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13,117 results for “Maps”
STalign: Alignment of spatial transcriptomics data using diffeomorphic metric mapping
<p>Spatial transcriptomics (ST) technologies enable high throughput gene expression characterization within thin tissue sections. However, comparing spatial observations across sections, samples, and technologies remains challenging. To address this challenge, we developed STalign to align ST datasets in a manner that accounts for partially matched tissue sections and other local non-linear distortions using diffeomorphic metric mapping. We apply STalign to align ST datasets within and across technologies as well as to align ST datasets to a 3D common coordinate framework. We show that STalign achieves high gene expression and cell-type correspondence across matched spatial locations that is significantly improved over landmark-based affine alignments. Applying STalign to align ST datasets of the mouse brain to the 3D common coordinate framework from the Allen Brain Atlas, we highlight how STalign can be used to lift over brain region annotations and enable the interrogation of compositional heterogeneity across anatomical structures. STalign is available as an open-source Python toolkit at <a href="https://github.com/JEFworks-Lab/STalign">https://github.com/JEFworks-Lab/STalign</a> and as supplementary software with additional documentation and tutorials available at <a href="https://jef.works/STalign">https://jef.works/STalign</a>.</p> <p>Here we have included alignment results that were used in performance analysis of STalign:</p> <p>We aligned Slice 2 Replicate 3 to Slice 2 Replicate 2 of the MERFISH mouse coronal brain sections available from Vizgen Data Release V1.0. May 2021 (<a href="https://info.vizgen.com/mouse-brain-map">https://info.vizgen.com/mouse-brain-map</a>).</p> <ul> <li>STalign_S2R3_to_S2R2.csv.gz contains cell ids, original cell centroid positions of S2R3, cell positions of S2R3 after alignment to S2R2 with STalign, cell positions of S2R3 after supervised affine alignment to S2R2, and counts for genes and blanks.</li> <li>STalign_S2R2.csv.gz contains cell ids, cell centroid positions of S2R2 and counts for genes and blanks.</li> </ul> <p>Additionally, we aligned Slice 2 Replicate 3 to a Visium dataset of an FFPE preserved adult mouse brain were obtained from the 10X Datasets website for <em>Spatial Gene Expression Dataset by Space Ranger 1.3.0</em> (<a href="https://www.10xgenomics.com/resources/datasets/adult-mouse-brain-ffpe-1-standard-1-3-0">https://www.10xgenomics.com/resources/datasets/adult-mouse-brain-ffpe-1-standard-1-3-0</a>).</p> <ul> <li>STalign_S2R3_to_Visium.csv.gz contains cell ids, original cell centroid positions of S2R3, cell positions of S2R3 after alignment to Visium H&E staining with STalign, and counts for genes and blanks.</li> </ul> <p>Furthermore, we performed alignments with the 50um resolution 3D Allen Reference Atlas Nissl common coordinate framework, CCF (<a href="https://help.brain-map.org/display/mouseconnectivity/API">https://help.brain-map.org/display/mouseconnectivity/API</a>). We applied STalign to align the Allen CCF to each of the 9 MERFISH slices (3 slice locations with 3 biological replicates) provided by Vizgen. Because the Allen CCF has annotated brain regions, we were able to lift over those brain region annotations to label all cells in the MERFISH datasets.</p> <p>Also, since the STalign mappings from the Allen CCF to the MERFISH slices are invertible, for each slice we can apply the inverse of the mapping to get cell positions in the Allen CCF coordinates.</p> <ul> <li>STalign_SXRX_with_structure_id_name.csv.gz contains cell ids for Slice X Replicate X, original cell centroid positions, cell xyz-coordinates in Allen CCF, brain structure id per cell, brain structure acronym</li> </ul> <p>To evaluate the 3D CCF alignment, we performed unified transcriptional clustering analysis and cell-type annotation. All MERFISH datasets were combined. Transcriptional clustering analysis and cell type annotation was performed using the SCANPY package [version 1.9.1]. Data were normalized to counts per million (scanpy: normalize_total) and log transformed (scanpy: log1p). PCA (scanpy: pca) was computed on the cell by gene matrix. A neighborhood graph of cells using the top 10 PCs and 10 nearest neighbors was created (scanpy: neighbors), and Leiden clustering was performed on this graph (scanpy: leiden) to identify 29 clusters. Differentially expressed genes were extracted from each cluster (scanpy: rank_genes_groups), and cell-types were annotated based on marker genes in each cluster.