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1,152 results for “Magnetic resonance imaging”
MASiVar: Multisite, Multiscanner, and Multisubject Acquisitions for Studying Variability in Diffusion Weighted Magnetic Resonance Imaging
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Data from the behavioural and Magnetic resonance imaging of the Ts66Yah and Ts65Dn male model of Down syndrome
<p>Please find enclosed the behavioural and Magnetic Resonnance Imaging (MRI) variables used for comparing the Ts66Yah DS models with the parental line Ts65Dn. The raw data are found as two CVS files</p> <p>- Behavioural phenoParameters_Ts65Dn_Ts66Yah.csv</p> <p>- MRI phenoParameters_Ts65Dn_Ts66Yah.csv</p> <p>while the processed data used for the GDAPHEN analysis (https://github.com/YaH44/GDAPHEN/releases/tag/Public) are available as Excel docs.</p> <p> </p> <p>The processing has been done with a low level of imputation for missing data detailed in the Formating_decision_phenoParameters_Ts65Dn_Ts66Yah. ...</p>
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>
Dataset In-vivo probabilistic atlas of human thalamic nuclei based on diffusion weighted magnetic resonance imaging
<p>This is the dataset related to the paper "In-vivo probabilistic atlas of human thalamic nuclei based on diffusion weighted magnetic resonance imaging", E. Najdenovska*, Y. Aléman-Gómez*, G. Battistella, M. Descoteaux, P. Hagmann, S. Jacquemont, P. Maeder, J.-P. Thiran, E. Fornari and M. Bach Cuadra, Sci. Data. 5:180270 doi: 10.1038/sdata.2018.270 (2018). *Equally contributed authors.</p> <p>We provide NifTI-1 files representing a digital atlas of seven thalamic subparts per hemisphere. More precisely, the files include the spatial probabilistic atlas maps for each thalamic subpart (Thalamus_Nuclei-HCP-4DSPAMs.nii.gz) and the maximum likelihood atlas (Thalamus_Nuclei-HCP-MaxProb.nii.gz) in MNI space. The region corresponding to each labeled thalamic part respectively is given in the look-up table Thalamic_Nuclei-ColorLUT.txt. The NIFTI files can be visualised with the main available tools such as tkmedit, freeview or 3D-Slicer.</p> <p>We also provide a step by step pseudo code for creating the atlas.</p>
Data for: Physics-based Reconstruction Methods for Magnetic Resonance Imaging
<p>Magnetic Resonance Imaging measurement data used in our paper about 'Physics-based Reconstruction Methods for Magnetic Resonance Imaging' (DOI: 10.1098/rsta.2020.0196). (In version 2 the IR-FLASH data set was replaced with one which is from the same volunteer and slice as the ME-SE data set.) </p> <p>The data is acquired from healthy volunteers and stored in the format of the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>).</p> <p>The acquisition parameters are shown in the following table:</p> <p>flip angle[◦] TR/TE/ Delta TE[ms] bandwidth [Hz/px] matrix spokes TA[s] FOV[mm] slice[mm]</p> <p>IR-FLASH 6 4.10/2.58 630 256 × 256 1020 4 192 5<br> ME-SE 90/180 2500/9.9/9.9 390 256 × 256 25 × 16 80 192 3<br> ME-FLASH 5 10.60/1.37/1.34 960 200× 200 33 × 7 0.35a 320 5<br> PC-FLASH 10 4.46/2.96 1250 210 × 210 2 × 7 15 320 5<br> fmSSFPb 15 4.5/2.25 840 192× 192 4 × 101 × 40 137 192 1</p>
Magnetic Resonance Imaging Copper Sulfate Dataset
<p>The data has been produced by the Institut für Mikrostrukturtechnik (IMT) at Karlsruher Institut für Technologie (KIT). This dataset represents the DICOM (Digital Imaging and Communications in Medicine) files, which belong to one MRI (Magnetic Resonance Imaging) study and contain a series of images that have been measured with different protocols. The samples shown by the images are tubes, which contain different concentrations of CuSO4. The DICOM file headers have metadata tags, which embody additional information about the study and the particular series.</p>
Magnetic Resonance Imaging Glucose Study Dataset
<p>The data has been produced by the Institut für Mikrostrukturtechnik (IMT) at Karlsruher Institut für Technologie (KIT). This dataset represents the DICOM (Digital Imaging and Communications in Medicine) files, which belong to one MRI (Magnetic Resonance Imaging) study and contain a series of images that have been measured with different protocols. The samples shown by the images are tubes, which contain different concentrations of Glucose. The DICOM file headers have metadata tags, which embody additional information about the study and the particular series.</p>
ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset.
