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.
106
datasets available to search
ShareScore release 0.9.0
Dataset results
106 results for “pathogenic variant”
Data files and videos for manuscript "Biallelic pathogenic variants in ROBO1 associate with syndromic CAKUT"
<p>#2021-11-20<br> #Summary<br> This ZIP-file contains the supplementary files of our ROBO1 study. <br> Suppl. File S1 details the next-generation sequencing-based gene panel comprising 426 CKD-associated genes performed in ID 1.<br> Suppl. File S2 contains a variant information and raw in silico data.<br> Suppl. Videos SV1A and SV1B show cardiac valve anomalies comprising mitral valve prolapse in ID 1.<br> Suppl. Video SV2 shows renal anomalies and hydroureter as documented in-vivo by microCT-scan of the mutant mice.</p> <p>#Folder structure<br> ./ (parent directory containing this README file and all subfolders)<br> ./Files/ (contains Excel Suppl. File S1 and Suppl.File S2)<br> ./Videos/ (contains the Suppl. Videos)</p> <p><br> #Files and checksums<br> Algorithm Hash Path<br> --------- ---- ----<br> MD5 2BCB0774F8303FC5DBA2313DFD19FBEE Files\FileS1_Panel-Content.xlsx<br> MD5 015F01832AE1C6610D9C1FB8902FDF17 Files\FileS2_Variants-and-Domains_2021-05-18.xlsx<br> MD5 8BBE3DE1C21F161BCE07A2AFB4A6F7CD Videos\Supplementary Video SV1\Supplementary Video SV1.docx<br> MD5 5EA354253D7E489B48EDFBDE59CFB798 Videos\Supplementary Video SV1\Supplementary Video SV1A.mov<br> MD5 61796C4A5F7EAD79D642ED9D11514A95 Videos\Supplementary Video SV1\Supplementary Video SV1B.mov<br> MD5 4098A23D5CE1C67C1D507D4DD5F4F866 Videos\Supplementary Video SV2\Lo_Robo mutant.mov<br> MD5 D0B219332F227DBECF1A2A1A6AD0925A Videos\Supplementary Video SV2\Supplementary Video SV2.mov</p>
Evaluating the relevance of sequence conservation in the prediction of pathogenic missense variants.
<p>Datasets: <em>CommonClinvar</em> and <em>NewClinvar</em></p> <p>Authors: Emidio Capriotti and Piero Fariselli</p> <p>Article: Evaluating the relevance of sequence conservation in the prediction of pathogenic missense variants.<br> </p>
Supplemental Material Surveillance Improves Outcomes for Carriers of SDHB Pathogenic Variants
<p><strong>Supplemental table for Surveillance improves outcomes for carriers of <em>SDHB</em> pathogenic variants: a multi-center study</strong></p>
Рис. 5. Варианты преΑсказанной Αоменной структуры скавенΑжер-рецепторов гемоцитов моΛΛюсков Planorbarius corneus. Сокращения (зΑесь и ΑаΛее): SR — богатый цистеином Αомен скавенΑжер-рецептора, Filament — Αомен промежуточного фиΛамента, TSP1 — повторы тромбоспонΑина типа 1, KR — крингΛ-Αомен, LDLa — Αомен рецептора Λипопротеинов низкой пΛотности кΛасса А Fig. 5. Variants of the predicted domain structure of scavenger receptors from hemocytes of Planorbarius corneus molluscs. Abbreviations (here and in what follows): SR — scavenger receptor Cys-rich domain, Filament — intermediate filament protein, TSP1 — thrombospondin type 1 repeats, KR — kringle domain, LDLa — low-density lipoprotein receptor domain class A in Pathogen recognition molecules from hemocytes of Planorbarius corneus molluscs (Planorbidae, Pulmonata)
Рис. 5. Варианты преΑсказанной Αоменной структуры скавенΑжер-рецепторов гемоцитов моΛΛюсков Planorbarius corneus. Сокращения (зΑесь и ΑаΛее): SR — богатый цистеином Αомен скавенΑжер-рецептора, Filament — Αомен промежуточного фиΛамента, TSP1 — повторы тромбоспонΑина типа 1, KR — крингΛ-Αомен, LDLa — Αомен рецептора Λипопротеинов низкой пΛотности кΛасса А Fig. 5. Variants of the predicted domain structure of scavenger receptors from hemocytes of Planorbarius corneus molluscs. Abbreviations (here and in what follows): SR — scavenger receptor Cys-rich domain, Filament — intermediate filament protein, TSP1 — thrombospondin type 1 repeats, KR — kringle domain, LDLa — low-density lipoprotein receptor domain class A
