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.
18
datasets available to search
ShareScore release 0.9.0
Dataset results
18 results for “MRI Raw data”
Raw Data for "RASER MRI: Magnetic Resonance Images formed Spontaneously exploiting Cooperative Nonlinear Interaction"
<p>This upload contains the raw data used for Fig. 3-5 in "RASER MRI: Magnetic Resonance Images formed Spontaneously exploiting Cooperative Nonlinear Interaction". Experimental conditions and details about the datasets are given in a "ReadMe.txt" file.</p>
MRI raw data for: A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry
<p>MRI raw data from three different magnetic field strength (1.5 T, 3 T, 7T; 7T data are in separate datasets) for the publication 'A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry', in which a phantom was presented that allows for an experimental evaluation of QSM reconstruction algorithms. The phantom contains susceptibility producing particles with dia- and paramagnetic properties embedded in an MRI visible medium (gelatin and agarose gel) and is suitable to assess the performance of algorithms that attempt to separate isotropic dia- and paramagnetic susceptibility at the sub-voxel level. The dataset additionally contains raw data for a phantom that only contains diamagnetic and paramagnetic particles, respectively, for magnetic field strengths of 1.5 T and 3 T (additional 7 T data are provided in separate datasets).</p>
Dynamic Contrast Enhanced MRI Raw Data Acquired with 3D Cones Trajectory
<p>This repository contains the raw data for the second dynamic contrast enhanced (DCE) MRI in <a href="https://arxiv.org/abs/1909.13482">Extreme MRI: Large-Scale Volumetric Dynamic Imaging from Continuous Non-Gated Acquisitions</a>. The data is stored as numpy arrays, containing k-space data (ksp.npy), coordinates (coord.npy), and density compensation factors (dcf.npy). Code to process and reconstruct the data is available here: <a href="https://github.com/mikgroup/extreme_mri">https://github.com/mikgroup/extreme_mri</a></p> <p>For more information about how the data is acquired, please see the linked paper.</p>
7T MRI raw data for: A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry
<p>MRI 7 T raw data for the publication 'A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry', in which a phantom was presented that allows for an experimental evaluation of QSM reconstruction algorithms. The phantom contains susceptibility producing particles with dia- and paramagnetic properties embedded in an MRI visible medium (gelatin and agarose gel) and is suitable to assess the performance of algorithms that attempt to separate isotropic dia- and paramagnetic susceptibility at the sub-voxel level. This dataset only contains additional raw data for a phantom that only contains diamagnetic and paramagnetic particles, respectively.</p>
Lung MRI Raw Data Acquired with 3D UTE Radial Trajectory
<p>This repository contains the raw data for the second lung MRI result in <a href="https://arxiv.org/abs/1909.13482">Extreme MRI: Large-Scale Volumetric Dynamic Imaging from Continuous Non-Gated Acquisitions</a>. The data is stored as numpy arrays, containing k-space data (ksp.npy), coordinates (coord.npy), and density compensation factors (dcf.npy). Code to process and reconstruct the data is available here: <a href="https://github.com/mikgroup/extreme_mri">https://github.com/mikgroup/extreme_mri</a></p> <p>For more information about how the data is acquired, please see the linked paper.</p>
MRI raw data: RF-spoiled radial FLASH sequence without cardiac gating and during free breathing
<p>RF-spoiled radial FLASH sequence without cardiac gating and during free breathing<br> Sequence parameters:<br> TR 2.0ms<br> TE 1.3 ms<br> Flip angle 8°<br> Matrix Size 128x128 (two-fold oversampling, resulting in 256 sample points for each radial spoke)<br> Radial Spokes: 125<br> In Plane Resolution 2mmx2mm<br> Slice Thickness 8mm<br> 3T System<br> 32 channel body array coil, compressed to 12 virtual channels using svd based coil compression<br> ote: This data set was acquired with a radial acquisition. The corresponding k-Space trajectory is also included (256x125 matrix k). The Non-Uniform-FFT Toolbox is needed for reconstruction of this data set.<br> Florian Knoll (florian.knoll@tugraz.at) Date: 2.2.2011</p>
MRI raw data: SE measurement of the knee of a healthy volunteer (8 channel coil)
<p>SE measurement of the knee of a healthy volunteer Sequence parameters:</p> <p>TR 1800ms<br> TE 18ms<br> Matrix Size 192x192<br> In Plane Resolution 0.78mmx0.78mm<br> Slice Thickness 2mm<br> 3T System<br> 8 channel knee coil<br> Florian Knoll (florian.knoll@tugraz.at) Date: 2.2.2011</p> <p>Acknowledgements to Tobias Block and Martin Uecker for their support with the in vivo radial spin echo data.</p>
MRI raw data: T2 weighted TSE scan of a healthy volunteer (32 channel head coil)
<p>T2 weighted TSE Scan of the brain of a healthy volunteer, 32 channel coil</p> <p>Sequence parameters:<br> TR 5000ms<br> TE 99ms<br> Turbo Factor 10<br> Matrix Size 256x256<br> In Plane Resolution 0.9mmx0.9mm<br> Slice Thickness 4mm<br> 3T System<br> 32 channel head coil</p> <p>Florian Knoll (florian.knoll@tugraz.at)<br> Date: 2.2.2011<br> </p>