<p>This multi-center dataset consists of 250 expert-annotated magnetic resonance imaging stroke cases. It is the training dataset for the Ischemic Stroke Lesion Segmentation Challenge (ISLES'22).</p> <p>For each case, an expert level annotation of the stroke lesions is included along with the following three imaging sequences: Fluid attenuated inversion recovery (FLAIR), diffusion weighted imaging (DWI, b=1000) and its corresponding apparent diffusion coefficient (ADC) map. All imaging data and annotations are released in the Neuroimaging Informatics Technology Initiative (NIfTI) format (https://nifti.nimh.nih.gov/nifti-1), according to the BIDS convention. All imaging data are released in the native space without prior registration. Prior to release, skull-stripping was performed to de-identify patients.</p> <p>Image acquisition was performed on one of the following devices: 3T Philips MRI scanners (Achieva, Ingenia), 3T Siemens MRI scanner (Verio) or 1.5T Siemens MAGNETOM MRI scanners (Avanto, Aera). All images were obtained by healthcare professionals as part of the clinical imaging routine for stroke patients at three different stroke centers and imaging data was collected retrospectively for different clinical studies. Computer-readable scanner metadata from the Digital Imaging and Communications in Medicine (DICOM) header in the JSON file format is provided with the datasets if available.</p> <p>For a full dataset description, see the <a href="https://arxiv.org/abs/2206.06694">ISLES'22 preprint</a>.</p> <p>More information about the ISLES'22 challenge can be found in <a href="https://isles22.grand-challenge.org/">grand challenge</a> and in our official <a href="http://www.isles-challenge.org/">challenge website</a>.</p> <h3>Please cite the following works when using this dataset:</h3> <ul> <li>de la Rosa, Ezequiel, et al. <strong>DeepISLES: a clinically validated ischemic stroke segmentation model from the ISLES'22 challenge.</strong> <em>Nature Communications</em> 16.1 (2025): 7357.</li> <li>Hernandez Petzsche, Moritz R., et al. <strong>ISLES 2022: A multi-center magnetic resonance imaging stroke lesion segmentation dataset.</strong> <em>Scientific data</em> 9.1 (2022): 762.</li> </ul>
Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment
<p>We uploaded the raw data related to extracted features of the manuscript "Granata V, Fusco R, De Muzio F, Brunese MC, Setola SV, Ottaiano A, Cardone C, Avallone A, Patrone R, Pradella S, Miele V, Tatangelo F, Cutolo C, Maggialetti N, Caruso D, Izzo F, Petrillo A. Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment. Radiol Med. 2023 Nov;128(11):1310-1332. doi: 10.1007/s11547-023-01710-w. Epub 2023 Sep 11. PMID: 37697033."</p>
Quantitative magnetic resonance imaging of Scots pine seeds and the assessment of germination potential
<p>This dataset contains all the raw source data and MATLAB analysis functions that comprise the study:</p> <p><strong>Quantitative magnetic resonance imaging of Scots pine seeds and the assessment of germination potential</strong></p> <p>Canadian Journal of Forest Research | DOI: 10.1139/cjfr-2021-0273.</p> <p>Tuomainen, TV (1), Himanen, K (2), Helenius, P (2), Kettunen, MI (3), Nissi, MJ (1,4)*<br> 1. University of Eastern Finland, Department of Applied Physics, Kuopio, Finland<br> 2. Natural Resources Institute Finland, Suonenjoki Unit, Suonenjoki, Finland.