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242 results for “Spatial Dataset”
Spatial patterns and effects of invasive plants on soil microbial activity and diversity along river corridors - dataset
<p>environmental data, plant community data, CLPP profiles, microbial activity data</p>
Dataset from Rummel et al.: "Spatially resolved salt intrusion mechanisms in a tidal estuary and the impact of channel deepening" - Part 1
<p>Model data from the numerical setup of the Weser River Estuary used in Rummel et al. (submitted to JGR:Oceans): "Spatially resolved salt intrusion mechanisms in a tidal estuary and the impact of channel deepening" - Part 1.</p> <p>The dates in the file names are connected to specific model runs and do not explain the modelled time period.</p> <p>Explanation of datasets:</p> <ul> <li>2D_elev* - 2D model output for the entire year 2016 for validation at one location each (associated station name included in file name), original topography.</li> <li>3D_stat* - 3D model output for the entire year 2016 for validation at one location each (associated station name included in file name), original topography.</li> <li>3D_cross_30_80* - 3D model output for one month of 2016 for the model domain from Weser km 30 to 80 including variables needed for the salt transport decomposition. <ul> <li>2024-05-23 - March 2016, original topography</li> <li>2024-06-07 - March 2016, dredged topography</li> <li>2024-06-06 - September 2016, original topography (different temporal resolution)</li> <li>2024-06-10 - September 2016, dredged topography</li> </ul> </li> <li>3D_channel* - 3D model output for the navigational channel in the entire model domain for the entire year 2016. <ul> <li>2024-04-02 - original topography</li> <li>2024-05-16 - dredged topography</li> </ul> </li> <li>3D_cross_55/65_2024-09-02* - 3D model output for September 2016, original topography for crosssections at Weser km 55 and 65 including variables needed for the salt transport decomposition.</li> </ul>
Dataset from Rummel et al.: "Spatially resolved salt intrusion mechanisms in a tidal estuary and the impact of channel deepening" - Part 2
<p>Model data from the numerical setup of the Weser River Estuary used in Rummel et al. (submitted to JGR: Oceans): "Spatially resolved salt intrusion mechanisms in a tidal estuary and the impact of channel deepening" - Part 2.</p> <p>The dates in the file names are connected to specific model runs and do not explain the modelled time period.</p> <p>This dataset contains daily averaged 3D model output for the entire year 2016 of the whole model domain with the original, not dredged topography.</p> <p> </p>
Dataset from Rummel et al.: "Spatially resolved salt intrusion mechanisms in a tidal estuary and the impact of channel deepening" - Part 3
<p>Model data from the numerical setup of the Weser River Estuary used in Rummel et al. (submitted to JGR: Oceans): "Spatially resolved salt intrusion mechanisms in a tidal estuary and the impact of channel deepening" - Part 3.</p> <p>The dates in the file names are connected to specific model runs and do not explain the modelled time period.</p> <p>This dataset contains daily averaged 3D model output for the entire year 2016 of the whole model domain with the dredged topography.</p>
Test dataset for "Spatial Integration of Multi-Omics Data from Serial Sections using the novel Multi-Omics Imaging Integration Toolset"
