spatial transcriptomics]
GEO Series GSE233814. Mus musculus. 5 samples. Type: Expression profiling by high throughput sequencing; Other.
Primary Aldosteronism: Spatial Multiomics Mapping of Genotype-Dependent Heterogeneity and Tumor Expansion of Aldosterone-Producing Adenomas
GEO Series GSE274314. Homo sapiens. 14 samples. Type: Other.
Spatial Compartmentalization at the Nuclear Periphery Characterized by Genome-wide Mapping
GEO Series GSE41583. Mus musculus. 10 samples. Type: Other; Genome binding/occupancy profiling by high throughput sequencing.
Cell-Type Profiling of the Sympathetic Nervous System Using Spatial Transcriptomics and Spatial Mapping of mRNA [Slide-seq]
GEO Series GSE230776. Gallus gallus. 1 samples. Type: Expression profiling by high throughput sequencing.
A prenatal window for enhancing spatial resolution of cortical barrel maps
GEO Series GSE260865. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
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.