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7,351 results for “stroke”
[Dataset] Stroke Caregiver Burden in East Coast Peninsular Malaysia, A Short-term Longitudinal Study
<p>Raw dataset for study entitled "INFORMAL CAREGIVERS BURDEN AMONG STROKE PATIENTS IN EAST-COAST MALAYSIA: A SHORT-TERM LONGITUDINAL STUDY"</p> <p>This study is part of study funded by Newton Ungku Omar Fund (2020-2021) under grant for “A Scalable Solution for Supporting Informal Stroke Caregivers in Malaysia: Systematic Development and Feasibility Study” Malaysian Ministry of Education (203.PPSP.678003) and Medical Research Council, United Kingdom (MR/T018968/1).</p> <p>Please note that this data is in raw csv form, imported from REDCap. due to REDCap system, the raw file need to be relabel and relevel to reflect the original score or response.</p> <p>Data dictionary provided for data relabel and relevel purpose.</p> <p>R script also available to convert the raw csv into dataset with appropriate label and level</p> <p> </p> <p> </p>
Dataset corresponding to scientific paper "Improved reperfusion following alternative surgical approach for experimental stroke in mice"
<p>Acquired raw experimental data using laser speckle contrast imaging (LSCI) following middle cerebral artery occlusion (MCAO) in mice. Data obtained from mice undergoing standard CCA ligation technique and mice undergoing CCA vessel repair technique<sup>1</sup>.</p> <p>The dataset is linked to paper "Improved reperfusion following alternative surgical approach for experimental stroke in mice". </p> <p>The dataset consists of the following:</p> <ul> <li>Raw LSCI flux values, from ipsilateral and contralateral hemispheres, measured at baseline, 24hours post-MCAO and 48hours post-MCAO. </li> <li>Normalised data expressing ispilateral hemisphere as a % of the control contralateral hemisphere.</li> <li>Mean normalised values for each subject. </li> </ul> <p> </p> <p><strong>References</strong></p> <ol> <li>Trotman-Lucas,M., Kelly, M.E., Janus, J., Fern, R., Gibson, C.L. (2017) 'An alternative surgical approach reduces variability following filament induction of experimental stroke in mice'. <em>Disease Models & Mechanisms,</em> 10, 931-938.</li> </ol> <p> </p>
Raw data acquired necessary to produce the plots introduced in the scientific paper: "Upper-limb kinematic reconstruction during stroke robot-aided therapy" (Medical & Biological Engineering & Computing)
<p>These files contain the raw data acquired necessary to produce the plots introduced the Figure 6 of the scientific paper: “Upper-limb kinematic reconstruction during stroke robot-aided therapy” (Medical & Biological Engineering & Computing).</p> <p>Fig. 6 shows the data recorded from two patients performing five forward/backward movements at InMotion2 robot before and after rehabilitation treatment. Mean values of the five execution have been reported in Fig. 6.</p>
A Systematic Review and Meta-Analysis of Mindfulness-Based (Baduanjin) Exercise for the Rehabilitation of Stroke Patients
<p><span>A Systematic Review and Meta-Analysis of Mindfulness-Based (Baduanjin) Exercise for the Rehabilitation of Stroke Patients</span></p>
Open data repository, Knab et al., Prediction of stroke outcome in mice based on non-invasive MRI and behavioral testing