<p>The uploaded tar file contains anonymized and reduced test data for the paper "Spatial Integration of Multi-Omics Data from Serial Sections using the novel Multi-Omics Imaging Integration Toolset". (doi: https://doi.org/10.1101/2024.06.11.598306; https://github.com/mwess/miit)</p> <p>Dataset description:<br>- 9 serial histology sections with the following stains: (HES, HE, HES, HES, HES, MTS, IHC, IHC, HES)<br>- Sections are indexed in the following way (due to some sections not being part of this project): 1,2,3,6,7,8,9,10,11<br>- Each serial section contains: <br> - landmarks with matching labels across all sections.<br> - semi-manually generated tissue masks <br>- Section 2 contain spatial transcriptomics data and one annotation file in geojson format.<br>- Sections 6 and 7 contain imzml data that were generated with MALDI-MSI in positive ion mode (section 6) and negative ion mode (section 7) and additional histology annotations.<br>- MALDI-MSI is reduced. The positive ion data contains only intensities and spectra for spermine. The negative ion mode data contains only intensities and spectra for citrate and zinc.<br>- ST data contains only locations of spots and scalefactors. (I.e. no count data is included.). Barcode ids are randomly generated. <br>- In addition, for each ST spot histopathological annotations and GSEA scores for the Citrate-Spermine Secretion gene signature are provided.</p> <p>Abbreviations:</p> <p>- HES = Hematoxylin-Erythrosine-Saffron<br>- HE = Hematoxylin-Eosin<br>- MTS = Masson's Trichrome Staining<br>- IHC = Immunohistochemistry<br>- ST = Spatial Transcriptomics, here refers to Visium10X arrays.<br>- MALDI-MSI = Matrix-Assisted Laser Desorption Ionization - Mass Spectrometry Imaging.</p> <p> </p>
HOMULA-RIR: A Room Impulse Response Dataset for Teleconferencing and Spatial Audio Applications Acquired Through Higher-Order Microphones and Uniform Linear Microphone Arrays
<p>In this paper, we present HOMULA-RIR, a dataset of room impulse responses (RIRs) acquired using both higher-order microphones (HOMs) and a uniform linear array (ULA), in order to model a remote attendance teleconferencing scenario. Specifically, measurements were performed in a seminar room, where a 64-microphone ULA was used as a multichannel audio acquisition system in the proximity of the speakers, while HOMs were used to model 25 attendees actually present in the seminar room. The HOMs cover a wide area of the room, making the dataset suitable also for applications of virtual acoustics. Through the measurement of the reverberation time and clarity index, and sample applications such as source localization and separation we demonstrate the effectiveness of the HOMULA-RIR dataset.</p>
Dataset for Spatial Heterogeneity of Uplift Pattern in the Western European Alps Revealed by InSAR Time Series Analysis
<p>ZIP file with InSAR raw and smoothed final velocity solution values</p>
Dataset for: Spatial tuning of face part representations within face-selective areas revealed by high-field fMRI
<p>Regions sensitive to specific object categories as well as organized spatial patterns sensitive to different features have been found across the whole ventral temporal cortex (VTC). However, it is unclear that within each object category region, how specific feature representations are organized to support object identification. Would object features, such as object parts, be represented in fine-scale spatial tuning within object category-specific regions? Here we used high-field 7T fMRI to examine the spatial tuning to different face parts within each face-selective region. Our results show consistent spatial tuning of face parts across individuals that within right posterior fusiform face area (pFFA) and right occipital face area (OFA), the posterior portion of each region was biased to eyes, while the anterior portion was biased to mouth and chin stimuli. Our results demonstrate that within the occipital and fusiform face processing regions, there exist systematic spatial tuning to different face parts that support further computation combining them.</p>
The datasets for the paper "Spatial and temporal distribution of lobate scarps in the lunar south polar region: Evidence for latitudinal variation of scarp geometry, kinematics and formation ages, continuous tectonic activity in the last 100 million years and seismically safe south pole Artemis human landing site" Geophysical Research Letters.
