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6,826 results for “head”
Temperature, Salinity, Sound Velocity, Location, Depth, Heading, and Velocity measured aboard the R/V Nanuq for the Northern Gulf of Alaska LTER site, 2020-2021
This dataset describes measurements from a thermosalinograph and navigation device used aboard the R/V Nanuq during cruises in Resurrection Bay and the Gulf of Alaska. Thermosalinograph data includes temperature, salinity, conductivity, and sound speed measurements every five seconds while the instrument was in use. Navigation data describes latitude, longitude, depth, heading, course over ground, and speed over ground. Data are collected on R/V Nanuq using the ship's GPS devices and a Seabird Electronics SBE-45 thermosalinograph (TSG) that samples uncontaminated pumped seawater. Temperature and conductivity data are sampled every 5 seconds and salinity and sound velocity are derived parameters. No data quality control has been applied to this data, so users should be cautious that conductivity, salinity and sound speed dropouts to due bubbles are common when the ship is plowing through large waves. Data are collected by a variety of projects, including from the NSF-funded Northern Gulf of Alaska Long Term Ecological Research (NGA LTER) program, the Exxon Valdez Oil Spill Trustee Council (EVOSTC) Gulf Watch Alaska GAK1 project, the UAF Sub-Arctic Oceanography Field Course, the Alaska Ocean Observing System (AOOS) glider program, and others.
Experimental data for the study: "Naturalistic visualization of reaching movements using head-mounted displays improves movement quality and proves high usability compared to conventional computer screens"
<p>The datasets contains the motor performance metrics and the questionnaire responses for two experiments involving a motor task with a VR controller (experiment 1, healthy old participants) or a rehabilitation assistive device (experiment 2, brain-injured patients) and three visualization technologies: an immersive virtual reality (IVR) head-mounted display (HMD), an augmented reality (AR) HMD, and a computer screen (2D screen). The study was performed in the Motor Learning and Neurorehabilitation Laboratory at the University of Bern. All data are stored in “csv” files. The variables inside the files are explained in “DataFrameDescription.rtf”. For questions, please contact L.MarchalCrespo@tudelft.nl.</p>
Multi-head CRF classifier for biomedical multi-class Named Entity Recognition on Spanish clinical notes
<p>This contains the merged dataset as described in the work "<strong>Multi-head CRF classifier for biomedical multi-class Named Entity Recognition on Spanish clinical notes"</strong>.</p> <p>This dataset consists of 4 seperate datasets:</p> <ul> <li><a href="../records/8224056" target="_blank" rel="noopener">MedProcNer</a></li> <li><a href="../records/7614764" target="_blank" rel="noopener">DisTEMIST</a></li> <li><a href="../records/4270158" target="_blank" rel="noopener">PharmaCoNER</a></li> <li><a href="../records/10635215" target="_blank" rel="noopener">SympTEMIST</a></li> </ul> <p>The dataset contains two tasks:</p> <p><strong>Task 1:</strong> This task is related to multi-class Named Entity Recognition. This dataset contains 5 possible classes: SYMPTOM, PROCEDURE, DISEASE, CHEMICAL and PROTEIN.</p> <p><strong>Task 2:</strong> This task is related to Named Entity Linking, where each code corresponds to a code within the SNOMED-CT corpus. The exact corpus used can be obtained <a href="https://download.nlm.nih.gov/umls/kss/IHTSDO20190131/SnomedCT_SpanishRelease-es_PRODUCTION_20190430T120000Z.zip" target="_blank" rel="noopener">here</a>. Further for the MedProcNER, SympTEMIST and DisTEMIST datasets, a gazetteer is provided in the original datasets. </p> <p>For more information on the construction of the dataset, aswell as dataloaders, we refer you to our <a href="https://github.com/ieeta-pt/Multi-Head-CRF" target="_blank" rel="noopener">GitHub repository</a>.<br><br>Further this also contains the embeddings from the <a href="https://huggingface.co/cambridgeltl/SapBERT-UMLS-2020AB-all-lang-from-XLMR-large" target="_blank" rel="noopener">SapBERT</a> model.</p> <p><strong>Please, cite:</strong></p> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <blockquote> <div>@article{jonker2024a, title = {Multi-head {{CRF}} classifier for biomedical multi-class named entity recognition on {{Spanish}} clinical notes}, author = {Jonker, Richard A. A. and Almeida, Tiago and Antunes, Rui and Almeida, Jo{\~a}o R. and Matos, S{\'e}rgio}, year = {2024}, journal = {Database}, publisher = {Oxford University Press} }</div> </blockquote> <div>Jonker, R. A. A., Almeida, T., Antunes, R., Almeida, J. R., & Matos, S. (2024). Multi-head CRF classifier for biomedical multi-class named entity recognition on Spanish clinical notes. (Submitted.) </div> <div> </div> <div> <p><strong>License</strong></p> <p>This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
Pauni (पौनि Bhandārā district) Maharashtra. Stone head at the site of the stūpa.
