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ShareScore release 0.9.0
Dataset results
49 results for “CSI”
Exposing the CSI - S7
<p>This dataset has been used to study a CSI-based activity-recognition framework in the paper "Exposing the CSI: A Systematic Investigation of CSI-based Wi-Fi Sensing Capabilities and Limitations". The dataset is split into several archives, one for each experiment considered in the paper. A description of all the experiments can be found in the paper, together with a map of the lab reporting the location of all the nodes and target positions.</p>
Exposing the CSI - S5
<p>This dataset has been used to study a CSI-based activity-recognition framework in the paper "Exposing the CSI: A Systematic Investigation of CSI-based Wi-Fi Sensing Capabilities and Limitations". The dataset is split into several archives, one for each experiment considered in the paper. A description of all the experiments can be found in the paper, together with a map of the lab reporting the location of all the nodes and target positions.</p>
Exposing the CSI - S3
<p>This dataset has been used to study a CSI-based activity-recognition framework in the paper "Exposing the CSI: A Systematic Investigation of CSI-based Wi-Fi Sensing Capabilities and Limitations". The dataset is split into several archives, one for each experiment considered in the paper. A description of all the experiments can be found in the paper, together with a map of the lab reporting the location of all the nodes and target positions.</p>
Exposing the CSI - S4
<p>This dataset has been used to study a CSI-based activity-recognition framework in the paper "Exposing the CSI: A Systematic Investigation of CSI-based Wi-Fi Sensing Capabilities and Limitations". The dataset is split into several archives, one for each experiment considered in the paper. A description of all the experiments can be found in the paper, together with a map of the lab reporting the location of all the nodes and target positions.</p>
Wi-Fi (CSI and RSSI) data of six basic knife activities for cooking (chopping, cubing, French cutting, julienning, mincing, and slicing)
<p>To gather the dataset, we asked two participants to perform six basic knife activities. The layout of the system experiment is provided in Fig. 4. As it illustrates, we put the receiver on the right side and the ESP32 transceiver on the left side of the performing area. The performing area is a cutting board (30 x 46 cm) in this experiment. Each participant performs each activity five times in the performing area. The data is recorded using a customized version of ESP32-CSI-tool [38] on the laptop that helps us to record and save each data in a separate file. After recording all 60 data entries, we used Python code to extract the clean data from all generated text by the tool. The clean data is stored in a database and creates the dataset.</p>
csi-vae dataset
<p>This dataset contains the data used in the paper "Accurate Passive Radar via an Uncertainty-Aware Fusion of Wi-Fi Sensing Data" (FUSION 2023) and "Learning to Sense: A Principled Approach to Wi-Fi Sensing Using Variational Auto-Encoders" (submitted to IEEE Transactions on Mobile Computing).</p> <ul> <li><strong>latent_space_dataset.zip </strong>contains data already processed by the variational autoencoders</li> <li><strong>KERAS_models.zip</strong> contains the weights of the EDL/MLP models used in the FUSION paper</li> <li><strong>VAE_models.zip</strong> contains the weights of the VAE models</li> </ul> <p>Version 1.0 includes the data from 5 activities used in our FUSION 2023 paper. Version 2.0 also includes the data from 12 different activities and the weights of different classifiers in <strong>models_12activities.zip</strong></p> <p> </p>
5-minute high-frequent data for SSE 50, CSI300, CSI500 and CSI 1000 indices
Open the record for dataset details and reuse information.