Рис. 2. Варианты преΑсказанной Αоменной структуры патогенраспознающих моΛекуΛ гемоцитов моΛΛюсков Planorbarius corneus. a — фибриногенпоΑобные беΛки, b — гаΛектины, c — F-Λектины. УсΛовные обозначения и сокращения, зΑесь и ΑаΛее: горизонтаΛьные красные поΛоски — сигнаΛьный пептиΑ, горизонтаΛьные розовые — обΛасть низкой сΛожности, вертикаΛьные синие поΛоски — трансмембранная обΛасть, FBG — фибриногеновый Αомен, FTP — Αомен фукоΛектина, EGF — Αомен эпиΑермаΛьного фактора роста, EGF_CA — каΛьцийсвязывающий EGF-поΑобный Αомен, PAN_AP — APPLE-поΑобный Αомен, SCAN — обΛасть, богатая Λейцином, GLECT — гаΛактозосвязывающий Λектин, CLECT — Λектин C-типа, Gal-bind — гаΛактозиΑ–связывающий Λектин, ML — MD-2- поΑробный Αомен распознавания ΛипиΑов Fig. 2. Variants of the predicted domain structure of pattern recognition molecules from hemocytes of Planorbarius corneus molluscs. a — fibrinogen-related proteins, b — galectins, c — F-lectins. Symbols and abbreviations (here and further): horizontal red stripes — signal peptide, horizontal pink stripes — a low complexity region, vertical blue stripes — transmembrane region, FBG — fibrinogen-related domain, FTP — fucolectin domain, EGF — epidermal growth factor-like domain, EGF_CA — calcium-binding EGF-like domain, PAN_AP — APPLE-like domain, SCAN — leucine rich region, Apple — APPLE domain, GLECT — galactose-binding lectin, CLECT — C-type lectin, Gal-bind — galactoside-binding lectin, ML — MD-2-related lipid-recognition domain in Pathogen recognition molecules from hemocytes of Planorbarius corneus molluscs (Planorbidae, Pulmonata)
Рис. 2. Варианты преΑсказанной Αоменной структуры патогенраспознающих моΛекуΛ гемоцитов моΛΛюсков Planorbarius corneus. a — фибриногенпоΑобные беΛки, b — гаΛектины, c — F-Λектины. УсΛовные обозначения и сокращения, зΑесь и ΑаΛее: горизонтаΛьные красные поΛоски — сигнаΛьный пептиΑ, горизонтаΛьные розовые — обΛасть низкой сΛожности, вертикаΛьные синие поΛоски — трансмембранная обΛасть, FBG — фибриногеновый Αомен, FTP — Αомен фукоΛектина, EGF — Αомен эпиΑермаΛьного фактора роста, EGF_CA — каΛьцийсвязывающий EGF-поΑобный Αомен, PAN_AP — APPLE-поΑобный Αомен, SCAN — обΛасть, богатая Λейцином, GLECT — гаΛактозосвязывающий Λектин, CLECT — Λектин C-типа, Gal-bind — гаΛактозиΑ–связывающий Λектин, ML — MD-2- поΑробный Αомен распознавания ΛипиΑов Fig. 2. Variants of the predicted domain structure of pattern recognition molecules from hemocytes of Planorbarius corneus molluscs. a — fibrinogen-related proteins, b — galectins, c — F-lectins. Symbols and abbreviations (here and further): horizontal red stripes — signal peptide, horizontal pink stripes — a low complexity region, vertical blue stripes — transmembrane region, FBG — fibrinogen-related domain, FTP — fucolectin domain, EGF — epidermal growth factor-like domain, EGF_CA — calcium-binding EGF-like domain, PAN_AP — APPLE-like domain, SCAN — leucine rich region, Apple — APPLE domain, GLECT — galactose-binding lectin, CLECT — C-type lectin, Gal-bind — galactoside-binding lectin, ML — MD-2-related lipid-recognition domain
Рис. 7. Варианты преΑсказанной Αоменной структуры моΛекуΛ аΑгезии гемоцитов моΛΛюсков Planorbarius corneus. УсΛовные обозначения и сокращения: 1–3 — β-интегрины, 4–5 — α-интегрины, 6–7 — сеΛектины, 8–11 — моΛекуΛы семейства САМ (сell adhesiom molecues), INB — субъеΑиницы β-интегрина, IntegrinBcyt — цитопΛазматический Αомен β-интегрина, CY — цистатинопоΑобный Αомен, Int alpha — Αомен α-интегрина, FN3 — Αомен фибронектина типа 3, CCP — Αомен контроΛя компΛемента Fig. 7. Variants of the predicted domain structure of adhesion molecules from hemocytes of Planorbarius corneus molluscs. Symbols and abbreviations: 1–3 — β-integrins, 4–5 — α–integrins, 6–7 — selectins, 8–11 — molecules of the СAM family (cell adhesion molecules), INB — β-integrin subunits, IntegrinBcyt — cytoplasmic domain of β-integrin, CY — cystatin-like domain, Int alpha — α-integrin domain, FN3 — fibronectin type 3 domain, CCP — complement control protein domain in Pathogen recognition molecules from hemocytes of Planorbarius corneus molluscs (Planorbidae, Pulmonata)