<p>Pauni (पौनि Bhandārā district) Maharashtra. Stone head at the site of the <em>stūpa</em>, kept in Jagannāth temple.</p>
Dataset: A database of near-field head-related transfer functions based on measurements with a laser spark source
<p>This is a database of near-field head-related transfer functions (HRTFs) of an artificial head, measured at four distances (0.2, 0.3, 0.4 and 0.5 m), with 49 positions recorded at each distance, for a total of 196 measurement points. The HRTFs were recorded using an acoustic pulse created by a laser-induced breakdown of air (LIB), which realizes a close to ideal, massless, monopole sound source. The repository contains the original measurement data (raw_data.zip), the derived HRTFs both with (NF_LIB_HRTF_LFE.sofa) and without (NF_LIB_HRTF_measured.sofa) a low-frequency extension (LFE) applied, as well as the MATLAB code used to process the measurement data and to apply the LFE (LIB_HRTF_DB.zip). The database is made publicly available to support future research into nearby sound localization, and virtual/augmented reality applications.</p> <p>Please see the accompanying paper for further details: Marschall et al. (2023), <a href="https://doi.org/10.1016/j.apacoust.2022.109173">A database of near-field head-related transfer functions based on measurements with a laser spark source</a>, Applied Acoustics. </p>
TCGA Head & Neck Squamous Cell Carcinoma (HNSC) Gene Expression
<p><strong>Abstract:</strong></p> <p>The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset contains information about HNSC, a type of cancer that originates in the squamous cells lining the mucosal surfaces of the head and neck region, including the oral cavity, throat, and larynx. The gene expression profile was measured experimentally using the Illumina HiSeq 2000 RNA Sequencing platform by the University of North Carolina TCGA genome characterization center. The Sample IDs serve as unique identifiers for each sample.</p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Instruction:</strong></p> <p>The log2(x+1) normalization was removed, and z-normalization was performed on the dataset using a Python script.</p> <p><strong>Acknowledgments:</strong></p> <p>Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8</p> <p>The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764</p> <p><strong>U-BRITE last update: </strong>07/13/2023</p>
TCGA Head & Neck Squamous Cell Carcinoma (HNSC) Clinical Data
<p><strong>Abstract:</strong></p> <p>The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset includes curated survival data from the Pan-cancer Atlas paper titled <a href="http://www.cell.com/cell/fulltext/S0092-8674(18)30229-0">"An Integrated TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR) to drive high quality survival outcome analytics"</a>. The paper highlights four types of carefully curated survival endpoints, and <a href="http://www.cell.com/action/showFullTableImage?isHtml=true&tableId=tbl3&pii=S0092867418302290">recommends the use of the endpoints of OS, PFI, DFI, and DSS for each TCGA cancer type</a>. The dataset also includes phenotypic information about HNSC. The Sample IDs are unique identifiers, which can be paired with the gene expression dataset. </p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Instruction:</strong></p> <p>The survival and phenotype data were merged into one file. Empty columns were removed. Columns with the same value for every sample were also removed. </p> <p><strong>Acknowledgments:</strong></p> <p>Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8</p> <p>Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11. <a href="https://doi.org/10.1016/j.cell.2018.02.052">https://doi.org/10.1016/j.cell.2018.02.052</a></p> <p>The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764</p> <p><strong>U-BRITE last update: </strong>07/13/2023</p>
PIE LTER Year 2017, 15 minute measurements of conductivity, water temperature at the Ipswich River head of tide, Sylvania Dam in Ipswich, MA.
