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Rare Genomic Copy Number Variants Implicate New Candidate Genes for Bicuspid Aortic Valve
<p>Whole genome genotyping data in dbGAP format and copy number variant calls in dbVar format.</p> <p>dbVar data includes CNV calls from cases with early onset bicuspid aortic valve disease (EBAV), cases from the International BAV Consortium (BAVCon), and controls from the dbGAP Wisconsin Longitudinal Study on Aging dataset (WLS).</p> <p>dbGAP files are divided into 12 batches of genotypes from EBAV subjects (EBAV1-12):</p> <p>1) PLINK output files (.map and .ped)</p> <p>2) GenomeStudio Final Report files</p> <p>3) One master pedigree file</p> <p>4) dbGAP subject mapping files</p> <p> </p>
Fig. 5 in Geometric morphometric analysis of cyclical body shape changes in color pattern variants of Cichla temensis Humboldt, 1821 (Perciformes: Cichlidae) demonstrates reproductive energy allocation
Fig. 5. Relative mean GSI vs. relative mean HSI of color pattern variants of Cichla temensis. Points for GSI represent the mean value for each CPV grade as compared to the range encountered. Points for HSI represent the mean value for each CPV grade compared to the range encountered.
Fig. 3 in Geometric morphometric analysis of cyclical body shape changes in color pattern variants of Cichla temensis Humboldt, 1821 (Perciformes: Cichlidae) demonstrates reproductive energy allocation
Fig. 3. Biplot of the uniform components in each direction (UniX and UniY) of morphometrical differences in 80 specimens of Cichla temensis in 4 color variation patterns (CPV) as measured by 9 Thin Plate Spline (TPS) distortion variables (V1-V9). Colored numbers indicate the CPV grade of individuals. The total spread of scores among individuals of each CPV are indicated by an envelope (solid line polygon) calculated as the minimum convex hull for that group. Position in the plot relative to other individuals indicates the degree of similarity in morph. Vectors point in the direction of gradient change for that TPS variable and the magnitude indicates the strength of the gradient. Angles between vectors indicate the TPS interset correlations.
Tissue-aware interpretation of genetic variants advances the etiology of rare diseases
<p>Pathogenic variants underlying Mendelian diseases often disrupt the normal physiology of a<br>few tissues and organs. However, variant effect prediction tools that aim to identify<br>pathogenic variants are typically oblivious to tissue contexts. Here we report a machine-<br>learning framework, denoted ‘Tissue Risk Assessment of Causality by Expression for<br>variants’ (TRACEvar, https://netbio.bgu.ac.il/TRACEvar/), that offers two advancements.<br>First, TRACEvar predicts pathogenic variants that disrupt the normal physiology of specific<br>tissues. This was achieved by creating 14 tissue-specific models that were trained on over<br>14,000 variants and combined 84 attributes of genetic variants with 495 attributes derived<br>from tissue omics. TRACEvar outperformed 10 well-established and tissue-oblivious variant<br>effect prediction tools. Second, the resulting models are interpretable, thereby illuminating<br>variants' mode-of-action. Application of TRACEvar to variants of 52 rare-disease patients<br>highlighted pathogenicity mechanisms and relevant disease processes. Lastly, interpretation<br>of large-scale models revealed that top-ranking determinants of pathogenicity included<br>attributes of disease-affected tissues, particularly cellular process activities. Hence, tissue<br>contexts and interpretable machine-learning models can greatly enhance the etiology of rare<br>diseases.</p> <p>Article link: https://www.embopress.org/doi/full/10.1038/s44320-024-00061-6</p> <p> </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
Fig. 2 in A new subtype of Entamoeba gingivalis: BE. gingivalis ST2, kamaktli variant^
Fig. 2 Unrooted phylogenetic tree reconstruction of Entamoeba species" based on 18S rRNA sequences. The values of the nodes indicate the bootstrap proportions and Bayesian posterior probabilities in the following order: maximum likelihood/maximum parsimony/Bayesian analysis. The sequences reported by the present study are indicated in bold. The asterisks indicate a new subtype BE. gingivalis ST2, kamaktli variant.^ Bar 0.1 substitutions per site
Electron microscopy images and morphometric data of SARS-CoV-2 variants in ultrathin plastic sections - Dataset 06 (SARS-CoV-2 Omicron B.1.1.529; BA.2)