<p><strong>Open data repository, Knab et al., Prediction of stroke outcome in mice based on non-invasive MRI and behavioral testing</strong></p> <p><strong>Latest version of files: repository_v2.0.zip, Behavior Data_v2.0.xlsx and MRI IDs Testing&Replication Cohort.xlsx (please ignore repository.zip)</strong></p> <p>Open data repository Knab et al. Prediction of stroke outcome in mice based on non-invasvive MRI and behavioral testing</p> <p>Open code and documentation of prediction models available via <a href="https://github.com/major-s/mouse-mcao-outcome-predictor">https://github.com/major-s/mouse-mcao-outcome-predictor</a></p> <p><strong>Content:</strong></p> <p>README.txt</p> <p>This information</p> <p><strong>dat</strong></p> <p>Contains MRI data in NIFTI format and secondary data from atlas registration. For documentation of atlas registration files see https://pubmed.ncbi.nlm.nih.gov/28829217/<br>Files used for the manuscript:<br>t2.nii: t2 weighted image acquired 24 h post stroke<br>masklesion.nii: manually delineated lesion<br>x_masklesion.nii: lesion in atlas space<br>ix_ANO.nii: Allen brain atlas in native space (i.e. matching t2.nii)<br>Lesion volume was calculated by volume of voxels unequal 0 in x_masklesion.nii<br>Overlap of regions defined by ix_ANO.nii with masklesion.nii were used for calculating percent damage in each atlas region</p> <p><strong>prediction_models</strong></p> <p>Contains separated training and test data as xlsx and csv files with lesion volumes in cubic mm of the Allen brain atlas space, percent damage per atlas region and behavioral data. The training data was used as input for training prediction models in MATLAB, the results were created using the test data.<br>The files have following sturcture:<br>Column 1: animal ID<br>Columns 2-537: MRI regions (column title corresponds to the region number as used in the Allen common coordinate framework)<br>Column 538: lesion volume<br>Column 539: initial performance (subacute deficit) = mean performance/deficit on days 2-6<br>Column 540: mean performance/deficit on days 2-6 = initial performance (subacute deficit) - this column equals column 539 but has different header which was used to train the residual from initial deficit<br>Column 541: residual performance/deficit<br>Column 542: test or training group<br>Consecutive rows contain data for each animal specified by the animal id</p> <p>The repository also contains all trained models, prediction results for the test data and tables with resulting median absolute error (MedAE) and 5th, 25th, 75th and 95 absolute error quantiles for each model.<br>The model files end with '_models.mat' and contain 50 independently trained models each. Each model version is specified by number 1-50.<br>The result files end with '_test_results.mat' or '_test_results.xlsx', files with MedAE and quantiles end with '_test_errors.xlsx' or '_test_errors.csv. The common part of filenames specifies the used paradigm<br>Folder 'subacute deficit prediction' contains:<br> - initial_performance_from_lesion_volume: prediction of subacute deficit using lesion volume<br> - initial_performance_from_segmented_mri: prediction of subacute deficit using segmented mri<br>Folder 'long-term outcome prediction' contains:<br> - lesion_volume: prediction of residual deficit using lesion volume<br> - segmented_mri: prediction of residual deficit using segmented_mri<br> - initial_performance: prediction of residual deficit using subacute deficit<br>Folder 'mri_inc_oob_imp' contains models trained using increasing number of mri segments sorted according to the out-of-bag importance. The number of used segments is given in the file name. The models, results and errors are separated in subfolders.</p> <p>Files with equal file name and different extension always contain the same data</p> <p><strong>templates</strong><br>Allen atlas, template, brain mask, hemisphere masks, tissue probability masks in NIFTI format including annotations of region IDs and parameter.m file for use in MATLAB toolbox ANTx2<br> </p>
Bibliography on COVID-19 and ischemic stroke
<p>Search on PubMed literature on COVID-19 and ischemic stroke.</p> <p>The uploaded database was generated on November 2, 2021. </p> <p>The database contains the following attributes:</p> <p>- PMID: PubMed identifier of the article. <br> - Autors: list of authors. <br> - Referència: bibliographic reference of the article. </p>