Year 2017, continuous measurements, every 15 minutes were made of conductivity, water temperature in the Ipswich River behind the head of tide Sylvania Dam in Ipswich, MA. The datalogger was retrieved in August, 2017 and returned to Dr. Green.
Datasets with and without deliberate head movements for detection and imputation of dropout in diffusion MRI
Open the record for dataset details and reuse information.
Head-to-Ball Impacts in Collegiate Soccer Players
Open the record for dataset details and reuse information.
Global Wheat Head Dataset - 2020 challenge version
<p>The latest version is V4.</p> <p>This is the only official version of the Global Wheat Head Dataset presented in David et al. (2020) . It's a corrected version of the dataset published on Kaggle, and the one used for the Codalab challenge.</p> <p>Test labels are available on request by filling the form <a href="https://docs.google.com/forms/d/e/1FAIpQLSciaWUwQDNFP199Xb0Iqt2fY67tQI0hAZBJCCfvwd5OuIVQ3A/viewform?usp=sf_link">here </a> or contacting <strong>etienne.david@outlook.com</strong></p> <p>If you use the dataset for your paper, please cite: <a href="https://doi.org/10.34133/2020/3521852">https://doi.org/10.34133/2020/3521852</a></p> <p>If you want to benchmark your solution and get localization and counting metrics, please submit to the codalab challenge: </p>
A Free Database of Head-Related Impulse Response Measurements in the Horizontal Plane with Multiple Distances (MAT-Version)
<p>Head related impulse response measurements with the KEMAR dummy head performed in an anechoic chamber with a resolution of 1°. The impulse responses are provided for different distances and are accompanied by headphone compensation filters.</p> <p>This entry stores the measurements in the MAT format for use in Matlab/Octave. The measurements are identical to the once stored in the SOFA format available at <a href="https://doi.org/10.5281/zenodo.55418">https://doi.org/10.5281/zenodo.55418</a></p>
Head-related impulse responses of a loudspeaker array
<p>Head-related impulse responses measured with a KEMAR dummy head of a loudspeaker array consisting of 13 loudspeakers. The dummy head was placed at three different positions, which allows to combine the measurements to an loudspeaker array consisting of 35 loudspeakers.</p> <p>The measurement equipment was exactly the same as in http://dx.doi.org/10.5281/zenodo.55418</p>
A Free Database of Head-Related Impulse Response Measurements in the Horizontal Plane with Multiple Distances
<p>Head related impulse response measurements with the KEMAR dummy head performed in an anechoic chamber with a resolution of 1°. The impulse responses are provided for different distances and are accompanied by headphone compensation filters.</p> <p>For details have a look at README.md.</p> <p>The same measurement can be downloaded as MAT files at <a href="https://doi.org/10.5281/zenodo.4459911">https://doi.org/10.5281/zenodo.4459911</a></p> <p>This dataset is further described in (see the PDF file)</p> <p>H. Wierstorf, M. Geier, A. Raake, S. Spors - A Free Database of Head-Related<br> Impulse Response Measurements in the Horizontal Plane with Multiple Distances.<br> In 130th AES Conv. 2011, eBrief 6.</p> <p> </p>
Dataset: Six years of surface remote sensing of stratiform warm clouds in marine and continental air over Mace Head, Ireland
<p>A total of 118 stratiform water clouds observed by ground-based remote sensing instruments at the Mace Head Atmospheric Research Station at the West coast of Ireland from 2009 to 2015 were analyzed in terms of microphysical and optical characteristics as well as the impact of aerosols on these properties. The microphysical and optical cloud properties in the files were obtained using the algorithm SYRSOC (SYnergistic Remote Sensing Of Clouds).</p>
Listening test results for sound field synthesis localization experiment -- head movement data
<p>This data set contains recorded head movements listeners did during several localisation tasks in the context of sound field synthesis. This is an add-on to the actual localisation results provided by [1].</p> <p>[1] Wierstorf, H. (2016). Listening test results for sound field synthesis localization experiment [Data set]. Zenodo. http://doi.org/10.5281/zenodo.55439</p>
Data set for "Optogenetic stimulation of cortex to map evoked whisker movements in awake head-restrained mice"
