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Dataset results
20 results for “biosensing”
Functionalising the electrical properties of Kombucha zoogleal mats for biosensing applications
<p><span>Data acquired from the electrical characterisation of pure and functionalised with graphene and zeolite Y Kombucha mats, <span> </span>including dynamic current-voltage (I-V) profiling, impedance, capacitance, and resistance measurements. </span></p> <p> </p>
Electrochemical Biosensing of Tuberculosis using CRISPR-Cas12a and redox-probe modified oligonucleotide
<p>We have submitted the manuscript in HELIYON </p> <p><span>Manuscript. Number: HELIYON-D-24-20798R3 </span></p> <p>Title: An electrochemical biosensor for the detection of tuberculosis specific DNA with CRISPR-Cas12a and redox-probe modified oligonucleotide. </p> <p>The dataset used in that study is available here. </p>
Datasets for Multimodal Biosensing for Vestibular Network-Based Cybersickness Detection
<p>These are the datasets about the experimental group (N=20, where N is the sample size, that is, 20 participants in total) and control group (N=20, where N is the sample size, that is, 20 participants in total) for our published paper entitled "Multimodal Biosensing for Vestibular Network-Based Cybersickness Detection", DOI: 10.1109/JBHI.2021.3134024. </p> <p>1. What do these datasets include?</p> <p>There are 40 participants' data in 40 folders, respectively. Each folder includes EEG data, Questionnaires, and answers as well as other non-EEG biometrics and memory test result of the cognitive task.</p> <p><br> 1.1 EEG data<br> All EEG data is either *.easy file or *.info file. The *.easy file is the raw EEG data. The *.info file is the information (e.g., EEG montage) paired with those raw EEG data. The *.info file can be simply readable by any Notepad software. The *.easy file can be readable by Neuroelectrics's plugin for EEGLAB (https://www.neuroelectrics.com/wiki/index.php/EEGLAB). Note, most of the participants' EEG data is a single easy file, but due to technical problems, few participants' EEG data consists of some separated easy files. Please use the function 'merge dataset' in the EEGLAB to merge them, and then use the following markers to extract segments of interest.</p> <p>All received EEG markers are integer numbers. Specifically, the markers are as follows:</p> <p>For control group:<br> a) The 'target' stimuli in the cognitive task: 16<br> b) The 'distractor' stimuli in cognitive task:32<br> c) The end of Baseline_1(that is, before the first cognitive task): 37<br> d) The end of Baseline_2(that is, before the round 1 neutral task): 47<br> e) The end of Baseline_3(that is, before the round 2 neutral task): 57<br> f) The end of Baseline_4(that is, before the second cognitive task): 67<br> g) The end of FMS reporting during the round 1 neutral task: 100<br> h) The end of FMS reporting during the round 2 neutral task: 101</p> <p>For experimental group:</p> <p>a) The 'target' stimuli in the cognitive task: 16<br> b) The 'distractor' stimuli in cognitive task:32<br> c) The end of Baseline_1(that is, before the first cognitive task): 37<br> d) The end of Baseline_2(that is, before the tunnel travel task): 47<br> e) The end of Baseline_3(that is, before the rollercoaster task): 57<br> f) The end of Baseline_4(that is, before the second cognitive task): 67<br> g) The end of FMS reporting during the Tunnel travel task: 100<br> h) The end of FMS reporting during the Rollercoaster task: 101</p> <p>1.2 Questionnaires</p> <p>1.2.1 SSQ<br> All SSQ questionnaires are named as xxx_QuestionnaireResult.csv, where xxx stands for the timestamps.</p> <p>1.2.2 FMS</p> <p>For control group:<br> All FMS questionnaires are named as xxx_Nature_FMS_backup.csv or xxx_Bio-data and FMS Nature.csv, where xxx stands for the timestamps and 'Nature' refers to the vection-free neutral task. The FMS scores in the xxx_FMS_backup.csv and xxx_Bio-data and FMS 'Task name'.csv are the exactly same. Unlike the xxx_Bio-data and FMS 'Task name'.csv contains the non-EEG biometrics as well, the xxx_FMS_backup.csv is pure FMS scores and easy to read.