<p>Dataset 06 comprises 164 transmission electron microscopy images of extracellular SARS-CoV-2 (isolate Omicron B.1.1.529; BA.2) particles in ultrathin plastic sections (45 nm) through Vero cell cultures. The images were recorded with dimensions of 4112 x 3008 pixels at a pixel size of 0.1641 nm and stored in 16-bit TIF format. It is recommended that an image viewer capable of reading 16-bit images, such as IrfanView, be used to visualize the images. The image files have been size calibrated and can be opened with the correct size calibration using ImageJ or Fiji with the Bioformats importer. A PDF document is provided with the image files, which describes the methods used for the generation of the images. Additionally, an XLSX file is included, offering morphometric particle measurements and the calculated statistical values for their distribution. The dataset was produced as dataset 06 for a comparative morphometric analysis of evolving SARS-CoV-2 variants. Further datasets used for the analysis are available in this repository (see dataset description document).</p>
Electron microscopy images and morphometric data of SARS-CoV-2 variants in ultrathin plastic sections - Dataset 04 (SARS-CoV-2 Beta B.1.351)
<p>Dataset 04 comprises 132 transmission electron microscopy images of extracellular SARS-CoV-2 (isolate Beta B.1.351) particles in ultrathin plastic sections (45 nm) through Vero cell cultures. The images were recorded with dimensions of 4112 x 3008 pixels at a pixel size of 0.1641 nm and stored in 16-bit TIF format. It is recommended that an image viewer capable of reading 16-bit images, such as IrfanView, be used to visualize the images. The image files have been size calibrated and can be opened with the correct size calibration using ImageJ or Fiji with the Bioformats importer. A PDF document is provided with the image files, which describes the methods used for the generation of the images. Additionally, an XLSX file is included, offering morphometric particle measurements and the calculated statistical values for their distribution. The dataset was produced as dataset 04 for a comparative morphometric analysis of evolving SARS-CoV-2 variants. Further datasets used for the analysis are available in this repository (see dataset description document).</p>
Electron microscopy images and morphometric data of SARS-CoV-2 variants in ultrathin plastic sections - Dataset 02 (SARS-CoV-2 Italy-INMI1)
<p>Dataset 02 comprises 154 transmission electron microscopy images of extracellular SARS-CoV-2 (isolate Italy-INMI1) particles in ultrathin plastic sections (45 nm) through Vero cell cultures. The images were recorded with dimensions of 4112 x 3008 pixels at a pixel size of 0.1641 nm and stored in 16-bit TIF format. It is recommended that an image viewer capable of reading 16-bit images, such as IrfanView, be used to visualize the images. The image files have been size calibrated and can be opened with the correct size calibration using ImageJ or Fiji with the Bioformats importer. A PDF document is provided with the image files, which describes the methods used for the generation of the images. Additionally, an XLSX file is included, offering morphometric particle measurements and the calculated statistical values for their distribution. The dataset was produced as dataset 02 for a comparative morphometric analysis of evolving SARS-CoV-2 variants. Further datasets used for the analysis are available in this repository (see dataset description document).</p>
Data - AlphaFold2 Predicts Alternative Conformation Populations in Green Fluorescent Protein Variants
<p><strong>MSAs.zip </strong>Multiple sequences alignments generated by AlphaFold2 structure prediction of 7 engineered GFPs.</p> <p><strong>AF2_models_column_masking.zip </strong>AlphaFold2 models of the alternative conformations of 7 engineered GFPs.</p> <p><strong>MD_trajectories_PyMOL.zip</strong> Molecular dynamics trajectories (PyMOL sessions) of the alternative conformations of 7 engineered GFPs.</p> <p><strong>MD_analysis.zip </strong>Root mean square deviation and per-residue root mean square fluctuations along molecular dynamics simulations of 7 engineered GFPs.</p> <p><strong>rmsd_values.zip</strong> Root mean square deviation relative to crystallographic GFP structure for AlphaFold2 models and molecular dynamics frames (global and central alpha-helix)</p>
Training data for 'Somatic variant calling' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial that demonstrates identification of somatic and germline variants from tumor and normal sample pairs.</p>
Implementation of Genomic Variant Calling Using GATK4, SPARK, WDL, CROMWELL and DOCKER Over Simulated Ebola NGS Dataset.