</p> <ul> <li>STalign_celltypeannotations_merfishslices_v2.csv.gz contains for all nine slices cell ids and cell type annotations</li> </ul> <p>This updated (v2) cell-type annotation file contains a new column with simplified cell-types. Briefly, we fixed typos, standardized lower case/upper case formats, merged subclasses of each cell-types. For example, subclasses of astrocytes­­, which are originally labeled as “Astrocytes”, “Astrocytes(1)”, “Astrocytes(2)”, “Astrocytes(3)”, are all labeled as “Astrocytes” in the added column.</p> <p>Note: Cell ids may have been mutated from original string of numbers through reading and writing across programming languages that handle numbers with different precision. If using R to read the files shared here, one can find the cells in STalign_celltypeannotations_merfishslices_v2.csv.gz that correspond with STalign_SXRX_with_structure_id_name.csv.gz when cell ids are formatted as a double in scientific notation, which is how R will read the file automatically.</p>
ChinaSoybean10:An Annual 10-m Soybean cropland Mapping Dataset in China from 2019 to 2022
<p>This dataset consists of soybean annual maps from 2019 to 2022 in China's main soybean producing areas with a pixel size of 10 m x 10 m, including Heilongjiang, Inner Mongolia, Jilin, Liaoning, Anhui, Henan, Shandong, Hubei, Jiangsu, and Sichuan.The maps use the ESPG: 4326 (WGS_1984) spatial reference system. The maps contain values of null and 1, representing non-crop land (including other crops) and soybeans. The maps are shown at the provincial administrative unit level, including the four prefecture-level cities in Eastern Inner Mongolia. You can analyze and visualize these maps with software like ArcGIS, QGIS, or similar applications.</p>
Data repository associated with 'A Functional Map of the Human Intrinsically Disordered Proteome'
<p><strong>ES_MAP.zip</strong></p> <ul> <li>a hierarchically clustered map of the human IDR-ome</li> <li>.cdt and .gtr files - outputs of Cluster3.0 software</li> <li>can be visualized using JavaTreeView (see Tutorial_ES.pdf)</li> </ul> <p><strong>TUTORIAL.zip</strong>, information on:</p> <ul> <li>visualization and analysis of the human IDR-ome map</li> <li>search for proteins of interest and exploratory analyses of clusters</li> <li>automatic export and analysis of exported clusters (code available at https://github.com/IPritisanac/ES_PW)</li> </ul> <p><strong>IDROME_SEQUENCES.zip</strong></p> <ul> <li>human proteome fasta file</li> <li>IDRome fasta file</li> <li>SPOT-Disorder v1.0 disorder boundaries <ul> <li>13 044 unique protein sequences with at least one IDR (>=30 amino acids)</li> <li>21 252 total unique human IDRs</li> </ul> </li> </ul> <p><strong>IDR_ALN.zip</strong></p> <ul> <li>alignments of IDR sequences across ENSEMBL orthologs</li> <li>19 459 IDR alignments</li> <li>UniProt ID and IDR boundaries for the human sequence are indicated in the name of the file</li> </ul> <p><strong>FAIDR_TSTATS.zip</strong></p> <ul> <li>hierarchical clustering of FAIDR t-statistics for 148 GO terms<br> <ul> <li>.cdt, .gtr files from Cluster3.0</li> <li>can be visualized using JavaTreeView</li> <li>reveals the most predictive molecular features for the top performing 148 models</li> </ul> </li> </ul> <p><strong>CLUSTERS_EXPLORE.zip</strong></p> <ul> <li>clusters obtained through exploratory analysis of the map provided in ES_MAP.zip</li> <li>93 exported clusters in .cdt file format</li> </ul> <p><strong>CLUSTERS_AUTO.zip</strong></p> <ul> <li>clusters extracted from the hierarchically clustered IDR-ome map at a range of distance thresholds (0.4 - 0.8) in .cdt file format</li> <li>distance refers to the uncentered correlation distance between vectors of Z-scores representing human IDRs</li> <li>clusters extracted at different distance thresholds are split into separate archives</li> <li>AUTO_GO_FEATS.xlsx - summary of GO-term overrepresentation and feature enrichment analyses; each distance threshold is in a separate sheet</li> </ul> <p><strong>FAIDR_HIGH_AUC_PPV_GO.zip</strong></p> <ul> <li>target files with annotations of 148 GO terms for which good quality FAIDR models could be obtained (AUC >= 0.7, PPV >= 0.4)</li> <li>file format: three columns; 1st: IDR ID (includes IDR boundaries); 2nd: protein UniProt ID; 3rd: annotation of the protein to a GO term (1 if known to be associated with the GO term, 0 if not)</li> </ul> <p> </p> <p> </p>
Ecosystem Map - SPEARHEAD - AMR stakeholders' mapping workshops
Open the record for dataset details and reuse information.