Data and Code for "Early complications after mild to moderate ischemic stroke and their impact on 3-months outcome: The prospective Stroke Unit Plus Cohort Study"
<p>This repository consists of the data and code for the publication "Early complications after mild to moderate ischemic stroke and their impact on 3-months outcome: The prospective Stroke Unit Plus Cohort Study"<br> <br> - Analysis code:<br> - Analysis.R<br> - Functions.R</p> <p>- Data:<br> - AnalysisSet in .Rdata, .csv, and .xlsx formats<br> <br> - Variable codebook in .xlsx format</p> <p>Responsibility for the upload lies with Prof. Jan Sobesky, e-mail: j.sobesky@ak-neuss.de<br> For inquiries regarding the data please contact Dr. Vince Madai, e-mail: vince_istvan.madai@bih-charite.de</p>
Accuracy and Reliability of Noninvasive Stroke Volume Monitoring via ECG-Gated 3D Electrical Impedance Tomography in Healthy Volunteers
<p>3D EIT dataset of ten healthy human volunteers, as described in the corresponding <a href="http://dx.doi.org/10.1371/journal.pone.0191870">journal publication at PLOS ONE</a> or the first author's <a href="http://dx.doi.org/10.5075/epfl-thesis-8343">PhD thesis at EPFL</a>. Please also read the attached ReadMe file.</p> <p>When using this data please cite the corresponding journal publication:</p> <blockquote> <p>Accuracy and Reliability of Noninvasive Stroke Volume Monitoring via ECG-Gated 3D Electrical Impedance Tomography in Healthy Volunteers, PLOS ONE, 2018, <a href="http://dx.doi.org/10.1371/journal.pone.0191870">https://dx.doi.org/10.1371/journal.pone.0191870</a></p> </blockquote>
Neural Protein Associations with Parkinson's, Stroke, and Alzheimer's: Insights from UK Biobank Data
<p>该数据集来自英国生物样本库 (UKB) 和英国生物样本库制药蛋白质组学项目 (UKB-PPP),包含来自 54,219 名参与者的全面蛋白质组学和人口统计信息。该数据集包括 2,941 种蛋白质分析物的测量值,代表 2,923 种独特蛋白质。选择了一组 217 种神经学相关蛋白和 10 种人口统计学和生活方式协变量进行分析。该研究侧重于帕金森病、中风和阿尔茨海默病,使用 ICD-10 代码确定病例。该数据集是研究蛋白质组学生物标志物与神经系统疾病之间关系的宝贵资源,可用于复制和进一步研究。</p>
[Dataset] Bibliometric Analysis of Stroke Caregivers
<p>Raw dataset for manuscript entitled "Research on Stroke Caregiver, A bibliometric Analysis". This manuscript is part of study entitled "INFORMAL CAREGIVERS BURDEN AMONG STROKE PATIENTS IN EAST-COAST MALAYSIA: A SHORT-TERM LONGITUDINAL STUDY"</p> <p>This study is part of study funded by Newton Ungku Omar Fund (2020-2021) under grant for “A Scalable Solution for Supporting Informal Stroke Caregivers in Malaysia: Systematic Development and Feasibility Study” Malaysian Ministry of Education (203.PPSP.678003) and Medical Research Council, United Kingdom (MR/T018968/1).</p> <p>The dataset is in BibTeX file format, which can be opened by most references management software (e.g., Zotero, Mendeley and EndNote) and also any text editor software.</p> <p> </p> <p>Detail of search term used to generate this file:</p> <p>Database: Clarivate's Web of Science<br> Term: (TI=(stroke) AND TI=(caregiver))<br> Date & Time: 07 December 2022, 09.00pm (GMT +8)<br> Filter: Article, Review, Proceeding<br> Result: 678<br> </p>
Repository of Raw Datasets for the Study of Anticoagulation and the Incidence of Stroke and Other Outcomes in Patients with Left Ventricular Thrombus
<p>The optimal duration of anticoagulation in patients with left ventricular thrombus (LVT) is unknown. The data package herein presented contains the data used to assess the effect of duration of anticoagulation in the incidence of stroke in patients with left ventricular thrombus (LVT) in a tertiary hospital. These data includes clinical and demographic information, treatment choices (vitamin K antagonists [VKA] versus direct oral anticoagulants [DOAC]), duration of treatment, reason for interruption of treatment, occurrence of stroke, acute myocardial infarction, bleeding events, thrombus resolution and recurrence, and death.<br> The raw dataset is available upon request to the corresponding author.</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>
