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ShareScore release 0.9.0
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20 results for “spatial coupling”
Geosci. Model Dev. paper data for Flipo et al., "Regional coupled surface-subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency-domain discharge data"
<p>Data and associated user guide, as part of the paper :</p> <p>Flipo N., Gallois N., Schuite J. Regional coupled surface-subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency-domain discharge data, Geoscientific Model Development.</p> <p>In consistency with the “Code and data availability” sub-section of the paper, all data necessary for the reproduction of<br> Figs. 7, 8c, 8d, 9, 10 and 11 are here provided.</p>
Spatial Room Impulse Response Dataset: A Robot's Journey Through Coupled Rooms of a Reverberant University Building
<p>This is a dataset of Spatial Room Impulse Responses obtained by a robot equipped with a microphone array.</p> <p>The measurements were conducted in a reverberant university building, the <em>Helmholtz</em> building at<em> Technische Universität Ilmenau</em> (coordinates: N50.6815788133375°, E10.939294371903342°). All the floors in the building are covered with bare stone tiles, the walls are not acoustically treated. Only the hallway has a suspended acoustic ceiling. The file "Pictures Overview.jpg" shows some impressions of the building. Note that the floorplan only shows parts of the building that were connected to the measurement area by open doors.</p> <p>The area covered by the robot is in a hallway on the top floor (2nd floor starting with ground floor) with two stairwells at both ends. To specifically study the behavior of coupled rooms and occluded sources, the sound sources were placed in adjacent sections of the building and on multiple floors. See the file "Measurement Overview.jpg" for an overview of the source positions and the receiver areas covered. Areas 2 and 3 were captured with a higher spatial resolution than area 1 to analyze the transition between the hallway and the staircases. The receiver positions form a uniform grid, the pitch between positions is shown in the following table. Due to time and technical constraints, only a maximum of 3 sources were used per run, so there are not all combinations of sources and receiver areas. Refer to the following table to see which source was active for which area and which zip file contains the according data:</p> <table> <tbody> <tr> <th>Filename</th> <th>Sources</th> <th>Receiver Area</th> <th>Receiver Positions [ct]</th> <th>Pitch [cm]</th> </tr> </tbody> <tbody> <tr> <td>Helmholtzbau_OG2_HM_HS.zip</td> <td>HM, HS</td> <td>Area 1</td> <td>143</td> <td>50</td> </tr> <tr> <td>Helmholtzbau_OG2_SML_SSL_SSU.zip</td> <td>SML, SSL, SSU</td> <td>Area 1</td> <td>154</td> <td>50</td> </tr> <tr> <td>Helmholtzbau_OG2_SMU_SML_HM.zip</td> <td>SMU, SML, HM</td> <td>Area 2</td> <td>88</td> <td>25</td> </tr> <tr> <td>Helmholtzbau_OG2_SSU_SSL_HS.zip</td> <td>SSU, SSL, HS</td> <td>Area 3</td> <td>92</td> <td>25</td> </tr> </tbody> </table> <p>As an example "Plot Reverberation Time.jpg" shows the reverberation times for all measured positions of Area 1 and 2 with speaker HM.</p>
Does the spatial sorting of dispersal traits affect the phenotype of the non-dispersing stages of the invasive frog Xenopus laevis through coupling?
<p>Over ten weeks, we surveyed the development of <em>X. laevis</em> tadpoles in the French invasive range by conducting experiments in outdoor mesocosms and in laboratory microcosms, from free-swimming larvae to metamorphosis. We tested the effect of the location of the parental pond in the colonised range (core or periphery) on morphological traits related to dispersal (SVL and hind limb length), time to metamorphosis (development) and survival to metamorphosis. This excel spreadsheet contains the metadata and data spreadsheets used in the analysis. In addition to the first metadata sheet, the file contains nine additional sheets each containing data used in the separate analysis as outlined in the manuscript. </p>
Increased spatial coupling of integrin and collagen IV in the immunoresistant clear-cell renal-cell carcinoma tumor microenvironment - Nanostring CosMx SMI Data
<p>Data export from Nanostring CosMx SMI, directly from Nanostring, in Seurat Object format for use in R. Clear cell renal cell carcinoma and papillary renal cell carcinoma were profiled before and after exposure to immunotherapy, with and without sarcomatoid features in clear cell tumors. Each tumor had a field of view in the stromal compartment and field of view in the tumor compartment.</p> <p>For appropriate clinical information associated with this study, please contact Dr. Brandon Manley.</p>
Data from: Mapping spatial patterns to energetic benefits in groups of flow-coupled swimmers
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DeepCellMap: a Deep Learning Approach Coupled to Spatial Statistics to unravel Microglial Spatial Organization in the Developing Human Brain
<p>DeepCellMap is a deep-learning-assisted tool that integrates multi-scale image processing with advanced spatial and clustering statistics. This pipeline is designed to map microglial organization during normal and pathological brain development but can be adapted to any cell type.} Using DeepCellMap, we can capture the morphological diversity of microglia, identify strong coupling between proliferative and phagocytic phenotypes, and show that distinct spatial clusters rarely overlap as human brain development progresses. Additionally, we uncover a novel association between microglia and blood vessels in fetal brains exposed to maternal SARS-CoV-2. These findings offer insights into whether various microglial phenotypes form networks in the developing brain to occupy space, and in conditions involving haemorrhages, whether microglia respond to, or influence changes in blood vessel integrity. DeepCellMap is available as open-source software and is a powerful tool for extracting spatial statistics and analyzing cellular organization in large tissue sections, accommodating various imaging modalities. </p>