<br> 3. University of Eastern Finland, Kuopio Biomedical Imaging Unit, A.I. Virtanen Institute for Molecular Sciences, Kuopio, Finland <br> 4. University of Oulu, Research Unit of Medical Imaging, Physics and Technology, Oulu, Finland</p> <p>*Corresponding author:<br> Mikko J. Nissi<br> Department of Applied Physics,<br> University of Eastern Finland<br> POB 1627<br> FI-70211, Kuopio, Finland<br> mikko.nissi@uef.fi<br> +358-50-5955517</p> <p>Keywords: Pinus sylvestris, seed germination, MRI, radiography, relaxation time mapping</p> <p> </p> <p><strong>Study and data description</strong></p> <p>Altogether 90 Scots pine (Pinus sylvestris L.) seeds were MR imaged using RAREVTR, MSME, MGE and ZTE pulse sequences with reference radiograph from each seed.</p> <p>The data includes MR images and relaxation time data as well as individual X ray radiographs of Scots pine seeds. </p> <p>The data includes all data ('fid' and '2dseq' for MRI, and .jpeg/.png for radiographs), metadata (acquisition and reconstruction MRI parameters), figures of manuscript, and calculated relaxation time maps (in MATLAB MAT-file format).</p> <p> </p> <p>Included folders and files in the zenodo_repo_scotspine_MRI_zip_20012022 are:</p> <ul> <li><strong>additional_info_scotspine</strong>: Information on the seed batches, their germination and structure in .xlsx file format. Translated into English from Finnish on 06.10.2021.</li> <li><strong>manuscript_figures:</strong> Figures in .eps vector file format (fig1.eps-fig7.eps)</li> <li><strong>matlab_scripts:</strong> Contains MATLAB functions and scripts for data analysis of the MRI data, processed together with 'aedes' GUI (aedes.uef.fi/, redirects to github.com).</li> <li><strong>mri_scotspine</strong>: Contains the MRI data using 5 mm and 10 mm RF coils at 11.7 T (Bruker). The folders 'discard_folder/' contain ZTE data that are not processed with carbon_collector.m MATLAB script (i.e. processed separately).</li> <li><strong>radiography_scotspine: </strong>Contains radiographs of invidual seeds in two folders: old (lower resolution, Faxitron MX-20, Faxitron Bioptics LLC, <em>Tucson, Az, USA</em>) and new (higher resolution, Faxitron MultiFocus, Faxitron Bioptics LLC, <em>Tucson, Az, USA</em>).</li> <li><strong>readme.txt: </strong>More information on the file and folder structure and datatypes.</li> </ul> <p> </p> <p>Please see the included readme.txt for further details.</p> <p> </p> <p>(Teemu Tuomainen, Jan 25, 2022)</p>
Data on the detection of clinically significant prostate cancer by magnetic resonance imaging (MRI)-guided targeted and systematic biopsy
<p>This is a dataset from the original publication “Reasons for missing clinically significant prostate cancer by targeted magnetic resonance imaging/ultrasound fusion-guided biopsy”. From 01/2014 to 04/2019 a sample collective of 785 patients with 3T multiparametric magnetic resonance imaging (mp-MRI) of the prostate and subsequent combined systematic biopsy (SB) and magnetic resonance imaging/ultrasound (US) fusion-guided biopsy (TB) was retrospectively analyzed. Prostate carcinoma (PCa) detection by TB and/or additional SB was analyzed.</p>
3D co-registration of ultra-low-field and high-field magnetic resonance images (data)
<p>Dataset used for "3D co-registration of ultra-low-field and high-field magnetic resonance images" submitted to PlosOne.</p>
Data for: Quantitative Magnetic Resonance Imaging by Nonlinear Inversion of the Bloch Equations
<p>Magnetic Resonance Imaging measurement data used in our work about "Quantitative Magnetic Resonance Imaging by Nonlinear Inversion of the Bloch Equations". The data is provided in a file format used by the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>).