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35 results for “Human senses”
Raw data (RF) provided for : Sensing Ultrasound Localization Microscopy reveals glomeruli in rats and humans
<p><strong>Abstract :</strong> Estimation of glomerular function is a key element in the diagnosis of kidney disease. However, the study of glomeruli in the clinic remains indirect through urine and blood tests. Recent imaging technique called Ultrasound Localization Microscopy (ULM) originated from the ability to record continuous movements of individual microbubbles in the bloodstream. Although it improved the resolution of vascular imaging up to tenfold, the imaging of the smallest vessels had yet to be reported.</p> <p>We acquired ultrasound sequences from living humans and rats and then applied filtering dividing the data set into slow-moving and fast-moving microbubbles. We performed a double tracking to highlight and characterize this new population of microbubbles with singular behaviors: we called this technique “sensing ULM” (sULM). We used post-mortem micro-CT for side-by-side confirmation in rats.</p> <p>In this study, we report the observation of microbubbles flowing in capillaries bundles, i.e. the glomeruli, in the kidney in living humans and rats. We introduce a set of analysis tools dedicated to extracting quantitative information from individual microbubbles, like the remanence time or the normalized distance.</p> <p>As glomeruli play a key role in kidney function, their observation could yield a deeper understanding of kidney diseases and provide a diagnostic tool for patients. More generally, it will bring imaging capabilities closer to the functional units of organs, which is one of the keys to understanding most diseases, like cancers, diabetes, or kidney failures. </p> <p><strong>Academic reference to be cited : </strong>Denis, Bodard, Hingot, Chavignon, Battaglia, Renault, Lager, Aissani, Hélénon, Correas, and Couture. <em>Sensing Ultrasound Localization Microscopy reveals glomeruli in rats and humans,</em> eBioMedicine, 2023.</p> <p><strong>Article</strong> : <a href="https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(23)00143-3/fulltext">https://www.thelancet.com/journals/ebiom/article/PIIS2352-3964(23)00143-3/fulltext</a></p> <p><strong>Related scripts and software application</strong> : <a href="https://github.com/EngineerJB/akebia">https://github.com/EngineerJB/akebia</a></p> <p><strong>Beamformed dataset</strong> : <a href="../record/6811910#.ZA9dV3bMLid">https://zenodo.org/record/6811910#.ZA9dV3bMLid</a></p> <p><strong>Corresponding authors : </strong></p> <ul> <li>Article : Louise Denis, <a href="mailto:louise.denis@sorbonne-universite.fr">louise.denis@sorbonne-universite.fr</a>, Sylvain Bodard, <a href="mailto:sylvain.bodard@aphp.fr">sylvain.bodard@aphp.fr</a></li> <li>Scripts, and codes : Louise Denis, <a href="mailto:louise.denis@sorbonne-universite.fr">louise.denis@sorbonne-universite.fr</a>, Jacques Battaglia, <a href="mailto:jacques.battaglia@sorbonne-universite.fr">jacques.battaglia@sorbonne-universite.fr</a></li> <li>Materials, collaborations, rights and others: Olivier Couture, <a href="mailto:olivier.couture@sorbonne-universite.fr">olivier.couture@sorbonne-universite.fr</a></li> </ul>
RUSSE'2018: Human-Annotated Sense-Disambiguated Word Contexts for Russian
<p>This dataset contains human-annotated sense identifiers for 2562 contexts of 20 words used in the <a href="https://russe.nlpub.org/2018/wsi/">RUSSE'2018</a> shared task on Word Sense Induction and Disambiguation for the Russian language; part of the <em>bts-rnc</em> evaluation dataset. These sense identifiers are disambiguated as according to the sense inventory of the <a href="http://gramota.ru/slovari/info/bts/">Large Explanatory Dictionary of Russian</a>.</p> <p>The annotation is done on December 1, 2017, on the <a href="https://tolokanyandex.com/">Yandex.Toloka</a> crowdsourcing platform. In particular, 80 pre-annotated contexts are used for training the human annotators, 2562 contexts are annotated by humans such that each context was annotated by 9 different annotators. The annotation reliability is indicated by a high value of Krippendorff's α = 0.83. After the annotation, every context was additionally inspected (“curated”) by the organizers of the shared task.