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144 results for “Activity Detection”
Bats actively use leaves as specular reflectors to detect acoustically camouflaged prey
<p>Measured target strength from 541 positions for 5 different frequency bands. Bat positions in incidence angles of for 33 flight paths.</p>
NeuroVox: Bilingual Brain-to-Speech Translation and Neural Activity Detection
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Data from: A surface acoustic wave (SAW)-based lab-on-chip for the detection of active α-glycosidase
Enzyme detection in liquid samples is a complex laboratory procedure, based on assays that are generally time- and cost-consuming, and require specialized personnel. Surface acoustic wave sensors can be used for this application, overcoming the cited limitations. To give our contribution, in this work we present the bottom-up development of a surface acoustic wave biosensor to detect active α-glycosidase in aqueous solutions. Our device, optimized to work at an ultra-high frequency (around 740 MHz), is functionalized with a newly synthesized probe 7-mercapto-1-eptyl-D-maltoside, bringing one maltoside terminal moiety. The probe is designed ad hoc for this application and tested in-cuvette to analyze the enzymatic conversion kinetics at different times, temperatures and enzyme concentrations. Preliminary data are used to optimize the detection protocol with the SAW device. In around 60 min, the SAW device is able to detect the enzymatic conversion of the maltoside unit into glucose in the presence of the active enzyme. We obtained successful α-glycosidase detection in the concentration range 0.15–150 U/mL, with an increasing signal in the range up to 15 U/mL. We also checked the sensor performance in the presence of an enzyme inhibitor as a control test, with a signal decrease of 80% in the presence of the inhibitor. The results demonstrate the synergic effect of our SAW Lab-on-a-Chip and probe design as a valid alternative to conventional laboratory tests.
EEG and EMG dataset for the detection of errors introduced by an active orthosis device (IJCAI'23 CC6 Competition)
<p>This dataset was a part of the IJCAI 2023 competition : CC6: IntEr-HRI: Intrinsic Error Evaluation during Human-Robot Interaction (<a href="https://ijcai-23.org/competitions/">IJCAI'23 Official Website</a>). This dataset repository is divided into 3 versions:</p> <ul> <li><strong><em>Version 1: </em>Training data + Metadata</strong></li> <li><strong><em>Version 2: </em>Test data</strong></li> <li><strong>Version 3: Complete dataset (EEG + EMG) </strong></li> </ul> <p><strong>For more detailed information about the competition, please visit our <a href="http://ijcai-23.dfki-bremen.de/competitions/inter-hri/">competition webpage</a>.</strong></p> <p>This dataset contains recordings of the electroencephalogram (EEG) data from eight subjects who were assisted in moving their right arm by an active orthosis. </p> <p>The orthosis-supported movements were elbow joint movements, i.e., flexion and extension of the right arm. While the orthosis was actively moving the subject's arm, some errors were deliberately introduced for a short duration of time. During this time, the orthosis moved in the opposite direction. The errors are very simple and easy to detect. EEG and EMG data are provided. The recorded EEG data follows the BrainVision Core Data Format 1.0, consisting of a binary data file (.eeg), a header file (.vhdr), and a marker file (.vmrk) (<a href="https://www.brainproducts.com/support-resources/brainvision-core-data-format-1-0/%7D%7D.">https://www.brainproducts.com/support-resources/brainvision-core-data-format-1-0/).</a> For ease of use, the data can be exported into the widely adopted BIDS format. Furthermore, for data analysis, processing, and classification, two popular options are available - MNE (Python) and EEGLAB (MATLAB). </p> <p><strong>If you use our dataset, cite our paper.</strong></p> <p>Frontiers in Human Neuroscience DOI: <a href="https://doi.org/10.3389/fnhum.2024.1304311">10.3389/fnhum.2024.1304311</a></p> <p>BibTeX citation:</p> <div> <div>@ARTICLE{10.3389/fnhum.2024.1304311,</div> <div>AUTHOR={Kueper, Niklas and Chari, Kartik and Bütefür, Judith and Habenicht, Julia and Rossol, Tobias and Kim, Su Kyoung and Tabie, Marc and Kirchner, Frank and Kirchner, Elsa Andrea},</div> <div>TITLE={EEG and EMG dataset for the detection of errors introduced by an active orthosis device},</div> <div>JOURNAL={Frontiers in Human Neuroscience},</div> <div>VOLUME={18},</div> <div>YEAR={2024},</div> <div>URL={https://www.frontiersin.org/articles/10.3389/fnhum.2024.1304311},</div> <div>DOI={10.3389/fnhum.2024.1304311},</div> <div>ISSN={1662-5161}</div> <div>}</div> </div>
Active Close Contact Investigation of Tuberculosis Through Computer-aided Detection and Stool Xpert MTB/RIF Among People Living in Ethiopia
ClinicalTrials.gov study NCT05818059. IPD Sharing: NO. Countries: 1. Publications: 1.