<p>This dataset provides the original data that were used for preparing the illustrations, figures and tables.</p>
dataset of the work entitled "The spatial distribution of rhizosphere microbial activities under drought: water availability is more important than root-hair controlled exudation"
<p>This is the dataset of the enzyme kinetics, and other biochemical properties obtained from zymography, <sup>14</sup>C images and water images of the work entitled "The spatial distribution of rhizosphere microbial activities under drought: water availability is more important than root-hair controlled exudation". </p>
Dataset for "Spatial distribution and physicochemical properties of respirable volcanic ash from the 16-17 August 2006 Tungurahua eruption (Ecuador), and alveolar epithelium response in-vitro" published in GeoHealth
<p>Data Repository for:</p> <p><strong>"Spatial distribution and physicochemical properties of respirable volcanic ash from the 16-17 August 2006 Tungurahua eruption (Ecuador), and alveolar epithelium response <em>in-vitro" </em></strong>published in GeoHealth.<br> </p> <p>Julia Eychenne<sup>1,2*</sup>, Lucia Gurioli<sup>1</sup>, David Damby<sup>3</sup>, Corinne Belville², Federica Schiavi<sup>1</sup>, Geoffroy Marceau<sup>2,4</sup>, Claire Szczepaniak<sup>5</sup>, Christelle Blavignac<sup>5</sup>, Mickael Laumonier<sup>1</sup>, Emmanuel Gardés<sup>1</sup>, Jean-Luc Le Pennec<sup>6,7</sup>, Jean-Marie Nedelec<sup>8</sup>, Loïc Blanchon², Vincent Sapin<sup>2,4 </sup></p> <p><sup>1</sup> Université Clermont Auvergne, CNRS, IRD, OPGC, Laboratoire Magmas et Volcans, F-63000 Clermont-Ferrand, France</p> <p><sup>2</sup> Université Clermont Auvergne, CNRS, INSERM, Institut de Génétique Reproduction et Développement, F-63000 Clermont-Ferrand, France</p> <p><sup>3</sup> U.S. Geological Survey, California Volcano Observatory, Moffett Field, CA, USA</p> <p><sup>4</sup> Biochemistry and Molecular Genetic Department, University Hospital, F-63000 Clermont-Ferrand, France</p> <p><sup>5</sup> Université Clermont Auvergne, UCA PARTNER, Centre Imagerie Cellulaire Santé, F-63000 Clermont-Ferrand, France</p> <p><sup>6</sup> Geo-Ocean, CNRS, Ifremer, UMR6538, F-29280 Plouzané, France</p> <p><sup>7</sup> IRD Office for Indonesia & Timor Leste, Jalan Kemang Raya n°4, Jakarta 12730, Indonesia</p> <p><sup>8</sup> Université Clermont Auvergne, Clermont Auvergne INP, CNRS, ICCFn, F-63000 Clermont-Ferrand, France</p> <p><strong>This repository includes the grainsize distributions of the individual tephra fall samples, the grainsize distribution of the respirable ash sample isolated from F2, the Raman point counting data and individual spectra, the SEM images and EDX maps of the respirable ash sample, the SEM and TEM images of the <em>in-vitro</em> experiments, and the data from the LDH assays, multiplex immunoassays and RT-qPCR.</strong></p>
A high spatial resolution dataset for anthropogenic atmospheric mercury emissions in China during 1998-2014
<p>This database contains gridded atmospheric mercury emissions in 30 provinces of China by sectors from 1998 to 2014 at the resolution of 1 km×1 km. We distribute atmospheric mercury emissions in four sectors, i.e., agriculture, industry, service industry, and residences, based on China's land use data, enterprise data, road data, and population data. Gridded estimates of the total Hg (THg) and the three species, i.e., gaseous elemental Hg (Hg<sub>0</sub>), gaseous oxidized mercury (Hg<sub>II</sub>), and particulate-bound mercury (Hg<sub>p</sub>), are given separately.</p>
Mapping the exposure of tourism to weather extremes: The need for a spatially-explicit gridded dataset for disaster risk reduction
<p>This dataset contains the spatially-explicit gridded database based on social media data for over 150 different tourism-related classes that depicts tourism density (supply and demand) and perceived satisfaction in Europe, and the related exposure to selected climate extreme events. Information on tourism density (supply and demand) and perceived satisfaction are categorised for Attractions, Culinary, and Hospitality, while the exposure analysis of those clases are provided in separate, specific files. The provided dataset is made accessible to support large-scale and regional tourism research and extends its relevance to other fields that are part of tourism as a complex system, such as risk assessment and vulnerability studies. For citing this work, please refer to the research article "Mapping the exposure of tourism to weather extremes: The need for a spatially-explicit gridded dataset for disaster risk reduction", DOI 10.1088/1748-9326/ad3e91. Suggested citation: "Camatti, N., Hrast Essenfelder, A., & Giove, S. (2024). Mapping the exposure of tourism to weather extremes: The need for a spatially-explicit gridded dataset for disaster risk reduction. Environmental Research Letters."</p>