</p> <p>The following words are represented: <em>акция</em> (action / stock), <em>байка</em> (yarn / tale), <em>гвоздика</em> (carnation / nail), <em>гипербола</em> (hyperbole), <em>град</em> (avalanche), <em>гусеница</em> (grub), <em>домино</em> (domino), <em>кабачок</em> (marrow / pub), <em>капот</em> (hood), <em>карьер</em> (mine / career), <em>кок</em> (cook), <em>крона</em> (top / crown), <em>круп</em> (croup), <em>мандарин</em> (mandarine), <em>рок</em> (fate / rock), <em>слог</em> (syllable), <em>стопка</em> (glass, stack), <em>таз</em> (bowl), <em>такса</em> (rate / badger-dog), <em>шах</em> (shah / check).</p> <p>The following files are included in this dataset:</p> <ul> <li>Toloka assignments (training: <em>tasks-train.tsv</em>, annotation: <em>tasks-test.tsv</em>)</li> <li>Toloka output (non-aggregated: <em>assignments_01-12-2017.tsv.xz</em>, aggregated: <em>aggregated_results_pool_1036853__2017_12_01.tsv</em>)</li> <li>annotator agreement report (<em>agreement.txt</em>)</li> <li>curated report (<em>report-curated.tsv.xz</em> and a supplementary file <em>tasks-eval.tsv.xz</em>)</li> <li>the final aggregated dataset (<em>bts-rnc-crowd.tsv</em>)</li> </ul> <p>The <em>bts-rnc-crowd.tsv</em> file has the following format: <em>id</em>, <em>lemma</em>, <em>sense_id</em>, <em>left</em> hand side context, <em>word</em> form, <em>right</em> hand side context, list of <em>senses</em>. The encoding is UTF-8 and the line breaks are LF (UNIX).</p>
1X4 radio channel data at 3.42 GHz for device-free human sensing
<p>Datasets for <em>CAMPAIGN-I and CAMPAIGN-II </em>from paper 'Beamsteering for Ad-Hoc Recognition of Multi-Human Targets Performing Distinct Activities'. The datasets belong to the <a href="http://ambientintelligence.aalto.fi/radiosense/">Radiosense</a> project.</p> <p>'.</p>
Dataset provided for : Sensing Ultrasound Localization Microscopy reveals glomeruli in rats and humans
<p><strong>Abstract :</strong> Estimation of glomerular function is a key element in the diagnosis of kidney disease. However, the study of glomeruli in the clinic remains indirect through urine and blood tests. Recent imaging technique called Ultrasound Localization Microscopy (ULM) originated from the ability to record continuous movements of individual microbubbles in the bloodstream. Although it improved the resolution of vascular imaging up to tenfold, the imaging of the smallest vessels had yet to be reported.</p> <p>We acquired ultrasound sequences from living humans and rats and then applied filtering dividing the data set into slow-moving and fast-moving microbubbles. We performed a double tracking to highlight and characterize this new population of microbubbles with singular behaviors: we called this technique “sensing ULM” (sULM). We used post-mortem micro-CT for side-by-side confirmation in rats.</p> <p>In this study, we report the observation of microbubbles flowing in capillaries bundles, i.e. the glomeruli, in the kidney in living humans and rats. We introduce a set of analysis tools dedicated to extracting quantitative information from individual microbubbles, like the remanence time or the normalized distance.</p> <p>As glomeruli play a key role in kidney function, their observation could yield a deeper understanding of kidney diseases and provide a diagnostic tool for patients. More generally, it will bring imaging capabilities closer to the functional units of organs, which is one of the keys to understanding most diseases, like cancers, diabetes, or kidney failures. </p> <p><strong>Academic reference to be cited : </strong>Denis, Bodard, Hingot, Chavignon, Battaglia, Renault, Lager, Aissani, Hélénon, Correas, and Couture. <em>Sensing Ultrasound Localization Microscopy reveals glomeruli in rats and humans,</em> eBioMedicine, 2023.</p> <p><strong>Related scripts and software application</strong> : <a href="https://github.com/EngineerJB/akebia">https://github.com/EngineerJB/akebia</a></p> <p><strong>(New ! published in June 2024) Raw data :</strong> <a href="../records/11395562">https://zenodo.org/records/11395562</a></p> <p><strong>Corresponding authors : </strong></p> <ul> <li>Article : Louise Denis, <a href="mailto:louise.denis@sorbonne-universite.fr">louise.denis@sorbonne-universite.fr</a>, Sylvain Bodard, <a href="mailto:sylvain.bodard@aphp.fr">sylvain.bodard@aphp.fr</a></li> <li>Scripts, and codes : Louise Denis, <a href="mailto:louise.denis@sorbonne-universite.fr">louise.denis@sorbonne-universite.fr</a>, Jacques Battaglia, <a href="mailto:jacques.battaglia@sorbonne-universite.fr">jacques.battaglia@sorbonne-universite.fr</a></li> <li>Materials, collaborations, rights and others: Olivier Couture, <a href="mailto:olivier.couture@sorbonne-universite.fr">olivier.couture@sorbonne-universite.fr</a></li> </ul>
