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67 results for “spatial information”
Adult neurogenesis improves spatial information encoding in the mouse hippocampus - hM4Di Silencing
<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 CNO-induced chemogenetic silencing experiments in hM4Di+ and hM4Di-(control) mice. There is another Zenodo dataset with the remaining hM4Di-(control) mice <a href="https://doi.org/10.5281/zenodo.10949624">here</a>. The folder for each mouse includes baseline (pre) and post-CNO (cno) recordings.</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>Refer to the original publication for additional information. The sex of individual mice is as follows:</p> <p> </p> <table> <tbody> <tr> <td><strong>Group</strong></td> <td><strong>Mouse #</strong></td> <td><strong>Sex</strong></td> <td><strong>Group</strong></td> <td><strong>Mouse #</strong></td> <td><strong>Sex</strong></td> </tr> <tr> <td>hM4Di+</td> <td>M1</td> <td>M</td> <td>hM4Di-</td> <td>M1</td> <td>M</td> </tr> <tr> <td> </td> <td>M2</td> <td>M</td> <td> </td> <td>M2</td> <td>M</td> </tr> <tr> <td> </td> <td>M3</td> <td>F</td> <td> </td> <td>M3</td> <td>M</td> </tr> <tr> <td> </td> <td>M4</td> <td>F</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>M5</td> <td>M</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>M6</td> <td>M</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>M7</td> <td>M</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>M8</td> <td>F</td> <td> </td> <td> </td> <td> </td> </tr> <tr> <td> </td> <td>M9</td> <td>F</td> <td> </td> <td> </td> <td> </td> </tr> </tbody> </table>
Dendritic prioritization through spatial stream network modeling informs targeted management of Himalayan riverscapes under brown trout invasion
<p>With the concept of 'riverscapes' long pending to be acknowledged in the 'landscape-centric' legislative framework of Himalayan nations, conservation of native riverine species stays practically unheeded. This necessitates urgent prioritization of stream networks to conserve the lotic taxa under invasion pressures. Himalayan riverscapes are pervaded with the invasive-exotic brown trout <i>Salmo trutta</i>,<i> </i>posing serious threats to the co-occurring native, the snow trout <i>Schizothorax richardsonii</i>. Using intensive surveys (218.7km) and geostatistical stream network models (n=537), we contrasted snow trout in two stream networks with and without invasives, for assessing differences in their spatial distribution. Our models indicate invasion-induced relegations of natives from the river mainstem into headwaters, with large sections of mainstem occupied by invasives. Furthermore, a concerningly small percentage of potential habitat left for natives to occupy in the mainstem is threatened, where a 100% overlap of native and invasive trout distributions is predicted. With a higher presence probability for the natives in headwaters of invaded watershed as compared to the non-invaded watershed, we highlight the headwater streams as<b> </b>potential refugia for the natives under invasion.</p> <p><i>Synthesis and Applications: </i>Our approach of basin-scale dendritic prioritization provides immediate management solutions to tackle brown trout invasion threats in Himalaya. We inform decisions on delineation of headwaters as invasion refugia for native fish, with assisted recovery of their fragmented populations in the river mainstems through targeted management of invasives</p>
Supporting information for U.S. Dairy LCA: Matrices for spatialized impacts
<p>These files provide matrices as supporting information to the manuscript "<a href="https://www.mdpi.com/2071-1050/15/3/1890">Spatialized Life Cycle Assessment of Fluid Milk Production and Consumption in the United States</a>", published in Sustainability: https://doi.org/10.3390/su15031890</p>
Supplementary animation file for INSPIRE: Intensity and spatial information-based deformable image registration
<p>Illustrative animation of the INSPIRE registration method applied to a retinal image.</p>
Data from: Revised time scales of RNA virus evolution based on spatial information
Open the record for dataset details and reuse information.
Dendritic prioritization through spatial stream network modeling informs targeted management of Himalayan riverscapes under brown trout invasion
Open the record for dataset details and reuse information.