Рис. 7. Варианты преΑсказанной Αоменной структуры моΛекуΛ аΑгезии гемоцитов моΛΛюсков Planorbarius corneus. УсΛовные обозначения и сокращения: 1–3 — β-интегрины, 4–5 — α-интегрины, 6–7 — сеΛектины, 8–11 — моΛекуΛы семейства САМ (сell adhesiom molecues), INB — субъеΑиницы β-интегрина, IntegrinBcyt — цитопΛазматический Αомен β-интегрина, CY — цистатинопоΑобный Αомен, Int alpha — Αомен α-интегрина, FN3 — Αомен фибронектина типа 3, CCP — Αомен контроΛя компΛемента Fig. 7. Variants of the predicted domain structure of adhesion molecules from hemocytes of Planorbarius corneus molluscs. Symbols and abbreviations: 1–3 — β-integrins, 4–5 — α–integrins, 6–7 — selectins, 8–11 — molecules of the СAM family (cell adhesion molecules), INB — β-integrin subunits, IntegrinBcyt — cytoplasmic domain of β-integrin, CY — cystatin-like domain, Int alpha — α-integrin domain, FN3 — fibronectin type 3 domain, CCP — complement control protein domain
DYNA: Disease-Specific Language Model for Variant Pathogenicity
<p>For coding variant effect predictions (VEPs), our approach centers on clinical variant sets specifically related to inherited cardiomyopathies (CM) and arrhythmias (ARM). We utilize a pre-compiled dataset comprised of rare missense pathogenic and benign variants, categorized using a cohort-based approach for diseases such as cardiomyopathy and arrhythmias, as detailed in the previous report by Zhang et al. ClinVar CM and ARM datasets include all missense variants in CM and ARM, respectively, are extracted from ClinVar (Landrum et al.). In the realm of non-coding VEPs, our focus shifts to splicing-related variants, utilizing a dataset from the multiplexed assay for exon recognition by Chong et al., which highlights the significant impact of rare genetic variants on splicing disruptions. Similarly, the ClinVar Splicing dataset, compiled from ClinVar, encompasses all benign sequences and pathogenic variants pertinent to splicing.</p> <p> </p> <p>For the ClinVar CM and ARM datasets, we translate the DNA sequences into protein sequences using the human genome assembly hg38 from https://www.ncbi.nlm.nih.gov/grc/human. We employed the GFF file, MANE.GRCh38.v1.1.ensembl\_genomic.gff.gz from https://www.ncbi.nlm.nih.gov/refseq/MANE, to annotate coding versus non-coding regions for each gene, as only coding DNA sequences are translated into proteins. Additionally, protein domains, cataloged in the Pfam database (Finn et al.), are essential for the functional characterization of proteins. These domains are identified by aligning the translated sequences to known domain structures, thereby facilitating deeper insights into protein function.</p> <p> </p>
Pathogenic and low frequency variants in children with central precocious puberty
<p><b>Background </b>Central precocious puberty (CPP) due<span> to premature activation of GnRH secretion results in early epiphyseal fusion and to a significant compromise in the achieved final adult height. CPP is usually idiopathic and is disproportionally observed in girls compared to boys. Currently, only few </span>genetic determinants of children with CPP have been described and the role they exert on the development of the disorder. In this original study rare variants in <i>MKRN3</i>, <i>DLK1</i>, <i>KISS1</i>, <i>KISS1R</i> and <i>MAGEL2</i> genes are reported in patients with CPP.</p> <p><b>Methods </b>Fifty-four index females and 2 index males with CPP underwent whole exome sequencing (WES) by Next Generation Sequencing (NGS). The identified rare variants were initially examined by <i>in silico</i> computational algorithms and confirmed by Sanger sequencing. Additionally, a genetic network for the <i>MKRN3</i> gene mimicking a holistic regulatory depiction of the crosstalk between <i>MKRN3</i> and other genes is designed.