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13,117 results for “Maps”
The world of GPCR dimers - mapping dopamine receptor D2 homodimers in different activation states and configuration arrangements
<p>G protein-coupled receptors (GPCRs) are known to dimerize, but the molecular and structural basis of GPCR dimers is not well understood. We developed a computational framework to generate models of the dopamine receptor D2 (D<sub>2</sub>R) homodimer in different activation states of the monomers and identified their most likely interfaces with molecular detail and contacts formed between interfacial residues. Such contacts were followed along a 3 replicates of 500 ns molecular dynamics simulation. </p> <p>The dataset presented here is a summary of all interfacial contacts, such as hydrogen bonds, pi-cation, pi-stacking, salt-bridges and t-stacking interactions, which occur within the interfaces of the different dimer configurations. The information can be viewed as dynamical flareplots, by uploading the .json files to https://gpcrviz.github.io/flareplot/. </p> <p>Contacts were measured using GetContacts (https://getcontacts.github.io/).</p> <p> </p> <p>6CM4-6CM4 = inactive-inactive</p> <p>6CM4-6U1N = inactive-arrestin</p> <p>6U1N-6U1N = arrestin-arrestin</p> <p>6VMS-6CM4 = active-inactive</p> <p>6VMS-6U1N = active-arrestin</p> <p>6VMS-6VMS = active-active</p>
T2 orientation anisotropy mapping of articular cartilage using qMRI
<p>Data and codes for publication entitled "T2 orientation anisotropy mapping of articular cartilage using qMRI"</p>
Africa tree cover map
<p><strong>Data </strong><strong>description</strong></p> <p>This file is a 100 m resolution map of % tree canopy cover per pixel for Africa in 2019. Tree cover values range from 0-100 % and nodata areas are masked as -1.</p> <p>The map is derived from predictions of tree cover using 3 m PlanetScope imagery, which were resampled to 1 m, and then aggregated to 100 m. Predictions at 1 m resolution (or anything between 1 and 100 m) are available on request.</p> <p>The image data was processed from raw scenes into merged 1x1 degree mosaics. Differences in the quality of raw scenes and processed mosaics can lead to artifacts in the predicted tree cover, visible as seamlines along scene or mosaic edges. Therefore the current version of this map should not be seen as a final product representing the exact tree cover at each location, but as a proof of concept of what is possible with PlanetScope data.</p> <p>The main benefit of this map is the inclusion of cover from scattered single trees in sparse cover areas such as savannahs. Here it can serve as a complement to existing forest cover maps to extend the mapping to include non-forest trees.</p> <p><strong>License</strong></p> <p>This tree cover map is made freely available for non-commercial purposes. All usage of the data must be attributed and should be cited with the paper citation. </p> <p>Please see the NICFI license for full terms of usage, available at: https://assets.planet.com/docs/Planet_ParticipantLicenseAgreement_NICFI.pdf</p> <p><strong>Version differences</strong></p> <p>We recommend to always use the latest version.</p> <p><strong>We are continuously improving the quality and consistency of the tree cover mapped</strong>, and are expecting to release regular version updates, based on an improved model and mosaics. We also plan to release different years in the future.</p> <p><strong>Version 0.1: ps_africa_treecover_2019_100m_v0.1</strong></p> <p>This version was used for the analyses in the paper. It used the first version of the mosaics, which suffered from the inclusion of lower-quality scenes and missing data.</p> <p>Known issues</p> <p>- missing data coverage: incomplete mosaics and mosaics missing scenes</p> <p>- inconsistent predictions between mosaics: visible mosaic edges, see Zimbabwe</p> <p>- underprediction in very sparse cover areas</p> <p>- overprediction of cover in denser shrublands</p> <p>- overprediction (artifacts) in mountains, desert dunes, croplands and some urban areas</p> <p>- flowering trees in closed forests are mapped as gaps</p> <p>- occasional confusion between understory or shrubs and trees in wood- and shrublands</p> <p><strong>Version 1.0: ps_africa_treecover_2019_100m_v1.0</strong></p> <p>This is the latest version available as of March 2023. It is based on improved mosaics and a revised model, leading to better consistency both between and within mosaics.</p> <p>However the current model underpredicts solid tree cover in shrublands and woodlands, leading to an overall underprediction in lower rainfall areas.</p> <p>Improvements over v0.1:</p> <p>- improved mosaic quality</p> <p>- reduced artifacts in croplands and urban areas (though some remain)</p> <p>- better consistency of forest area mapping</p> <p>- better detection rate of small trees in dry areas</p> <p>Known issues</p> <p>- underprediction of tree clusters in shrublands</p> <p>- some (few) false predictions of small trees in dry areas</p> <p>- occasional inconsistent predictions within mosaics: seamlines along scene edges</p> <p>- areas of underprediction in dense tropical forest due to lower quality scenes, see DRC</p> <p>- overprediction (artifacts) in mountains, and occasionally desert dunes</p> <p>- flowering trees in closed forests are mapped as gaps</p> <p>- occasional confusion between understory or shrubs and trees in wood- and shrublands</p> <p>- trees without leaves may not be mapped correctly</p>
SPARCQ: A new approach for fat fraction mapping using asymmetries in the phase-cycle bSSFP signal profile