Physical Fitness in Subacute Stroke (Phys-Stroke)
<p>Raw data of efficacy endpoint data and analyses scripts</p> <p> </p> <p>In this version a '.csv' File with the baseline data is added to enable more open access to the provided data.</p>
Tabla strokes dataset
<p>Tabla strokes which are recorded from the professional tabla players. Tabla set is tuned to C# scale. </p>
Publication rate and consistency of registered trials of motor-based stroke rehabilitation
<p><strong>Table e1 - Eligible records</strong></p> <p>A list of registered randomized controlled trials (RCTs) meeting the following criteria:</p> <ol> <li>RCTs of motor-based interventions in individuals with stroke (including transient ischemic attack);</li> <li>Started on or after 1 July 2005;</li> <li>Completed before 1 April 2017 (actual or expected end date);</li> <li>Included human adult participants (≥18 years old);</li> <li>Included at least one outcome (primary or secondary) related to motor control, mobility, or physical functioning and performance of upper and/or lower extremities or the body as a whole; and</li> <li>Registered in English.</li> </ol> <p>This list was obtained by searching the following registries between 23 November 2017 and 22 February 2018: the International Clinical Trials Registry Platform, Clinicaltrials.gov (USA), Australian New Zealand Clinical Trial Registry, Chinese Clinical Trial Registry, Clinical Research Information Service (Republic of Korea), Clinical Trial Registry of India, Cuban Public Registry of Clinical Trials, European Union Clinical Trials Register, German Clinical Trials Register, Iranian Registry of Clinical Trials, International Standard Randomised Controlled Trials Number registry (UK), Center for Clinical Trials-Japan Medical Association, University Hospital Medical Information Network-Clinical Trial Registry (Japan), Thai Clinical Trials Registry, Netherlands Trials Registry, Pan African Clinical Trials Registry, Peruvian Clinical Trials Registry, and Sri Lanka Clinical Trials Registry. </p> <p><em>Variable definitions</em></p> <p>UIN: Unified identification number (obtained from the trial registry)</p> <p>Status: whether or not a peer-reviewed publication reporting the trial findings for the primary outcome/outcome was found</p> <ul> <li>Paper available: a publication reporting the trial findings for the primary outcome/objective was found</li> <li>Paper available (not English): a publication, published in a language other than English, reporting the trial findings for the primary outcome/objective was found</li> <li>Secondary paper available: a publication reporting study findings is available, but not for the primary objectives/outcome</li> <li>Results available: trial findings for the primary objective/outcome are available in a non-peer reviewed format (e.g., conference publication, non-peer reviewed journal, or uploaded to the trial registry)</li> <li>Discontinued: the trial registry record indicates that the trial was discontinued, so no publication is expected</li> <li>No paper/results: none of the above apply</li> </ul> <p>Source: database or method we used to find the publication reporting the trial findings for the primary objective/outcome</p> <ul> <li>Pubmed: the publication was found by searching for the UIN in Pubmed</li> <li>EMBASE/OVID: the publication was found by searching for the UIN in Embase or OVID Medline</li> <li>Google Scholar: the publication was found by searching for the UIN in Google Scholar</li> <li>Registry: the publication was listed in the trial registry</li> <li>Internet: the publication was found by