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.
1,855
datasets available to search
ShareScore release 0.9.0
Dataset results
1,855 results for “Framework”
The dataset of figures for "The GPU version of LICOM3 under HIP framework and its large-scale application" (updated)
<p>A high-resolution (1/20°) global ocean general circulation model with Graphics processing units (GPUs) code implementations is developed based on the LASG/IAP Climate system Ocean Model version 3 (LICOM3) under Heterogeneous-compute Interface for Portability (HIP) framework. The dynamic core and physics package of LICOM3 are both ported to the GPU, and 3-dimensional parallelization is applied. The HIP version of the LICOM3 (LICOM3-HIP) is 42 times faster than what the same number of CPU cores dose, when 384 AMD GPUs and CPU cores are used. The LICOM3-HIP has excellent scalability; it can still obtain speedup of more than four on 9216 GPUs comparing to 384 GPUs. In this phase, we successfully performed a test of 1/20° LICOM3-HIP using 6550 nodes and 26200 GPUs, and at the grand scale, the model’s time to solution can still obtain an increasing, about 2.72 simulated years per day (SYPD). The high performance was due to putting almost all of computation processes inside GPUs, and thus greatly reduces the time cost of data transfer between CPUs and GPUs. At the same time, a 14-year spin-up integration following the phase 2 of Ocean Model Intercomparison Project (OMIP-2) protocol of surface forcing has been conducted, and the preliminary results have been evaluated. We found that the model results have little differences from the CPU version. Further comparison with observations and lower-resolution LICOM3 results suggests that the 1/20° LICOM3-HIP can not only reproduce the observations, but also produce much smaller scale activities, such as submesoscale eddies and frontal scales structures.</p>
Dataset: "Probabilistic Framework for Integration of Mass Spectrum and Retention Time Information in Small Molecule Identification"
<p>The SQLite database contains the pre-computed tandem mass spectra (MS2) and retention order scores used for the experiments in the publication: "<a href="https://doi.org/10.1093/bioinformatics/btaa998">Probabilistic Framework for Integration of Mass Spectrum and Retention Time Information in Small Molecule Identification</a>" by Bach et al. (2020).</p> <p>A detailed description of the database structure is given in the 'README.md' and can also be found in the <a href="https://github.com/aalto-ics-kepaco/msms_rt_score_integration/tree/master/data">code-repository associated with the publication</a>. The database layout is illustrated in the 'db_layout.png' file.</p> <p>The SQLite file 'ms_and_rt_score_DB_bach_etal_2020.db.gz' is compressed using <a href="https://en.wikipedia.org/wiki/Gzip">gzip</a>.</p>
Characterization data for the manuscript "A data-driven perspective on the colours of metal-organic frameworks"
<p>Visualize the data in this dataset: <a href="https://www.c6h6.org/zenodo/record/4044212">open entry</a>. </p>
Data for: Performance Benchmarking of Application Monitoring Frameworks
<p>Application-level monitoring of continuously operating software systems provides insights into their dynamic behavior, helping to maintain their performance and availability during runtime. Such monitoring may cause a significant runtime overhead to the monitored system, depending on the number and location of used instrumentation probes. In order to improve a system’s instrumentation and to reduce the caused monitoring overhead, it is necessary to know the performance impact of each probe.<br /> While many monitoring frameworks are claiming to have minimal impact on the performance, these claims are often not backed up with a detailed performance evaluation determining the actual cost of monitoring. Benchmarks can be used as an effective and affordable way for these evaluations. However, no benchmark specifically targeting the overhead of monitoring itself exists. Furthermore, no established benchmark engineering methodology exists that provides guidelines for the design, execution, and analysis of benchmarks.