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114 results for “Molecular heterogeneity”
Heterogenized Molecular Rhodium Phosphine Catalysts within Metal–Organic Frameworks for Alkene Hydroformylation
<p>Supplementary information for associated publication</p>
Supplementary Information for Heterogeneous Parallelization and Acceleration of Molecular Dynamics Simulations in GROMACS
<p>Supplementary information for<br> Páll, S., Zhmurov, A., Bauer, P., Abraham, M., Lundborg, M., Gray, A., Hess, B, & Lindahl, E.. (2020). Heterogeneous Parallelization and Acceleration of Molecular Dynamics Simulations in GROMACS. The Journal of Chemical Physics, 2020</p> <p>Contains benchmark methodology description as well as all inputs used in the application performance benchmarks included the paper.</p>
Heterogeneity in the rate of molecular sequence evolution substantially impacts the accuracy of detecting shifts in diversification rates
<p>As species richness varies along the tree of life, there is a great interest in identifying factors that affect the rates by which lineages speciate or go extinct. To this end, theoretical biologists have developed a suit of phylogenetic comparative methods that aim to identify where shifts in diversification rates had occurred along a phylogeny and whether they are associated with some traits. Using these methods, numerous studies have predicted that speciation and extinction rates vary across the tree of life. In this study we show that asymmetric rates of sequence evolution rates lead to systematic biases in the inferred phylogeny, which in turn lead to erroneous inferences regarding lineage diversification patterns. The results demonstrate that as the asymmetry in sequence evolution rates increases, so does the tendency to select more complicated models that include the possibility of diversification rate shifts. These results thus suggest that any inference regarding shifts in diversification pattern should be treated with great caution, at least until any biases regarding the molecular substitution rate have been ruled out.</p>
Mutation and methylation data for study: Assessment of the molecular heterogeneity of E-cadherin expression in invasive lobular breast cancer
<p>Processed mutation data and DNA methylation beta values published with this study.</p>
Dataset of the article 'Spectroelectrochemistry of water oxidation kinetics in molecular versus heterogeneous oxide iridium electrocatalysts'
<p>Dataset of the article 'Spectroelectrochemistry of Water Oxidation Kinetics in Molecular versus Heterogeneous Oxide Iridium Electrocatalysts', <a href="https://doi.org/10.1021/jacs.2c02006">https://doi.org/10.1021/jacs.2c02006</a>.</p> <p>Abstract: Water oxidation is the step limiting the efficiency of electrocatalytic hydrogen production from water. Spectroelectrochemical analyzes are employed to make a direct comparison of water oxidation reaction kinetics between a molecular catalyst, the dimeric iridium catalyst [Ir<sub>2</sub>(pyalc)<sub>2</sub>(H<sub>2</sub>O)<sub>4</sub>-(µ-O)]<sup>2+</sup> (<strong>Ir<sub>Molecular</sub></strong>,<strong><sub> </sub></strong>pyalc = 2-(2’pyridinyl)-2-propanolate) immobilized on a mesoporous indium tin oxide (ITO) substrate, with that of an heterogenous electrocatalyst, an amorphous hydrous iridium (<strong>IrO<sub>x</sub></strong>) film. For both systems, four analogous redox states were detected, with the formation of Ir(4+)-Ir(5+) being the potential-determining step in both cases. However, the two systems exhibit distinct water oxidation reaction kinetics, with potential-independent first-order kinetics for <strong>Ir<sub>Molecular</sub></strong> contrasting with potential-dependent kinetics for <strong>IrO<sub>x</sub></strong>. This is attributed to water oxidation on the heterogenous catalyst requiring co-operative effects between neighboring oxidized Ir centers. The ability of <strong>Ir<sub>Molecular</sub></strong> to drive water oxidation without such co-operative effects is explained by the specific coordination environment around its Ir centers. These distinctions between molecular and heterogenous reaction kinetics are shown to explain the differences observed in their water oxidation electrocatalytic performance under different potential conditions.</p>