</p> <p><b>Results </b>Three previously described pathogenic <i>MKRN3</i> variants in the coding region of the gene occurred in 12 index females with CPP. With the p.Gly312Asp pathogenic variant of the <i>MKRN3 </i>gene being the most prevalent and exclusively found among the Cypriot CPP cohort, it is projected to be the result of founder effect phenomenon. In seven additional CPP patients from the same cohort several other likely and rare pathogenic upstream variants in the <i>MKRN3</i> gene were also observed. In addition to the <i>MKRN3</i> variants, a total of 16 other rare variants in <i>DLK1</i>, <i>KISS1</i> and <i>MAGEL2 </i>were also identified in other CPP patients from the same cohort. Interestingly, the frequent variant rs10407968 (p.Gly8Ter) of the <i>KISS1R</i> gene appeared to be less frequent in the cohort of patients with CPP.</p> <p><b>Conclusion</b> The results of the present study denote the key role of the imprinted <i>MKRN3</i> gene in puberty. Additionally, pathogenic variants can also exist in the noncoding region of the MKRN3 gene such as the proximal promoter and 5'-UTR region and which can also be considered as contributing factors to CPP. Overall, the results of present study have emphasised the necessity of the allied genetic and clinical approach which is necessary for the management and treatment of CPP.</p>
Maverick Variant Pathogenicity Data Resources
<p>MAVERICK is a Mendelian Approach to Variant Effect pRedICtion built in Keras. It classifies protein-altering variants as either dominant disease-causing, recessive disease-causing, or benign. Here, we provide the pre-computed scores for all missense and nonsense SNVs in Gencode Basic V33 on GRCh37 and lifted over to GRCh38 as well as the datasets on which MAVERICK was trained and primarily evaluated. </p>
Perturbations in fatty acid metabolism and collagen production infer pathogenicity of a novel MBTPS2 variant in Osteogenesis imperfecta
<p>(i) PCR-sequencing: Chromatograms were generated by PCR-sequencing of a region within exon 4 of MBTPS2 using DNA extracted from a healthy control and the proband's fibroblasts</p> <p>(ii) Gene expression was quantified by qRT-PCR using RNA extracted from fibroblasts. Transcript levels of each gene of interest was calculated using the 2^-deltaCt method with normalization to the average Ct values of endogenous control genes GAPDH, IPO8 and TBP.</p> <p>(iii) Cellular fatty acid content was quantified by GC-MS/MS. Each table represents one technical replicate. Absolute values of each fatty acid are listed in the tables; relative ratios of various fatty acids are calculated at the bottom of each table. </p> <p>(iv) Immunocytochemistry images of ECM proteins (COL1 = collagen type I; COL4 = collagen type IV; COL5 = collagen type V; a2b1 = integrin a2b1) and binding of collagen-hybridising peptide (R-CHP).</p>
BRCA1-specific machine learning model predicts variant pathogenicity with high accuracy - Supplementary material
<p>Figure S1: Distribution of the reviewed 141 <em>BRCA1</em> missense variants; Figure S2: The Shapely values for the <em>BRCA1</em> XGBoost models; Figure S3: The Shapely values of the <em>BRCA1</em> XGBoost model used to predict the functional assays’ results for variants of uncertain significance; Table S1: The receiver operating characteristic (ROC) curve analysis for the different in silico predictions; Table S2: Cross validation of the BRCA1 model in 5 different random training and test samples; Table S3: Pathogenicity prediction and prioritization of the 31,058 unreviewed BRCA1 variants from the BRCA Exchange database.</p>
Pathogenic and low frequency variants in children with central precocious puberty
Open the record for dataset details and reuse information.