<p>Data set for: Auffret M, Ravano VL, Rossi GMC, Hankov N, Petersen MFA, Petersen CCH (2017) Optogenetic stimulation of cortex to map evoked whisker movements in awake head-restrained mice. Neuroscience, http://dx.doi.org/10.1016/j.neuroscience.2017.04.004</p> <p>There are 9 files in this data upload:</p> <ol> <li>'2017_Auffret_Neuroscience.pdf' - this is a pdf version of the online publication.</li> <li>'Auffret_data.mat' - this is a Matlab data structure, which contains all the data for the publication.</li> <li>'Auffret_data.npy' - this is a Python data structure, which contains all the data for the publication. The Python data was generated from 'Auffret_data.mat' by 'DataViewer.py'.</li> <li>'Auffret_data.xlsx' - this is an Excel file, which contains all the data for the publication. This Excel file was generated from 'Auffret_data.mat'.</li> <li>'DataViewer.fig' - this is a Matlab Figure file, which is the GUI layout for 'DataViewer.m'.</li> <li>'DataViewer.m' - this is a Matlab Code, which displays the data contained in 'Auffret_data.mat'.</li> <li>'DataViewer.py' - this is a Python Code, which generates 'Auffret_data.npy' from 'Auffret_data.mat', and displays an example trial.</li> <li>'FigureViewer.fig' - this is a Matlab Figure file, which is the GUI layout for 'FigureViewer.m'.</li> <li>'FigureViewer.m' - this is a Matlab Code, which analyses the data in 'Auffret_data.mat', and displays the results in the same way as the published figures (Auffret et al., 2017).</li> </ol>
Data and code for the paper "Macroscopic parameterization of positive streamer heads in air"
<p>This dataset contains the following:</p> <ul> <li><strong>ODE_solutions_no_photoi</strong>: the simulation results corresponding to figure 3 (see the README in the folder)</li> <li><strong>ODE_solutions_photoi</strong>: the simulation results corresponding to figure 6 (see the README in the folder)</li> <li><strong>input</strong>: the electron transport data used in the simulations (ionization coefficient, attachment coefficient, electron mobility) as a function of electric field strength</li> <li><strong>ODE_model.py</strong>: The version of the ODE model used in the paper</li> </ul> <p>The most recent version of the ODE model can be found at <a href="https://github.com/MD-CWI/streamer-head-ode">https://github.com/MD-CWI/streamer-head-ode</a></p>
Dataset for "IRIS analyser assessment reveals sub-hourly variability of isotope ratios in carbon dioxide at Baring Head, New Zealand's atmospheric observatory in the Southern Ocean"
<p>Dataset for</p> <p>Sperlich, P., Brailsford, G. W., Moss, R. C., McGregor, J., Martin, R. J., Nichol, S., Mikaloff-Fletcher, S., Bukosa, B., Mandic, M., Schipper, I., Krummel, P. and Griffiths, A. D.: IRIS analyser assessment reveals sub-hourly variability of isotope ratios in carbon dioxide at Baring Head, New Zealand's atmospheric observatory in the Southern Ocean, Atmos. Meas. Tech., https://doi.org/10.5194/amt-15-1-2022, 2022.</p>
A 3D human head dataset for non-coplanar keypoints detection
<p>This MRI volumes ("nii.gz" format) were part of the IXI dataset (<a href="https://brain-development.org/ixi-dataset/">IXI Dataset – Brain Development (brain-development.org)</a>). For our work, we focused on the T1-weighted images, which provided an higher degree of anatomical details. We chose and manually annotated 4 non-coplanar points inside these volumes, in order to train a 3D CNN to detect them. Such points could be exploited to perform alignment tasks. The annotation was carried out using 3D Slicer, and we encourage to use it for the visualization of the volumes and the relative annotations ("json" format). Below a description of the 4 keypoints:</p> <ul> <li>Keypoint 1: The cerebral aqueduct in correspondence of the transverse plane slice where the mammillary bodies are two well defined little balls.</li> <li>Keypoint 2: The point of contact between the two ventricles anterior horns before going into lateral ventricles (visualize on coronal plane)</li> <li>Keypoint 3: The right eye center in correspondence of the largest diameter circle (on the coronal plane)</li> <li>Keypoint 4: The left eye center in correspondence of the largest diameter circle (on the coronal plane)</li> </ul> <p>The dataset consists of 507 volumes with related annotations.</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.