Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
38
datasets available to search
ShareScore release 0.9.0
Dataset results
38 results for “HCP”
HCP-YA Tractography Atlas (Figures)
<p>Yeh, F. C., Panesar, S., Fernandes, D., Meola, A., Yoshino, M., Fernandez-Miranda, J. C., ... & Verstynen, T. (2018). Population-averaged atlas of the macroscale human structural connectome and its network topology. NeuroImage, 178, 57-68.</p>
HCP-YA Tractography Atlas (NIFTI Files)
<p>Yeh, F. C., Panesar, S., Fernandes, D., Meola, A., Yoshino, M., Fernandez-Miranda, J. C., ... & Verstynen, T. (2018). Population-averaged atlas of the macroscale human structural connectome and its network topology. NeuroImage, 178, 57-68.</p>
Segmentations of the core of the acoustic radiation in HCP data
<p><strong>Description of the repository</strong>:</p> <p>The goal of our paper (<a href="https://doi.org/10.3389/fneur.2022.934650">https://doi.org/10.3389/fneur.2022.934650</a>) was to segment the acoustic radiation (AR), one of the most important white matter fiber bundles of the hearing system. This repository contains the segmentations masks of the AR we created from 105 subjects of the Human Connectome Project (HCP) young adult dataset (<a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a>). These subjects are exactly the same used by Wasserthal et al. (2018) <a href="https://doi.org/10.1016/j.neuroimage.2018.07.070">https://doi.org/10.1016/j.neuroimage.2018.07.070</a>.</p> <p>In the file "data_training.tar.gz", one directory was created per HCP subject. Every directory contains the file "bundle_masks_AR.nii.gz" that contains the binary masks for the left and right AR.</p> <p>In our paper, we used these masks to train TractSeg. The file best_weights_ep110.npz contains the weights after training TractSeg that can be used in inference for targeting the AR. For using these weights on new data, one can use TractSeg with the option "--exp_name best_weights_ep110.npz". Please read the documentation of TractSeg and our paper for more information.</p> <p> </p> <p>If you use the training data or the pre-trained network, please cite our publication:</p> <p>Malin Siegbahn, Cecilia Engmér Berglin, Rodrigo Moreno. Automatic segmentation of the core of the acoustic radiation in humans. Frontiers in Neurology (2022) 13:934650. doi: 10.3389/fneur.2022.934650</p>
In-house CHO HCP Platform: a promising approach for HCP ELISA monitoring
Open the record for dataset details and reuse information.
SLAC/MEC LJ55 experiment on hcp-Fe plasticity under shock compression
<p>Raw data for experiment LJ55 at SLAC/MEC on hcp-Fe strength and plasticity: X-ray diffraction data and X-ray beam energies.</p> <p>Corresponding publication is published in <em>Physical Review Letters</em>: S. Merkel, S. Hok, C. Bolme, D. Rittman, K. J. Ramos, B. Morrow, H. J. Lee, B. Nagler, E. Galtier, E. Granados, A. Hashim, W. L. Mao, and A. E. Gleason, Femtosecond Visualization of hcp-Iron Strength and Plasticity under Shock Compression, <em>Physical Review Letters</em>, 127, 205501 (2021), [doi: <a href="http://dx.doi.org/10.1103/PhysRevLett.127.205501">10.1103/PhysRevLett.127.205501</a>]</p>
HippUnfold HCP-YA Training Data
<p>Hippocampal Tissue segmentations, T1w, T2w, and mean DWI b1000 images in upsampled 0.3mm left and right bounding boxes around the hippocampus. Subjects are from the HCP-1200 Young Adult dataset. These data were used to train the current HippUnfold U-net segmentation models.</p> <p>Unzip all the zip files, folders will be created for the labelmaps, T1w, T2w, and b1000 images for each hemisphere and subject. Left hipp labels are already L-R flipped to the right side.</p> <p>Labelmap lookup table:</p> <pre>index name abbreviation 1 hippocampal grey matter GM 2 SRLM or 'dark band' SRLM 3 neocortex (entorhinal or parahippocampal) MTLC 4 pial surface Pial 5 hippocampal-amygdalar transition area HATA 6 indusium griseum IndGris 7 cysts Cyst 8 dentate gyrus DG</pre> <p> </p>
Results of MD simulations of collision cascades in hcp Zr at 600 K.