Study for the Prediction of Active Rejection in Organs Using Donor-derived Cell-free DNA Detection
ClinicalTrials.gov study NCT03984747. IPD Sharing: NO. Countries: 1. Publications: 1.
Clinical Study Comparing PillCam® Crohn's Capsule Endoscopy to Ileocolonoscopy (IC) Plus MRE for Detection of Active CD in the Small Bowel and Colon in Subjects With Known CD and Mucosal Disease.
ClinicalTrials.gov study NCT03241368. IPD Sharing: NO. Countries: 3. Publications: 1.
Micro-Ultrasound for Detecting Clinically Significant Prostate Cancer in Active Surveillance
ClinicalTrials.gov study NCT07386171. IPD Sharing: NO. Countries: 1. Publications: 12.
Alpha modulation of spiking activity across multiple brain regions in mice performing a tactile selective detection task
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Miniaturization eliminates detectable impacts of drones on bat activity
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Discrete fire events, their severity, and their ignitions, as derived from MODIS MCD 14ML active-fire detection data for Indonesia, 2002-2019
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Data from: A surface acoustic wave (SAW)-based lab-on-chip for the detection of active α-glycosidase
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ShimFall&ADL: Triaxial accelerometer fall and activities of daily living detection dataset
<p> </p> <p><strong>ShimFall&ADL dataset</strong></p> <p> </p> <p><strong>Version </strong>1.0 (2020-06-19)</p> <p><strong>Please cite as:</strong> "T. Althobaiti, S. Katsigiannis, N. Ramzan, Triaxial accelerometer-based Fall and Activities of Daily Life detection using machine learning, Sensors, 20(13), 3777, 2020. doi: 10.3390/s20133777"</p> <p> </p> <p><strong>Disclaimer</strong><br> While every care has been taken to ensure the accuracy of the data included in the ShimFall&ADL dataset, the authors and the University of the West of Scotland do not provide any guaranties and disclaim all responsibility and all liability (including without limitation, liability in negligence) for all expenses, losses, damages (including indirect or consequential damage) and costs which you might incur as a result of the provided data being inaccurate or incomplete in any way and for any reason. 2020, University of the West of Scotland, Scotland, United Kingdom.</p> <p><br> <strong>Contact</strong><br> For inquiries regarding the ShimFall&ADL dataset, please contact:<br> Dr Stamos Katsigiannis, Stamos.Katsigiannis@uws.ac.uk, University of the West of Scotland<br> Prof. Naeem Ramzan, Naeem.Ramzan@uws.ac.uk, University of the West of Scotland</p> <p> </p> <p><strong>Acknowledgment</strong></p> <p>The authors would like to thank Md. Hasan Shahriar for the data collection under his MSc project.</p> <p> </p> <p><strong>Dataset summary</strong><br> The ShimFall&ADL dataset contains recordings from 35 individuals, acquired using a chest-strapped Shimmer v2 tri-axial accelerometer, recording at a 50Hz sampling rate. Experiments were conducted in a controlled environment at a research lab in the University of the West of Scotland. Thirty five (35) healthy individuals were recruited among young or mid-aged volunteers, aged between 19 and 34 years old, having a body weight between 52 and 113 kg, and a body height between 1.45 and 1.82 m.</p> <p>Participants performed the following activities of daily living (ADL):<br> Jumping<br> Lying down<br> Bending/picking up<br> Sitting to a chair<br> Standing up from a chair<br> Walking</p> <p>Participants performed the following falls:<br> Steep (hard)<br> Front (soft)<br> Front (hard)<br> Left (soft)<br> Left (hard)<br> Right (soft)<br> Right (hard)<br> Back (soft)<br> Back (hard)</p> <p><br> <strong>Data</strong><br> Each ".dat" file in the dataset corresponds to one event for one individual and contains 101 accelerometer samples corresponding to the event. Each row of the file corresponds to one 3-channel sample, dividing the x, y, z axes values using the "\t" character, as follows:<br> Row 1: x1\ty1\tz1<br> Row 2: x2\ty2\tz2<br> ...<br> Row N: xN\tyN\tzN</p> <p>The files within the dataset are named as follows:<br> adl_<ADL activity>_<Participant ID>.dat<br> <Fall Type>fall_<soft,hard>_<Participant ID>.dat</p> <p>For example, the file "adl_standingfromchair_18.dat" corresponds to the accelerometer recording of the 18th participant, performing the "standing up from chair" ADL. The file, "leftfall_soft_11.dat" corresponds to the accelerometer recording of the 11th participant, performing a soft left fall.</p> <p><br> <strong>Additional information</strong><br> For additional information regarding the creation of the ShimFall&ADL dataset, please refer to the associated publication: "T. Althobaiti, S. Katsigiannis, N. Ramzan, Triaxial accelerometer-based Fall and Activities of Daily Life detection using machine learning, Sensors, 20(13), 3777, 2020. doi: 10.3390/s20133777"</p>
