Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
242
datasets available to search
ShareScore release 0.9.0
Dataset results
242 results for “Spatial Dataset”
Spatial Plus Cross-Validation experiments datasets and codes (new)
<p>This rar/zip file includes all materials of Spatial Plus Cross-Validation experiments. </p> <p>They are ordered by the first number of folder's name.</p> <p>In each folder, the order of running code scripts are labeled by the first number of code's name.</p>
Dynamic encoding of social threat and spatial context in the hypothalamus - calcium activity recordings and behavioural dataset
Open the record for dataset details and reuse information.
Dataset for: Spatial and environmental effects on Coho Salmon life-history trait variation
Open the record for dataset details and reuse information.
Spatial datasets for Victorian kelp dynamics
Open the record for dataset details and reuse information.
A dataset of spatially resolved transcriptomics of post-natal cardiac development in mice
GEO Series GSE298650. Mus musculus. 5 samples. Type: Other.
Spatial transcriptomics dataset of primary tumours from MDA-MB-231 xenograft model
GEO Series GSE300613. Homo sapiens; Mus musculus. 2 samples. Type: Other.
Common Mitochondrial Deletions in RNA-Seq: Evaluation of Bulk, Single-Cell and Spatial Transcriptomic Datasets
GEO Series GSE226663. Homo sapiens. 10 samples. Type: Expression profiling by high throughput sequencing; Other.
The supplementary material (dataset S1 and dataset S2) of article with the title of "Bacterioplankton community variation in Bohai Bay (China) is explained by joint effects of environmental and spatial factors"
<p>Dataset S1 Geochemical and physical variables of the sampling sites in this study</p> <p>Dataset S2 The relative baundance of 36 OTUs (with relative abundance > 1%) </p>
Bayesian regional flood frequency analysis with GEV hierarchical models under spatial dependency structures (code and dataset)
<p>The provided material contains the dataset and the models' code used in the article named “Bayesian regional flood frequency analysis with GEV hierarchical models under spatial dependency structures”.</p>
Dataset for "Modelling impacts of spatially variable erosion drivers on suspended sediment dynamics"
<p>Dataset for publication:</p> <p>Battista, P. Molnar, and P. Burlando. Modelling impacts of spatially variable erosion drivers on suspended sediment dynamics. Earth Surface Dynamics, 8(3):619{635, jul 2020a. ISSN 2196-632X. doi: 10.5194/esurf-8-619-2020.</p>
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>
The effect of spatial energy spread on sound image size and speech intelligibility [dataset]
<p>This dataset contains the data and supplementary material for the publication titled "The effect of spatial energy spread on sound image size and speech intelligibility" published in the Journal of the Acoustical Society of America.</p> <p>This repository contains the results from the three experiments as well as a figure showing the adaptive tracks of the adaptive procedure and the estimated psychometric functions in experiment 3. The subject identifiers are common across the three experiments.</p> <p><strong>Experiment 1: </strong></p> <p>Factors: <em>Subject/Listener, ambisonics order, audio/stimulus type, spatial location of stimulus, room condition, repetition</em></p> <p>Measured variables: <em>Source image size, source direction (azimuth), source distance</em></p> <p><strong>Experiment 2:</strong></p> <p>Factors: <em>Subject/Listener, ambisonics order, interferer location, room condition</em></p> <p>Measured variable: <em>Speech reception threshold (SRT)</em></p> <p><strong>Experiment 3:</strong></p> <p>Factors: <em>Subject/Listener, ambisonics order, repetition</em></p> <p>Measured variable: <em>Speech reception angle (SRA)</em></p> <p>The figure (exp3_adaptiveTracks.png) shows the adaptive tracks in experiment 3. Each panel shows one of the ambisonics orders. The x-axis is the trial number and the y-axis the separation angle between target and interfering talkers. Each color indicates one subject.</p> <p>The figure (exp3_psychometricFcns.png) shows the estimated psychometric functions in experiment 3. Each panel shows one of the ambisonics orders. The x-axis is the separation angle between target and interfering talkers and the y-axis is the percent correct words. Each color indicates one subject. The black line indicates the median over the subjects and repetitions and the black cross the SRA predicted with this method.</p>
Data from: Spatial and quantitative datasets of the pancreatic β-cell mass distribution in lean and obese mice
A detailed understanding of pancreatic β-cell mass distribution is a key element to fully appreciate the pathophysiology of models of diabetes and metabolic stress. Commonly, such assessments have been performed by stereological approaches that rely on the extrapolation of two-dimensional data and provide very limited topological information. We present ex vivo optical tomographic data sets of the full β-cell mass distribution in cohorts of obese ob/ob mice and their lean controls, together with information about individual islet β-cell volumes, their three-dimensional coordinates and shape throughout the volume of the pancreas between 4 and 52 weeks of age. These data sets offer the currently most comprehensive public record of the β-cell mass distribution in the mouse. As such, they may serve as a quantitative and topological reference for the planning of a variety of in vivo or ex vivo experiments including computational modelling and statistical analyses. By shedding light on intra- and inter-lobular variations in β-cell mass distribution, they further provide a powerful tool for the planning of stereological sampling assessments.
