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13,117 results for “Maps”
FIGURE 7. Distribution map. A in Two new species of Thismia (Thismiaceae) from the Brazilian Amazon Forest
FIGURE 7. Distribution map. A. Distribution of T. calcarata and T. variabilis. B. Delimitation of the Parque Estadual Chandless, Manoel Urbano, Acre, Brazil. C. Delimitation of the Reserva Florestal Humaitá, Porto Acre, Acre, Brazil (satellite images by Google sattelite obtained in 2022).
Mapped ATAC-seq data for mock and HSV-1 strain 17 infection and infection with null mutants of HSV-1
<p>Sample annotation:</p> <table> <tbody> <tr> <td>Mock_1</td> <td>mock infection, replicate 1</td> </tr> <tr> <td>Mock_2</td> <td>mock infection, replicate 2</td> </tr> <tr> <td>WT_1</td> <td>HSV-1 wt, strain 17, replicate 1</td> </tr> <tr> <td>WT_2</td> <td>HSV-1 wt, strain 17, replicate 2</td> </tr> <tr> <td>WT_plus_PAA_1</td> <td>HSV-1 wt, strain 17, +PAA, replicate 1</td> </tr> <tr> <td>WT_plus_PAA_2</td> <td>HSV-1 wt, strain 17, +PAA, replicate 2</td> </tr> <tr> <td>dICP0_1</td> <td>HSV-1 lacking expression of ICP0, replicate 1</td> </tr> <tr> <td>dICP0_2</td> <td>HSV-1 lacking expression of ICP0, replicate 2</td> </tr> <tr> <td>dICP22_1</td> <td>HSV-1 lacking expression of ICP22, HSV-1 strain F mutant R325, replicate 1</td> </tr> <tr> <td>dICP22_2</td> <td>HSV-1 lacking expression of ICP22, HSV-1 strain F mutant R325, replicate 2</td> </tr> <tr> <td>dICP22_3</td> <td>HSV-1 lacking expression of ICP22, HSV-1 strain F mutant R325, replicate 3</td> </tr> <tr> <td>dICP22_4</td> <td>HSV-1 lacking expression of ICP22, HSV-1 strain F mutant R325, replicate 4</td> </tr> <tr> <td>dICP22_plus_PAA_1</td> <td>HSV-1 lacking expression of ICP22, HSV-1 strain F mutant R325, +PAA, replicate 1</td> </tr> <tr> <td>dICP22_plus_PAA_2</td> <td>HSV-1 lacking expression of ICP22, HSV-1 strain F mutant R325, +PAA, replicate 2</td> </tr> <tr> <td>dICP27_1</td> <td>HSV-1 lacking expression of ICP27, KOS, replicate 1</td> </tr> <tr> <td>dICP27_2</td> <td>HSV-1 lacking expression of ICP27, KOS, replicate 2</td> </tr> <tr> <td>dVHS_1</td> <td>HSV-1 lacking expression of UL41, replicate 1</td> </tr> <tr> <td>dVHS_2</td> <td>HSV-1 lacking expression of UL41, replicate 2</td> </tr> </tbody> </table>
FIGURE:1 Map of Sampling sites, the green dots represent the Shogran and brown dot represents Khanspur, from where the specimens of Hypholoma himalayense were collected in Hypholoma himalayense, a new and noteworthy species from the Himalayan moist temperate forests of Pakistan
FIGURE:1 Map of Sampling sites, the green dots represent the Shogran and brown dot represents Khanspur, from where the specimens of Hypholoma himalayense were collected
WikiPathways WPRDF BridgeDb ID mapping triples
<p>Created from the Nightly WPRDF data from 2023-03-31 by loading the RDF into a local Blazegraph instance and running the below query and exporting this as `.ttl` file:</p> <p>```sparql</p> <p>PREFIX bdb: <http://vocabularies.wikipathways.org/wp#bdb></p> <p>CONSTRUCT { ?s ?p ?o } WHERE {<br> VALUES ?p {<br> bdb:Wikidata bdb:Ensembl bdb:Uniprot bdb:EntrezGene<br> bdb:ChEBI bdb:Chemspider bdb:ComplexPortal bdb:HgncSymbol<br> bdb:Hmdb bdb:InChIKey bdb:KeggCompound bdb:LipidMaps<br> bdb:PubChem bdb:Rhea bdb:Uniprot bdb:Reactome<br> }<br> ?s ?p ?o .<br> }</p> <p>```</p>
Training material for the mapping and quantification of single-cell ATAC-seq 10X Datasets
<p>The data provided here is part of the Galaxy Training Network tutorial that analyses 10x genomics single-cell ATAC-seq data from the 10x platform. </p> <p>Due to time constraints during training, the datasets were subsampled to reads that map to chrY.</p> <p>The 10x Genomics Datasets follow the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution</a> license.</p>
Mapping forests with different levels of naturalness using machine learning and landscape data mining - GRASS GIS DB
