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.
160
datasets available to search
ShareScore release 0.7.1
Dataset results
160 results for “Cell size”
Geografisches hexagonales Gitter mit 1 Quadratkilometer Zellengröße für Deutschland - Geographical hexagonal grid with one square kilometer cell size for Germany
<p>Ein räumliches Gitter stellt eine abstrakte Definition von einheitlich großen Bezugszellen für statistische Auswertungen dar. Dieses macht Auswertungen und Vergleiche gegenüber räumlichen Abgrenzungen auf der Basis von administrativen Grenzen wie Kreisen oder Gemeinden unterschiedlicher Größen einfacher. Geografische Gitterdefinitionen werden eingesetzt, um eine vordefinierte Raumbezugsstruktur zu haben mit einer einheitlicheren Verteilung von Zellen in der Fläche.</p> <p><br> Ein Gitter mit sechseckigen, eben hexagonalen Zellen bietet eine Alternative zu rechteckigen statistischen Gittern wie das GeoGitter vom Bundesamt für Kartographie und Geodäsie (BKG, 2020). Ein Hexagon-Gitter bietet neben der Eigenschaft homogene Einheiten für statistische Analysen zu bilden (Schindler et al. 2008) mit sechs unmittelbaren Nachbarzellen vorteilhaftere Voraussetzungen für Nachbarschaftsanalysen im Vergleich zu quadratischen Zellen mit vier Kantennachbarn (White et al. 1992). Als Beispiel werden im Bild 1 mittels Hexagonen-Gitter Volumenunterschiede der aufragenden Vegetation pro Zelle dargestellt.</p>
Stacks of microCT Scans, Cell size, weight, volume and thallus size data supporting the paper 'Mechanical regulation of tissue flatness in Marchantia'
<div> <div> <div> <p>These data are the supporting elements to the following paper: 'Mechanical regulation of tissue flatness in Marchantia'</p> </div> </div> </div> <p> .tif files contain MicroCT (MCT) scans of 16-day-old <em>Marchantia polymorpha</em> thalli. Three genotypes were analysed here: <strong><em>fer-2</em></strong> mutant (from Mecchia et al., 2022), <strong>FER-OE #9</strong> (proMpEF1::MpFERONIA-mCitrine trangenic line 9)<strong> </strong>from Mecchia et al., 2022), and Tak-1 (WT line). These plants were grown in 3 different media: Gamborgh B5 + vitamins and 0.6, 1.2 and 2.5% agar, and one stress condition consisting of the adjunction of a thin PDMS film at 4, to mimich external mechanical stimulus (only performed on thalli grown on 1.2% agar).</p> <p>MicroCT scans were performed at the faculity of odontology of Université Paris-Cité (Plateform imagerie du vivant) with the technical support of Lotfi Slimani and Baptiste Casel. https://piv.u-paris.fr/micro-ct-haute-resolution/ </p> <p>All files already have embeded scales.</p> <p>Each file name consists of a unique ID number in the following form:</p> <p>P+<LETTER>+<NUMBER>-<CONDITION></p> <p>-LETTER: One letter = one imaging session</p> <p>-NUMBER: Individual and Genotype: 33-40 -> Tak1; 200-207-><em>fer-2</em>; 41-49 -> FER-OE</p> <p>-CONDITION : AGAR0.6/AGAR2.5/PDMS. Absence of condition indicates growth on standard medium (1.2% agar). PDMS indicated growth on standard medium and supplementation of a topping PDMS film at day 4)</p> <p> </p> <p>-Volume data were calculated from MicroCT scans</p> <p>-thallus projected surfaces were calculated from MicroCT scans</p> <p><a href="https://zenodo.org/api/records/13981438/draft/files/Lambda%20curvature%20calculation.ipynb/content" target="_blank" rel="noopener noreferrer">-Lambda curvature calculation.ipynb</a> is suited for MorphographX mesh exported .txt files.</p> <p> </p> <p> </p> <p> </p>
Paper data and code of manuscript: Intraspecific variation on heat tolerance in a model ectotherm: effects of body mass, cell size, oxygen and sex