</p> <p>For experimental group:</p> <p>All FMS questionnaires are named as xxx_Gabor_FMS_backup.csv or xxx_Rollercoaster_FMS_backup.csv or xxx_Bio-data and FMS GaborRacer.csv or xxx_Bio-data and FMS Rollercoaster.csv, where xxx stands for the timestamps and Gabor or GaborRacer refer to the tunnel travel task. The FMS scores in the xxx_FMS_backup.csv and xxx_Bio-data and FMS 'Task name'.csv are the exactly same. Unlike the xxx_Bio-data and FMS 'Task name'.csv contains the non-EEG biometrics as well, the xxx_FMS_backup.csv is pure FMS scores and easy to read.</p> <p>1.3 Non-EEG biometrics<br> All non-EEG biometrics are named as xxx_Bio-data 'Task name'.csv, where 'Task name' refers to 'VA' (that is the cognitive task), or 'Nature' (for the control group) or 'GaborRacer' (for the experimental group) or 'Rollercoaster' (for the experimental group). The column information is as follows:<br> Column A: time<br> Column Q: PPG raw data (the left index finger)<br> Column R: Fingertip temperature (the left middle finger)<br> Column S: Forehead temperature<br> Column T: VR hermetic space temperature</p> <p>The data in other columns are not used in this study.</p> <p>1.4 The memory test result of the cognitive task</p> <p>Memory test result: xxx_CheckMemoryResult.csv, where xxx stands for the timestamps.</p> <p>Column A: time</p> <p>Column B: memory test result (Correct/Wrong)</p> <p>Column C: the number of the target (green sunfish)</p> <p>Column D: the reported number of the target in the memory test</p>
Data From: BioSense: An Automated Sensing Node for Organismal and Environmental Biology
<p><strong>Article title</strong></p> <p>BioSense: An Automated Sensing Node for Organismal and Environmental Biology</p> <p> </p> <p><strong>Authors</strong></p> <p>Andrea Contina<sup>a,b</sup>, Eric Abelson<sup>b</sup>, Brendan Allison<sup>b</sup>, Brian Stokes<sup>b</sup>, Kenedy F. Sanchez<sup>c</sup>, Henry M.Hernandez<sup>d</sup>, Anna M. Kepple<sup>b</sup>, Quynhmai Tran<sup>b</sup>, Isabella Kazen<sup>d</sup>, Katherine A. Brown<sup>e,f</sup>, Je'aime H. Powell<sup>g</sup>, Timothy H. Keitt<sup>b</sup></p> <p> </p> <p><strong>Affiliations</strong></p> <p><span>a Permanent address: School of Integrative Biological and Chemical Sciences, The University of Texas Rio Grande Valley, Brownsville, TX 78520, USA. </span></p> <p><span>b Department of Integrative Biology, The University of Texas at Austin, Austin, TX 78703, USA. </span></p> <p><span>c Carnegie Mellon University, Pittsburgh, PA 15213, USA.</span></p> <p><span>d Department of Physics, The University of Texas at Austin, Austin, TX 78712, USA. </span></p> <p><span>e The Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA.</span></p> <p><span>f Cavendish Laboratory, University of Cambridge, Cambridge CB3 0HE, UK</span></p> <p><span>g Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX 78758, USA.</span></p> <p> </p> <p><strong>Corresponding author’s email address <br></strong></p> <p><em>Timothy Keitt, </em><a href="mailto:tkeitt@utexas.edu"><em>tkeitt@utexas.edu</em></a><em>. <br></em></p> <p> </p> <p><strong>Abstract </strong></p> <p>Automated remote sensing has revolutionized the fields of wildlife ecology and environmental science. Yet, a cost-effective and flexible approach for large scale monitoring has not been fully developed, resulting in a limited collection of high-resolution data. Here, we describe BioSense, a low-cost and fully programmable automated sensing platform for applications in bioacoustics and environmental studies. Our design offers customization and flexibility to address a broad array of research goals and field conditions. Each BioSense is programmed through an integrated Raspberry Pi computer board and designed to collect and analyze avian vocalizations while simultaneously collecting temperature, humidity, and soil moisture data. We illustrate the different steps involved in manufacturing this sensor including hardware and software design and present the results of our laboratory and field testing in southwestern United States.</p>
PyGBe-LSPR—Computational nanoplasmonics for biosensing applications (problem datasets)
<p>Problem folders including all the input files necessary to reproduce the computations in the paper PyGBe-LSPR—Computational nanoplasmonics for biosensing applications. </p>
Feasibility Study to Use Biosensing Devices to Monitor PA and Resp. Function in Smokers w and w/o Resp. Symptoms/COPD
ClinicalTrials.gov study NCT04081961. IPD Sharing: YES. Countries: 1. Publications: 1.