Normalized CT images and reference segmentations of thoracic and lumbar vertebrae from the CSI 2014 workshop
<p>This is the dataset of the vertebra segmentation challenge of the <a href="http://csi-workshop.weebly.com/challenges.html">CSI 2014 workshop</a> that was held in conjunction with MICCAI 2014.</p> <ul> <li><strong>1-10</strong>: Training set, scans of 10 young adult (16-35 years old)</li> <li><strong>11-15</strong>: Test set, scans of 5 young adult (20-35 years old)</li> <li><strong>16-20</strong>: Test set, scans of 5 patients with vertebral compression fractures </li> </ul> <p>Scans were acquired at the Department of Radiological Sciences, University of California, Irvine, School of Medicine and were published under the <a href="http://opendatacommons.org/licenses/pddl/1.0/">ODC Public Domain Dedication and License</a> on <a href="http://spineweb.digitalimaginggroup.ca/">SpineWeb</a> (datasets 2 and 15). The dataset and challenge is further described in this publication: <a href="http://dx.doi.org/10.1016/j.compmedimag.2015.12.006">A multi-center milestone study of clinical vertebral CT segmentation</a></p> <p>The data that is published here has been normalized:</p> <ul> <li>Voxel values are Hounsfield values and have been clipped to [-1000, 3095]</li> <li>Image origin has been set to 0,0,0</li> <li>Image orientation has been standardized to RAI orientation</li> <li>Small islands and other obvious mistakes have been removed</li> <li>Segmentation masks have been smoothed with a 2x2x2 median filter</li> <li>Vertebrae have been anatomically labeled (8 = T1, 9 = T2, ..., 24 = L5)</li> <li>Because not always all visible vertebrae were segmented in the original data, only segmentations of the thoracic and lumbar vertebrae have been retained</li> </ul> <p><strong>License</strong></p> <p>This dataset is released under the <a href="https://opendatacommons.org/licenses/by/1-0/">Open Data Commons Attribution License</a> (which the original license allows me to do). When using this dataset for publication of any kind, please reference the following paper to meet the attribution requirement:</p> <blockquote> <p>Yao J, Burns JE, Forsberg D, Seitel A, Rasoulian A, Abolmaesumi P, Hammernik K, Urschler M, Ibragimov B, Korez R, Vrtovec T, Castro-Mateos I, Pozo JM, Frangi AF, Summers RM, Li S. A multi-center milestone study of clinical vertebral CT segmentation. Comput Med Imaging Graph. 2016; 49:16-28. doi: 10.1016/j.compmedimag.2015.12.006.</p> </blockquote> <p>There is no need to reference this upload, referencing the original authors is sufficient.</p> <p><strong>Notes</strong></p> <p>The dataset contains 2 cases with only 4 lumbar vertebrae in which L5/S1 has not been segmented (i.e., label 25 is missing).</p> <p>The resolution and segmentation quality of the diseased cases (16-20) is quite low.</p>
CSI traces
<p>Wi-Fi CSI traces for presence and activity detection. Each zip archive contains 30 subchannels data.</p>
Coronary and Structural Interventions Ulm - Transcatheter Mitral Valve Repair (CSI-Ulm-TMVR)
ClinicalTrials.gov study NCT03104660. IPD Sharing: NO. Countries: 1. Publications: 2.
Evaluation of Memantine in the Preservation of Memory and Neurocognition Following CSI
ClinicalTrials.gov study NCT06275035. IPD Sharing: NO. Countries: 1. Publications: 2.
Step Change -CSI 4 – Infectious Disease Outbreak Preparedness the Italy – Infographic
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Step Change - CSI 3 – Energy Communities in Germany – Infographic
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Step Change - CSI 2 – Non-Alcoholic Fatty Liver Disease in the UK – Infographic
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Step Change -CSI 5 – Off-Grid Renewable Energy in Agriculture in Uganda – Infographic
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Step Change CSI 1 - Wildlife Conservation in Slovenia - Infographic
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CSI-Glucagon for Prevention of Hypoglycemia in Children With Congenital Hyperinsulinism
ClinicalTrials.gov study NCT02937558. IPD Sharing: NO. Countries: 1. Publications: 0.
Study to Evaluate the Safety, Tolerability, and Efficacy of OTX-CSI in Subjects With Dry Eye Disease.
ClinicalTrials.gov study NCT04362670. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Gene expression altered by csi-miR156a overexpression in citrus callus
GEO Series GSE98687. Citrus hindsii. 8 samples. Type: Expression profiling by high throughput sequencing.
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>
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.