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1,868 results for “Spatial Data”
Data from: The effects of human-altered habitat spatial pattern on frugivory and seed dispersal: a global meta-analysis
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Data from: Floral spatial morphological characteristics preferences facilitate mating and predation in flower-visiting spiders
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Data and code from: Traits and phylogenies modulate the environmental responses of wood-inhabiting fungal communities across spatial scales
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Data from: Predicting disease risk areas through co-production of spatial models: the example of Kyasanur Forest Disease in India’s forest landscapes
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Data for: Modeling of spatial pattern and influencing factors of cultivated land quality based on spatial-temporal big data (PONE-D-21-21084R1)
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Data from: Philopatry influences the genetic population structure of the blacktip shark (Carcharhinus limbatus) at multiple spatial scales
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Data from: Overlap of spatial and temporal spawning distributions of spring and summer Chinook Salmon results in hybridization in the upper Columbia River
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Data from: Accounting for uncertainty in marine ecosystem service predictions for spatial prioritisation
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Data from: Joint spatial modeling of cluster size and density for a heavily hunted primate persisting in a heterogeneous landscape
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Data from: Organic electro-scattering antenna: Wireless and multisite probing of electrical potentials with high spatial resolution
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Data from: Assessing spatial patterns of soil erosion in a high‐latitude rangeland
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Data from: Drivers of intra-individual spatial variability in methane emissions from tree trunks in upland forest
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Data for: Thermal vulnerability in a mountain stream network: Temporal, spatial, and biological data
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A machine learning approach to integrating genetic and ecological data in tsetse flies (Glossina pallidipes) for spatially explicit vector control planning
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Data from: Genetic and spatial variation in vegetative and floral traits across a hybrid zone
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Highly multiplexed histology reveals phenotypic and spatial characteristics of human Innate Lymphoid Cells in chronic inflammation - MELC colon data-set
<p> 50 marker MELC Run in human colon. Each image shows the same field of view, sequentially stained with the depicted fluorescence-labelled antibodies, including surface proteins and transcription factors. Images contain 2024 x 2024 pixels and are generated using an inverted wide-field fluorescence microscope with a 20x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Images have not been normalized and intensities have not been adjusted.</p>
Data Supplement: U.S. state-level projections of the spatial distribution of population consistent with Shared Socioeconomic Pathways.
<p>These data are to supplement the following in-press publication: </p> <p>Zoraghein, H., and O'Neill B. (2020). U.S. state-level projections of the spatial distribution of population consistent with Shared Socioeconomic Pathways. Sustainability.</p> <p>The data herein were generated using the `population_gravity` model which can be found here: <a href="https://github.com/IMMM-SFA/population_gravity">https://github.com/IMMM-SFA/population_gravity</a></p> <p>CONTENTS:</p> <p><strong>zoraghein-oneill_population_gravity_inputs_outputs.zip</strong></p> <ul> <li>contains a directory for each U.S. state for inputs and outputs</li> <li><strong>inputs</strong> contain the following: <ul> <li><strong><state-name>_<urban or rural>_<yr>_1km.tif: </strong> Urban and Rural population GeoTIF rasters at a 1km resolution <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> <li><strong><state-name>_mask_short_term.tif: </strong> Mask GeoTIF rasters at a 1km resolution that contain values from 0.0 to 1.0 for each 1 km grid cell to help calculate suitability depending on topographic and land use and land cover characteristics <ul> <li>value per grid cell: values from 0.0 to 1.0 (float) that are generated from topographic and land use and land cover characteristics to inform suitability as outlined in the companion publication</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> <li><strong><state-name>_<ssp>_popproj.csv: </strong> Population projection CSV files for urban, rural, and total population (number of humans; float) for SSPs 2, 3, and 5 for years 2010-2100</li> <li><strong><state-name>_coordinates.csv: </strong>CSV file containing the coordinates for each 1 km grid cell within the target state. File includes a header with the fields XCoord, YCoord, FID.,Where data types and field descriptions are as follows: (XCoord, float, X coordinate in meters),(YCoord, float, Y coordinate in meters),(FID, int, Unique feature id)</li> <li><strong><state-name>_within_indices.txt: </strong>text file containing a file structured as a Python list (e.g. [0, 1]) that contains the index of each grid cell when flattened from a 2D array to a 1D array for the target state.