<p>This repository contains <em>in vitro</em> and <em>in vivo</em> MRI data acquired on a 3T clinical system (MAGNETOM Prisma<sup>fit</sup>, Siemens Healthcare, Erlangen, Germany) using a commercially available 18-channel body coil. All data includes both magnitude and phase information in DICOM format.</p> <p>Data was collected and used in a study untitled "<strong>SPARCQ: A new approach for fat fraction mapping using asymmetries in the phase-cycled bSSFP signal profile</strong>" by Rossi <em>et. al.</em> (2023).</p> <p>The detailed contents of the repository are listed below:</p> <ul> <li><strong>/PHANTOM</strong>: 3D acquisitions of a custom fat-water phantom with 6 vials of different peanut oil and water concentrations <ul> <li><strong>/PCbSSFP</strong> N<sub>PC</sub>=37 Phase-Cycled bSSFP acquisitions with phase increments [0°:10°:360°]</li> <li><strong>/MEGRE</strong> MultiEcho GRE acquisition with 13 monopolar echoes TE<sub>1</sub>/<span class="math-tex">\(\Delta\)</span>TE=1.34/1.98 ms</li> </ul> </li> <li><strong> /V1</strong> to <strong>/V6</strong>: 3D knee acquisitions from n=6 healthy volunteers <ul> <li><strong>/Scan </strong>N<sub>PC</sub>=37 Phase-Cycled bSSFP acquisitions with phase increments [0°:10°:360°]</li> <li><strong>/Rescan</strong> N<sub>PC</sub>=37 Phase-Cycled bSSFP acquisitions with phase increments [0°:10°:360°] after volunteer repositioning (repeatability experiment)</li> <li><strong>/Dixon</strong> In-phase, out-of-phase, fat-only and water-only images reconstructed from a Turbo Spin Echo Dixon sequence</li> </ul> </li> <li> <strong>/V7 </strong>and <strong>/V8</strong>: 3D knee acquisitions from n=2 healthy volunteers <ul> <li><strong>/Scan </strong>N<sub>PC</sub>=37 Phase-Cycled bSSFP acquisitions with phase increments [0°:10°:360°]</li> <li><strong>/MEGRE </strong>MultiEcho GRE acquisition with 13 monopolar echoes TE<sub>1</sub>/<span class="math-tex">\(\Delta\)</span>TE=1.34/1.98 ms</li> </ul> </li> </ul> <p>************************************************************************************************************************************************************************************************************************************************************</p> <p><strong>Link to publication: <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.29813">https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.29813</a> </strong></p> <p>************************************************************************************************************************************************************************************************************************************************************</p> <p>Data was collected and approved for sharing according to institutional rules (Ethics Commitee, CHUV, Lausanne, Switzerland).</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>
Mapping Synthetic Binding Proteins Epitopes on Diverse Protein Targets by Protein Structure Prediction and Protein-Protein Docking
<p>The predicted 3D structures of 145 SBPs and the 96 models of SBPs in complex with protein targets.</p>
Map 1 in Aloe andersonii, a new cliff-dwelling aloe from Mpumalanga, South Africa
Map 1. The known geographical distribution of Aloe andersonii.
Supplementary material 2 from: Eusébio RP, Fonseca PE, Rebelo R, Mathias ML, Reboleira ASPS (2023) How to map potential mesovoid shallow substratum (MSS) habitats? A case study in colluvial MSS. Subterranean Biology 45: 141-156. https://doi.org/10.3897/subtbiol.45.96332
Literature review data Mesovoid Shallow Substrate's faunal communities
FIGURE39. Distribution map of Dimophora spp. from Japan in Revision of the genus Dimophora Förster (Hymenoptera: Ichneumonidae: Cremastinae) from Japan
FIGURE39. Distribution map of Dimophora spp. from Japan
MAP 14 in Taxonomic revision of the genus Parena Motschulsky, 1860 (Coleoptera, Carabidae, Lebiini, Metallicina)
MAP 14. Distributions for species in the Parena (Parena) plagiata group
MAP 12 in Taxonomic revision of the genus Parena Motschulsky, 1860 (Coleoptera, Carabidae, Lebiini, Metallicina)
MAP 12. Distributions for species in the Parena (Parena) stigmatica group
MAP 11 in Taxonomic revision of the genus Parena Motschulsky, 1860 (Coleoptera, Carabidae, Lebiini, Metallicina)
MAP 11. Distributions for species in the Parena (Parena) latecincta group
MAP 10 in Taxonomic revision of the genus Parena Motschulsky, 1860 (Coleoptera, Carabidae, Lebiini, Metallicina)
MAP 10. Distributions for species in the Parena (Parena) nigrolineata group.
MAP 9 in Taxonomic revision of the genus Parena Motschulsky, 1860 (Coleoptera, Carabidae, Lebiini, Metallicina)
MAP 9. Distributions for species in the Parena (Parena) bicolor group.
MAP 7 in Taxonomic revision of the genus Parena Motschulsky, 1860 (Coleoptera, Carabidae, Lebiini, Metallicina)
MAP 7. Distributions for species in the Parena (Bothynoptera) tesari group
MAP 6 in Taxonomic revision of the genus Parena Motschulsky, 1860 (Coleoptera, Carabidae, Lebiini, Metallicina)
MAP 6. Distributions for species in the Parena (Bothynoptera) dorsigera group.
MAP 8 in Taxonomic revision of the genus Parena Motschulsky, 1860 (Coleoptera, Carabidae, Lebiini, Metallicina)
MAP 8. Distributions for species in the Parena (Bothynoptera) tripunctata group.
MAP 13 in Taxonomic revision of the genus Parena Motschulsky, 1860 (Coleoptera, Carabidae, Lebiini, Metallicina)
MAP 13. Distributions for species in the Parena (Parena) scutata group
MAP 4 in Taxonomic revision of the genus Parena Motschulsky, 1860 (Coleoptera, Carabidae, Lebiini, Metallicina)
MAP 4. Distributions for species in the Parena (Crossoglossa) testacea group.
MAP 2 in Taxonomic revision of the genus Parena Motschulsky, 1860 (Coleoptera, Carabidae, Lebiini, Metallicina)
MAP 2. Distributions for species in the Parena (Crossoglossa) cavipennis group.
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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.