<p>When using the data or code from this manuscript, please cite it as:</p><p><strong>Leiva FP</strong>, Santos M, Rezende E, & Verberk WCEP. 2021. Paper data and code of manuscript: Intraspecific variation on heat tolerance in a model ectotherm: effects of body mass, cell size, oxygen and sex. Zenodo. <a href="https://doi.org/10.5281/zenodo.5120028">https://doi.org/10.5281/zenodo.5120028</a>.</p>
Text-fig. 10. Platanoxylon cf. haydenii, a, e, h: UF 279-34470; b, c, d, f g: UF 279-34469. a, b: Diffuse porous wood with vessels solitary and in small multiples, which are mostly tangential or oblique, diffuse and diffuse-in-aggregates axial parenchyma., TS. c–e: Scalariform perforation plates. f, g: Opposite intervessel pits, TLS. h: Two size classes of rays, TLS. Platanus sp., UF 279- 24552. i: Predominantly solitary vessels, diffuse and diffuse-in-aggregates parenchyma, growth ring boundary distinct, noded rays, TS. j: Simple perforation plates (PP), RLS. k: Body of ray with procumbent ray cells, RLS. l: Scalariform perforation plate, RLS. m: Rays of two sizes, wide rays>10-seriate, TLS. Scale bars: 200 µm in a, b, h, i, m; 100 µm in j, k: 50 µm in c, d, e, f, l. in A Diverse Assemblage Of Late Eocene Woods From Oregon, Western Usa
Text-fig. 10. Platanoxylon cf. haydenii, a, e, h: UF 279-34470; b, c, d, f g: UF 279-34469. a, b: Diffuse porous wood with vessels solitary and in small multiples, which are mostly tangential or oblique, diffuse and diffuse-in-aggregates axial parenchyma., TS. c–e: Scalariform perforation plates. f, g: Opposite intervessel pits, TLS. h: Two size classes of rays, TLS. Platanus sp., UF 279- 24552. i: Predominantly solitary vessels, diffuse and diffuse-in-aggregates parenchyma, growth ring boundary distinct, noded rays, TS. j: Simple perforation plates (PP), RLS. k: Body of ray with procumbent ray cells, RLS. l: Scalariform perforation plate, RLS. m: Rays of two sizes, wide rays>10-seriate, TLS. Scale bars: 200 µm in a, b, h, i, m; 100 µm in j, k: 50 µm in c, d, e, f, l.
Text-fig. 2. Celtis popsii sp. nov., UF 279-34460. a: Growth ring with earlywood of multiple rows of vessels solitary and in radial multiples; latewood vessels in wavy tangential bands, TS. b: Growth ring boundary, latewood vessels in multiples with axial parenchyma confluent, TS. c: Simple perforation plates, alternate intervessel pits, polygonal in outline, TLS. d: Vessel-ray parenchyma pits to right of VRP, oval in outline, with slightly reduced borders, RLS. e: Rays tending to two sizes, some multiseriate rays with distinct sheath cells, multiseriate rays usually with 1 marginal row of square to upright cells, occasionally with 4 or more; uniseriate rays less than 10 cells high, TLS. f: Detail of multiseriate ray with distinct sheath cells, vessel element end walls, TLS. Scale bars: 200 µm in a, e; 100 µm in b; 50 µm in c, f; 20 µm in d. in A Diverse Assemblage Of Late Eocene Woods From Oregon, Western Usa
Text-fig. 2. Celtis popsii sp. nov., UF 279-34460. a: Growth ring with earlywood of multiple rows of vessels solitary and in radial multiples; latewood vessels in wavy tangential bands, TS. b: Growth ring boundary, latewood vessels in multiples with axial parenchyma confluent, TS. c: Simple perforation plates, alternate intervessel pits, polygonal in outline, TLS. d: Vessel-ray parenchyma pits to right of VRP, oval in outline, with slightly reduced borders, RLS. e: Rays tending to two sizes, some multiseriate rays with distinct sheath cells, multiseriate rays usually with 1 marginal row of square to upright cells, occasionally with 4 or more; uniseriate rays less than 10 cells high, TLS. f: Detail of multiseriate ray with distinct sheath cells, vessel element end walls, TLS. Scale bars: 200 µm in a, e; 100 µm in b; 50 µm in c, f; 20 µm in d.
