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1,102 results for “human use”
Differential gene expression data of commercial compounds used to assess the performance of human TeraTox assay
<p>The dataset supplements the publication `Optimization of the <em>TeraTox</em> assay for preclinical teratogenicity assessment`. </p> <ul> <li>2022-02-18-TeraTox-commercial-logFC.gct: log2FC matrix of genes by compounds (in concentration ranges)</li> <li>2022-02-18-TeraTox-commercial-pScore.gct: p-scores (log 10 transformed p-values with the sign of logFC) of genes by compounds</li> <li>2022-02-18-TeraTox-commercial-featureData.txt: feature annotation in TSV format</li> <li>2022-02-18-TeraTox-commercial-phenoData.txt: sample annotation in TSV format</li> <li>2021-06-10-gcGeneFactorAnno-withPositiveCoefs.tsv: gene membership of germ-layer factors, with germ-layer annotation and average expression in copies per million (cpm).</li> </ul> <p>Citation: Jaklin, Manuela, Jitao David Zhang, Nicole Schäfer, Nicole Clemann, Paul Barrow, Erich Küng, Lisa Sach-Peltason, Claudia McGinnis, Marcel Leist, and Stefan Kustermann. “Optimization of the TeraTox Assay for Preclinical Teratogenicity Assessment.” <em>Toxicological Sciences</em> 188, no. 1 (July 1, 2022): 17–33. <a href="https://doi.org/10.1093/toxsci/kfac046">https://doi.org/10.1093/toxsci/kfac046</a>.</p>
MAMEM Phase I Dataset - A dataset for multimodal human-computer interaction using biosignals and eye tracking information
<p>This dataset combines multimodal biosignals and eye tracking information gathered under a human-computer interaction framework. The dataset was developed in the vein of the MAMEM project that aims to endow people with motor disabilities with the ability to edit and author multimedia content through mental commands and gaze activity. The dataset includes EEG, eye-tracking, and physiological (GSR and Heart rate) signals along with demographic, clinical and behavioral data collected from 36 individuals (18 able-bodied and 18 motor-impaired). Data were collected during the interaction with specifically designed interface for web browsing and multimedia content manipulation and during imaginary movement tasks. Alongside these data we also include evaluation reports both from the subjects and the experimenters as far as the experimental procedure and collected dataset are concerned. We believe that the presented dataset will contribute towards the development and evaluation of modern human-computer interaction systems that would foster the integration of people with severe motor impairments back into society.</p>
Diffraction images used to solve the structures published in the article "Structure of human endo-α-1,2-mannosidase (MANEA), an antiviral host-glycosylation target"
<p>Raw diffraction images used for generating the structures published in the article "Structure of human endo-α-1,2-mannosidase (MANEA), an antiviral host-glycosylation target" (available <a href="https://doi.org/10.1073/pnas.2013620117">here</a>). Full single-crystal datasets, including images that were not used in the final analyses, are published. The software used for the processing of each dataset is listed in their respective PDB entries. Datasets 6ZJ1 and 6ZJ5 were cut anisotropically using STARANISO, other datasets were processed isotropically.</p> <p> </p> <p>If you find this useful, please contact me at <a href="mailto:lukasz.sobala@hirszfeld.pl">lukasz.sobala@hirszfeld.pl</a>, I am just interested in how these data are used!</p>
Taxonomic list of Brazilian fruit-bearing plants for human use
<h3>Lista taxonômica de plantas frutíferas para consumo humano, com curadoria da equipe do projeto <a href="https://www.inaturalist.org/projects/pomar-urbano">Pomar Urbano</a>. </h3> <p><em>[see English description below]</em></p> <p><br>As planilhas estão organizadas da seguinte forma:</p> <p><strong>PT_lista_especies_aceitas_v.3.0</strong>: contém os nomes de todas as espécies atualmente indexadas na base de dados do <a href="https://www.inaturalist.org/projects/pomar-urbano">Pomar Urbano</a>.</p> <p><strong>PT_lista_especies_adicionadas_v.3.0</strong>: contém os nomes das novas espécies que passam a integrar a base de dados do Pomar Urbano a partir da versão 3.0.