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126 results for “cross validation”
Data files belonging to the paper "Dealing with clustered samples for assessing map accuracy by cross-validation"
<p>Mapping of environmental variables often relies on map accuracy assessment through cross-validation with the data used for calibrating the underlying mapping model. When the data points are spatially clustered, conventional cross-validation leads to optimistically biased estimates of map accuracy. Several papers have promoted spatial cross-validation as a means to tackle this over-optimism. Many of these papers blame spatial autocorrelation as the cause of the bias and propagate the widespread misconception that spatial proximity of calibration points to validation points invalidates classical statistical validation of maps. In the paper related to these data, we present and evaluate alternative cross-validation approaches for assessing map accuracy from clustered sample data. </p> <p> </p> <p>The study area is western Europe, constrained in the north at 52° latitude and at -10° and 24° longitude The projection is IGNF:ETRS89LAEA (Lambert azimuthal equal area projection).</p> <p> </p> <p><strong>Files:</strong></p> <p>agb.tif = above ground biomass (AGB) map from version 3 of the 2017 CCI-Biomass product (<a href="https://catalogue.ceda.ac.uk/uuid/5f331c418e9f4935b8eb1b836f8a91b8">https://catalogue.ceda.ac.uk/uuid/5f331c418e9f4935b8eb1b836f8a91b8</a>)<br> AGBstack.tif = covariates used for predicting AGB<br> aggArea.tif = coarse grid used for simulation in the model-based methods<br> ocs.tif = soil organic carbon stock (OCS) map (0-30 cm) from Soilgrids (<a href="https://www.isric.org/explore/soilgrids">https://www.isric.org/explore/soilgrids</a>)<br> OCSstack.tif = covariates used for predicting OCS<br> strata.xxx = 100 compact geo-strata (ESRI shape) created with the spcosa package; used for generating clustered samples<br> TOTmask.tif = mask of the area covered by the covariates</p> <p> </p> <p><strong>Details and data sources of the covariates in AGBstack.tif and OCSstack.tif:</strong></p> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Source</strong></p> </td> <td> <p><strong>Note</strong></p> </td> </tr> <tr> <td> <p>ai</p> </td> <td> <p>Aridity Index</p> </td> <td> <p><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></p> </td> <td>Version 2.1</td> </tr> <tr> <td> <p>bio1</p> </td> <td> <p>Mean annual air temperature [°C]</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>bio5</p> </td> <td> <p>Mean daily maximum air temperature of the warmest month [°C]</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>bio7</p> </td> <td> <p>Annual range of air temperature [°C]</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>bio12</p> </td> <td> <p>Annual precipitation [kg/m<sup>2</sup>]</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>bio15</p> </td> <td> <p>Precipitation seasonality [kg/m<sup>2</sup>]</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>gdd10</p> </td> <td> <p>Growing degree days heat sum above 10°C</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>clay</p> </td> <td> <p>Clay content [g/kg] of the 0-5cm layer</p> </td> <td> <p><a href="https://soilgrids.org/">https://soilgrids.org/</a></p> <p> </p> </td> <td> <p>Only used for AGB</p> </td> </tr> <tr> <td> <p>sand</p> </td> <td> <p>Sand content [g/kg] of the 0-5cm layer</p> </td> <td><a href="https://soilgrids.org/">https://soilgrids.org/</a></td> <td>as above</td> </tr> <tr> <td> <p>pH</p> </td> <td> <p>Acidity (Ph(water)) of the 0-5cm layer</p> </td> <td><a href="https://soilgrids.org/">https://soilgrids.org/</a></td> <td>as above</td> </tr> <tr> <td> <p>glc2017</p> </td> <td> <p>Landcover 2017</p> </td> <td> <p><a href="https://land.copernicus.eu/global/products/lc">https://land.copernicus.eu/global/products/lc</a>, reclassified to: closed forest, open forest, natural non-forest veg., bare & sparse veg. cropland, built-up, water</p> </td> <td> <p>Categorical variable</p> </td> </tr> <tr> <td> <p>dem</p> </td> <td> <p>Elevation</p> </td> <td> <p><a href="https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-eu-dem">https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-eu-dem</a></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>cosasp</p> </td> <td> <p>Cosine of slope aspect</p> </td> <td> <p>Computed with the terra package from elevation</p> </td> <td>Computed @25m resolution; next aggregated to 0.5km</td> </tr> <tr> <td> <p>sinasp</p> </td> <td> <p>Sine of slope aspect</p> </td> <td>Computed with the terra package from elevation</td> <td>as above</td> </tr> <tr> <td> <p>slope</p> </td> <td> <p>Slope</p> </td> <td>Computed with the terra package from elevation</td> <td>as above</td> </tr> <tr> <td> <p>TPI</p> </td> <td> <p>Topographic position index</p> </td> <td>Computed with the terra package from elevation</td> <td>as above</td> </tr> <tr> <td> <p>TRI</p> </td> <td> <p>Terrain ruggedness index</p> </td> <td>Computed with the terra package from elevation</td> <td>as above</td> </tr> <tr> <td> <p>TWI</p> </td> <td> <p>Topographic wetness index</p> </td> <td> <p>Computed with SAGA from 500m resolution (aggregated) dem</p> </td> <td> </td> </tr> <tr> <td> <p>gedi</p> </td> <td> <p>Forest height</p> </td> <td> <p><a href="https://glad.umd.edu/dataset/gedi">https://glad.umd.edu/dataset/gedi</a></p> </td> <td> <p>Zone: NAFR</p> </td> </tr> <tr> <td> <p>xcoord</p> </td> <td> <p>X coordinate</p> </td> <td> <p>Using a mask created from the other covariates</p> </td> <td> </td> </tr> <tr> <td> <p>ycoord</p> </td> <td> <p>Y coordinate</p> </td> <td>Using a mask created from the other covariates</td> <td> </td> </tr> <tr> <td> <p>Dcoast</p> </td> <td> <p>Distance from coast</p> </td> <td> <p>Using a land mask created from the other covariates</p> </td> <td> </td> </tr> </tbody> </table> <p> </p>
CROSS-VALIDATION OF FUNCTIONAL MRI and PARANOID-DEPRESSIVE SCALE: BRAIN SIGNATURES FROM MULTIVARIATE ANALYSIS
<p>Brain signatures identified by bottom-up unsupervised machine learning: three principal components based on activations yielded from the three kinds of diagnostically relevant stimuli are used in order to produce cross-validation markers which may effectively predict the variance on the level of clinical populations and eventually delineate diagnostic and classification groups. The stimuli represent items from a paranoid-depressive self-evaluation scale, administered simultaneously with functional magnetic resonance imaging (fMRI).</p> <p>We have been able to separate the two investigated clinical entities – schizophrenia and recurrent depression by use of multivariate linear model and principal component analysis. This is a confirmation of the possibility to achieve bottom-up classification of mental disorders, by use of the brain signatures relevant to clinical evaluation tests.</p>
Blind Prediction Competition - Sera.ta - Seismic Response of Masonry Cross Vaults: Shaking table tests and numerical validations
<p>Masonry vaults play a much relevant role in the seismic response of heritage masonry buildings, ranging from housing to the greatest cathedrals. Acting as both a ceiling and a structural horizontal diaphragm with significant mass, their mechanical behaviour affects the overall seismic response of buildings, in terms of strength, stiffness, and ductility. Moreover, local damage and collapse of vaults may produce significant losses in terms of cultural assets and casualties. In spite of the importance of this topic, the evaluation of the complex three-dimensional behaviour of vaults is still an important challenge for researchers. The main objectives of the present research project are:<br> 1) to better understand the seismic behaviour of masonry cross vaults by means of shaking table tests on both full-scale and small-scale models;<br> 2) to assess the capability of different modelling/analysis approaches to predict the seismic response of these masonry structures.</p> <p>In particular, three sets of shaking table tests are planned:<br> a. Tests on a 1:1 scale model of a brick unreinforced masonry cross vault: to investigate the behaviour of brick masonry cross vaults under different seismic inputs, in terms of damage, displacement capacity and peak acceleration.<br> b. Tests on a 1:1 scale model of a brick reinforced masonry cross vault: to evaluate the effectiveness of reinforcing techniques to repair the vaults tested in a).</p> <p>In addition to the experimental tests, a blind prediction competition is performed to assess the efficacy of different modelling strategies and analysis techniques. The final aims are to improve the safety assessment procedures proposed for historic masonry buildings in Eurocode 8.3 and to provide better seismic assessment techniques and strengthening measures.</p>