a superficial internet search for the UIN</li> <li>Protocol: the publication was found through a cited reference search for the published protocol</li> <li>Author: the publication was found by contacting the trial investigators</li> <li>Other: the publication was found by some other means</li> <li>None: no publication reporting the trial findings for the primary objective/outcome was found</li> </ul> <p><strong>Table e2 - Published papers consistency</strong></p> <p>The subset of trials from Table e1 where an English-language publication reporting the trial findings for the primary objective/outcome was found.</p> <p><em>Variable definitions</em></p> <p>UIN: Unified identification number (obtained from the trial registry)</p> <p>DOI: digital objective identifier of the publication</p> <p>First_author: last name of the first author of the publication</p> <p>Year: year of the publication</p> <p>Journal: journal of the publication (abbreviated journal names are used, where available)</p> <p>Reg_Trial_End_Date: end date of the trial, as stated in the trial registry (format DD-MMM-YY)</p> <p>Date_Submitted: date when the paper was submitted to the journal for publication (where available; format: DD-MMM-YY)</p> <p>Time_To_Submit: difference, in days, between Reg_Trial_End_Date and Date_Submitted</p> <p>Date_Published: date when the paper was published, either online or in print, whichever is earlier (format: DD-MMM-YY)</p> <p>Time_To_Publish: difference, in days, between Reg_Trial_End_Date and Time_To_Publish</p> <p>UIN_Paper_Location: location of the UIN in the publication</p> <p>Reg_Pilot: whether the trial was defined as a pilot or feasibility study in the registry record (0=no, 1=yes)</p> <p>Paper_Pilot: whether the trial was defined as a pilot or feasibility study in the publication (0=no, 1=yes)</p> <p>Consistency_pilot: whether Reg_Pilot = Paper_Pilot (0=no, 1=yes)</p> <p>Consistency_Primary_Objective: whether the trial registry record and publication were consistent in terms of the primary objective (0=no, 1=yes)</p> <p>Consistency_Primary_Outcome: whether the trial registry record and publication were consistent in terms of the primary outcome (0=no, 1=yes)</p> <p>Target_N: target sample size, as indicated in the trial registry record</p> <p>Paper_N: number of participants recruited to the study, as indicated in the publication</p> <p>Consistency_N: whether the trial registry record and publication were consistent in terms of the sample size (i.e., Paper_N is within +/-10% of Target_N; 0=no, 1=yes)</p> <p>N_direction: for those trials that were inconsistent in terms of the sample size, whether Paper_N was less than (Under) or more then (Over) Target_N</p> <p>Eligibility_Consistency: whether the trial registry record and publication were consistent in terms of the eligibility criteria</p> <p>UIN_in_paper: the UIN that was included in the paper, exactly as it was published</p> <p>UIN_consistency: whether UIN = UIN_in_paper (0=no, 1=yes)</p> <p>Reg_Type: whether the trial was registered before recruiting the first participant (Prospective), after recruiting the first participant but before the trial was completed (Retro - pre-completion), or after the trial was completed (Retro - post-completion)</p> <p><strong>Table e3 - Inconsistency details</strong></p> <p>The subset of trials from Table e2 where the trial registry record and publication were inconsistent on only one of the criteria examined. This table provides further details of these inconsistencies, and details of any explanations for changes to the protocol since registration, if available.</p>
Dataset of the scientific paper "A Comparative Analysis of 2D and 3D Tasks for Virtual Reality Therapies Based on Robotic-Assisted Neurorehabilitation for Post-stroke Patients" (Front. Aging Neurosci.)