◂Fig. 1 Morphology of thecate and coccoid cells, with labelled thecal plates. a–c, i, m Light microscopy, d–h, k–l scanning electron microscopy. a Ventral view of strain GeoM*788; b dorsal view of strain GeoM*793; c apical view of strain GeoK*044; d ventral view of strain GeoK*037; e dorsal view of strain GeoM*788; f apical view of strain GeoK*024, with the dehiscence of epithecal opening indicated by a blue line; g antapical view of strain GeoK*044; h leftlateral view of strain GeoM*866; i motile cell of strain GeoK*037; k–m coccoid cells showing variability in shape and size of strains k GeoM*866, l GeoM*793 and m GeoK*024. Abbreviations: n′: apical plate, n′′: precingular plate, n′′′: postcingular plate, n′′′′: antapical plate, na: anterior intercalary plate, nC: cingular plate, Sa: anterior sulcal plate, Sd: right sulcal plate, Sp: posterior sulcal plate. Ss: left sulcal plate. Scale bar: 10 µm. UA: 15 kV in Morphological and molecular variability of Peridinium volzii Lemmerm. (Peridiniaceae, Dinophyceae) and its relevance for infraspecific taxonomy
◂Fig. 1 Morphology of thecate and coccoid cells, with labelled thecal plates. a–c, i, m Light microscopy, d–h, k–l scanning electron microscopy. a Ventral view of strain GeoM*788; b dorsal view of strain GeoM*793; c apical view of strain GeoK*044; d ventral view of strain GeoK*037; e dorsal view of strain GeoM*788; f apical view of strain GeoK*024, with the dehiscence of epithecal opening indicated by a blue line; g antapical view of strain GeoK*044; h leftlateral view of strain GeoM*866; i motile cell of strain GeoK*037; k–m coccoid cells showing variability in shape and size of strains k GeoM*866, l GeoM*793 and m GeoK*024. Abbreviations: n′: apical plate, n′′: precingular plate, n′′′: postcingular plate, n′′′′: antapical plate, na: anterior intercalary plate, nC: cingular plate, Sa: anterior sulcal plate, Sd: right sulcal plate, Sp: posterior sulcal plate. Ss: left sulcal plate. Scale bar: 10 µm. UA: 15 kV
Fig. 4 in Genome size of chrysophytes varies with cell size and nutritional mode
Fig. 4 Comparison of genome size within different taxonomic groups. Mixotrophic (blue) and heterotrophic (dark red) chrysophytes rank among the smallest eukaryotic genomes (values obtained from [1] Mohanta and Bae 2015; Egertová and Sochor 2017; [2] Gregory 2017; [3] Bennett 2012; [4] Courties et al. 1994)
Fig. 1 in Genome size of chrysophytes varies with cell size and nutritional mode
Fig. 1 Cell volumes [μm 3] of different chrysophytes. Different colors represent the different nutritional modes present. Phototrophic chrysophytes (light green) do have highest cell volumes compared to
Fig. 2 in Genome size of chrysophytes varies with cell size and nutritional mode
Fig. 2 Genome size [pg] of investigated chrysophytes. Different colors represent the different nutritional modes present. Heterotrophic chrysophytes (dark red) tend to have smaller genome sizes, compared to phototrophic chrysophytes (light green), while mixotrophic chrysophytes (blue) show intermediate genome sizes. *Dinobryon sociale var. americana cf. div. schauinslandii; HF = Heterotrophic flagellate
Fig. 5 in Genome size of chrysophytes varies with cell size and nutritional mode
Fig. 5 Model of evolution of genome size, cell volume, and nutritional mode of chrysophytes: nutrient limitations may have driven genome size reduction in the ancestors of mixotrophic (and heterotrophic) chrysophytes, as well as the evolution of phagotrophic mechanisms to attain additional nutrients. Cell size reduction is supposedly a more gradual process, coming into play in taxa which were already able to obtain nutrients by phagotrophy, which optimized food uptake by the optimization of the predator-prey size ratio. This may have triggered the evolution of obligate heterotrophs in many chrysophyte lineages independently
Wings: 68, Loew system for wing cells (Mydas lividus Curran); 69, Comstock Needham system for wing veins tNernomouiae pantherinus Gerstaecker); 70, Apiophora paulseni Philippi, a: open 2nd submarginal cell; b: reduced size of axillary lobe; 71., Mitrodetus sp.; 72 Pseudonomoneura sp. in The American Genera of Mydidae (Diptera), with the Description of three new Genera and two new Species
Wings: 68, Loew system for wing cells (Mydas lividus Curran); 69, Comstock Needham system for wing veins tNernomouiae pantherinus Gerstaecker); 70, Apiophora paulseni Philippi, a: open 2nd submarginal cell; b: reduced size of axillary lobe; 71., Mitrodetus sp.; 72 Pseudonomoneura sp.