A dataset of measured spatial room impulse responses for the transition between coupled rooms
<p>For a detailed description of the measurement and analysis methods, please see:</p> <p>McKenzie, T., Schlecht, S. J., and Pulkki, V. (2021). Acoustic Analysis and Dataset of Transitions between Coupled Rooms. <em>IEEE International Conference on Acoustics, Speech and Signal Processing.</em></p> <p> </p> <p>This dataset contains measured spatial room impulse responses for the transition between coupled rooms. Four coupled room pairs are included:</p> <ul> <li>Meeting Room to Hallway</li> <li>Office to Anechoic Chamber</li> <li>Office to Kitchen</li> <li>Office to Stairwell</li> </ul> <p>All were recorded at the Aalto University campus using a Genelec 8331A coaxial loudspeaker and an mh acoustics Eigenmike (32 capsule spherical microphone array for fourth order spherical harmonic capture). Each transition features 101 measurements in 5cm intervals from 2.5m inside the first room to 2.5m inside the second room. Transitions are repeated four times corresponding to four different source positions: two inside each room, one which has no continuous line-of-sight with the microphone when in the opposing room, and one which retains a direct continuous line-of-sight with the microphone for all measurement positions. </p> <p>The spatial room impulse responses are downloadable in either Spatially Oriented Format for Acoustics (SOFA) and Wav formats. Supplementary data includes amplitude plots, direct-to-reverberant graphs, estimated direction-of-arrival plots and scaled illustrations of room geometries and source positions. </p> <p>Changelog: </p> <p>V 1.0 - Initial version<br> V 1.1 - Added plots only download option<br> V 1.2 - Improved time alignment of impulse responses and changed normalisation to a single value relative to the maximum of the entire dataset<br> V 1.3 - SOFA files updated to latest Matlab API (1.1.3), 'SingleRoomDRIR' convention, with SourcePosition data corrected. The SOFA files for each transition are also now downloadable separately, in case the entire dataset is not required. <br> V 1.4 - SRIRs have been denoised using the technique described in https://www.aes.org/e-lib/browse.cfm?elib=21800 and available at https://github.com/chris-hld/Directional-Multi-Slope-Room-Impulse-Response-Denoising. This is particularly noticeable for measurements with a low SNR, such as where there is a large distance between source and receiver and occluded direct path. Note that the wav files have not been included in this release - see previous releases if wav files (not denoised) are required. SOFA files are renamed and available to download separately.</p>
Data from: Spatial variation and linkages of soil and vegetation in the Siberian Arctic tundra – coupling field observations with remote sensing data
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Data from: Estimating range expansion of wildlife in heterogeneous landscapes: a spatially explicit state-space matrix model coupled with an improved numerical integration technique
Open the record for dataset details and reuse information.
Dataset for Analysis of Various Spatial Resolutions for Modelling Sector-Coupled Energy Systems
<p>Dataset for preprocessing Balmorel data in this Danish case study.</p>
Increased spatial coupling of integrin and collagen IV in the immunoresistant clear-cell renal-cell carcinoma tumor microenvironment - Validation mIF
<p>mIF object from <em>spatialTIME</em> R package, calculating univariate Ripley's K on multiplex immunofluorescence images stained with FOXP3, CD8, CD68, ITGAV, COL4, VIM, SMA, and PCK. Data was processed with InForm and HALO. </p> <p>Clear cell renal cell carcinoma and papillary renal cell carcinoma were profiled before and after exposure to immunotherapy, with and without sarcomatoid features in clear cell tumors. Each tumor had a field of view in the stromal compartment and field of view in the tumor compartment.</p> <p>For appropriate clinical information associated with this study, please contact Dr. Brandon Manley.</p>
Spatial coupling of microbes and immune cells in solid malignancies [Cold_hot_tumors_mouse]
GEO Series GSE189925. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Determination of the spatial organization of the FXN gene locus by using Chromosome Conformation Capture technique coupled with High-throughtput sequencing (3C-seq).
GEO Series GSE42961. Homo sapiens. 5 samples. Type: Other.
Spatial coupling of microbes and immune cells in solid malignancies [LCM_stroma_epithelium]
GEO Series GSE155723. Homo sapiens. 16 samples. Type: Expression profiling by high throughput sequencing.
Spatial coupling of microbes and immune cells in solid malignancies [Whole_slides]
GEO Series GSE155722. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
Spatial coupling of microbes and immune cells in solid malignancies.
GEO Series GSE213736. Mus musculus. 16 samples. Type: Expression profiling by high throughput sequencing.
Perturb-map coupled with spatial transcriptomics identifies mutation associated gene signatures in a mouse model of lung adenocarcinoma
GEO Series GSE193460. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing; Other.
Spatial Coupling of mTOR and Autophagy Augments Secretory Phenotypes
GEO Series GSE28464. Homo sapiens. 6 samples. Type: Expression profiling by array.
Spatial coupling of microbes and immune cells in solid malignancies [LCM_cold_hot_tumor_nest]
GEO Series GSE155724. Homo sapiens. 20 samples. Type: Expression profiling by high throughput sequencing.
Spatial coupling of microbes and immune cells in solid malignancies
GEO Series GSE155725. Mus musculus; Homo sapiens. 57 samples. Type: Expression profiling by high throughput sequencing.
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.