<br /> This thesis introduces a benchmark approach to measure the performance overhead of application-level monitoring frameworks. The core contributions of this approach are 1) a definition of common causes of monitoring overhead, 2) a general benchmark engineering methodology, 3) the MooBench micro-benchmark to measure and quantify causes of monitoring overhead, and 4) detailed performance evaluations of three different application-level monitoring frameworks. Extensive experiments demonstrate the feasibility and practicality of the approach and validate the benchmark results. The developed benchmark is available as open source software and the results of all experiments are available for download to facilitate further validation and replication of the results.</p> <p>This dataset supplements the thesis and contains the results of all experiments, including the raw result data, the results of additional experiments, and the configuration of our benchmarks.</p>
Plasma density turbulence obtained from a Hasegawa-Wakatani drift-wave turbulence model within the BOUT++ framework
<p>A Hasegawa-Wakatani drift-wave turbulence model [1] within the BOUT++ framework [2] was used to generate a set of turbulent plasma density profiles. Drift-wave turbulence is thought to be the dominant mechanism responsible for the anomalous transport observed in the edge of tokamak plasmas.</p> <p>The motivation to generate this rather large set of density profiles was to properly study the influence of plasma density fluctuations on traversing microwaves which requires ensemble-averaging to get statistically relevant results.</p> <p>The density data is given in normalized units (1.0 would correspond to 100 % fluctuation degree) on a mean-free background and the spatial coordinates are given in units of the ion Larmor radius.</p> <p>The form of equations used was the un-modified Hasegawa-Wakatani model [3]. Input parameters were kappa = 1.0 (normalised background density gradient), alpha = 0.5 (adiabaticity parameter), and diffusion constant D=1e-2.</p> <ol> <li>Wakatani, M., Hasegawa, A. (1984). <em>A collisional drift wave description of plasma edge turbulence.</em> Phys. Fluids <strong>27</strong>(3), 611. doi:10.1063/1.864660</li> <li>Dudson, B. <em>et al.</em> (2009). <em>BOUT++: A framework for parallel plasma fluid simulations</em>. Comp. Phys. Comm. <strong>180</strong>(9), 1467. doi:10.1016/j.cpc.2009.03.008</li> <li>Numata, R., Ball, R., & Dewar, R. L. (2007). <em>Bifurcation in electrostatic resistive drift wave turbulence</em>. Phys. Plasmas <strong>14</strong>(10), 102312. doi:10.1063/1.2796106</li> </ol>
Supplementary information for "Anharmonic origin of large thermal displacements in the metal-organic framework UiO-67"
<p>Supplementary information for DOI: 10.1021/acs.jpcc.7b04757</p> <p>POSCAR-XXX: DFT optimised structures</p> <p>Phonons-XXX.zip: Folders containing the force constants (FORCE_SETS), the resulting phonon frequencies (mesh.yaml), phonon partial density of states (partial_dos.dat), animations of all phonon modes (anime.ascii) e.g. to be visualized in VMD and gifs of selected phonon modes. </p> <p>XDATCAR-XXX: MD trajectories</p>
Framework and resource for more than 11,000 gene-transcript-protein-reaction associations in human metabolism
<ul> <li>Eight versions of COBRA-compliant SBML files are available for each Recon 2M.1 and Recon 2M.2 depending on the use of: MNXref versus BiGG IDs; Entrez gene IDs (GPR associations) versus Ensembl transcript IDs versus RefSeq transcript IDs versus UCSC transcript IDs (TPR associations for the last three database IDs).</li> <li>892 personal GEMs (only T-GEMs) built with Recon 2.2</li> <li>1,784 personal GEMs (both P-GEMs and T-GEMs) built with Recon 2M.1</li> <li>892 personal GEMs (only T-GEMs) built with Recon 2M.2</li> </ul> <p> </p> <p><strong>Publication</strong></p> <p>Jae Yong Ryu<sup>1</sup>, Hyun Uk Kim<sup>1</sup> & Sang Yup Lee<sup>*</sup>. Framework and resource for more than 11,000 gene-transcript-protein-reaction associations in human metabolism., <em>Proc. Natl. Acad. Sci. U.S.A.</em>, 2017, http://www.pnas.org/content/early/2017/10/23/1713050114</p> <p> </p>
WeBCMD: A Cross-Platform Interface for the BCMD Modelling Framework