COREQ checklist: Focus group for 'Streamlining Concept Mapping for Clinical Data Enrichment: A Process-focused approach in medical Data Warehouses'
<p>Presentation of the 32 items on the consolidated criteria for reporting qualitative research (COREQ) checklist. The information is used for the report on a focus group that was conducted as part of the preparation of a publication. The title of the article is (as of submission on 18.03.2024): 'Streamlining Concept Mapping for Clinical Data Enrichment: A Process-focused approach in Medical Data Warehouses'.</p>
Fig. 1. Map showing a in Graveyards of Giant Pandas at the Bottom of the Sea? A Strange-Looking New Species of Colonial Ascidians in the Genus Clavelina (Tunicata: Ascidiacea)
Fig. 1. Map showing a sampling locality, Tonbara, off Kumejima Island, Japan.
Supporting material for: Geostratigraphic mapping of the intrusive Valentine Domes on the Moon
<p>This dataset is the supporting information for the article called: Geostratigraphic mapping of the intrusive Valentine Domes on the Moon. It contains a QGIS project, the vector data, and the rastar data used to create the geostratigraphic maps of the zone.</p>
Surface drifters and high resolution global simulations mapping of internal tide surface energy
<p>File " gdp_energy.nc " contains surface semidiurnal internal tides binned-averaged energy levels estimated from the Global Drifter Program dataset. </p> <p>File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_SSV_hf_binned_dl1.0_attrs.nc</a> " contains semidiurnal internal tides squared binned-averaged surface meridional velocity estimated from LLC4320 outputs and simulated drifters. Bins size is 1deg x 1deg .</p> <p>File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_SSU_hf_binned_dl1.0_attrs.nc</a> " contains semidiurnal internal tides squared binned-averaged surface zonal velocity estimated from LLC4320 outputs and simulated drifters. Bins size is 1deg x 1deg .</p> <p>File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_SSV_hf_binned_dl2.0_attrs.nc</a> " contains semidiurnal internal tides squared binned-averaged surface meridional velocity estimated from LLC4320 outputs and simulated drifters. Bins size is 2deg x 2deg .</p> <p>File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_SSU_hf_binned_dl2.0_attrs.nc</a> " contains semidiurnal internal tides squared binned-averaged surface zonal velocity estimated from LLC4320 outputs and simulated drifters. Bins size is 2deg x 2deg .</p> <p>File " <a href="../api/records/10851200/draft/files/energy_SSV_hf_binned_dl1.0_attrs.nc/content" target="_blank" rel="noopener noreferrer">energy_hf_binned_dl1.0_attrs.nc</a> " contains semidiurnal internal tides binned-averaged kinetic energy levels estimated from LLC4320 outputs and simulated drifters. Bins size is 1deg x 1deg .</p> <p>For all files semidiurnal signal is obatined from band-pass filtering.</p>
Global map of foliar N:P
<p>Map associated to the manuscript:</p> <p><span>Vallicrosa, H.</span>, <span>Sardans J.</span>, <span>Maspons J.</span>, & <span>Peñuelas J.</span> (<span>2022</span>). <span>Global distribution and drivers of forest biome foliar nitrogen to phosphorus ratios (N:P)</span>. <em>Global Ecology and Biogeography</em>, <span>31</span>, <span>861</span>–<span>871</span>. <a href="https://doi.org/10.1111/geb.13457">https://doi.org/10.1111/geb.13457</a></p> <p> </p>
A 10 m resolution land cover map of the Tibetan Plateau with detailed vegetation types
<p>A 10 m resolution land cover map of the Tibetan Plateau with 12 vegetation types and 3 non-vegetation types for the year 2022 (TP_LC10-2022) by leveraging state-of-the-art remote sensing approaches including the Sentinel-1 and Sentinel-2 imagery, environmental and topographic datasets, and Random Forest model using Google Earth Engine platform.</p>
Mapping Deformation Processes using InSAR PS+DS Timeseries Estimation in Northern California, U.S.