Pre-built leave-out-out cross-validation imputation reference panel datasets - LmTag paper
<p>Pre-built leave-out-out cross-validation imputation reference panel datasets - LmTag paper (complement for a tutorial at https://github.com/datngu/LmTag)</p> <p>This repo includes chromosome 10 reference panel data constructed for 3 populations:</p> <p>- EAS</p> <p>- EUR</p> <p>- SAS</p>
Dataset for Repeated double cross validation applied to the PCA-LDA classification of SERS spectra: a case study with serum samples from hepatocellular carcinoma patients
<p>This dataset contains all the spectra used in the paper "Repeated double cross validation applied to the PCA-LDA classification of SERS spectra: a case study with serum samples from hepatocellular carcinoma patients", plus the R code to import the TXT (ASCII) files into a dataset, preprocess data, set-up and cross validate the PCA-LDA model and generate the figures shown in the paper.</p> <p>Data are available in 2 different formats: </p> <p>- 1 compressed archive ("dataset.zip") containing all the 144 TXT files (1 file = 1 spectrum) </p> <p>- 1 single CSV file (“dataset.csv”) with all the 144 spectra in the form of a table. The data are structured as follow, with each row being 1 spectrum, preceded by metadata: "acquisition_date", "substrate_batch", "class", "sample_code".</p> <p>The code for R is available as a single file "Rcode.R".</p> <p> </p>
Data for the Article: Cross-validation of a semantic segmentation network for natural history collection specimens
<p>This deposit contains six datasets which were used for testing and validating a semantic segmentation network. The purpose was to evaluate the suitability of the segmentation network for use in the processing of images from Natural History Collections.</p>
DocTOR models and cross-validation dataset
<p>Dataset necessary for DocTOR utility.</p> <p>DocTOR (Direct fOreCast Target On Reaction), is a utility written in python3.9 (using the conda workframe) that allows the user to upload a list of Uniprot IDs and Adverse reactions (from the available models) in order to study the relationship between the two.</p> <p>On output the program will assign a positive or negative class to the protein, assessing its possible involvement in the selected ADRs onset.</p> <p>DocTOR exploits the data coming from T-ARDIS [https://doi.org/10.1093/database/baab068] to train different Machine Learning approaches (SVM, RF, NN) using network topological measurements as features.</p> <p>The prediction coming from the single trained models are combined in a meta-predictor exploiting three different voting systems.</p> <p>The results of the meta-predictor together with the ones from the single ML method will be available in the output log file (named "predictions_community" or "predictions_curated" based on the database type).</p> <p>The DocTOR utility is avaiable at https://github.com/cristian931/DocTOR</p>
Histological validation of per-bundle water diffusion metrics within a region of fiber crossing following axonal degeneration
<p>Interactive plots showing the correlation between histological parameters of optic nerves and chiasm, and metrics derived from diffusion MRI in a rat model of unilateral retinal ischemia.</p> <p> </p> <p>There are two .html files, each containing an interactive figure, one for data pertaining to the optic nerve, the other for the chiasm. The left panel shows the correlation matrix. Click on any cell to see the corresponding scatter plot on the right panel. </p>
Postdiction Competition - Sera.ta - Seismic Response of Masonry Cross Vaults: Shaking table tests and numerical validations
<p>Once the blind prediction has been completed (DOI 10.5281/zenodo.7624666), new data are given to the research groups to perform the post diction simulations. The main objectives are:</p> <ul> <li>to calibrate the numerical model based on the dynamic properties;</li> <li>to emprove the capability of different modelling/analysis approaches and better justify the numerical assumptions.</li> </ul> <p>Below you can find the data set for the post diction analysis namely:</p> <ul> <li>Unstrengthened specimen results: </li> <li>Strengthened specimen results: </li> <li>Results of mix mortars used for the TRM technique:</li> </ul> <p> </p>
Data from: Experimental validation of a linear momentum and bluff-body model for high-blockage cross-flow turbine arrays
Open the record for dataset details and reuse information.