</p> <p><strong>PT_lista_especies_removidas_v3.0</strong>: contém os nomes das espécies removidas da versão 3.0 da lista, e que portanto não fazem mais parte do banco de dados do projeto. </p> <p> </p> <p><strong>Metadados usados nas planilhas:</strong></p> <ul> <li><em>Nome científico</em>: O nome científico completo, com autoria e data, se conhecidos.</li> <li><em>Família</em>: O nome científico completo da família.</li> <li><em>Nome vernacular</em>: nome comum, popular.</li> <li><em>Origem</em><strong>: </strong>Declaração sobre se um organismo foi introduzido em um local e tempo específicos por meio da atividade direta ou indireta dos seres humanos modernos.</li> <li><em>Distribuição geográfica</em>: área geográfica ou região onde uma espécie ocorre no Brasil. Foram considerados como valores válidos para este campo apenas as macrorregiões do Brazil, a saber: S = Sul, SE = Sudeste, CO = Centro-Oeste, NE = Nordeste, N = Norte.</li> <li><em>Última atualização</em>: A data mais recente em que a entrada no catálogo foi alterada, atualizada ou modificada.</li> </ul> <h3>--------------------------------------------------------------------------------------------------------------------------------------<br><br>Taxonomic list of fruit-bearing plants for human consumption, curated by the <a href="https://www.inaturalist.org/projects/pomar-urbano">Pomar Urbano project</a></h3> <p><em>[Vernacular names are presented only in Portuguese; for properly processing in data management tools, downloading a Portuguese language package might be necessary]</em></p> <p>The spreadsheets are organized as follows:</p> <p>EN_list_accepted_species_v.3.0: contains the names of all species currently indexed in the <a href="https://www.inaturalist.org/projects/pomar-urbano">Pomar Urbano</a> database.</p> <p>EN_new_added_species_v.3.0: contains the names of new species that are included in the Pomar Urbano database starting from version 3.0.</p> <p>EN_removed_species_v.3.0: contains the names of species that were present in the version 2.0 of the list and are therefore no longer part of the version 3.</p> <p> </p> <p><strong>Metadata used in the spreadsheets</strong>:</p> <p><em>Scientific Name</em>: The complete scientific name, including authorship and date, if known. <em>ExactMatch</em>: <a href="http://rs.tdwg.org/dwc/terms/scientificName">dwc:scientificName</a>. </p> <p><em>Family</em>: The full scientific name of the family. <em>ExactMatch</em>: <a href="http://rs.tdwg.org/dwc/terms/family">dwc:family.</a></p> <p><em>Vernacular Name</em>: Common or popular name. <em>ExactMatch</em>: <a href="http://rs.tdwg.org/dwc/terms/vernacularName">dwc:vernacularName</a></p> <p><em>Establishment Means</em>: Statement about whether an organism has been introduced to a specific place and time through the direct or indirect activity of modern humans. <em>ExactMatch</em>: <a href="http://rs.tdwg.org/dwc/terms/establishmentMeans">dwc:establishmentMeans</a></p> <p><em>Higher geography</em>: The geographical area or region where a species occurs in Brazil. Only the macroregions of Brazil are considered valid values for this field within this dataset, namely: S = South, SE = Southeast, CO = Central-West, NE = Northeast, N = North. <em>CloseMacth</em>: <a href="http://rs.tdwg.org/dwc/terms/higherGeography">dwc:higherGeography</a></p> <p><em>Last Update</em>: The most recent date on which the catalog entry was changed, updated, or modified. <em>ExactMatch</em>: <a href="http://purl.org/dc/terms/modified">dct:modified</a></p> <p> </p>
Dataset of "Towards Artefact Aware Human Motion Capture using Inertial Sensors Integrated into Loose Clothing"