Vibration and IMU Sensing Human Activity Dataset
<p>This dataset contains fine-grained human daily activity data collected by infrastructure vibration sensors and one on-wrist IMU sensor. This dataset is collected from six persons from two domestic homes, in total, there are 12 sub-datasets.</p> <p>For the naming, "p" means person and "l" means location.</p> <p>Each dataset has 11 columns, 1o of them stands for sensors' reading.</p> <p>* Due to the uploading platform, please<strong> <em>ignore</em> </strong>all files in the folder '__MACOSX', and files whose names start with '._'. These are computer system files, not parts of the shared dataset. </p> <p>** If you are going to use this dataset for any publications, we will appreciate you to cite this dataset properly.</p> <p>************************************************************</p> <p>The following content is copied from README.txt in the compressed folder:</p> <p>-----------------------<br> Labels:</p> <p>Keyboard typing 1<br> Using mouse 2<br> Handwriting 3<br> Cutting vegetables 4<br> Stir-frying vegetables 5<br> Wiping the table 6<br> Sweeping floor 7<br> Using vacuum to vacuum floor: 8<br> Open and close drawer: 9</p> <p>None Activity: 10</p> <p>-----------------------<br> 11 Columns:<br> 1: Activity label<br> 2: Vibration sensor put on the Living Area floor<br> 3: Vibration sensor put on the Living Area table<br> 4: Vibration sensor put on the Studying Area floor<br> 5: Vibration sensor put on the Studying Area desk<br> 6, 7, 8: Accelerometer X,Y,Z<br> 9, 10, 11: Gyroscope X,Y,Z</p> <p>-----------------------<br> All signals are zero-meaned.<br> The vibration sensors' sampling rate is roughly around 6500Hz, and the IMU sensors' original sampling rate is roughly around 235Hz.</p> <p>************************************************************</p> <p>New in Version 2:</p> <p>- Added extracted features from IMU data and vibration data for reference.</p> <p>- IMU signal is applied with a sliding window of 1.5 seconds with 0.75 seconds overlapping, then the feature is extracted in each window. The feature's description can be found here: https://dl.acm.org/doi/abs/10.1145/3410530.3414320</p> <p>- The vibration signal is applied with event detection to extract events in the vibration signal. For each event, we normalize it by its energy, then extract 10~490 Hz frequency amplitude as the feature.</p> <p> </p> <p>Disclaimer: Both event detection and feature extraction are empirical, we don't guarantee it is an optimal one.</p>
Measurement of T1ρ dispersion with compressed sensing and magnetization prepared radial balanced steady-state free precession in spontaneous human osteoarthritis
<div>This dataset contains key analysis and plotting scripts, data, and sample images.</div> <div> </div> <div>Measurement of T1ρ dispersion with compressed sensing and magnetization prepared radial balanced steady-state free precession in spontaneous human osteoarthritis</div> <div> </div> <div>Magnetic Resonance in Medicine Journal | DOI: 10.1002/mrm.30206</div> <div> </div> <div>§ Swetha Pala(1), § Antti Paajanen(1), Aapo Ristaniemi(1), Ervin Nippolainen(1), Isaac O. Afara(1), Olli Nykänen (1), Mikko J. Nissi (1*)</div> <div> </div> <div>1Department of Technical Physics, University of Eastern Finland</div> <div>§Shared authorship </div> <div> </div> <div> </div> <div> </div> <div>*Corresponding author</div> <div>Mikko J. Nissi</div> <div>Department of Technical Physics</div> <div>University of Eastern Finland, Kuopio Finland</div> <div>POB 1627</div> <div>70211 Kuopio</div> <div>mikko.nissi@uef.fi</div> <div>+358-50-5955517</div> <div> </div> <div> </div> <div>Keywords: Quantitative MRI, T1ρ relaxation, T1ρ dispersion, Compressed-sensing, radial acquisition.