Data from: A genetic polymorphism affecting reliance on personal versus public information in a spatial learning task in Drosophila melanogaster
Open the record for dataset details and reuse information.
Spatially Resolved Multi-Omics Single-Cell Analyses Inform Mechanisms of Immune Dysfunction in Pancreatic Cancer
GEO Series GSE205049. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing.
The spatially informed mFISHseq assay resolves biomarker discordance and predicts treatment response in breast cancer
GEO Series GSE283522. Homo sapiens. 1254 samples. Type: Expression profiling by high throughput sequencing.
Spatial gradients of chromatin accessibility endow embryonic cells with positional information [Multiome]
GEO Series GSE224722. Gallus gallus. 2 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Spatial single cell transcriptomic analysis of a lineage-traceable mouse model of DICER1 Syndrome informs tumor developmental hierarchy [RNA-Seq]
GEO Series GSE309820. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Spatial gradients of chromatin accessibility endow embryonic cells with positional information [RNA-seq]
GEO Series GSE224436. Gallus gallus. 168 samples. Type: Expression profiling by high throughput sequencing.
Spatially resolved multi-omics single-cell analyses inform mechanisms of immune-dysfunction in pancreatic cancer
GEO Series GSE205354. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
Spatial transcriptomic and morpho-functional information derived from single mouse FFPE slides allows in-depth fingerprinting of lung fibrosis
GEO Series GSE288947. Mus musculus. 13 samples. Type: Expression profiling by high throughput sequencing.
Data from: Modeling spatial patterns of soil respiration in maize fields from vegetation and soil property factors with the use of remote sensing and geographical information system
To examine the method for estimating the spatial patterns of soil respiration (Rs) in agricultural ecosystems using remote sensing and geographical information system (GIS), Rs rates were measured at 53 sites during the peak growing season of maize in three counties in North China. Through Pearson's correlation analysis, leaf area index (LAI), canopy chlorophyll content, aboveground biomass, soil organic carbon (SOC) content, and soil total nitrogen content were selected as the factors that affected spatial variability in Rs during the peak growing season of maize. The use of a structural equation modeling approach revealed that only LAI and SOC content directly affected Rs. Meanwhile, other factors indirectly affected Rs through LAI and SOC content. When three greenness vegetation indices were extracted from an optical image of an environmental and disaster mitigation satellite in China, enhanced vegetation index (EVI) showed the best correlation with LAI and was thus used as a proxy for LAI to estimate Rs at the regional scale. The spatial distribution of SOC content was obtained by extrapolating the SOC content at the plot scale based on the kriging interpolation method in GIS. When data were pooled for 38 plots, a first-order exponential analysis indicated that approximately 73% of the spatial variability in Rs during the peak growing season of maize can be explained by EVI and SOC content. Further test analysis based on independent data from 15 plots showed that the simple exponential model had acceptable accuracy in estimating the spatial patterns of Rs in maize fields on the basis of remotely sensed EVI and GIS-interpolated SOC content, with R2 of 0.69 and root-mean-square error of 0.51 µmol CO2 m−2 s−1. The conclusions from this study provide valuable information for estimates of Rs during the peak growing season of maize in three counties in North China.