A Large TV Dataset for Speech and Music Activity Detection
<p>Automatic speech and music activity detection (SMAD) is an enabling task that can help segment, index, and pre-process audio content in radio broadcast and TV programs. However, due to copyright concerns and the cost of manual annotation, the limited availability of diverse and sizeable datasets hinders the progress of state-of-the-art (SOTA) data-driven approaches. We address this challenge by presenting a large-scale dataset containing Mel spectrogram, VGGish, and MFCCs features extracted from around 1600 hours of professionally produced audio tracks and their corresponding noisy labels indicating the approximate location of speech and music segments. The labels are derived from several sources such as subtitles. A test set curated by human annotators is also included as a subset for evaluation. To the best of our knowledge, this dataset is the first large-scale, open-sourced dataset that contains features extracted from professionally produced audio tracks and their corresponding frame-level speech and music annotations. </p>
Genome graphs detect human polymorphisms in active epigenomic states during influenza infection: validation
<p>Sanger sequencing and qPCR validation data for "Genome graphs detect human polymorphisms in active epigenomic states during influenza infection" manuscript.</p>
Fish eDNA detections in ports mirror fishing fleet activities and highlight the spread of non-indigenous species in the Mediterranean Sea. Environmental DNA Metabarcoding DATASET
<p>Environmental DNA metabarcoding data set. Environmental samples were collected in 2018 (one sample replicate of 2L surface water) and 2019 (four replicates of 1 L surface water) from several Mediterranean ports and data were generated using 12S rRNA and mitochondrial COI. For each sampling campaign (2018 and 2019) and marker two sheets are presented (with the same numeration): the first one represents all the ASV/MOTUs after the bioinformatics pipelines; the second one shows the curated assignment. The last two sheets are the OTU tables (presence-absence).</p>
Machine activity detection evaluation dataset.
<p>Dataset for the evaluation of the one-shot time series machine state detector.</p>
Electrical Alignment Signatures of Ice Particles before Intracloud Lightning Activity Detected by Dual-polarized Phased Array Weather Radar
<p>lightning data from LIDEN system and the data of the Dual-polarized Phased Array Weather Radar (DP-PAWR) on August 20, 2019. </p>
Detecting Transient Deformation at the Active Volcano Ol Doinyo Lengai in Tanzania with the TZVOLCANO Network: Supplementary software, data, model files
<p><span>These are supplementary data, code, and model files associated with the manuscript "</span><span>Detecting Transient Deformation at the Active Volcano Ol Doinyo Lengai in Tanzania with the TZVOLCANO Network<span>" in consideration for publication in the Geophysical Research Letters. tzvolcano_code_and_models.zip contains all necessary Targeted Projection Operator (TPO) software, input, and output files for the GNSS inversions presented in our manuscript necessary to reproduce the results. The TPO program is a Unix/Linux code developed by <span>Kang-Hyeun Ji working at the Korea Institute for Geoscience and Mineral Resources, Daejeon, South Korea. The source code is available in the supplementary Zenodo repository. We also include input and output model files for the USGS code dMODELS for reproducibility. Please see the README.txt file for more details.</span></span></span></p> <p><span>This study was funded by the US National Science Foundation grant number EAR-1943681 to Virginia Tech, internal university funds via Ardhi University, and Ministry of Science and ICT of Korea Basic Research Project GP2021-006 to the Korea Institute of Geosciences and Mineral Resources. We acknowledge and thank the EarthScope Consortium for archiving and making TZVOLCANO GNSS datasets freely available, supported by the National Science Foundation’s Seismological Facility for the Advancement of Geoscience (SAGE) Award under Cooperative Support Agreement EAR-1851048 and Geodetic Facility for the Advancement of Geoscience (GAGE) Award under NSF Cooperative Agreement EAR-1724794.</span></p>
Metabolomic Profile and Proteasic Activity as Biomarkers for Early Detection of Arterial Vasospas in Arterial Vasospasm After Aneurysmal Subarachnoid Hemorrhage
ClinicalTrials.gov study NCT02397759. IPD Sharing: Not stated. Countries: 1. Publications: 2.
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.