<p>The GRASS GIS database containing the input raster layers needed to reproduce the results from the manuscript entitled:</p> <p><strong>"Mapping forests with different levels of naturalness using machine learning and landscape data mining"</strong> (under review)</p> <p>Abstract:</p> <p><em>To conserve biodiversity, it is imperative to maintain and restore sufficient amounts of functional habitat networks. Hence, locating remaining forests with natural structures and processes over landscapes and large regions is a key task. We integrated machine learning (Random Forest) and wall-to-wall open landscape data to scan all forest landscapes in Sweden with a 1 ha spatial resolution with respect to the relative likelihood of hosting High Conservation Value Forests (HCVF). Using independent spatial stand- and plot-level validation data we confirmed that our predictions (ROC AUC in the range of 0.89 - 0.90) correctly represent forests with different levels of naturalness, from deteriorated to those with high and associated biodiversity conservation values. Given ambitious national and international conservation objectives, and increasingly intensive forestry, our model and the resulting wall-to-wall mapping fills an urgent gap for assessing fulfilment of evidence-based conservation targets, spatial planning, and designing forest landscape restoration.</em></p> <p>This database was compiled from the following sources:</p> <p>1. <strong>HCVF</strong>. A database of High Conservation Value Forests in Sweden. Swedish Environmental Protection Agency.</p> <p>source: <a href="https://geodata.naturvardsverket.se/nedladdning/skogliga_vardekarnor_2016.zip">https://geodata.naturvardsverket.se/nedladdning/skogliga_vardekarnor_2016.zip</a></p> <p>2. <strong>NMD</strong>. National Land Cover Data. Swedish Environmental Protection Agency.</p> <p>source: <a href="https://www.naturvardsverket.se/en/services-and-permits/maps-and-map-services/national-land-cover-database/">https://www.naturvardsverket.se/en/services-and-permits/maps-and-map-services/national-land-cover-database/</a></p> <p>3. <strong>DEM</strong>. Terrain Model Download, grid 50+. Lantmateriet, Swedish Ministry of Finance.</p> <p>source: <a href="https://www.lantmateriet.se/en/geodata/geodata-products/product-list/terrain-model-download-grid-50/">https://www.lantmateriet.se/en/geodata/geodata-products/product-list/terrain-model-download-grid-50/</a></p> <p>4. <strong>GFC</strong>. Global Forest Change. Global Land Analysis and Discovery, University of Maryland.</p> <p>source: <a href="https://glad.earthengine.app">https://glad.earthengine.app</a></p> <p>5. <strong>LIGHTS</strong>. A harmonized global nighttime light dataset 1992–2018. Land pollution with night-time lights expressed as calibrated digital numbers (DN).</p> <p>source: <a href="https://doi.org/10.6084/m9.figshare.9828827.v2">https://doi.org/10.6084/m9.figshare.9828827.v2</a></p> <p>6. <strong>POPULATION</strong>. Total Population in Sweden. Statistics Sweden.</p> <p>source: <a href="https://www.scb.se/en/services/open-data-api/open-geodata/grid-statistics/">https://www.scb.se/en/services/open-data-api/open-geodata/grid-statistics/</a></p> <p> </p> <p>To learn more about the GRASS GIS database structure, see:</p> <p><a href="https://grass.osgeo.org/grass82/manuals/grass_database.html">https://grass.osgeo.org/grass82/manuals/grass_database.html</a></p>
Supplementary material 4 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
Abundance of invertebrates collected in colluvial Mesovoid Shallow Substratum (MSS) at the Arrábida National Park
Supplementary material 3 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
Results of criteria met for each location pinpointed as potential colluvial Mesovoid Shallow Substratum (MSS)
Supplementary material 1 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
Characterization of each of the locations found in situ as colluvial Mesovoid Shallow Substratum (MSS): latitude, longitude and estimated area (m2)
Supplementary material 5 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
Total invertebrate abundance, collected in colluvial Mesovoid Shallow Substratum (MSS) at the Arrábida National Park
Warm Jupiter Flux Maps
<p>Gif and mp4 animations showing maps of the bolometric thermal emission from 3D atmospheric models of a hypothetical warm Jupiter, throughout one orbit (i.e., year) of the planet. The models are described in detail in the associated publication. They all assume the planet has the mass and radius of Jupiter and is on a 10-day orbit around a Solar twin. Each model has a different combination of assumed rotation rate and obliquity (aka axial tilt). The rotation rates are synchronous and 12, 4, 2, and 1 times slower than Jupiter. (Synchronous is close to 24 times slower than Jupiter.) The obliquities are 0, 10, 30, 60, and 90 degrees. Each model has two different forms for the outgoing flux maps: one that is a cylindrical projection (in the frame rotating with the planet), showing how the flux map changes throughout the planet's orbit, and one that is an orthogonal projection, showing how the dayside of the planet appears throughout the planet's orbit (as if viewed from the star). In most models a black star indicates the location of the substellar point. In the cylindrical projections from the model rotating at Jupiter's speed there is a black line indicating the substellar latitude, as the forcing pattern assumes a diurnal average and irradiates all longitudes equally.</p>