Open the record for dataset details and reuse information.
Regolith-landform map of the Tanami Region, Australia
<p>Images used for mapping and modelling the regolith-landform of the Tanami Region, Australia.</p> <p>Zip file: Temporally mergered Landsat TM; RGB, DS754 and Gozzard ratios (ECW)</p> <p><span><span>Zip file: Relief imagess (PNG, PGW); </span></span><span>Slope images (PNG, PGW); </span><span>TWI images (PNG, PGW); </span><span>Flow accumulation images (PNG, PGW)</span></p>
Regolith-landform map of the Tanami Region, Australia
<p>Figure 10 in "Geochemical pathways defined by predictive regolith-landform models using TanDEM-X data in the Tanami Region, Australia". Mapped and modelled for reproduction at 1:25,000-scale.</p>
Map of the Vegetation of the Natural Reserve of Monte Catillo (central Italy)
<p><strong>Map of the Vegetation of the Natural Reserve of Monte Catillo (central Italy)</strong></p> <p><strong> </strong></p> <p><strong>Authors:</strong></p> <p>Sabina Burrascano, Lorenzo Caucci, Giulio Ferrante </p> <p>Department of Environmental Biology, Sapienza University of Rome</p> <p>Maria Vinci </p> <p>Città metropolitana di Roma Capitale</p> <p><strong> </strong></p> <p><strong>Reference system:</strong> EPSG 3004 - Monte Mario / Italy zone 2</p> <p><strong>Spatial scale: </strong>the map was drawn at 1:2,000</p> <p><strong>Minimum Mapping Unit:</strong> 0.2 hectares</p> <p><strong>Provided styles:</strong> use "Carta_vegetazione_aprile2024.qml" for Italian legend; or "Carta_veg_ENG.qml" for English legend</p> <p><strong> </strong></p> <p>Vegetation maps has the purpose of assessing and representing the distribution and extension of the types of vegetation of an area, after investigating it through field surveys, both physiognomic (annotation of the dominant species) and floristic-vegetational (complete censuses of all vascular plant species). Vegetation maps have gained a major role in the Geographical Information Systems of national and local administrations, especially in protected areas, due to their usefulness as tools for environmental monitoring, management and territorial planning.</p> <p>The collaboration between the Metropolitan City of Rome Capital and the Department of Environmental Biology of Sapienza University of Rome has led, among other results, to the creation of a novel map of the vegetation of the Monte Catillo Natural Reserve, updating the pre-existing one which dates back to 20051. The new map was created at a spatial scale of 1:2,000 based on both orthophotos, i.e. aerial photos taken in 2014, and satellite images taken in 2022 and 2023. The minimum mapping unit was set at 0.2 hectares.</p> <p>A EUNIS code was assigned to each type of vegetation based on the correspondence between field observations and the habitat descriptions reported in the EUNIS database. </p> <p>EUNIS (European Nature Information System) is a classification system developed by the European Environmental Agency for the description and categorization of habitat types in Europe to facilitate the exchange of information between European countries and improve the management and conservation of biodiversity at the continental level.</p> <p>The EUNIS system is used to classify natural, semi-natural and artificial habitats, as well as species and habitat types of importance for biodiversity conservation. It follows a hierarchical scheme that allows different levels of detail to be applied to each habitat based on the degree of correspondence between what was detected in the field and what is described in the classification. For example, a Mediterranean scrub habitat dominated by Pistacia lentiscus and Phyllyrea sp. could be classified as follows:</p> <p>Level 1: S - Heaths, scrub and tundra;</p> <p>Level 2: S5 - Maquis, shrub matorral and thermo-Mediterranean maquis;</p> <p>Level 3: S51 - Mediterranean scrub and shrubby matorral;</p> <p>Level 4: S512 - Shrubby matorral of Olea europaea and Pistacia lentiscus;</p> <p>Level 5: S5123 - Pistacia lentiscus and Phillyrea shrub matorral.