</div> <div> </div> <div>Included folders and files are:</div> <div>- Article_figures: all figures published in the manuscript (.eps format)</div> <div>- Data: MRI data files from 27 human cadaver samples with subfolders and files:</div> <div>- Human samples data: raw data files, along with generic analysis ROIs, zone divison inside specific samples folder, and within the parameter related data folder there are smaple specific analysis ROIs, computed profiles per spin lock amplitude. </div> <div>- CS reconstructed data files: </div> <div>- DataTables_used_for_analysis: Contains data tables per AF and reference data used for data analysis </div> <div>- Scripts: Matlab functions used for data processing and T1ρ computation, aedes plugins, and data analysis with subfolders and files:</div> <div> - Aedes_plugins: Plugins for aedes (http://aedes.uef.fi) for calculation of profiles from ROI. </div> <div> - Data analysis: Key scripts used for analysis and plotting.</div> <div> - Common functions: Some common functions that are required by the scripts/plugins. </div> <div> </div> <div>- README.txt: this file describing the contents of the dataset.</div> <div> </div> <div> </div> <div>See more info in separate readme files included in sub-folders.</div> <div> </div> <div> </div> <div>(Swetha Pala, 02 July 2024)</div> <p> </p>
The molecular basis of pH sensing by the human fungal pathogen Candida albicans TOK potassium channel
Open the record for dataset details and reuse information.
Ultrasound Imaging Based Sensing of Human Ankle Motion Intent and Control Strategies for Ankle Assistance
ClinicalTrials.gov study NCT05249595. IPD Sharing: NO. Countries: 1. Publications: 4.
Functional Magnetic Resonance Imaging of Human Hypothalamic Responses to Oral Temperature and Glucose Sensing
ClinicalTrials.gov study NCT03181217. IPD Sharing: NO. Countries: 1. Publications: 1.
Pan-cancer analysis reveals the prognostic potential of THAP9/THAP9-AS1 Sense-Antisense gene pair in human cancer.
<p>Supplementary data & images.</p>
Data from: Human visual exploration reduces uncertainty about the sensed world
Open the record for dataset details and reuse information.
MYCT1 Controls Environmental Sensing in human Hematopoietic Stem Cells [scRNA-seq]
GEO Series GSE232361. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.
Lentiviral Vectors Escape Innate Sensing but Trigger p53 In Human Hematopoietic Stem Cells
GEO Series GSE92652. Homo sapiens. 93 samples. Type: Expression profiling by high throughput sequencing.
HILPS, a novel long non-coding RNA essential for global oxygen sensing in human
GEO Series GSE230388. Homo sapiens. 63 samples. Type: Expression profiling by high throughput sequencing.
MYCT1 Controls Environmental Sensing in human Hematopoietic Stem Cells.
GEO Series GSE232362. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
Ultra-Deep Single-Cell Analyses Reveals Novel Mechanisms of Gut Enteroendocrine Cell Sensing Dysfunction in Human Obesity
GEO Series GSE154405. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
Revealing the impact of Pseudomonas aeruginosa quorum sensing molecule 2’-aminoacetophenone on human bronchial-airway epithelium and pulmonary endothelium using a human-airway-on-a-chip
GEO Series GSE290885. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
Expression of sense and cis-natural antisense transcripts in the human B lymphoblastic cell line IM-9 after X-ray irradiation
GEO Series GSE288225. Homo sapiens. 4 samples. Type: Expression profiling by array.
Time Course and Nature of Nutrient Sensing During Fasting in Humans
ClinicalTrials.gov study NCT01387919. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Expression of Matrix Metalloproteinase 13, Tissue Inhibitor of Metalloproteinases 3, and Calcium-sensing Receptor in Human Trabecular Meshwork
ClinicalTrials.gov study NCT02638181. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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
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DANDI Archive for NWB datasets
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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.