Determination and Evaluation of the Non-Additivity in Wetting of Molecularly Heterogeneous Surfaces
<p>All the data relevant to the paper "Determination and Evaluation of the Non-Additivity in Wetting of Molecularly Heterogeneous Surfaces" (PNAS, 2019) are available here.</p> <p>The details about the data collection and analysis are available in the paper.</p> <p>The data are divided into two zipped folders: one of them contains all the experimental data (experiments.zip) and the other one all the files related to the molecular dynamics simulations (MD.zip). Each of the two folders contains a README.txt file, with details about the folder and its structure.</p>
Profiling the Heterogeneity of Colorectal Cancer Consensus Molecular Subtypes using Spatial Transcriptomics: datasets
<p>You can find here the datasets used in the publication: </p> <p><em><strong>Valdeolivas, A., Amberg, B., Giroud, N. et al. Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics. npj Precis. Onc. 8, 10 (2024). https://doi.org/10.1038/s41698-023-00488-4</strong> </em></p> <p>This contents the raw Spatial Transcriptomics data, spot categorization made by pathologist, the results of the deconvolution and intermediary files required to run the analysis described in our manuscript and available in Github: </p> <p><a href="https://github.com/alberto-valdeolivas/ST_CRC_CMS">https://github.com/alberto-valdeolivas/ST_CRC_CMS</a></p> <p>In particular, you will find here several zip compressed files with the following content: </p> <p>- <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/Intermediary_FileObjects.zip?versionId=989cd48d-45f6-46b9-9f90-1927af392a7e">Intermediary_FileObjects.zip</a>: The intermediary files generated in the scripts hosted in the github repo and required to run some later scripts. </p> <p>- <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/IntermediaryFiles_ST_CRC_LiverMetastasis.zip">IntermediaryFiles_ST_CRC_LiverMetastasis.zip</a>: The intermediary files generated in the scripts hosted in the github repo and required to run some of the scripts dealing with the external CRC ST dataset used in our manuscript. </p> <p>- <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/Pathology_SpotAnnotations.zip?versionId=ce657a54-9fec-4633-9d89-31f1479b93b7">Pathology_SpotAnnotations.zip</a>: The categories assigned by the pathologists to all the spots across our set ST samples to a different anatomical category (tumor, stroma, non-neoplastic mucosa...) </p> <p>-<a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A121573_Rep1.zip?versionId=dbfaad0f-784b-44c9-91d1-713f063d64e3">SN048_A121573_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A121573_Rep2.zip?versionId=ae997080-ca69-44c3-86aa-65bc1d5ef120">SN048_A121573_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A416371_Rep1.zip?versionId=e453ed45-22d8-4d60-b7f3-4daa9212cc88">SN048_A416371_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A416371_Rep2.zip?versionId=be395926-eee8-4670-b355-125e72bf6281">SN048_A416371_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A551763_Rep1.zip?versionId=6b7fa01a-a0d9-43e7-8d1d-8c56cb422374">SN123_A551763_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A595688_Rep1.zip?versionId=f625a286-fbc7-48f6-a57d-7d0df67a0574">SN123_A595688_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A798015_Rep1.zip?versionId=3540f1e5-9cf4-412c-887c-b1d0cc4e03c5">SN123_A798015_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A938797_Rep1_X.zip?versionId=de59c354-fea2-4843-a5f9-5e7a8d863e51">SN123_A938797_Rep1_X.