Genome Sizes of Bacterial Species Detected in Cell-Free DNA of Patients with Acute Leukemia and Sepsis, Including Those Undergoing Bone Marrow Transplantation
<p>Next Generation Sequencing (NGS) analysis of Cell-Free DNA provides valuable insights into a spectrum of pathogenic species (particularly bacterial) in blood. Patients with Sepsis often face problems like delays in treatment regimens (combination or cocktail of antibiotics) due to the long turnaround time (TAT) of classical and standard blood culture procedures. NGS gives results with lower TAT along with high-depth coverage. The use of NGS may be a possible solution to deciding treatment regimens for patients without losing precious time and more accurately possibly saving lives.</p> <p>Our curated dataset is of bacterial species or strains detected along with their genome size in 107 AML patients diagnosed with Sepsis clinically. Cell-free DNA profiles of patients were built and sequencing was done in Illumina (NovaSeq and NextSeq). Bioinformatic analysis was performed using two classification algorithms namely kraken2 and kaiju. For kraken2 based classification reference bacterial index developed by Carlo Ferravante et al (Zenodo 2020) (link: https://zenodo.org/records/4055180) was used, while for kaiju-based classification reference database named "nr_euk" dated "2023-05-10" (link: https://bioinformatics-centre.github.io/kaiju/downloads.html) was used.</p> <p>Genome size annotation is important in metagenomics since for the use of depth of coverage (abundance), genome size is required. In metagenomic classification algorithms like kraken/kraken2 and kaiju output computes reads assigned only and not abundance. In kaiju, the problem is more complicated since the reference database does not have a fasta file but only an index file from which alignment is done. </p> <p>To address the above challenges to compute "depth of coverage" or simply abundance, we build a Genome size annotator tool (https://github.com/patkarlab/Genome-Size-Annotation) which provides genome size for each species detected given its taxid is available. In this tool, the NCBI Datasets tool, NCBI Genome API check tool, and Data Mining from AI search engines like perplexity.ai are used. </p> <p>We have curated two datasets</p> <p>Kraken2 dataset named "FINAL METAGENOMIC DATA MASTERSHEET - kraken_genome_annotation"<br>Kaiju dataset named "FINAL METAGENOMIC DATA MASTERSHEET - kaiju_genome_annotation"</p> <p>*Please note that for kraken2 curated dataset, we used data mining from the AI search engine perplexity.ai while for kaiju we did not use perplexity, ai, and any species whose genome size was not found was labeled "NA"</p>
Text-fig. 13. Scanning electron microscope (SEM) images of pollen or spore clump with pollen grains or spores of unknown affinity that occur separately or adhering together in dyads, triads and tetrads; Torres Vedras locality, Portugal. a) Clump of pollen or spores that yielded the pollen or spores in this Text-figure; b–f) Grains adhering together in twos, threes or fours (b–e) or occurring singly and apparently with a proximal trilete mark (f); note that the adhering grains are connected by a smooth bandlike covering, perhaps remains of the microspore mother cell; note also abundant orbicules of various sizes among and over the grains. Specimen, TV44-S148149. Scale bars 300 Μm (a), 30 Μm (b–f). in The Early Cretaceous Mesofossil Flora Of Torres Vedras (Ne Of Forte Da Forca), Portugal: A Palaeofloristic Analysis Of An Early Angiosperm Community
Text-fig. 13. Scanning electron microscope (SEM) images of pollen or spore clump with pollen grains or spores of unknown affinity that occur separately or adhering together in dyads, triads and tetrads; Torres Vedras locality, Portugal. a) Clump of pollen or spores that yielded the pollen or spores in this Text-figure; b–f) Grains adhering together in twos, threes or fours (b–e) or occurring singly and apparently with a proximal trilete mark (f); note that the adhering grains are connected by a smooth bandlike covering, perhaps remains of the microspore mother cell; note also abundant orbicules of various sizes among and over the grains. Specimen, TV44-S148149. Scale bars 300 Μm (a), 30 Μm (b–f).