MRI raw data: T2 weighted TSE scan of a healthy volunteer (4 channel head coil)
<p>T2 weighted TSE Scan of the brain of a healthy volunteer, 4 channel coil</p> <p>Sequence parameters:<br> TR 5000ms<br> TE 99ms<br> Turbo Factor 10<br> Matrix Size 256x256<br> In Plane Resolution 0.9mmx0.9mm<br> Slice Thickness 4mm<br> 3T System<br> 4 channel head coil</p> <p>Florian Knoll (florian.knoll@tugraz.at)<br> Date: 2.2.2011<br> </p>
MRI Raw Data of Cardiac Radial Real-Time BSSFP Sequence (Part 1/5)
<p>Other parts of this dataset are available at:</p> <div> <div>* Part 1: 10.5281/zenodo.10492333</div> <div>* Part 2: 10.5281/zenodo.10912299</div> <div>* Part 3: 10.5281/zenodo.10492343</div> <div>* Part 4: 10.5281/zenodo.10492455</div> <div>* Part 5: 10.5281/zenodo.10493095</div> </div>
MRI Raw Data of Cardiac Radial Real-Time BSSFP Sequence (Part 3/5)
<p>Other parts of this dataset are available at:</p> <div> <div>* Part 1: 10.5281/zenodo.10492333</div> <div>* Part 2: 10.5281/zenodo.10912299</div> <div>* Part 3: 10.5281/zenodo.10492343</div> <div>* Part 4: 10.5281/zenodo.10492455</div> <div>* Part 5: 10.5281/zenodo.10493095</div> </div>
MRI Raw Data of Cardiac Radial Real-Time BSSFP Sequence (Part 4/5)
<p>Other parts of this dataset are available at:</p> <div> <div>* Part 1: 10.5281/zenodo.10492333</div> <div>* Part 2: 10.5281/zenodo.10912299</div> <div>* Part 3: 10.5281/zenodo.10492343</div> <div>* Part 4: 10.5281/zenodo.10492455</div> <div>* Part 5: 10.5281/zenodo.10493095</div> </div>
MRI Raw Data for 2024 ISMRM Workshop on Motion Correction
Open the record for dataset details and reuse information.
MRI raw data to publication: Fuzzy ripple artifact in high resolution fMRI: identification, cause, and mitigation.
<p>These Data are refering to the maunscript "Fuzzy ripple artifact in high resolution fMRI: identification, cause, and mitigation" authored by </p> <p>Renzo Huber1, Rüdiger Stirnberg2, A Tyler Morgan1, David A Feinberg3,4-5, Samantha J Ma6, Philipp Ehses3, Omer Faruk Gulban2,7, Kenshu Koiso2, Isabel Gephart1, Stephanie Swegle1, Susan Wardle1, Emily Ma2, Andrew Persichetti1, Alexander JS Beckett4-5, Tony Stöcker3, Nicolas Boulant8, Benedikt A Poser2, Peter Bandettini1</p> <p><strong> </strong></p> <p>1 NIMH, NIH, Bethesda, United States,</p> <p>2 German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany,</p> <p>3 Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States, </p> <p>4 Advanced MRI Technologies, Sebastopol, CA, United States,</p> <p>5 CN, FPN, University of Maastricht, The Netherlands,</p> <p>6 Siemens Medical Solutions USA, Inc., Berkeley, CA, USA,</p> <p>7 Brain Innovation, Maastricht, The Netherlands,</p> <p>8 CEA, NeuroSpin, University Paris Saclay, France.</p> <p> </p> <h1>Abstract</h1> <p><strong>Purpose:</strong> High resolution fMRI is an emerging research field focused on capturing functional signal changes across cortical layers. However, the data acquisition is limited by low spatial frequency EPI artifacts; termed as Fuzzy Ripples. These artifacts limit the practical applicability of acquisition protocols with higher spatial resolution, faster acquisition speed, and they challenge imaging in lower brain areas. </p> <p><strong>Methods: </strong>We characterize Fuzzy Ripple artifacts across commonly used sequences and distinguish them from conventional EPI Nyquist ghosts, off-resonance effects, and GRAPPA artifacts. To investigate their origin, we employ dual polarity readouts.</p> <p><strong>Results: </strong>Our findings indicate that Fuzzy Ripples are primarily caused by kx-specific imperfections in gradient trajectories, which can be exacerbated by inductive coupling between third-order shims and readout gradients. We also find that these artifacts can be mitigated through complex-valued averaging of dual polarity EPI or by disconnecting the third-order shim.</p> <p><strong>Conclusion:</strong> The proposed mitigation strategies allow for overcoming current limitations in layer-fMRI protocols: </p> <p>(1) Achieving resolutions beyond 0.8mm is feasible, and even at 3T, we achieved 0.53mm voxel functional connectivity mapping. </p> <p>(2) Temporal acquisition speed can be increased to GRAPPA 8. </p> <p>(3) Sub-millimeter fMRI is achievable in lower brain areas, including the cerebellum.</p> <p> </p>
DEEP IMPACT 9P/TEMPEL CRUISE - RAW MRI CALIB DATA V1.0
This data set contains raw science calibration images acquired by the Deep Impact Medium Resolution Instrument Visible CCD during the cruise phase of the mission.
DEEP IMPACT 9P/TEMPEL ENCOUNTER - RAW MRI DATA V1.0
This data set contains raw 9P/Tempel 1 and science calibration images acquired by the Deep Impact Deep Impact Medium Resolution Instrument Visible CCD during the 9P encounter phase of the mission.
DEEP IMPACT 9P/TEMPEL ENCOUNTER - RAW MRI DATA V1.0
This data set contains raw 9P/Tempel 1 and science calibration images acquired by the Deep Impact Deep Impact Medium Resolution Instrument Visible CCD during the 9P encounter phase of the mission.
DEEP IMPACT 9P/TEMPEL CRUISE - RAW MRI CALIB DATA V1.0
This data set contains raw science calibration images acquired by the Deep Impact Medium Resolution Instrument Visible CCD during the cruise phase of the mission.
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.