<p> There are three files with the following information:<br> - data_2d.bin, binary file with information of the different parameters of the nine subjects during 2d tasks<br> - data_3d.bin, binary file with information of the different parameters of the nine subjects during 3d tasks<br> - survey.bin, binary file with the score of the System Usability Scale (SUS) survey of each subject</p>
Dataset for Monitoring and Visualizing Stroke Rehabilitation Progress using Wearable Sensors (IMU)
<div> <p>This dataset is associated with a manuscript that is currently under peer review.</p> <p> </p> <p>Article Abstract:</p> <p>Stroke is one of the leading causes of death and disability worldwide, and recovering mobility is an important goal during post-stroke rehabilitation. In this work, we present a study to verify the feasibility of monitoring and visualizing longitudinal stroke gait rehabilitation progress using wearable sensors. Wearable devices such as inertial measurement units (IMUs) are easy-to-use and cost-effective tools for quantifying mobility. However, there is a need for research on longitudinal monitoring of stroke rehabilitation progress with wearables, as well as generating clinically relevant insights using appropriate visualizations. To this aim, we recruited ten stroke patients in their early rehabilitation stage. We collected and analyzed the IMU-derived gait features across two visits, and presented visualizations of the foot movement trajectories as well as the spatio-temporal gait parameters in the average, symmetry, and variation domains to quantify changes in gait. Our visualization and quantification methods are evaluated and validated by clinical experts, and prove to be promising in aiding clinicians to monitor rehabilitation progression.</p> <p> </p> <p>Data description:</p> <p>The dataset consists data from ten stroke patients who completed both visits. The "raw" data folder contains tri-axial acceleration and angular velocity data from the IMUs. In addition, information about the participants such as demographics (e.g., body height and body weight), FAC scores at both visits, and evaluations of gait improvement are documented in the file "participant_info.csv".</p> <p>The “interim” folder contains IMU data that has been manually segmented to remove irrelevant movements before and after each walking session during a visit, based on visual inspection of raw IMU signals. For quality control, the segmented accelerometer and gyroscope data of each sensor were plotted, and the plots were saved in the same folder as the IMU signals. In addition, during the first execution of gait parameter extraction, calculated 3D feet trajectories were cached in the "interim" folder, so that for future executions, the cached trajectories can be loaded directly, reducing the computational efforts for re-calculation. The file "stance_magnitude_thresholds_manual.csv" documents the angular velocity thresholds used to identify stance phases for the gait analysis algorithm for each participant. The threshold values were determined manually by observing the angular velocity signals. </p> <p>The “processed” folder contains stride-by-stride spatio-temporal gait parameters extracted for each of the four walking conditions, and aggregated gait parameters in terms of coefficients of variation and symmetry for all walking conditions for each participant. </p> <p> </p> </div>
NIHSS_802_stroke
<p>This dataset contains information of 802 people with acute ischemic stroke. This information consists of age, sex, race, NIHSS, and infarct characteristics, acessed by DWI-MRI (hemisphere and arterial territory affected, lesion volume in cc and as in percentage of the brain affected). </p>
The HistLight global lightning stroke density reconstruction (1836–2015)
<p><strong>The HistLight global lightning stroke density reconstruction (1836–2015)</strong></p> <p>This repository contains a global estimate of past lightning stroke density based on the <a href="https://doi.org/10.5281/zenodo.4774528">WGLC</a> and the <a href="https://www.psl.noaa.gov/data/gridded/data.20thC_ReanV3.html">20th Century Reanalysis</a>. The reconstruction was made by regressing monthly mean observed lightning density from the WGLC against monthly mean Convective Available Potential Energy (CAPE) from the 20th Century Reanalysis during the six-year period of overlap (2010-2015). The linear regression was made on a log(WGLC) : log(CAPE) basis. In areas where the explanatory power of the regression model was not statistically significant (P>0.05), no CAPE-based reconstruction was made, and the missing values were filled with the 2010-2015 climatological monthly mean lightning from the WGLC.</p> <p>Further details of the preparation of this dataset will be described in a forthcoming manuscript.</p> <p><strong>Technical features</strong></p> <p>The data are stored in a <a href="https://www.unidata.ucar.edu/software/netcdf/">NetCDF</a> (version 4) file and have the following attributes:</p> <ul> <li>Spatial extent: Entire Earth</li> <li>Spatial reference system (SRS): Unprojected (geographic, WGS84)</li> <li>Spatial resolution: half-degree</li> <li>Temporal extent: 1836-2015 (time coordinate refers to the first day of the month)</li> <li>Temporal resolution: monthly</li> </ul> <p><strong>Variables included in this release</strong></p> <ul> <li>Lightning density (lght) (strokes km<sup>-2</sup> day<sup>-1</sup>)</li> </ul>
Fibrinogen depletion coagulopathy predicts major bleeding after thrombolysis for ischemic stroke: a multicentre study
<p>Dataset from "Fibrinogen depletion coagulopathy predicts major bleeding after thrombolysis for ischemic stroke: a multicentre study", Romoli & Vandelli et al., Stroke 2022</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.