</p> <p><br> Further information about the individual datasets:</p> <p>data_GSM_t1<br> Type: Gold-Standard T1 measurement<br> Object: T2 sphere of the NIST phantom (Model 130)<br> Sequence: IR Single-Echo Spin-Echo<br> TR|TE [ms]: 8000|15<br> FOV [mm]: 200<br> T_INV [ms]: 30:250:2530</p> <p>data_GSM_t2<br> Type: Gold-Standard T2 measurement<br> Object: T2 sphere of the NIST phantom (Model 130)<br> Sequence: Single-Echo Spin-Echo<br> TR|TE [ms]: 8000|(15:40:455)<br> FOV [mm]: 200</p> <p>data_05b_b1map<br> Type: B1 Map<br> Object: T2 sphere of the NIST phantom (Model 130)<br> Sequence: Preconditioned RF pulse with TurboFLASH Readout<br> TR|TE [ms]: 2000|2.14<br> FA [deg]: 8<br> FOV [mm]: 200</p> <p>data_05b_kspace<br> Type: Radial Single-Shot Dataset<br> Object: T2 sphere of the NIST phantom (Model 130)<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.88|2.44<br> FA [deg]: 45<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 200<br> #Tiny GA: 7</p> <p>data_06_b1map<br> Type: B1 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Preconditioned RF pulse with TurboFLASH Readout<br> TR|TE [ms]: 2000|2.14<br> FA [deg]: 8<br> FOV [mm]: 200</p> <p>data_06_irbssfp_long<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 10.8|5.4<br> FA [deg]: 45<br> T_RF [ms]: 2.5<br> BWTP: 4<br> FOV [mm]: 200<br> #Tiny GA: 7</p> <p>data_06_irbssfp_short<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.88|2.44<br> FA [deg]: 45<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 200<br> #Tiny GA: 7</p> <p>data_06_irflash<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR FLASH<br> TR|TE [ms]: 4.1|2.58<br> FA [deg]: 6<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 200<br> #Tiny GA: 7</p> <p>data_s03_b1map<br> Type: B1 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Preconditioned RF pulse with TurboFLASH Readout<br> TR|TE [ms]: 2000|2.14<br> FA [deg]: 8<br> FOV [mm]: 200</p> <p>data_s03_irflash<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR FLASH<br> TR|TE [ms]: 3.75|2.26<br> FA [deg]: 8<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_2_5ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 6.14|3.07<br> FA [deg]: 35<br> T_RF [ms]: 2.5<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_2_1ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 5.5|2.75<br> FA [deg]: 35<br> T_RF [ms]: 2.1<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_1_6ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 5.0|2.5<br> FA [deg]: 35<br> T_RF [ms]: 1.6<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_1_2ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.6|2.3<br> FA [deg]: 35<br> T_RF [ms]: 1.2<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_0_6ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4|2<br> FA [deg]: 35<br> T_RF [ms]: 0.6<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p>data_s03_irbssfp_0_4ms<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 3.8|1.9<br> FA [deg]: 35<br> T_RF [ms]: 0.4<br> BWTP: 1<br> FOV [mm]: 200<br> #Tiny GA: 13</p> <p> </p>
Data for: Quantitative Multi-Parameter Mapping in Magnetic Resonance Imaging
<p>Magnetic Resonance Imaging measurement data used in the PhD thesis "Quantitative Multi-Parameter Mapping in Magnetic Resonance Imaging". The data is provided in a file format used by the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>).<br> </p> <p>Further information about the individual datasets:</p> <p>data_invivo_b0map<br> Type: B0 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Two GRE Acquisitions with different TE, "gre_field_mapping" Sequence<br> TR|TE1|TE2 [ms]: 400|4.92|7.38<br> FA [deg]: 60<br> FOV [mm]: 240</p> <p>data_invivo_b1map<br> Type: B1 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Preconditioned RF pulse with TurboFLASH Readout<br> TR|TE [ms]: 6830|2.19<br> FA [deg]: 8<br> FOV [mm]: 240</p> <p>data_invivo_irflash<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR FLASH<br> TR|TE [ms]: 3.8|2.26<br> FA [deg]: 8<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 7</p> <p>data_invivo_irbssfp<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 