Substrate stiffness reduces particle uptake by epithelial cells and macrophages in a size-dependent manner through mechanoregulation
<p>Cells continuously exert forces on their environment and respond to changes in mechanical forces by altering their behavior. Many pathologies such as cancer and fibrosis are hallmarked by dysregulation in the extracellular matrix, driving aberrant behavior through mechanotransduction pathways. We demonstrate that substrate stiffness can be used to regulate cellular endocytosis of particles in a size-dependent fashion. Culture of A549 epithelial cells and J774A.1 macrophages on polystyrene/glass (stiff) and polydimethylsiloxane (soft) substrates indicated that particle uptake is increased up to six times for A549 and two times for macrophages when cells are grown in softer environments. Furthermore, we altered surface characteristics through the attachment of submicron-sized particles as a method to locally engineer substrate stiffness and topography. To investigate the biomechanical changes which occurred within adherent epithelial cells, i.e. characterization of A549 cell spreading and focal adhesion maturation. Consequently, decreasing substrate rigidity and particle-based topography lead to a reduction of focal adhesion size. Moreover, expression levels of Yes-associated protein were found to correlate with the degree of particle endocytosis. A thorough appreciation of the mechanical cues may lead to improved solutions to optimize nanomedicine approaches for treatment of cancer and other diseases with abnormal mechanosignaling.</p>
Data from: Cell size, photosynthesis and the package effect: an artificial selection approach
Open the record for dataset details and reuse information.
Data from: Are acute and acclimated thermal effects on metabolic rate modulated by cell size? A comparison between diploid and triploid zebrafish larvae
Being composed of small cells may carry energetic costs related to maintaining ionic gradients across cell membranes as well as benefits related to diffusive oxygen uptake. Here we test the hypothesis that these costs and benefits of cell size in ectotherms are temperature dependent. To study the consequences of cell size for whole-organism metabolic rate we compared diploid and triploid zebrafish larvae differing in cell size. A fully factorial design was applied combining three different rearing and test temperatures that allowed us to distinguish acute from acclimated thermal effects. Individual oxygen consumption rates of diploid and triploid larvae across declining levels of oxygen availability were measured. We found that both acute and acclimated thermal effects affected the metabolic response. In comparison to triploids, diploids responded more strongly to acute temperatures, especially when reared at the highest temperature. These observations support the hypothesis that animals composed of smaller cells (i.e. diploids) are less vulnerable to oxygen limitation in warm aquatic habitats. Furthermore, we found slightly improved hypoxia tolerance in diploids. By contrast, warm-reared triploids had higher metabolic rates when they were tested at acute cold temperature, suggesting that being composed of larger cells may provide metabolic advantages in the cold. We offer two mechanisms as a potential explanation of this result, related to homeoviscous adaptation of membrane function and the mitigation of developmental noise. Our results suggest that being composed of larger cells provides metabolic advantages in cold water, while being composed of smaller cells provides metabolic advantages in warm water.