<p>This dataset was used in the publication:<br> <strong>Towards Artefact Aware Human Motion Capture using Inertial Sensors Integrated into Loose Clothing</strong><br> presented at the IEEE International Conference on Robotics and Automation 2022</p> <p><strong>Abstract:</strong><br> Inertial motion capture has become an attractive alternative to optical motion capture for human joint angle estimation outside the laboratory. Usually inertial sensors are assumed to be tightly fixed to the body segments, which can be cumbersome regarding setup-time and ease-of-use. However, integrating the sensors directly into clothing, usually, results in additional clothing motion relative to the motion of the underlying bones that should be captured.<br> In this work we propose the <em>Difference Mapping</em> distributions approach that corrects the segment orientations of a given inertial motion capture system that assumes tightly coupled sensors.<br> The approach allows to reduce the joint angle errors due to clothing artefacts by at least 77.2 percent for people with similar morphology performing a similar task as seen in the training data, including an ergonomic assessments scenario at work places with 10 participants. <br> Moreover, we show that the uncertainty of the distribution can be used to measure the reliability of the predicted map if e.g. the motion is further away from the training data to allow for an artefact aware inertial motion tracking approach.<br> The experimental data for this study is available online</p> <p> </p> <p><strong>Data structure:</strong><br> The data contains trials of 12 subjects for different motions, wearing at the same time a tight setup with inertial sensors and a loose working suit with integrated inertial sensors. It contains the raw IMU data, raw Magnetometer data and the estimated segment orientations using a Sensor Fusion engine provided by Sci-Track.<br> Please note, that in the publication only the first 10 subjects were used and the upper body information was used only. The Sternum sensor of the tight setup of subjects 11, 12 and 13 tilted slowly during the long-term measurements. For this reason only 10 subjects were included in the study. However all remaining sensor of the tight setup were not tilted during recording. In particular the lower body recordings of all subjects are not corrupted.<br> <br> Code samples, a visualizer and further useful information is provided under the following git repository:<br> https://github.com/lorenzcsunikl/Dataset-of-Artefact-Aware-Human-Motion-Capture-using-Inertial-Sensors-Integrated-into-Loose-Clothing</p>
Impact of medical radionuclide discharges on people and the environment: scenario data used in the non-human biota impact assessment
<p>This dataset contains the input data for the D-DAT model: activity concentrations in water for the simulated Molse Nete scenario. It also contains the dynamic model-calculated activity concentrations in sediment and the non-human biota. These are the primary data upon which the dose calculations werte performed, and they can be used to reproduce these calculations. The related preprint article is also given in this repository: https://zenodo.org/records/10488393.</p> <p> </p> <p> </p> <p> </p>
Data supplement for "Land use intensification increasingly drives the spatiotemporal patterns of the global human appropriation of net primary production in the last century"
<p>This data supplements the publication "Land use intensification increasingly drives the spatiotemporal patterns of the global human appropriation of net primary production in the last century" by Thomas Kastner, Sarah Matej, Matthew Forrest, Simone Gingrich, Helmut Haberl, Thomas Hickler, Fridolin Krausmann, Gitta Lasslop, Maria Niedertscheider, Christoph Plutzar, Florian Schwarzmüller, Jörg Steinkamp, Karl-Heinz Erb.</p> <p>For details, please refer to the included readme file and to the publication (<a href="https://doi.org/10.1111/gcb.15932">https://doi.org/10.1111/gcb.15932</a>)</p> <p>In this new Version 1.01, we changed the file structure to make the data more accessible, we added data on means across modulations as used in the paper, and we include csv files with national totals for the different HANPP components.</p>
Data set for publication: Determination of Virulence-Associated Genes and Antimicrobial Resistance Profiles in Brucella Isolates Recovered from Humans and Animals in Iran Using NGS Technology
<p>This dataset includes information on resistance profiling, as well as antimicrobial resistance (AMR) genes and virulence-related factors that were identified in <em>Brucella</em> isolates recovered from humans and animals in different regions of Iran using classical phenotyping and next-generation sequencing (NGS) technology.</p>
Analysis of human humoral responses in a typhoid vaccine efficacy trial used for SIMON analysis