Image Dataset for 'AI-enabled biosensing for rapid pathogen detection: from liquid food to agricultural water'
<p>This dataset is presented in the following publication. Please cite this publication if you use the dataset.</p> <p><em>Jiyoon Yi, Nicharee Wisuthiphaet, Pranav Raja, Nitin Nitin, J. Mason Earles. (2023). AI-enabled biosensing for rapid pathogen detection: from liquid food to agricultural water. Water Research, 120258. doi: <a href="https://doi.org/10.1016/j.watres.2023.120258">10.1016/j.watres.2023.120258</a></em></p>
BioSense Culture Collection of Fungi (BSCCF) and Bacteria (BSCCB)
<p>The BSCCF/B serves as deposit of important fungal/bacterial strains used by Biosense Institute for research purposes. </p> <p>For further information please contact Dr Ljiljana Janjušević: ljiljana.janjusevic@biosense.rs </p>
Lysozyme-sensitive plasmonic hydrogel nanocomposite for colorimetric dry-eye inflammation biosensing_[Biosensing]
<p>Biosensing of a lysozyme-sensitive plasmonic hydrogel</p>
Lysozyme-sensitive plasmonic hydrogel nanocomposite for colorimetric dry-eye inflammation biosensing_[Photoresponsivity]
<p>Photoresponsivity of a lysozyme-sensitive plasmonic hydrogel</p>
Lysozyme-sensitive plasmonic hydrogel nanocomposite for colorimetric dry-eye inflammation biosensing_[Colorimetric Tests]
<p>Colorimetric tests for a lysozyme-sensitive plasmonic hydrogel.</p>
An Observational Post-marketing Study Using Commercially Approved Biosense Webster (BWI) Medical Devices for the Treatment of Participants With Cardiac Arrhythmias
ClinicalTrials.gov study NCT04750798. IPD Sharing: YES. Countries: 12. Publications: 0.
A Noninvasive Multimodal Biosensing Device for Screening and Monitoring Response to Treatment of Infectious Respiratory Diseases
ClinicalTrials.gov study NCT05035420. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Assessing the Feasibility of Multi-modal Biosensing for Monitoring Mobility and Cognition in Older Adults
ClinicalTrials.gov study NCT07217951. IPD Sharing: NO. Countries: 1. Publications: 14.
activeDCM - Interventional Clinical Trial of Individualized Activity and Exercise Programs to Improve Outcome in Dilated Cardiomyopathy Guided by Longitudinal Biosensing With Apple Watch
ClinicalTrials.gov study NCT04359238. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Data from: Seedless gold nanostars with seed-like advantages for biosensing applications
Open the record for dataset details and reuse information.
PSG Validation Study of Zensorium Biosensing Wearable Device
ClinicalTrials.gov study NCT04200495. IPD Sharing: NO. Countries: 1. Publications: 0.
A Split Luciferase Biosensing Platform for Detection and Imaging of Chromatin Loops in Individual Live Cells
GEO Series GSE295357. Homo sapiens. 12 samples. Type: Other.
Clinical Evaluation of the CARTO™ XP EP Navigation System v9 and Biosense Webster SOUNDSTAR 3D Diagnostic Ultrasound. A Feasibility Study
ClinicalTrials.gov study NCT00449085. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Shadow mask assisted 193 nm Excimer laser processing of commercial Cyclo-Olefin-Copolymer (COC) substrates for low-cost micro-fluidic biosensing devices.
<p>An Abstract submitted to The 24th International Symposium on Laser Precision Microfabrication (LPM2023)</p>
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.