45<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 7</p> <p>data_invivo_irbssfp_shim<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 45<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 7<br> missing shim break</p> <p>data_vfa_b0map<br> Type: B0 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Two GRE Acquisitions with different TE, "gre_field_mapping" Sequence<br> TR|TE1|TE2 [ms]: 400|4.92|7.38<br> FA [deg]: 60<br> FOV [mm]: 240</p> <p>data_vfa_b1map<br> Type: B1 Map<br> Object: Single-slice of volunteers brain<br> Sequence: Preconditioned RF pulse with TurboFLASH Readout<br> TR|TE [ms]: 6830|2.19<br> FA [deg]: 8<br> FOV [mm]: 240</p> <p>data_vfa_irflash<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR FLASH<br> TR|TE [ms]: 3.8|2.26<br> FA [deg]: 8<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_20<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 20<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_40<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 40<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_45<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 45<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_50<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 50<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_60<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 60<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_70<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 70<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p> <p>data_vfa_irbssfp_77<br> Type: Radial Single-Shot Dataset<br> Object: Single-slice of volunteers brain<br> Sequence: IR bSSFP<br> TR|TE [ms]: 4.5|2.25<br> FA [deg]: 77<br> T_RF [ms]: 1<br> BWTP: 4<br> FOV [mm]: 240<br> #Tiny GA: 13</p>
Data Supplement: GIRFReco.jl: An Open-Source Pipeline for Spiral Magnetic Resonance Image (MRI) Reconstruction in Julia
<p><strong>Dataset for GIRFReco.jl Paper</strong><br> <br> Please download this and extract to an appropriate location prior to running the demonstration code in GIRFReco.jl. The extracted folder will serve as the root directory in the demo code.</p>
Manganese Enhanced Magnetic Resonance Imaging reveals light-induced brain asymmetry in embryo
<p>The idea that sensory stimulation to the embryo (in utero or in ovo) may be crucial for brain development is widespread. Unfortunately, up to now evidence was only indirect because mapping of embryonic brain activity in vivo is challenging. Here we applied for the first time Manganese Enhanced Magnetic Resonance Imaging (MEMRI), a functional imaging method, to the eggs of domestic chicks. We revealed both spontaneous and light-induced brain asymmetry by comparing embryonic brain activity in vivo of eggs that were stimulated by light or maintained in the darkness. Our protocol paves the way to investigation of the effects of a variety of sensory stimulations on brain activity in embryo.</p>
Manganese Enhanced Magnetic Resonance Imaging reveals light-induced brain asymmetry in embryo
Open the record for dataset details and reuse information.
Magnetic Resonance Imaging Scan of the Brain of a Slow Loris (Nycticebus)
<p>Magnetic Resonance Imaging Scan of the Brain of a Slow Loris (<i>Nycticebus</i>) from http://braincatalogue.org/Slow_loris</p>
Magnetic Resonance Imaging Scan the Brain of a Chapman's Zebra (Equus quagga chapmani)
<p>Magnetic Resonance Imaging Scan of the Brain of a Chapman's Zebra (Equus quagga chapmani) from http://braincatalogue.org/Chapman's_zebra</p>
Magnetic Resonance Imaging Scan of the Brain of a Sloth Bear (Melursus ursinus)
<p>Magnetic Resonance Imaging Scan of the Brain of a Sloth Bear (<i>Melursus ursinus</i>) from http://braincatalogue.org/Sloth_bear</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.