<p>The VAST dataset contains data from 72 individuals enrolled in the clinical study to evaluate humoral responses in a typhoid vaccine efficacy trial in a controlled human <em>Salmonella </em>Typhi infection model (see original publication: <a href="https://doi.org/10.3389/fimmu.2019.02582">https://doi.org/10.3389/fimmu.2019.02582</a>). Only day 0 (day of the challenge) log-transformed data were used in the SIMON analysis, as described in the publication (<a href="https://doi.org/10.1101/2020.08.16.252767">https://doi.org/10.1101/2020.08.16.252767</a>). Individuals were vaccinated with either a purified Vi polysaccharide (Vi-PS) vaccine (35 individuals) or the Vi tetanus toxoid conjugate (Vi-TT) vaccine (37 individuals) one month prior to oral challenge with live <em>Salmonella </em>Typhi. Out of 72 individuals, 26 developed an acute typhoid infection following the challenge.</p>
Raw data employed to perform the algorithm used in the scientific paper: "Kinematic reconstruction of the human arm joints in robot-aided therapies with Hermes robot"
<p>This file contains the raw data necessary to perform the algorithm introduced in the scientific paper:</p> <p>PAPER: Kinematic reconstruction of the human arm joints in robot-aided therapies with Hermes robot</p> <p>Authors: Arturo Bertomeu-Motos, Ricardo Morales, Luis D. Lledó, Jorge A. Díez, Jose M. Catalan, Nicolas Garcia-Aracil.</p> <p>Conference: EMBC 2015, IEEE 37th International Conference in Medicine and Biology Society, August 2015.</p> <p>Raw data acquired necessary to perform thee algorithm introduced in this paper.</p> <p>a) Robot Joints: Robot joints generated to develop the simulation, in radians (j1-j7 colums). This robot is referenced in the paper.<br> b) Direct Upper Limb Joints: Upper limb joints generated to develop the simulation, in radians (q1-q7 columns). This data is used to simulate the accelerometer value.</p>
Dataset: Assessing Background Contamination of Sample Tubes used in Human Biomonitoring by Non-targeted Liquid Chromatography–High Resolution Mass Spectrometry
<p>Data set of the Publication: </p> <div> <div>Krauss, Martin, Carolin Huber, Tobias Schulze, Martina Bartel-Steinbach, Till Weber, Marike Kolossa-Gehring, und Dominik Lermen (2024): Assessing background contamination of sample tubes used in human biomonitoring by non-targeted liquid chromatography–high resolution mass spectrometry. <em>Environment International</em> 183: 108426. <a href="https://doi.org/10.1016/j.envint.2024.108426">https://doi.org/10.1016/j.envint.2024.108426</a>.</div> </div> <p>- raw LC-HRMS data in mzML format for positive and negative mode.</p> <p>- merged MS/MS spectra of whole data set after MZMine 2.53 processing in mgf format.</p> <p> </p>
Dataset: WiFi-based Human Activity Recognition using Raspberry Pi
<p>This dataset contains 980 802.11 Channel State Information captures for 11 activities performed in a small apartment by 1 subject. For full description, check README.md.</p>
Supplementary materials (set 2 of 2) in support of "Signalling Emotions with a Breathing Soft Robot" (Data set and materials used for human-robot interaction experiment)
<p>Supplementary materials (set 2 of 2) in support of "Signalling Emotions with a Breathing Soft Robot" authored by Troels Aske Klausen, Ulrich Farhadi, Evgenios Vlachos, and Jonas Jørgensen.</p> <p>Contents of set 2:<br> - Data set and materials used for the human-robot interaction experiment and for data analysis</p> <p>Files:<br> - "Questionnaire.pdf": Questionnaire used for data collection.<br> - "Video links.txt": Weblinks to stimuli videos used.<br> - "Data set.xls": Collected raw data.<br> - "Matlab_DataAnalysis.mlx": Matlab script used to analyze raw data.<br> - "Linear_Arousal.png": Linear fit between the scoring of arousal and BPM.<br> - "Linear_Dominance.png": Linear fit between the scoring of dominance and BPM.<br> - "Linear_Pleasure.png": Linear fit between the scoring of pleasure and BPM.</p> <p>The experiment procedure is described in the paper.<br> The soft robot used for the experiment is open source and can be manufactured using design files available on Zenodo: 10.5281/zenodo.5565201</p>
HBV-only reads from cultured human hepatocytes infected with HBV used for testing HBVouroboros functionalities.