<p>Ebola genome is manually mutated to contain non-structural as well as structural variants. One ebola genome contains non-structural variants - 10 SNPs, 10 INDELs, 05 TRANSLOCATIONs, 05 INSERSIONs and their reverse complements. Similarly, other two set of mutated genomes contain structural variants. Each set contains seven mutated ebola genome each one for large deletion, insertion, duplication, translocation, inversion, complex variant1 (consecutive three mutations - insertion, duplication and deletion) and complex variants2 (consecutive three mutations - deletion, duplication and deletion). All insertions are novel sequence insertion.</p> <p> </p>
Reads and truth variant set for benchmarking variant calling/genotyping
<p><em>downsampled.fasta</em> is created by converting this file (ftp://<a href="http://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG002_NA24385_son/NIST_HiSeq_HG002_Homogeneity-10953946/HG002Run01-11419412/HG002run1_S1.bam">ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/AshkenazimTrio/HG002_NA24385_son/NIST_HiSeq_HG002_Homogeneity-10953946/HG002Run01-11419412/HG002run1_S1.bam</a>) to fasta and picking every second read (to get half the coverage and half the number of reads).</p> <p><em>The HG002_GRCh37_GIAB_highconf_CG</em>... file is created by picking variants on chromosome from this file (ftp://<a href="http://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/release/AshkenazimTrio/HG002_NA24385_son/NISTv3.3.2/GRCh37/HG002_GRCh37_GIAB_highconf_CG-IllFB-IllGATKHC-Ion-10X-SOLID_CHROM1-22_v.3.3.2_highconf_triophased.vcf.gz">ftp-trace.ncbi.nlm.nih.gov/giab/ftp/release/AshkenazimTrio/HG002_NA24385_son/NISTv3.3.2/GRCh37/HG002_GRCh37_GIAB_highconf_CG-IllFB-IllGATKHC-Ion-10X-SOLID_CHROM1-22_v.3.3.2_highconf_triophased.vcf.gz</a>).</p> <p> </p> <p><strong>These two files can be used in benchmarking variant calling/genotyping.</strong></p>
Lifespan Fecundity data for The Combined Effects of Macronutrient Ratios and the chico1 Variant on Life History Traits in Drosophila melanogaster
<p>Data sheets for Lifespan Fecundity data for The Combined Effects of Macronutrient Ratios and the chico1 Variant on Life History Traits in Drosophila melanogaster. Chico_life_extention_ds and Chico_CP_life_extention_REP_ds are data sheets from project one that keep track of deaths that occurred in the experiment. Deaths of males, deaths of females, and censors were recorded. Hour = hour of collection, Minute = minute of collection, Days_alive = number of days flies have been inside the vials after initial collection, last_flip = day of last time flies were flipped, Label = id of the vial, repl = replicant group, deadF = number of females that died before that days collection, deadM = number of males that died before each collection, cens = number of censors before each collection, counter = person who counted the flies, Year = year of collection, Month = month of collection, Day = day of collection, notes = observations during collection.</p> <p> LDF_flipping_and_counting_data is a data sheet keeping track of deaths that occured in project two. Deaths of females, males, and censors were recorded. Month = month of collection, Day = day of collection, Year = year of collection, Days_alive = number of days flies have been inside vials, Flipped = were the flies flipped with Y meaning Yes and N meaning No, flipper = person who flipped the flies, DeadF = number of dead females before collection, DeadM = number of dead males before collection, Censor = number of censors before collection, Hour = hour of collection, Minute = minute of collection, Label = id of the vial, Notes = observations during collection.</p> <p>LDF_egg_counting_data is a data sheet keeping track of the number of eggs counted on every image in experiment 2. Image_ID i= image identification number, Label = id of the vial, Day = day of collection, Month = month of collection, Year = year of collection, Counter = person who counted the eggs, Egg_total = number of eggs counted on the photo, notes = observations during collection.</p> <p>Images.zip is a zipped folder of all images that were used to count the number of eggs laid over a ~16-hour time period once per week until the death of all flies in the vial. These images are organized by the date the picture was taken. These pictures were counted using the cell counter extension for ImageJ and counted. Counts were recorded in the LDF_egg_counting_data data sheet.</p>
Nanopore deep sequencing as a tool to characterize and quantify aberrant splicing caused by variants in inherited retinal dystrophy genes
Open the record for dataset details and reuse information.
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>
Data from: Repairing a deleterious domestication variant in a floral regulator of tomato by base editing
<p>This repository contains the data necessary to run the analysis described in the publication "Repairing a deleterious domestication variant in a floral regulator of tomato by base editing" by Glaus et al., 2024. A preprint is available on bioRxiv (doi: <a href="https://doi.org/10.1101/2024.01.29.577624" rel="nofollow">https://doi.org/10.1101/2024.01.29.577624</a>)</p> <p> </p> <p>82_acc_Spim0.1_filtered.vcf.gz -- variant call results for 82 genomes with LA1589 as reference</p> <p>82_acc_Spim0.1_filtered_SIFT_out.tar.gz -- sift4g prediction results for 82 genomes with LA1589 as reference (SIFTannotations.xls and SIFTpredictions.list)</p> <p>sift_lib_LA1589.tar.gz -- sift4g library for the LA1589 genome</p> <p>SolpimLA1589_liftoff.tar.gz -- liftoff annotation of LA1589 genome</p> <p> </p> <p>In case of any questions, please contact Sebastian Soyk (sebastian.soyk@unil.ch)</p>
FIG. 6 in Caribou hunting and utilization in West Greenland: Past and present variants
FIG. 6. — Caribou utilization in Angujâartorfiup Nunâ after 2000 AD. (Rangifer tarandus after Beauval & Coutureau © 2003, Archeozoo.org)
ScienceDex guides
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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.