Mapping and modeling human colorectal carcinoma interactions with the tumor microenvironment
<p>This dataset can be interactively explored with VisCello (https://github.com/qinzhu/VisCello). Please install VisCello and use e.g. "cello("path_to/hcc_main_cello")" to load the human colon cancer single cell dataset.</p> <p> </p>
FIG. 4 in Spatial Variation of False Map Turtle (Graptemys pseudogeographica) Bacterial Microbiota in the Lower Missouri River, United States
FIG. 4. Gut bacteria community relative abundances shown with each bar representing an individual sample in the corresponding location. Each color is representative of a corresponding genera. Where genera is not classified, the source family is given preceding ''unclassified.'' Bars remain unnormalized with remaining space composed of excluded low-representation groups (Ĺ2% of total abundance).
FIG. 3 in Spatial Variation of False Map Turtle (Graptemys pseudogeographica) Bacterial Microbiota in the Lower Missouri River, United States
FIG. 3. Gut bacteria community relative abundances shown with each bar representing an individual sample in the corresponding location. Each color is representative of a corresponding phylum (or in the case of ''Bacteria_unclassified,'' unidentified members of the bacteria). Bars remain unnormalized with remaining space composed of excluded low representation groups (Ĺ2% of total abundance).
FIG. 1 in Spatial Variation of False Map Turtle (Graptemys pseudogeographica) Bacterial Microbiota in the Lower Missouri River, United States
FIG. 1. Locations where cloacal microbiota samples were taken from False Map Turtles (Graptemys pseudogeographica) along the lower Missouri River between South Dakota and Nebraska, United States. Sites include: 1) James River, 2) Goat Island, and 3) Vermillion River. Major habitat types along the Missouri River are indicated (green = riparian forests; tan = sandbars) and modified from Dixon et al. (2015).
FIG. 2. Nonmetric multidimensional scaling calculated with the Bray-Curtis distance metric using a in Spatial Variation of False Map Turtle (Graptemys pseudogeographica) Bacterial Microbiota in the Lower Missouri River, United States
FIG. 2. Nonmetric multidimensional scaling calculated with the Bray-Curtis distance metric using a square root transformation and Wisconsin double-standardization. Location is represented by color, and sex is represented by shape. Stress of fit for the ordination is reported at 0.145. Axis titles represent the two dimensions to which the data have been ordinated.
Processed dataset used to train models in the study "Integrative modeling of lncRNA-chromatin interaction maps reveals diverse mechanisms of nuclear retention"
<p>The processed dataset used to train models in the study "Integrative modeling of lncRNA-chromatin interaction maps reveals diverse mechanisms of nuclear retention".</p>
BridgeDb: pathway identifier mapping database derived from Wikidata
<p>Release of a BridgeDb gene identifier mapping database between Wikidata and Ensembl.</p> <pre>[INFO]: Database finished. INFO: old database is Wikidata 1.0.0 (build: 20230506) INFO: new database is Wikidata 1.0.0 (build: 20230506) INFO: Number of ids in Wd (Wikidata): 153715 (unchanged) INFO: Number of ids in En (Ensembl): 153637 (unchanged) INFO: new size is 95 Mb (changed +0.0%) INFO: total number of identifiers is 307352 INFO: total number of mappings is 307430 </pre>
Random Filter Mappings as Optimization Problem Feature Extractors
<p>Artifacts produced as part of the paper Random Filter Mappings as Optimization Problem Feature Extractors</p>
Dataset for Poultry barn mapping
<p>These datasets are part of <strong>Poultry barn mapping, </strong>which are used for detecting poultry barns from high-resolution aerial imagery and an accompanying dataset of predicted barns over the United States.</p>
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.