<p>The dataset consists of bulk-RNA reads extracted from cultured human hepatocytes infected with HBV. The data is used to test the performance of the HBVouroboros software (https://github.com/bedapub/HBVouroboros).</p> <p>Note that fastq files are comprised of reads that map to HBV genome, as such sample files for negative controls of HBV infection are empty files. We include these for the sake of completeness and to reflect the experimental design.</p>
Spatiotemporal Prediction of COVID-19 Cases using Inter- and Intra-County Proxies of Human Interactions (dataset)
<p>This repository contains data (features) necessary to run STXGB model and accompanies the paper titled "Spatiotemporal Prediction of COVID-19 Cases using Inter- and Intra-County Proxies of Human Interactions".</p> <p> </p> <p>STXGB is a spatiotemporal autoregressive model that predicts county-level new cases of COVID-19 in the coterminous US in 1- to 4-week prediction horizons using spatiotemporal lags of infection rates, human interactions, human mobility, and socioeconomic composition of counties as predictive features.</p>
Dataset: Analysis of IFTTT Recipes to Study How Humans Use Internet-of-Things (IoT) Devices
<p>This archive contains the files submitted to the 4th International Workshop on Data: Acquisition To Analysis (DATA) at SenSys. Files provided in this package are associated with the paper titled "Dataset: Analysis of IFTTT Recipes to Study How Humans Use Internet-of-Things (IoT) Devices"</p> <p>With the rapid development and usage of Internet-of-Things (IoT) and smart-home devices, researchers continue efforts to improve the ''smartness'' of those devices to address daily needs in people's lives. Such efforts usually begin with understanding evolving user behaviors on how humans utilize the devices and what they expect in terms of their behavior. However, while research efforts abound, there is a very limited number of datasets that researchers can use to both understand how people use IoT devices and to evaluate algorithms or systems for smart spaces. In this paper, we collect and characterize more than 50,000 recipes from the online If-This-Then-That (IFTTT) service to understand a seemingly straightforward but complicated question: ''What kinds of behaviors do humans expect from their IoT devices?'' The dataset we collected contains the basic information of the IFTTT rules, trigger and action event, and how many people are using each rule.</p> <p>For more detail about this dataset, please refer to the paper listed above.</p>
Data from: Non-invasive Assessment of Cartilage Damage of the Human Knee using Acoustic Emission Monitoring: a Pilot Cadaver Study
<p>This dataset accompanies the following article: "Non-invasive Assessment of Cartilage Damage of the Human Knee using Acoustic Emission Monitoring: a Pilot Cadaver Study," in <em>IEEE Transactions on Biomedical Engineering</em>, doi: 10.1109/TBME.2023.3263388.</p> <p>Knee acoustic emissions (AE) recorded in the 100-450 kHz and 15-200kHz frequency ranges from a cadaver specimen knee in flexion/extension. Four stages of artificially inflicted cartilage damage and two sensor positions were investigated. </p> <p><em><strong>Stages of artificially inflicted cartilage damage:</strong></em> the cartilage surface damage on the medial compartment, KL III; the cartilage surface damage on the medial compartment plus patellofemoral surface, KL III; the cartilage surface damage on the medial compartment plus on the patellofemoral surface KL IV; the cartilage surface damage on the medial compartment plus on the patellofemoral surface and lateral compartment.</p> <p><strong><em>Sensor positions</em></strong>: medial and lateral knee</p>
Datasets used for the manuscript: "Sibling competition, dispersal and fitness outcomes in humans"
<p>Datasets used for the manuscript: “Sibling competition, dispersal and fitness outcomes in humans”, 10.1038/s41598-023-33700-3</p>
Raw and post-processing data for using auditory models to mimic human listeners in reverse correlation experiments from the fastACI toolbox
<p><strong>Description</strong>: The current dataset provides all the stimuli (folder ../01-Stimuli/), raw data (folder ../02-Raw-data/) and post-processed data (../03-Post-proc-data/) used in the Forum Acusticum 2013 paper titled "Using auditory models to mimic human listeners in reverse correlation experiments from the fastACI toolbox" by the same authors. In this paper, we replicated the tone-in-noise experiment by Ahumada et al. (1975) but using an artificial listener instead of collecting data from real participants. The behavioural data were mimicked using an artificial listener based on 'king2019' (King et al., 2019) as a front-end model using a template-matching decision to indicate whether a 500-Hz tone was (or not) present in each of the noisy trials. This study offers a step-by-step guide of how can be an artificial listener integrated into fastACI.</p> <p><strong>Use these data</strong>: Download all these data, locate them in a local directory of your computer. If you have MATLAB and you downloaded a local copy of the fastACI toolbox (open access at: <a href="https://github.com/aosses-tue/fastACI">https://github.com/aosses-tue/fastACI</a>) you can recreate the figures of our paper. After downloading and initialising the toolbox (type 'startup_fastACI;', without quotation marks in MATLAB), run the script <strong>g20230501_FA_Artificial_listener_paper_figs.m</strong> (provided in this dataset) and follow the instructions on the screen to generate one of the four study figures. This script calls the function <strong>publ_osses2023b_FA_figs.m</strong> from the toolbox. </p> <p> </p>
Vocal drum sounds in Human Beatboxing: an acoustic and articulatory exploration using electromagnetic articulography
<p>This dataset constitutes the supplementary material of a paper in review in the Journal of the Acoustical Society of America (JASA)</p>
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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.