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A551763_Rep2.zip?versionId=9da50bec-8ba4-41b0-a29e-4fc778cf12b7">SN124_A551763_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A595688_Rep2.zip?versionId=29c3e99e-7db2-4c02-9004-dc9d8abf3c27">SN124_A595688_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A798015_Rep2.zip?versionId=a0cf2cca-f3c9-4c45-b311-1ddc81371e35">SN124_A798015_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A938797_Rep2.zip?versionId=e6e4e2bc-1593-4c0f-ac37-b00cc2fc1124">SN124_A938797_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN84_A120838_Rep1.zip?versionId=ec31a69e-d0ce-4e4c-82dc-e7f2e617631a">SN84_A120838_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN84_A120838_Rep2.zip?versionId=89c89532-7bb1-47e4-8900-b5c12a7c4ba0">SN84_A120838_Rep2.zip</a>: The output of Space Ranger, including processed count data matrices and histological images, for the ST data generated in this study</p> <p>- <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/DeconvolutionResults_ST_CRC_BelgianCohort.zip">DeconvolutionResults_ST_CRC_BelgianCohort.zip</a>, <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/DeconvolutionResults_ST_CRC_KoreanCohort.zip">DeconvolutionResults_ST_CRC_KoreanCohort.zip</a>, <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/DeconvolutionResults_ST_CRC_LiverMetastasis.zip">DeconvolutionResults_ST_CRC_LiverMetastasis.zip</a>: These files contain the main results obtained when using the Cell2Location deconvolution approach in our samples (with two different references: Korean and Belgian cohorts) and in the external set of CRC ST samples (only Korean cohort)</p> <p> </p> <p>- We have also uploaded the whole slide images (WSI). These are the files with an ndpi extension: </p> <p><br><a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V10B01-048_new%20CRC_2021_02_16.ndpi?versionId=d5c8cbd3-40de-43da-8370-329def9e4f14">Visium Frozen_SN V10B01-048_new CRC_2021_02_16.ndp ...</a> (samples A121573_Rep1, A121573_Rep2, A416371_Rep1 and A416371_Rep2), <a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V19S23-084.ndpi?versionId=6d91b1f9-56e9-45c3-a2e6-4714975678fb">Visium Frozen_SN V19S23-084.ndpi</a> (samples A120838_Rep1 and A120838_Rep2), <a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V19S23-123.ndpi?versionId=c535482c-0a3c-4ba5-a056-f96796c366b0">Visium Frozen_SN V19S23-123.ndpi</a> (samples A551763_Rep1, A595688_Rep1, A798015_Rep1, A938797_Rep1) and <a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V19S23-124.ndpi?versionId=49ce857c-47bb-4930-bb0c-09213e4acf28">Visium Frozen_SN V19S23-124.ndpi</a> (samples A551763_Rep2, A595688_Rep2, A798015_Rep2 and A938797_Rep2)</p> <p>- We have now included the fastq and Bam files for the different samples, excluding replicate 1 of the A938797 sample whose fastq files are missing: </p> <p><strong>IMPORTANT: Fastq files are in version 1, while bam files are in version 2 of the dashboards reported below: </strong></p> <ol> <li>Sample <a href="https://doi.org/10.5281/zenodo.13991781">S1_Cec</a> (A551763)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.14006187">S2_Col_R </a>(A595688)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.13987002">S3_Col_R </a>(A416371) </li> <li>Sample <a href="https://doi.org/10.5281/zenodo.13990328">S4_Col_Sig </a>(A120838)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.13989699">S5_Rec </a>(A121573)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.14008051">S6_Rec </a>(A938797)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.14006810">S7_Rec/Sig</a> (A798015)</li> </ol> <p> </p> <p> </p>
Heterogeneity in the rate of molecular sequence evolution substantially impacts the accuracy of detecting shifts in diversification rates
Open the record for dataset details and reuse information.
Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics: fastq & bam files - Sample S5_Rec
<p>You can find here the fastq and bam files related to the datasets used in the publication: </p> <p>In this particular upload, you can find the fastq (version1) and bam (version2) files of the two replicates of sample S5_Rec (A121573)</p> <p><strong>Valdeolivas, A., Amberg, B., Giroud, N. <em>et al.</em> Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics. <em>npj Precis. Onc.</em> 8, 10 (2024). https://doi.org/10.1038/s41698-023-00488-4</strong></p> <p> </p> <p> </p>
Combining molecular data sets with strongly heterogeneous taxon coverage enlightens the peculiar biogeographic history of stoneflies (Insecta: Plecoptera)