Source code for StrVCTVRE: a supervised learning method to predict the pathogenicity of human genome structural variants
Open the record for dataset details and reuse information.
Comprehensive identification of pathogenic variants in retinoblastoma by long- and short-read sequencing
<p>Retinoblastoma (RB) is the most common intraocular malignancy in childhood. The causal variants in RB are mostly characterized by previously used short-read sequencing (SRS) analysis, which has technical limitations in identifying structural variants (SVs) and phasing information. Long-read sequencing (LRS) technology has significant advantages over SRS in detecting SVs, phased genetic variants, and methylation. In this study, we comprehensively characterized the genetic landscape of RB using combinatorial LRS and SRS of 16 RB tumors and 16 matched blood samples. We detected a total of 232 somatic SVs, with an average of 14.5 SVs per sample across the cohort. We identified 20 distinct pathogenic variants, including <span>three</span> novel small variants and <span>five</span> <span>somatic</span> SVs. Furthermore, our analysis shows that the vast majority (93.8%) of <em>RB1</em>-disrupted patients fit the two-hit hypothesis through diverse types, including the biallelic hypermethylated promoter as well as small and large compound heterozygous mutations which were missing in SRS analysis. By constructing the evolutionary history of all genetic variants, we reveal the evolution trends that <em>RB1</em> disruption early and followed by copy number changes, including amplifications of Chr2p, and deletions of Chr16q, during RB tumorigenesis. Altogether, we characterize the comprehensive genetic landscape of RB, providing novel insights into the genetic alterations and mechanisms contributing to RB initiation and development. Our work also establishes a framework to analyze genomic landscape of cancers based on LRS data. <br><br>We took IGV screenshots of SV and SNV/indels. The IGV naming conventions for SV is: sample name, chromosome 1, start, chromosome 2, end, SV type, SV length, SV breakpoint. The IGV naming convention for SNV/indels is: sample name, chromosome, position, reference allele, alternate allele.</p>
GCK variants and their annotations for "Underestimated risk of secondary complications in pathogenic and glucose-elevating GCK variant carriers with type 2 diabetes"
<p>This file provides annoatations for GCK variants as dsecribed in "Underestimated risk of secondary complications in pathogenic and glucose-elevating GCK variant carriers with type 2 diabetes"</p>
Human genetic variants and age are the strongest predictors of humoral immune responses to common pathogens and vaccines
<p>Online Supplementary Dataset of the manuscript "Human genetic variants and age are the strongest predictors of humoral immune responses to common pathogens and vaccines"</p>
V374A KCND3 pathogenic variant associated with paroxysmal ataxia exacerbations
<p><b>Objective</b></p> <p>Ataxia channelopathies share common features such as slow motor progression and variable degrees of cognitive dysfunction. Mutations in <i>KCND3, </i>encoding the K<sup>+</sup> channel, Kv4.3, are associated with spinocerebellar ataxia 19 (SCA19), allelic with spinocerebellar ataxia 22 (SCA22). Mutations in <i>KCNC3</i>, encoding another K<sup>+</sup> channel, Kv3.3, cause spinocerebellar ataxia 13 (SCA13). First, a comprehensive phenotype assessment was carried out in a family with autosomal dominant ataxia harboring two genetic variants in <i>KCNC3 </i>and <i>KCND3</i>. In order to evaluate the physiological impact of these variants on channel currents, <i>in vitro</i> studies were performed.</p> <p><b>Methods</b></p> <p>Clinical and psychometric evaluations, neuroimaging, and genotyping of a family (mother and son) affected by ataxia were carried out. Heterozygous and homozygous Kv3.3 A671V and<a name="_Hlk33945077">Kv4.3 V374A </a>variants were evaluated in <i>Xenopus laevis</i> oocytes using two-electrode voltage-clamp. The influence of Kv4 conductance on neuronal activity was investigated computationally using a Purkinje neuron model.