MDSINE2 Cross-Validation Analysis (Healthy Cohort)
<p>MDSINE2 Inference Analysis files. This archive contains all output files from the cross-validation inference (with comparator analysis included) for Healthy cohort. (Both healthy and dysbiotic cohorts are required to run the Jupyter Notebook (`fig4_semisynthetic_v2_cache.ipynb`) on our MDSINE2_Paper repo. For the Dysbiotic cohort files, refer to <a href="https://zenodo.org/records/16915340" target="_blank" rel="noopener">https://zenodo.org/records/16915340</a>.</p> <p>For the full project/source pipeline, refer to <a href="https://github.com/gerberlab/MDSINE2_Paper">https://github.com/gerberlab/MDSINE2_Paper</a>.</p> <p>The archive here was created using the command `tar --zstd -cvf`, and then split using the unix "split" command. To unpack these files, please use the following command:</p> <pre><code>cat cross_validation_healthy.tar.zst.part* > cross_validation_healthy.tar.zst tar -I zstd -xvf cross_validation_healthy.tar.zst</code></pre> <p>These files should be unpacked and placed in the paper repository directory (wherever you did `git clone`), so that the `datasets` directory is directly inside `MDSINE2_Paper` repository directory. </p> <p>------------</p> <p>Related zenodo records:</p> <p><a href="https://doi.org/10.5281/zenodo.8208502">https://doi.org/10.5281/zenodo.8208502</a> -- Full MDSINE2 inference on Healthy cohort</p> <p><a href="https://doi.org/10.5281/zenodo.16915340">https://doi.org/10.5281/zenodo.16915340</a> -- MDSINE2 Cross-Validation run (Dysbiotic cohort)</p> <p><a href="https://doi.org/10.5281/zenodo.16915311">https://doi.org/10.5281/zenodo.16915311</a> -- MDSINE2 semisynthetic dataset</p>
Data reported in development and cross-validation of a veterans mental health risk factor screen
<p>Background. VA primary care patients are routinely screened for current symptoms of PTSD, depression, and alcohol disorders, but many who screen positive do not engage in care. In addition to stigma about mental disorders and a high value on autonomy, some veterans may not seek care because of uncertainty about whether they need treatment to recover. A screen for mental health risk could provide an alternative motivation for patients to engage in care.</p> <p>Results. Twelve items assessing dissociation, emotional lability, life stress, and moral injury correctly classified 86% of those who later had elevated PTSD and/or depression symptoms (sensitivity) and 75% of those whose later symptoms were not elevated (specificity). Performance was also very good for 110 veterans who identified as members of ethnic/racial minorities.</p> <p>Conclusions. Mental health status was prospectively predicted in VA primary care patients with high accuracy using a screen that is brief, easy to administer, score, and interpret, and fits well into VA's integrated primary care. When care is readily accessible, appealing to veterans, and not perceived as stigmatizing, information about mental health risk may result in higher rates of engagement than information about current mental disorder status.</p>
Genotypes of Aedes aegypti mosquitoes derived from SNP chip and low-coverage whole genome sequencing for platform cross-validation
<p>The mosquito <em>Aedes aegypti </em>is the primary vector of many human arboviruses such as dengue, yellow fever, chikungunya, and Zika, which affect millions of people world-wide. Population genetics studies on this mosquito have been important in understanding its invasion pathways and success as a vector of human disease. The Axiom aegypti1 SNP chip was developed from a sample of geographically diverse <em>Ae. aegypti </em>populations to facilitate genomic studies on this species. Here we evaluate the utility of the Axiom aegypti1 SNP chip for population genetics and compare it with a low-depth shot-gun sequencing approach using mosquitoes from the species' native (Africa) and invasive range (outside Africa). These analyses indicate that the results from the SNP chip are highly reproducible and have a higher sensitivity to capture alternative alleles than a low-coverage whole-genome sequencing approach. Although the SNP chip suffers from ascertainment bias, results from population structure, ancestry, demographic, and phylogenetic analyses using the SNP chip were congruent with those derived from low coverage whole genome sequencing, and consistent with previous reports on Africa and outside Africa populations using microsatellites. More importantly, we identified a subset of SNPs that can be reliably used to generate merged databases, opening the door to combined analyses. We conclude that the Axiom aegypti1 SNP chip is a convenient, more accurate, low-cost alternative to low-depth whole genome sequencing for population genetic studies of <em>Ae. aegypti</em> that do not rely on full allelic frequency spectra. Whole genome sequencing and SNP chip data can be easily merged, extending the usefulness of both approaches. </p>
Data for "Identifying and Fitting Eclipse Maps of Exoplanets with Cross-Validation" (Hammond et al. 2024)
<p>This archive contains the data and scripts needed to reproduce the analysis in "Identifying and Fitting Eclipse Maps of Exoplanets with Cross-Validation" (Hammond et al. 2024).</p> <p>Contents</p> <p>data/: Input data and posterior distributions of different model fits</p> <p>datasets/: Observational datasets</p> <p>figures/: Folder to save figures in</p> <p>fluxes/: Saved lightcurves for auxiliary plotting purposes</p> <p>archive_paper_plotter.ipynb: Example script to plot fitted eclipse maps</p> <p>eclipse_pixel_sampling.py: Script to fit eclipse map and test k-fold CV score</p> <p>paper_eclipse_suite.py: Script to use simulated or observational data to fit an eclipse map</p>
Dissimilarity-adaptive cross-validation experiments and datasets