<p class="Standard1">Extant members of the ancient insect order of stoneflies exhibit a disjunct, antitropical distribution, with one major lineage exclusively occurring in the Southern Hemisphere and the other, with few exceptions, on the Northern continents. Here, we address the biogeographic distribution and phylogenetic relationships of stoneflies using a phylogenetic workflow that combines both transcriptomic and Sanger sequence datasets with heterogeneous taxon coverage. We used a dataset comprising 2997 genes derived from the transcriptomes of 30 species and Sanger sequences of seven genes for 498 species. The backbone phylogeny was mainly inferred from the transcriptomic data, whereas the Sanger nucleotide sequence data provided high species density for divergence time estimation and diversification analyses. Our results show that the biogeographic pattern we observe today is primarily more likely shaped by long-distance over-land dispersal than by vicariance. We inferred that the ancestors of extant stoneflies originated in the Northern Hemisphere approximately 265 Ma and were presumably restricted to this area due to climatic and geographic boundaries. Our analyses suggest that with the break-up of Pangaea around 200 Ma and the associated climatic and geographical changes, two groups of stoneflies, the Anarctoperlaria and the Notonemouridae, dispersed to Gondwana and subsequently went extinct on the northern continents. Both groups likely dispersed across Gondwana before its break-up into the modern continents. At least one member of another group of 'northern' stoneflies, the Acroneuriinae, seems to have migrated from North America to South America around 67 Ma. We found four major net diversification rate shifts, indicating rapid radiation patterns that hampered a robust phylogenetic placement of these stonefly groups. Our study provides the first conclusive evolutionary explanation for the unique distribution pattern of stoneflies.</p>
Dataset for the paper: "Using molecular oxygen and Fe-N/C heterogeneous catalysts to achieve Mukaiyama epoxidations via in situ produced organic peroxy acids and acylperoxy radicals"
<p>The data in this spreadsheet was used to produce the figures in the paper </p> <p>Authors: M. Gong, Y. Guo, D. Malko, G. J.P. Britovsek, J. Rubio-Garcia, J. Dawson and A. Kucernak</p> <p>Title: Using molecular oxygen and Fe-N/C heterogeneous catalysts to achieve Mukaiyama epoxidations via in situ produced organic peroxy acids and acylperoxy radicals</p> <p>Journal: Catalysis Science & Technology</p> <p>DOI: 10.1039/D2CY00356B</p> <p>Please cite the above reference if you wish to use this data </p> <p> </p> <p>DOI of data: 10.5281/zenodo.6384017</p>
Data used for the paper Daniel Malko, Yanjun Guo, Pip Jones, George Britovsek, and Anthony Kucernak, "Heterogeneous iron containing carbon catalyst (Fe-N/C) for epoxidation with molecular oxygen|", Journal of Catalysis, 2019, DOI:10.1016/j.jcat.2019.01.008
<p>The data in this spreadsheet was used to produce the figures in the paper</p> <p>Daniel Malko, Yanjun Guo, Pip Jones, George Britovsek, and Anthony Kucernak</p> <p>Heterogeneous iron containing carbon catalyst (Fe-N/C) for epoxidation with molecular oxygen</p> <p>Journal of Catalysis</p> <p>DOI:10.1016/j.jcat.2019.01.008</p> <p>Please cite the above reference if you wish to use this data<br> DOI of this data file is: 10.5281/zenodo.2539183</p>
Unravelling the Molecular Structure and Confining Environment of an Organometallic Catalyst Heterogenized within Amorphous Porous Polymers
<p>Raw data corresponding to Figure 4c of the following publicaiton: </p> <div> <div> <div>Jabbour, R.; Ashling, C. W.; Robinson, T. C.; Khan, A. H.; Wisser, D.; Berruyer, P.; Ghosh, A. C.; Ranscht, A.; Keen, D. A.; Brunner, E.; Canivet, J.; Bennett, T. D.; Mellot-Draznieks, C.; Lesage, A.; Wisser, F. M. Unravelling the Molecular Structure and Confining Environment of an Organometallic Catalyst Heterogenized within Amorphous Porous Polymers. <em>Angewandte Chemie International Edition</em> <strong>2023</strong>, <em>62</em> (44), e202310878. <a href="https://doi.org/10.1002/anie.202310878">https://doi.org/10.1002/anie.202310878</a>.</div> </div> </div>
Breast Cancer Proteomics and Molecular Heterogeneity
ClinicalTrials.gov study NCT01840293. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Combining molecular data sets with strongly heterogeneous taxon coverage enlightens the peculiar biogeographic history of stoneflies (Insecta: Plecoptera)
Open the record for dataset details and reuse information.