</p> <p><b>Results</b></p> <p>The main clinical findings were consistent with adult-onset ataxia with cognitive dysfunction, and acetazolamide (ACZ)-responsive paroxysmal motor exacerbations in the index case. Despite cognitive deficits, FDG-PET displayed hypometabolism mainly in the severely atrophic cerebellum. Genetic analyses revealed the new variant <a name="_Hlk35066877">c.1121T>C (V374A), in <i>KCND3</i> and c.2012T>C (A671V) in <i>KCNC3</i></a>. <i>In vitro </i>electrophysiology experiments on <i>Xenopus</i> oocytes demonstrated that the V374A mutant was non-functional when expressed on its own. Upon equal co-expression of WT and V374A channel subunits, Kv4.3 currents were significantly reduced in a dominant negative manner, without alterations of the gating properties of the channel. In contrast, Kv3.3 A671V, when expressed alone, exhibited moderately reduced currents compared to WT, with no effects on channel activation or inactivation. Immunohistochemistry demonstrated adequate cell membrane translocation of the Kv4.3 V374A variant, thus suggesting an impairment of channel function, rather than of expression. Computational modeling predicted an increased Purkinje neuron firing frequency upon reduced Kv4.3 conductance.</p> <p><b>Conclusions</b> Our findings suggest that Kv4.3 V374A, is likely pathogenic and associated with paroxysmal ataxia exacerbations, a new trait for SCA19/22. The present FDG PET findings contrast with a previous study demonstrating widespread brain hypometabolism in SCA19/22.</p>
Amplicon sequence variants (ASV) of gut pathogens in hooded cranes and domestic geese
<p>Driven by habitat loss from anthropogenic activities, wintering migratory birds forage together with poultry in paddy fields, and thus impose risks of cross transmitting pathogens. To date, there is little evidence for such risks of pathogen transmission between wild birds and poultry. Using the high-throughput sequencing, we report on detected potential pathogens of both wild hooded cranes <em>Grus monacha</em> and sympatric domestic geese <em>Anser</em> <em>anser</em> <em>domesticus</em> during the wintering period and infer the possibility of cross-species pathogen transmission. The results revealed that the number of shared amplicon sequence variants (ASVs) of potential pathogens between the gut microbiota of the two species was low during the early wintering stage (17.2%; 5 ASVs shared) but increased to 56.3% (18 ASVs shared) during the late wintering stage. That is, potential pathogens in the gut microbial communities of the two species became more similar through co-foraging in paddy fields, supporting cross-transmission of pathogens between hooded cranes and domestic geese during the wintering period. Importantly, transmission appeared to be largely from wild hooded cranes to domestic geese, although some potential pathogens may have become specialized to the domestic goose in late wintering stage. Humans are also facing the risks of contracting these potential pathogens from migratory birds through their frequent contact with domestic poultry. It is, therefore, necessary to closely monitor this pathway of pathogen transmission from wild birds to domestic animals and even to humans.</p>
Supplemental Material Outcomes of SDHB pathogenic variant carriers a systematic review and meta-analysis
<p>Supplemental Material Outcomes of SDHB pathogenic variant carriers a systematic review and meta-analysis</p>
Natural History With Focus on Oncological Risk Evaluation in Pediatric Patients With PTEN Pathogenic Variants - Observational Study
ClinicalTrials.gov study NCT06805734. IPD Sharing: YES. Countries: 1. Publications: 3.
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.