<p>This data includes all datasets and codes for implementing dissimilarity-adaptive cross-validation experiments, datasets and code, Reademe.txt explains each file's meaning. Appendix includes the descriptions of datasets. </p>
Python functions -- cross-validation methods from a data-driven perspective
<p>This is the organized python functions of proposed methods in Yanwen Wang PhD research. Researchers can directly use these functions to conduct spatial+ cross-validation (SP-CV), dissimilarity quantification by adversarial validation (AVD), and dissimilarity-adaptive cross-validation (DA-CV). The description of how to run codes is in Readme.txt. The descriptions of functions are in functions.docx.</p>
Dataset for article: Gait Speed Assessment in the 10-meter Walk Test for Older Adults Using a Computer Vision-based System: A Cross-sectional Study on Validity, Reliability, and Usability
<p>This dataset provides the Validity, Reliability, and Usability for an assessment of gait speed detection system in the 10-meter Walk Test for Older Adults.</p> <p>The dataset is formatted for easy import into microsoft excel software consist of:<br>Supplementary1.xlsx - Validity <br>Supplementary2.xlsx - Reliability<br>Supplementary3.xlsx - Usability test</p>
A cross sectional study on adaptation and initial validation of a test to evaluate health claims among high school students – Croatian version
<p>Objectives: We validated the Croatian version of the test using multiple-choice questions (MCQs) from the Claim Evaluation Tools item bank of the Informed Health Choices project, and measured the ability of high school students to appraise health claims.</p> <p>Setting: 16 high schools from the urban agglomeration of the city of Split, Croatia.</p> <p>Participants: Final year high school students of at least 18 years of age.</p> <p>Interventions: 18 MCQs from the item bank considered relevant for high school students were translated. After face-validity testing, the questionnaire was piloted and sent to a convenient sample of 302 high school students.</p> <p>Primary and secondary outcome measures: Difficulty and discrimination indices were calculated for each MCQ to determine the validity of translation and the weight of MCQs. We assessed basic metric characteristics and performed initial validation of the test. Two tests were created, the full (18 MCQs) and the short version (12 MCQs). We analysed differences in test score according to gender and school.</p> <p>Results: The response rate was 96% (75% female respondents). Metric characteristics of both tests were satisfactory (Cronbach α=0.71 for the full and α=0.73 for the short version). The mean score (± standard deviation) for the full version was 11.15±3.43 and 8.13±2.76 for the short version. There were 6 easy and 12 moderately difficult questions. Questions concerning effectiveness and dissimilar comparison groups were answered correctly by fewer than 40% of students. Female students and those from grammar and health schools scored higher on both tests.</p> <p>Conclusions: Both tests showed good metric characteristics and may be used for quick and reliable assessments of adolescents' ability to appraise health claims. They may be used to identify needs and inform development of educational activities to foster critical thinking about health among adolescents</p>
Development and validation of the Health Belief Model questionnaire to promote smoking cessation for nasopharyngeal cancer prevention: a cross-sectional study
<p>Objective: Nasopharyngeal cancer (NPC) risk factors caused by lifestyle choices are substantial yet avoidable. Using the Health Belief Model as a conceptual framework, this study develops and validates a questionnaire to predict smokers' intentions to quit in Sarawak, Malaysia (HBM).</p> <p>Design: A cross sectional study.</p> <p>Setting: Urban and suburban areas in Sarawak, Malaysia.</p> <p>Participants: Following a thorough literature study, the preliminary items for the instrument were created. Prior to beginning the content validity by a panel of 10 experts, the instrument was translated into the Malay language utilising the forward-backwards approach. 10 smokers carried out face validity in both quantitative and qualitative ways. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to assess the instrument's concept validity. Phase 1 of the EFA involved 100 smokers, whereas phase 2 of the CFA involved 171 smokers. Cronbach's alpha coefficients were used to measure internal consistency and assess reliability.</p> <p>Results: The factor loading of each item remained within the acceptable threshold in the exploratory stage. The final revised CFA yielded the following model fit indices that suitably fitted the seven-factor model: Chi Square: 641.705; df= 500; P< 0.001; CFI = 0.953; TLI: 0.948; RMSEA= 0.041. With the exception of one pairwise construct, satisfactory convergent validity and divergent validity were demonstrated. Phases 1 and 2 both have Cronbach's alpha values over 0.7, suggesting acceptable internal reliability.</p> <p>Conclusions: The study confirmed the validity and reliability of the instrument in predicting smokers' tendency to adopt healthy behaviour of smoking cessation to lower risk of developing cancer. The instrument consists of 34 items, dividing into two sections: 6 HBM components and health behavioural intention. The instrument is regarded as innovation and can be applicable in other smoking-related malignancies in different susceptible populations and geographical locations.</p>
Spatial Plus Cross-Validation experiments datasets and codes
<p>This zip file includes all materials of Spatial Plus Cross-Validation experiments. </p> <p>They are ordered by the first number of folder's name.</p> <p>In each folder, the order of running code scripts are labeled by the first number of code's name.</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.