<p>This is a database consisting of pre- and post-processing scripts, input files and results from molecular dynamics (MD) simulations of collision cascades in LAMMPS. The database is associated with the paper: <a href="https://arxiv.org/abs/2405.03332">Molecular dynamics simulations of neutron induced collision cascades in Zr</a>. The material studied was hcp Zr at the temperature of 600 K. A detailed description of the record can be found in the <code>pdf</code> file, as well as in Emacs <code>org</code> and Jupyter notebooks (<code>ipynb</code>). Files also include Python code describing how to access and process the data.</p>
Dataset: HashiCorp, Inc. (HCP) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
72 white matter bundles segmented by 7 different methods in one HCP subject
<p>This dataset contains segmentations of 72 white matter tracts from 7 different automatic segmentation methods for one subject (623844) from the Human Connectome Project (HCP) young adult dataset (https://www.humanconnectome.org/study/hcp-young-adult). Segmentations are given for the original HCP data ("HCP Quality") and for a downsampled version of the HCP data with reduced resolution (2.5mm isotrop) and less gradients (32x b=1000mm/s^2) ("Clinical Quality").</p> <p>The data is part of the following project: https://github.com/MIC-DKFZ/TractSeg/<br> If you use the data please cite the paper mentioned on the project page.</p> <p>Each file is 4D nifti image. The 4th dimension determines the bundle:</p> <p>1: AF_left (Arcuate fascicle)<br> 2: AF_right<br> 3: ATR_left (Anterior Thalamic Radiation)<br> 4: ATR_right<br> 5: CA (Commissure Anterior)<br> 6: CC_1 (Rostrum)<br> 7: CC_2 (Genu)<br> 8: CC_3 (Rostral body (Premotor))<br> 9: CC_4 (Anterior midbody (Primary Motor))<br> 10: CC_5 (Posterior midbody (Primary Somatosensory))<br> 11: CC_6 (Isthmus)<br> 12: CC_7 (Splenium)<br> 13: CG_left (Cingulum left)<br> 14: CG_right <br> 15: CST_left (Corticospinal tract<br> 16: CST_right <br> 17: MLF_left (Middle longitudinal fascicle)<br> 18: MLF_right<br> 19: FPT_left (Fronto-pontine tract)<br> 20: FPT_right <br> 21: FX_left (Fornix)<br> 22: FX_right<br> 23: ICP_left (Inferior cerebellar peduncle)<br> 24: ICP_right <br> 25: IFO_left (Inferior occipito-frontal fascicle) <br> 26: IFO_right<br> 27: ILF_left (Inferior longitudinal fascicle) <br> 28: ILF_right <br> 29: MCP (Middle cerebellar peduncle)<br> 30: OR_left (Optic radiation) <br> 31: OR_right<br> 32: POPT_left (Parieto‐occipital pontine)<br> 33: POPT_right <br> 34: SCP_left (Superior cerebellar peduncle)<br> 35: SCP_right <br> 36: SLF_I_left (Superior longitudinal fascicle I)<br> 37: SLF_I_right <br> 38: SLF_II_left (Superior longitudinal fascicle II)<br> 39: SLF_II_right<br> 40: SLF_III_left (Superior longitudinal fascicle III)<br> 41: SLF_III_right <br> 42: STR_left (Superior Thalamic Radiation)<br> 43: STR_right <br> 44: UF_left (Uncinate fascicle) <br> 45: UF_right <br> 46: CC (Corpus Callosum - all)<br> 47: T_PREF_left (Thalamo-prefrontal)<br> 48: T_PREF_right <br> 49: T_PREM_left (Thalamo-premotor)<br> 50: T_PREM_right <br> 51: T_PREC_left (Thalamo-precentral)<br> 52: T_PREC_right <br> 53: T_POSTC_left (Thalamo-postcentral)<br> 54: T_POSTC_right <br> 55: T_PAR_left (Thalamo-parietal)<br> 56: T_PAR_right <br> 57: T_OCC_left (Thalamo-occipital)<br> 58: T_OCC_right <br> 59: ST_FO_left (Striato-fronto-orbital)<br> 60: ST_FO_right <br> 61: ST_PREF_left (Striato-prefrontal)<br> 62: ST_PREF_right <br> 63: ST_PREM_left (Striato-premotor)<br> 64: ST_PREM_right <br> 65: ST_PREC_left (Striato-precentral)<br> 66: ST_PREC_right <br> 67: ST_POSTC_left (Striato-postcentral)<br> 68: ST_POSTC_right<br> 69: ST_PAR_left (Striato-parietal)<br> 70: ST_PAR_right <br> 71: ST_OCC_left (Striato-occipital)<br> 72: ST_OCC_right</p>
Elemental vacancy diffusion database from high-throughput first-principles calculations for fcc and hcp structures