</li> <li><strong><state-name>_<ssp>_params.csv: </strong>CSV file containing the calibration parameters (alpha_rural, beta_rural, alpha_urban, beta_urban; float) for the `population_gravity` model for each year from 2010-2100 in 10-year time-steps as described in the companion publication</li> </ul> </li> <li><strong>outputs</strong> contain the following: <ul> <li><strong>jones_oneill</strong> directory; these are the comparison datasets used to build Figures 7 and 8 in the companion publication <ul> <li>contains three directories: SSP2, SSP3, and SSP5 that each contain a GeoTIF representing total population (number of humans; float) at 1km resolution for years 2050 and 2100. <ul> <li><strong><state-name>_1km_<ssp>_total_<year>_jones_oneill.tif:</strong> <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> </ul> </li> </ul> </li> <li><strong>model</strong> directory; these are the model outputs from `population_gravity` for SSP2, SSP3, and SSP5 that each contain a GeoTIF representing urban, rural, and total population (number of humans; float) at 1km resolution for years 2020-2100 in 10-year time-steps. <ul> <li><strong><state-name>_1km_<ssp>_<urban, rural, or total>_<year>_jones_oneill.tif:</strong> <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> </ul> </li> </ul> </li> </ul> <p> </p> <p><strong>zoraghein-oneill_population_gravity_national-ssp-maps.zip</strong></p> <ul> <li>Results of the `population_gravity` model mosaicked to the National scale at a 1km resolution and the comparison Jones and O'Neill research. These are used to generate Figure 6 of the companion paper <ul> <li><strong>National_1km_<ssp>_<urban, rural, or total>_<year>_jones_oneill.tif:</strong> <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> <li><strong>National_1km_<ssp>_<urban, rural, or total>_<year>.tif:</strong> <ul> <li>value per grid cell: number of humans (float)</li> <li>crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected</li> <li>nodata value: -3.40282e+38</li> </ul> </li> </ul> </li> </ul> <p> </p>
Behavioural and neurophysiological data from experiments on the impact of spatial frequency information to action comprehension
<p>Understanding object-directed actions performed by others is central to everyday life. This ability is thought to rely on the interaction between the dorsal action observation network (AON) and a ventral object recognition pathway. On this view, the AON would encode action kinematics, and the ventral pathway, the most likely intention afforded by the objects. However, experimental evidence supporting this model is still scarce. Here, we aimed to disentangle the contribution of dorsal vs. ventral pathways to action comprehension by exploiting their differential tuning to lowspatial frequencies (LSFs) and high-spatial frequencies (HSFs). We filtered naturalistic action images to contain only LSF or HSF and measured behavioral performance and corticospinal excitability (CSE) using transcranial magnetic stimulation (TMS). Actions were embedded in congruent or incongruent scenarios as defined by the compatibility between grips and intentions afforded by the contextual objects. Behaviorally, participants were better at discriminating congruent actions in intact than LSF images. This effect was reversed for incongruent actions, with better performance for LSF than intact and HSF. These modulations were mirrored at the neurophysiological level, with greater CSE facilitation for congruent than incongruent actions for HSF and the opposite pattern for LSF images. Finally, only for LSF did we observe CSE modulations according to grip kinematics. While results point to differential dorsal (LSF) and ventral (HSF) contributions to action comprehension for grip and context encoding, respectively, the negative congruency effect for LSF images suggests that object processing may influence action perception not only through ventral-to-dorsal connections, but also through a dorsal-to-dorsal route involved in predictive processing.</p>
Automated identification of the mouse brain's spatial compartments from in situ sequencing data
<p>Example data [1] for testing in situ sequencing graph-based decoding pipeline and decoding results for reproducing publication analyses. Image decoding pipeline and notebooks are available at: <a href="https://github.com/wahlby-lab/graph-iss">https://github.com/wahlby-lab/graph-iss</a></p> <p>------------</p> <p>[1] Ke, Rongqin, et al. "In situ sequencing for RNA analysis in preserved tissue and cells." <em>Nature methods</em> 10.9 (2013): 857.</p>
Data from: Forest structure determines spatial changes in avian community along an elevational gradient in tropical Africa
Aim To test if tree species richness and forest structure drive spatial variation in avian communities along a tropical elevation gradient and to present information about the role of detailed forest parameters. Location A 2000-m long elevational gradient of tropical forest on Mt. Cameroon, west-central Africa. Taxon Birds and trees. Methods We performed bird censuses and vegetation mapping at the same plots across six forested sites at elevations of 350, 650, 1,100, 1,500, 1,850, and 2,200 m a.s.l., with 16 plots per elevation. We tested the effects of elevation, forest structure and tree diversity on the species richness, functional diversity and β-diversity of birds (Bray-Curtis dissimilarity). We used conditional inference trees based on random forests (RF) to investigate these relationships across all elevation sites as well as within elevations. Results Both tree and bird species richness declined monotonically with elevation. Vegetation structure correlated with elevation, and all vegetation attributes significantly differed among elevations. The RF explained 70% of the variance in avian species richness, with the most important predictors being elevation, proportion of dead trees, tree species richness and herb layer coverage. We found that elevation (and shrub layerE2) was a particularly important predictor of avian functional diversity. We identified no important predictor of bird species richness after standardization within elevations, and the proportion of dead trees was the sole important predictor of functional diversity. Within-elevation β-diversity in avian community composition was determined by the dissimilarity of the tree community and differences in leaf area index, solar radiation and spatial distance. The functional dissimilarity was best explained by leaf area index. Main conclusions Apart from elevation itself, spatial distance even within elevations correlated with compositional and functional variation among avian assemblages. Forest structural traits can have a significant influence on distribution of birds. Thus, gaps in the spatial distribution of species such as along elevations might be caused by fine-scale recognition of suitable habitats.
ScienceDex guides
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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.