Data From: Investigating the Effect of GLU283 Protonation State on the Conformational Heterogeneity of CCR5 by Molecular Dynamics Simulations
<p>This dataset contains MD simulation results for CCR5 receptor in different states. There are three states this GPCR could be in: 1.apo state, i.e. not bound to any other protein or ligand 2.holo state, i.e. bound by maraviroc (MRV) 3.GP120 bound state, i.e. bounded to HIV envelope protein GP120 and human receptor CD4. </p> <p>For apo state simulations, three different starting structure were used and the simulation results for them are given in three different folders named after the PDB ID of initial structures. </p> <p>One critical residue of CCR5 receptor, GLU283, was considered in two different protonation state, hence there are two folders for each studied system, GLU283 and GLH283 for differently protonated systems. </p> <p>The MD trajectories of studied systems are in dcd format and due to size issues waters, ions and membrane atoms were removed. </p> <p>For each system, three replica MD simulations were performed, hence, there are three folders with names REPLICA1, REPLICA2, REPLICA3.</p> <p> </p>
Data from: Reporting tumor molecular heterogeneity in histopathological diagnosis
Background: Detection of molecular tumor heterogeneity has become of paramount importance with the advent of targeted therapies. Analysis for detection should be comprehensive, timely and based on routinely available tumor samples. Aim: To evaluate the diagnostic potential of targeted multigene next-generation sequencing (TM-NGS) in characterizing gastrointestinal cancer molecular heterogeneity. Methods: 35 gastrointestinal tract tumors, five of each intestinal type gastric carcinomas, pancreatic ductal adenocarcinomas, pancreatic intraductal papillary mucinous neoplasms, ampulla of Vater carcinomas, hepatocellular carcinomas, cholangiocarcinomas, pancreatic solid pseudopapillary tumors were assessed for mutations in 46 cancer-associated genes, using Ion Torrent semiconductor-based TM-NGS. One ampulla of Vater carcinoma cell line and one hepatic carcinosarcoma served to assess assay sensitivity. TP53, PIK3CA, KRAS, and BRAF mutations were validated by conventional Sanger sequencing. Results: TM-NGS yielded overlapping results on matched fresh-frozen and formalin-fixed paraffin-embedded (FFPE) tissues, with a mutation detection limit of 1% for fresh-frozen high molecular weight DNA and 2% for FFPE partially degraded DNA. At least one somatic mutation was observed in all tumors tested; multiple alterations were detected in 20/35 (57%) tumors. Seven cancers displayed significant differences in allelic frequencies for distinct mutations, indicating the presence of intratumor molecular heterogeneity; this was confirmed on selected samples by immunohistochemistry of p53 and Smad4, showing concordance with mutational analysis. Conclusions: TM-NGS is able to detect and quantitate multiple gene alterations from limited amounts of DNA, moving one step closer to a next-generation histopathologic diagnosis that integrates morphologic, immunophenotypic, and multigene mutational analysis on routinely processed tissues, essential for personalized cancer therapy.
Data from: Integrated molecular imaging reveals tissue heterogeneity driving host-pathogen interactions
All diseases are characterized by distinct changes in tissue molecular distribution. Molecular analysis of intact tissues traditionally requires pre-existing knowledge of, and reagents for, the targets of interest. Conversely, label-free discovery of disease-associated tissue analytes requires destructive processing for downstream identification platforms. Tissue-based analyses therefore sacrifice discovery to gain spatial distribution of known targets, or sacrifice tissue architecture for discovery of unknown targets. To overcome these obstacles, we developed a multi-modality imaging platform for discovery-based molecular histology. We apply this platform to a model of disseminated infection triggered by the important pathogen Staphylococcus aureus, leading to the discovery of infection-associated alterations in the distribution and abundance of proteins and elements in tissue. These data provide an unbiased, three-dimensional analysis of how disease impacts the molecular architecture of complex tissues, enable culture-free diagnosis of infection through imaging-based detection of bacterial and host analytes, and reveal molecular heterogeneity at the host-pathogen interface.
Cellular and molecular heterogeneities and signatures, and pathological trajectories of fatal COVID-19 lungs defined by spatial single-cell transcriptome analysis
<p>Spatial in-situ data analysis.</p>
Data from: Heterogeneous rates of molecular evolution and diversification could explain the Triassic age estimate for angiosperms
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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.