<p><br /> This work demonstrates how databases of diffusion-related properties can be developed from high-throughput ab initio calculations. The formation and migration energies for vacancies of all adequately stable pure elements in both the face-centered cubic (fcc) and hexagonal close packing (hcp) crystal structures were determined using ab initio calculations. For hcp migration, both the basal plane and z-direction nearest-neighbor vacancy hops were considered. Energy barriers were successfully calculated for 49 elements in the fcc structure and 44 elements in the hcp structure. These data were plotted against various elemental properties in order to discover significant correlations. The calculated data show smooth and continuous trends when plotted against Mendeleev numbers. The vacancy formation energies were plotted against cohesive energies to produce linear trends with regressed slopes of 0.317 and 0.323 for the fcc and hcp structures respectively. This result shows the expected increase in vacancy formation energy with stronger bonding. The slope of approximately 0.3, being well below that predicted by a simple fixed bond strength model, is consistent with a reduction in the vacancy formation energy due to many-body effects and relaxation. Vacancy migration barriers are found to increase nearly linearly with increasing stiffness, consistent with the local expansion required to migrate an atom. A simple semi-empirical expression is created to predict the vacancy migration energy from the lattice constant and bulk modulus for fcc systems, yielding estimates with errors of approximately 30%.</p> <p>Files:</p> <p>figure_excel_files.zip:</p> <p>Excel files for figures in the publication, and excel files of main data tables for FCC and HCP vacancy formation energies and vacancy migration energies.</p> <p>fcc_hvf_hvm.tar.gz and hcp_hvf_hvm.tar.gz:</p> <p>Raw VASP files corresponding to FCC and HCP vacancy formation energies and vacancy migration energies.<br /> <br /> bulk_modulus.tar.gz:</p> <p>Raw VASP files corresponding to FCC bulk modulus calculations.<br /> </p>
HCP-MGH Subject dMRI Data (SRC Files)
<p>A multishell diffusion scheme was used, and the b-values were 1000 ,3000 ,5000 and 10000 s/mm2. The number of diffusion sampling directions were 64, 64, 128, and 256. The in-plane resolution was 1.5 mm. The slice thickness was 1.5 mm.</p> <p>This data set was originally from the connectome db website (https://db.humanconnectome.org/). The MGH HCP team has released diffusion imaging and structural imaging data acquired from 35 young adults using the customized MGH Siemens 3T Connectome scanner, which has 300 mT/m maximum gradient strength for diffusion imaging.</p> <p> </p> <p>The data are shared under the WU-Minn HCP open access data use term (4) at https://www.humanconnectome.org/study/hcp-young-adult/document/wu-minn-hcp-consortium-open-access-data-use-terms</p> <p>Please acknowledge the source to the WU-Minn HCP.</p>
Atomic models of elementary bcc to hcp transition with {13-41} mirror plane
<p>Atomic structures of bcc to hcp transition using the supercells constructed with {13-41} hcp planes. Transition</p>
Combined P-V-T Data for hcp-Fe with Reevaluated Pressures and Pressure Uncertainties
<p>This dataset consists of unit-cell volumes, temperatures, and reevaluated pressures, and their related uncertainties for <em>hcp-</em>Fe. The volumes and temperatures are taken from previous studies (indicated under the column "Original Publication"), and the pressures were reevaluated using the internally consistent pressure scales of Fei <em>et al</em>., 2007. The pressure uncertainties presented here propagate uncertainties in the volume and temperature measurements, as well as the uncertainty and covariance in the equation of state parameters used to determine the pressure. Also listed are the beamline the data were originally collected at, and the compression technique (DAC= Diamond Anvil Cell; MAP= Multi-Anvil Press).</p>
Immunogenicity Trial of Recombinant Influenza Vaccine Among HCP in Israel
ClinicalTrials.gov study NCT04523324. IPD Sharing: NO. Countries: 1. Publications: 2.