Spatially-diverse High-dimensional Channel State Information (CSI) based Dataset for Human Activity Recognition (HAR)
<p><strong>Introduction</strong></p> <p>The data collection for this novel channel state information (CSI)-based human activity recognition (HAR) dataset was conducted in a rigorously controlled laboratory environment. The space, measuring 5 meters x 8 meters x 3 meters, served as a well-defined testing ground. Four strategically positioned ESP32 devices formed transceiver pairs in a diagonal network, functioning as both transmitters and receivers, which are separated by a distance of 1.5 meters. The transmitters, powered by external power banks for consistent operation, were mounted on tripods at a height of 1.5 meters on the north and east corners. Their corresponding receivers, connected to laptops via USB for real-time CSI data acquisition, were positioned on the south and west corners.</p> <p>This dataset captures a wider range of activities (including subtle movements) and accounts for variations in body type and environmental conditions. It achieves this by collecting CSI data in a controlled environment using multiple transmitter-receiver pairs positioned at different orientations. This setup captures CSI information across 166 subcarriers used in Wi-Fi Wi-Fi IEEE 802.11n on channel 11, providing a richer and more nuanced view of human movement compared to traditional datasets. This data is expected to lead to the development of more robust and generalizable HAR models with higher accuracy and real-world applicability.</p> <p><strong>Description of Dataset</strong></p> <p>The dataset is housed within a directory named "SHD-HAR-Dataset-main" in the repository. This directory is further divided into "raw" and "amplitude" subdirectories. Data in "raw" and "amplitude" directories is further categorized based on participant orientation relative to the transceivers ("front/side"). It's important to note that the samples are synchronized between the "front" and "side" folders. This is because both devices collected data simultaneously for a particular activity. To summarize, the file "activityX.csv" in the "front" folder was collected at the same time as the corresponding "activityX.csv" file in the "side" folder, where "X" represents a unique identifier for each sample. </p> <p>The "raw" directory stores the unprocessed CSI data for each activity sample. These samples are stored as individual CSV files ("activityX.csv") containing 300-450 packets of CSI data obtained within 5 seconds. Each packet encompasses 25 distinct data fields, resulting in a total of 300-450 x 25 data entries per activity stored within the ".csv" file. </p> <p>The "amplitude" directory contains the processed signal amplitude data for 166 subcarriers. This directory mirrors the structure of the "raw" directory, with subdirectories for "front" and "side" orientations and "activityX.csv" files for each sample. However, the data within these files is transformed into a 300-450 x 166 matrix, representing the extracted signal amplitudes from the original CSI data. This format significantly reduces dimensionality while preserving key information for HAR analysis.</p>
Adult neurogenesis improves spatial information encoding in the mouse hippocampus - hM4Di Controls Part 2
<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 CNO-induced chemogenetic silencing experiments in hM4Di-(control) mice. There is another Zenodo dataset with the remaining hM4Di-(control) mice <a href="https://doi.org/10.5281/zenodo.10913386">here</a>. The folder for each mouse includes baseline (pre) and post-CNO (cno) recordings.</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>Refer to the original publication for additional information. The sex of individual mice is as follows:</p> <table> <tbody> <tr> <td><strong>Group</strong></td> <td><strong>Mouse #</strong></td> <td><strong>Sex</strong></td> </tr> <tr> <td>hM4Di-</td> <td>M4</td> <td>F</td> </tr> <tr> <td> </td> <td>M5</td> <td>F</td> </tr> <tr> <td> </td> <td> </td> <td> </td> </tr> </tbody> </table>
Supplementary files for: A geographic information system-based global variable renewable potential assessment using spatially resolved simulation
<p>File 1: Supplementary materials on methods</p> <p>File 2: Definition of the zones in this study</p> <p>File 3: Statistics on land cover pixels updated from OSM</p> <p>File 4: Validation of the eligibility masks with existing installation sites</p> <p>File 5: Validation of the estimated CF profiles for the European countries</p> <p>File 6: Summary of the available area, technical potential and capacity potential of wind and solar power in this study</p> <p>File 7: Aggregation of the estimated 10-year hourly CF into 288 month-hour profile</p> <p>File 8: The estimated 10-year hourly CF of all available technologies and CF tranches in each zone</p>
Spatial single cell transcriptomic analysis of a lineage-traceable mouse model of DICER1 Syndrome informs tumor developmental hierarchy [Xenium]
GEO Series GSE289001. Mus musculus. 72 samples. Type: Other.
Spatial single cell transcriptomic analysis of a lineage-traceable mouse model of DICER1 Syndrome informs tumor developmental hierarchy [scRNA-seq]
GEO Series GSE288990. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
ScienceDex guides
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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.