Cell size-dependent species sensitivity to nanoparticles underlies changes in phytoplankton diversity and productivity
<p>Nanoparticle pollution has been shown to affect various organisms. However, the effects of nanoparticles on species interactions, and the role of species traits, such as body size, in modulating these effects, are not well understood. We addressed this issue using competing freshwater phytoplankton species exposed to copper oxide nanoparticles. Increasing nanoparticle concentration resulted in decreased phytoplankton species growth rates and community productivity (both abundance and biomass). Importantly, we consistently found that nanoparticles had greater negative effects on species with smaller cell sizes, such that nanoparticle pollution weakened the competitive dominance of smaller species and promoted species diversity. Moreover, nanoparticles reduced the growth rate differences and competitive ability differences of competing species, while having little effect on species niche differences. Consequently, nanoparticle pollution reduced the selection effect on phytoplankton community abundance but increased the selection effect on community biomass. Our results suggest cell size as a key functional trait to consider when predicting phytoplankton community structure and ecosystem functioning in the face of increasing nanopollution.</p>
Supporting information for: Discrimination ability of central visual field testing using stimulus size I, II, and III and relationship with macular ganglion cell thickness in chiasmal compression
<p><strong>Purpose</strong><strong>: </strong>To compare the relationship between macular ganglion cell layer (mGCL) thickness and 10-2 visual field (VF) sensitivity using different stimulus sizes in patients with temporal hemianopia from chiasmal compression.</p> <p><strong>Methods:</strong><strong> </strong>A cross-sectional study was conducted involving 30 eyes from 25 patients with temporal VF loss on 24-2 SITA standard automated perimetry due to previous chiasmal compression and 30 healthy eyes (23 controls). Optical coherence tomography (OCT) of the macular area and 10-2 VF testing using Goldmann stimulus size I (GI), II (GII), and III (GIII) were performed in the Octopus 900 perimeter. For the sake of analysis, mGCL thickness and VF data were segregated into four quadrants (two temporal and two nasal) and two halves (temporal and nasal) centered on the fovea, and the groups were compared using generalized estimated equations. The discrimination ability of GI, GII, and GIII was evaluated, as was the correlation between mGCL and 10-2 VF sensitivity using GI, GII, and GIII. </p> <p><strong>Results:</strong><strong> </strong>All mGCL parameters were significantly reduced in patients compared to controls. 10-2 VF test sensitivity using GI, GII, and GIII was significantly lower in patients than in controls (p≤0.008) for all parameters, except the three nasal divisions when using GI (p=0.41, 0.07 and 0.18) Significant correlations were found between temporal VF sectors (all stimulus sizes) and the corresponding nasal mGCL measurements, with similar discrimination ability. Significant correlations were also observed between all three nasal VF divisions and the corresponding temporal mGCL thickness when using stimulus sizes I and II, but not stimulus size III.</p> <p><strong>Conclusions</strong><strong>:</strong> On 10-2 VF testing, GII outperformed GI and GIII with regard to discrimination ability and structure-function correlation with mGCL thickness in chiasmal compression. Our findings suggest that the use of GII can enhance the diagnostic power of 10-2 VF testing, although further studies are necessary to support this conclusion.</p>
Diatom cell-size composition as a novel tool for quantitative estimates of water table in peatlands
<p>Diatom cell-size composition is an indicator of aquatic environmental changes, but has been rarely investigated, especially in semi-terrestrial peatlands. In this study, both taxonomic composition and cell-size composition of diatoms were analyzed in 41 samples from two montane peatlands, northeastern China. Redundancy analyses revealed that diatom taxonomic composition was significantly related to the depth to the water table (DWT) and Ca<sup>2+</sup>, while cell-size composition was significantly associated with DWT and Si. DWT was the most important factor and its sole effect explained 26.2% and 17.9% of the total variance in taxonomic composition and cell-size composition, respectively. Accordingly, diatom-based water-table transfer functions were developed based on taxonomic composition and cell-size composition, respectively. The maximum likelihood (ML) model based on diatom taxonomic composition had the best performance, with the correlation coefficient value (R2) of 0.78 and the root mean squared error of prediction (RMSEP) of 6.66 cm. The ML model based on cell-size composition had similar performance, with the R2 of 0.78 and the RMSEP of 6.87cm, suggesting that diatom cell-size composition can be a new quantitative means to track past water-table changes.</p>
Data S6. Cell Size Validation Dataset
<p>This dataset contains the input values to compute the 2010 MTI in different cell sizes options, and the final MTI values. </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.