Adult neurogenesis improves spatial information encoding in the mouse hippocampus - EE dataset
<p><strong>In vivo two-photon imaging dataset for Frechou et al. "Adult neurogenesis improves spatial information encoding in the mouse hippocampus"</strong></p> <p>This dataset includes data from mice that were housed in an enriched environment (EE). </p> <p>For each recording we included raw imaging data consisting of:</p> <ul> <li>Individual frames (.tif files) from 3 consecutive 3 min Ca2+ imaging movies (which were concatenated for analysis)</li> <li>Microscope settings metadata (Experiment.xml)</li> <li>Mouse location data (Episode001.h5 in SyncData folder) containing rotary encoder and RFID data</li> </ul> <p>Some analyzed data is also included:</p> <ul> <li>Suite2p analysis data (<strong>Suite2p</strong> folder)</li> <li><strong>fluorescence.npy </strong>contains raw fluorescence data (the F output of Suite2p data extraction). Rows are individual cells and columns are frames (i.e. timepoints) acquired at 15.253 Hz.</li> <li><strong>positions.npy </strong>contains the position of the mouse on the treadmill belt indexed from 0 to 100.</li> </ul> <p>Both NumPy(.npy) files are the output of the Preprocessing.py code, part of the analysis pipeline used for data analysis in the original publication, which can be found at <a href="https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis">https://github.com/GoncalvesLab/Frechou-et-al-Neurogenesis</a></p> <p>All imaged mice were female. Refer to the original publication for additional information. </p>
Technology comparison (image-based spatial transcriptomics)- annotated datasets
<p>This repository contains all the AnnData datasets, regionally annotated, used in the comparison of image-based spatial transcriptomics technologies (Marco Salas et al. 2024)</p>
A New Dataset for Spatial, Temporal and Tactical Analysis of Female Handball Player Movements
<p>This repository presents a comprehensive dataset detailing the precise indoor positioning of players from a female amateur handball team across 10 real matches. Utilising Ultra-Wideband (UWB) technology, the dataset captures each player's x and y coordinates every second throughout the games. A preliminary game analysis is included, specifying the initiation and termination times of each team's possession. This analysis provides a variety of labels crucial for training machine learning algorithms, encompassing distinctions such as attack or defense, structured or unstructured play, goal outcomes, and the differentiation between counter-attacks and static phases. The dataset comprises 84691 positioning measurements, offering a valuable resource for in-depth study and analysis of player dynamics and game strategies in female handball.</p>
HAMSTER: Hyperspectral Albedo Maps dataset with high Spatial and TEmporal Resolution
<p>This dataset contains 365 hyperspectral albedo maps of Earth with a temporal resolution of 1 day. The hyperspectral albedo maps are built from a 10-year average of the MODIS Surface Reflectance dataset (MCD43D42-48, version 6.1). Using a Principal Component Analysis (PCA) regression algorithm we combine different hyperspectral laboratory and in-situ measurements datasets of various dry soils, vegetation surfaces and mixure of both to reconstruct the albedo maps in the entire wavelength range from 400 to 2500 nm. The hyperspectral albedo maps have a spatial resolution of 0.25° in latitude and longitude.</p> <p>Additional hyperspectral albedo maps with a coarser spatial and spectral resolutions are available in the "Data sets" supplemetary material of "HAMSTER: Hyperspectral Albedo Maps dataset with high Spatial and TEmporal Resolution" (Roccetti et al., 2024, https://doi.org/10.5194/egusphere-2024-167) or upon request to the corresponding author.</p>
TAU Spatial Sound Events 2019 - Ambisonic and Microphone Array, Development Datasets