Dataset of spatial transcriptomics of lung adenocarcinoma for analyzing the tumor microenvironment using topological analysis
<p>The human lung adenocarcinoma dataset for 'STopover captures spatial colocalization and interaction in the tumor microenvironment using topological analysis in spatial transcriptomics data'. </p>
supplementary dataset for "Review of quantitative applications of the concept of the water planetary boundary at different spatial scales"
<p>This supporting information includes 6 spread sheets:</p> <p>Table S1 288_database search The studies collected through an adjusted search strategy from ISI Web of Science v.5.35.<br> Table S2 114_1st filter The studies passed the 1st filter and their category (Category I ).<br> Table S3 52_2nd filter The studies passed the 2ed filter and their category (Category II ).<br> Table S4 26_additional screening The studies collected through manual searching from the citations of 288 studies, as well as the range included in previous literature reviews.<br> Table S5 28_final The studies passed the three filters and three additional reports.<br> Table S6 488_data records The 488 data records collected from the 28 studies.<br> </p>
Input dataset - Comparative Study of Spatial and Non-spatial Modelling in Price Prediction
<p>Raw and pre-processed dataset for case-study: Comparative Study of Spatial and Non-spatial Modelling in Price Prediction.</p> <p><a href="https://github.com/HassanAli99/Spatial-vs-NonSpatial-Price-Prediction">GitHub</a> </p>
Datasets used in Detecting Plumes in Mobile Air Quality Monitoring Time Series with Density-based Spatial Clustering of Applications with Noise v01
<p>This repository contains the following data sets related to Detecting Plumes in Mobile Air Quality Monitoring Time Series with DBSCAN published in . Please cite the following: .</p> <p>Validated_Data.csv: A .csv file containing the validation set used in the study. Column headings are the following:</p> <p>"Lat1": GPS latitude of car location in degrees.<br> "Long1": GPS longitude of car location in degrees.<br> "LST": Measurement time stamp. Time zone US/Central.<br> "BC": Black carbon measurements in ng/m^3<br> "CO2": Carbon dioxide measurements in ppm.<br> "UFP": Ultrafine particle count in particles/cc.<br> "NOx": Oxides of nitrogen, defined as the sum of NO and NO2, in ppb.<br> "Anomaly": What has been manually flagged as "Anomaly" (2) or "Normal" (1).<br> "Uniq_Fac": Factor from 1-30 mapping to different days of the campaign. For example, all measurements with Uniq_Fac = 1 belong to the same day.</p> <p>Labeled_DBSCAN_Anomalies.csv: A .csv file containing points labeled as anomalies by the DBSCAN algorithm described in the manuscript. Columns are the following.</p> <p>"BC": Black carbon measurement (ng/m^3)<br> "CO2": Carbon dioxide measurement (ppm)<br> "NOx": Oxides of nitrogen, defined as the sum of NO and NO2 (ppb)<br> "UFP": Ultrafine particle count (p/cc)<br> "Anomaly": Whether the DBSCAN algorithm has labeled this point as "Anomaly" (2) or "Normal" (1)<br> "Uniq_Fac": Factor spanning from 1-277 grouping measurements taken on separate days by car. E.g. all measurements with Uniq_Fac=1 were grouped and analyzed together.<br> "LST": Timestamp (US/Central)<br> "Road_Class": TigerLINE census road class designation for the given point. Possible road classes are S1100 - Primary Road, S1200 - Secondary Road, S1400 - Local Road, S1630 - Ramps, S1640 - Service Drives, S1730 - Private Roads<br> "X": Universal Transverse Mercator Easting for Zone 15N (m).<br> "Y": Universal Transverse Mercator Northing for Zone 15N (m).</p> <p>*_To_Be_Validated.csv: A series of files where * denotes the following.</p> <p>"DB": DBSCAN Algorithm<br> "QOR": QOR Algorithm<br> "QAND": QAND Algorithm<br> "Drew": Drewnick Algorithm</p> <p>Each file contains the following columns:</p> <p>"Lat1": GPS latitude of car location in degrees.<br> "Long1": GPS longitude of car location in degrees.<br> "LST": Measurement time stamp. Time zone US/Central.<br> "BC": Black carbon measurements in ng/m^3<br> "CO2": Carbon dioxide measurements in ppm.<br> "UFP": Ultrafine particle count in particles/cc.<br> "NOx": Oxides of nitrogen, defined as the sum of NO and NO2, in ppb.<br> "Anomaly": What has been flagged as "Anomaly" (2) or "Normal" (1).<br> "Uniq_Fac": Factor from 1-30 mapping to different days of the campaign. For example, all measurements with Uniq_Fac = 1 belong to the same day.</p>
IDH-mutant gliomas arise from glial progenitor cells harboring the initial driver mutation (Related accession no. GSE275791) - Spatial transcriptomics dataset
GEO Series GSE302642. Homo sapiens. 6 samples. Type: Other.
Data from: Spatial and quantitative datasets of the pancreatic β-cell mass distribution in lean and obese mice
Open the record for dataset details and reuse information.
A spatial transcriptomics dataset of the endometrium from repeated implantation failure patients
GEO Series GSE287278. Homo sapiens. 8 samples. Type: Other.
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.