Immunogenicity Trial of Egg- Versus Non-Egg-Based Influenza Vaccines Among HCP
ClinicalTrials.gov study NCT03722589. IPD Sharing: YES. Countries: 1. Publications: 3.
Clinical Trial to Evaluate the Immunogenicity of an Adjuvanted Influenza Vaccine Among HCP
ClinicalTrials.gov study NCT06054269. IPD Sharing: NO. Countries: 1. Publications: 1.
Claustrum Segmentations of HCP Young Adult Dataset
<p>The dataset consists segmentations of the left and right claustrum, for 1068 from the Human Connectome Project Young Adults dataset. The segmentations were generated automativally. </p> <p>The dataset also includes mannual segmentation of the left and right claustrum for 10 subjects. </p> <p>The segmentations are available as Nifti files. The name of each file states the HCP ID of that subjects. </p> <p>The segmentations in the same space and resolution of each subject's T1w_acpc_dc_restore file. </p> <p> </p> <p> </p>
Investigation of slip transfer across HCP grain boundaries with application to cold dwell facet fatigue
<p>Data for "Investigation of slip transfer across HCP grain boundaries with application to cold dwell facet fatigue"<br> http://dx.doi.org/10.1016/j.actamat.2017.01.021</p> <p>This Data folder contains 4 data files:<br> (1) Data_for_Figure_3.xlsx<br> (2) Data_for_Figure_4.xlsx<br> (3) Data_for_Figure_9.xlsx<br> (4) Data_for_Figure_13.xlsx</p> <p>-<br> If readers need further information, please feel free to contact:<br> zebang.zheng12@imperial.ac.u</p>
Human Connectome Project (HCP) Intrahemispheric FC
Open the record for dataset details and reuse information.
HCP-YA 1065 Subjects dMRI Data (2mm FIB Files)
<p>The minimally-preprocessed data (Glasser et al., 2013) from Human Connectome Projects (Q1-Q4 release, 2015) were acquired by Washington University in Saint Louis and University of Minnesota (Van Essen et al., 2012). The diffusion MRI scan was conducted on a Siemens 3T Skyra scanner using a 2D spin-echo single-shot multiband EPI sequence with a multi-band factor of 3 and monopolar gradient pulse. The spatial resolution was 1.25 mm isotropic. TR = 5500 ms, TE = 89.50 ms. The b-values were 1000, 2000, and 3000 s/mm2. The total number of diffusion sampling directions was 90, 90, and 90 for each of the shells in addition to 6 b0 images. The preprocessed data were corrected for eddy current and susceptibility artifact. The accuracy of b-table orientation was examined by comparing fiber orientations with those of a population-averaged template (Yeh et al. Neuroimage, 2018). The restricted diffusion was quantified using restricted diffusion imaging (Yeh et al., MRM, 77:603–612 (2017)). The diffusion data were reconstructed using generalized q-sampling imaging (Yeh et al., IEEE TMI, ;29(9):1626-35, 2010) with a diffusion sampling length ratio of 1.7.</p>
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
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.