<p>This package consists of two development datasets, <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> and <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong>. These datasets contain recordings from an identical scene, with <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> providing four-channel First-Order Ambisonic (FOA) recordings while <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong> provides four-channel directional microphone recordings from a tetrahedral array configuration. Both formats are extracted from the same microphone array. The recordings in the two datasets consist of stationary point sources from multiple sound classes each associated with a temporal onset and offset time, and DOA coordinate represented using azimuth and elevation angle. These development datasets are part of the <a href="https://github.com/sharathadavanne/seld-dcase2019">DCASE 2019 Sound Event Localization and Detection Task</a>.</p> <p>Both the development set consists of 400, one minute long recordings sampled at 48000 Hz, and divided into four cross-validation splits of 100 recordings each. These recordings were synthesized using spatial room impulse response (IRs) collected from five indoor locations, at 504 unique combinations of azimuth-elevation-distance. Furthermore, in order to synthesize the recordings, the collected IRs were convolved with <a href="http://www.cs.tut.fi/sgn/arg/dcase2016/task-sound-event-detection-in-synthetic-audio#audio-dataset">isolated sound events dataset from DCASE 2016 task 2</a>. Finally, to create a realistic sound scene recording, natural ambient noise collected in the IR recording locations was added to the synthesized recordings such that the average SNR of the sound events was 30 dB.</p> <p>The IRs were collected in Finland by Tampere University between 12/2017 - 06/2018. The data collection received funding from the European Research Council, grant agreement 637422 EVERYSOUND.</p> <p><strong>Download instructions</strong></p> <p>The three files, <strong><em>foa_dev.z01</em></strong>,<strong><em> foa_dev.z02</em></strong> and <strong><em>foa_dev.zip</em></strong>, correspond to audio data of <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> development dataset.<br> The two files, <strong><em>mic_dev.z01</em></strong> and, <strong><em>mic_dev.zip</em></strong>, correspond to audio data of <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong> development dataset.<br> The <strong><em>metadata_dev.zip</em></strong> is the common metadata for both <strong>TAU Spatial Sound Events 2019 - Ambisonic</strong> and <strong>TAU Spatial Sound Events 2019 - Microphone Array</strong> development datasets.</p> <p>Download the zip files corresponding to the dataset of interest and use your favorite compression tool to unzip these split zip files.<br> </p>
Dataset of the journal article "Spatial Reuse in IEEE 802.11ax WLANs"
<p>This dataset contains both the inputs and the outputs from the conference article "Spatial Reuse in IEEE 802.11ax WLANs", authored by Francesc Wilhelmi, Sergio Barrachina, Cristina Cano, Ioannis Selinis and Boris Bellalta. The article has been sent to IEEE Surveys & Tutorials.</p> <p>Regarding the Komondor's input, we provide the files used in the Komondor simulator, as well as the execution scripts, for generating the results presented in the paper. We find "toy" and "random" scenarios, which cover different parts of the paper. In the random case, we have 39,800 different scenarios, which correspond to 4 network densities, 3 strategies on applying SR, 3 traffic loads, 50 deployments, and 21 different OBSS/PD values. More details are provided in the article. </p> <p>Apart from the Komondor's input, we also provide other Matlab files used in the context of the SFCTMN analytical model (refer to <a href="https://github.com/sergiobarra/SFCTMN/releases/tag/v1.0_11ax_SR">https://github.com/sergiobarra/SFCTMN/releases/tag/v1.0_11ax_SR</a>). </p> <p>Contact information: francisco.wilhelmi@upf.edu</p>
Example dataset and expected outcomes of Spatially resolved in situ profiling of mRNA life cycle at transcriptome scale in intact cells and tissues
<p>Here are the example datasets and expected outcomes included in "<strong>Spatially resolved in situ profiling of mRNA life cycle at transcriptome scale in intact cells and tissues</strong>" from Ren et al. Please refer to the README.txt file for more detailed information. Corresponding computational tools are available at <a href="https://github.com/wanglab-broad/starfinder">https://github.com/wanglab-broad/starfinder.</a></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.