</p> <p>If the species composition detected in the field does not reflect any of the features described at level 5, the habitat will be reported with a level 4 and so on.</p> <p>Due to the biogeographical peculiarities of the Monte Catillo Natural Reserve, a lack of correspondence was found for several vegetation types occurring within the Reserve. For example, no code has been found in the EUNIS system that identifies the cork oak (Quercus suber) forest with Styrax officinalis understorey. The same was true for the pseudomaquis vegetation for which the most detailed code indicates strictly Balkan species not present in Italy.</p> <p>The map was developed in a GIS (Geographic Information System) environment, and allows the extraction and analysis of quantitative data, such as the extension of the main types of vegetation which can thus be monitored over time even in response to specific disturbances such as intense fires that periodically affect the Reserve.</p> <p> </p> <p><strong>References</strong></p> <p>1. Provincia di Roma, (2006). Piano di assetto della Riserva Naturale di Monte Catillo. Approvato con Deliberazione del Commissario ad acta del 26 Novembre 2015 pubblicato sul BURL del 19 Gennaio 2016, n. 5, supplemento 2.</p> <p> </p>
Raman spectroscopic mapping and atomic force microscopy statistical analyses for the determination of WS2 flake thickness
<p><span>This database contains the statistical analyses carried out in order to evaluate the thickness of the WS</span><sub><span>2</span></sub><span> nanoflakes. In order to have a reliable comparison we determine the methodology of comparing Raman spectroscopic mapping and atomic force microscopy (AFM). </span></p> <p><span>In case of Raman spectroscopy, the thickness is evaluated by employing the separation of the two vibrational mode, namely E</span><sub><span>2g</span></sub><span> and A</span><sub><span>1g</span></sub><span>. This method is well-established and the number of layers has been previously tabulated in different articles. </span></p> <p><span>Each spectrum of the Raman analysis, is performed with a 100X objective in a confocal microscope, with a 473 nm laser excitation, a laser power of 0.5 mW and an acquisition time of 1 s.</span></p> <p><span>The AFM analysis are carried out in tapping mode with a 512-pixel x 512-pixel resolution and and a scan rate of 1Hz per line.</span></p>
Maps and real-space refined models for 2024 Frontiers in Molecular Biosciences Perspective
<p>Maps and real-space refined models for our 2024 Frontiers in Molecular Biosciences perspective examining the impact of ML-based map modification tools on cryoEM maps.</p>
Map 1 in Genus Chimarra Stephens, 1829 (Insecta: Trichoptera: Philopotamidae) from Suntalekhola Biodiversity Camp, Neora Valley, National Park, West Bengal
Map 1. Location of collection site in West Bengal.
Presentation in 46th ARO: Mesoscale Selective Plane-Illumination Microscopy for Thickness Map of Sub-Layers of the Human Tympanic Membrane
<p>This upload is for the poster presentation of Merlin Schär at the 46th Annual Midwinter Conference of the Association for Research in Otolaryngology on the development and application of mesoscale selective plane-illumination microscopy for imaging of the sublayers of the human tympanic membrane. The upload contains the poster, the related imaging data with corresponding metadata for scanning and image processing, as well as supplementary figures illustrating the stitching of subvolume scans and the merged 3D rendering.</p>
IRIS Carbon Mapping Project: Curated Dataset
<p>Dataset to support IRIS Carbon Mapping Project final report.</p>
Dataset: Computational resources in the development of e-health IoT applications: A systematic mapping study
<p>Files related to systematic mapping titled Computational resources in the development of e-health IoT applications: A systematic mapping study</p>
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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.