<p>Datasets for the WeBCMD example use cases.</p> <p>Those named *-output-*.csv are intended for comparing with model output whilst synthetic-input-data.csv is intended to drive the model for the BrainSignals/Systemic Data use case.</p>
Scalability, dynamicity and performance evaluation results of Mantus framework
<p>Datasets used for experimental results (Figure 5): (a) Compositional weaver efficiency; (b) incremental weaving efficiency; (c) relative overhead of weaving in workflow; (d) weaver efficiency vs. aspect complexity.</p> <p>Type of data: raw and processed</p> <p>Hardware/software used: Intel Xeon E5-2650 Haswell at 2.60GHz with 64 GB of RAM; Testing input for all Mantus benchmarks: OpenStack-based ORBITS template described in paper, composed of a controller node and of 3 different group instances of compute nodes (Xen, KVM, LXC), with two virtual networks and relative network resources.</p> <p>Data format: CSV</p> <p>Source: Experiments</p> <p> </p>
Designing With: AI, ML and DV - Interactive Framework Tools Dataset
<p>This dataset presents a compilation of 182 AI tools, specifically designed for incorporation into design education. The collection was meticulously assembled through a process of rigorous mining of existing internet repositories and academic literature, resulting in a diverse array of AI tools that are applicable to a multitude of design methodologies.</p>
The Kconfig Variability Framework as a Feature Model: Sampled Configurations for Manual Evaluation
<p>This dataset contains plain text files with sampled solutions used during the manual evaluation of the transformation rules presented in https://doi.org/10.5445/IR/1000162110. To reproduce the manual evaluation process yourself, please copy over the respective Kconfig files in a local copy of the Linux kernel Git repository and run `make menuconfig`. You need to insert an invisible `MODULES` configuration symbol to ensure that tristate configuration symbols are handled correctly by Kconfig. Additionally, you need to remove the default Linux Kconfig file and rename the Kconfig file for which you want to reproduce the evaluation process accordingly (simply remove the number prefix).</p><p>Configurations marked with KCONFIG_NONSOLUTION cannot be reconstructed in `menuconfig`, wherein configurations marked with KCONFIG_SOLUTION should be reproducable in the `menuconfig` interface.</p><p>We additionally provide the generated feature models for the 9 selected Kconfig files, alongside with the Kconfig files themselves. Kconfig{1,2,3,4,5} can be automatically evaluated with Kfeature, as they contain no tristate confsyms.</p><p>The upstream version of Kfeature can be found on Codeberg: https://codeberg.org/6b6279/Kfeature</p>
Efficient genomics based 'end-to-end' selective tree breeding framework
<p>Since their initiation in the 1950s, worldwide selective tree breeding programs followed the recurrent selection scheme of repeated cycles of selection, breeding (mating), and testing phases and essentially remained unchanged to accelerate this process or address environmental contingences and concerns. Here, we introduce an "end-to-end" selective tree breeding framework that: 1) leverages strategically preselected GWAS-based sequence data capturing trait architecture information, 2) generates unprecedented resolution of genealogical relationships among tested individuals, and 3) leads to the elimination of the breeding phase through the utilization of readily available wind-pollinated (OP) families. Individuals' breeding values generated from multi-trait multi-site analysis were also used in an optimum contribution selection protocol to effectively manage genetic gain/co-ancestry trade-offs and traits' correlated response to selection. The proof-of-concept study involved a 40-year-old spruce OP testing population growing on three sites in British Columbia, Canada, clearly demonstrating our method's superiority in capturing most of the available genetic gains in a substantially reduced timeline relative to the traditional approach. The proposed framework is expected to increase the efficiency of existing selective breeding programs, accelerate the start of new programs for ecologically and environmentally important tree species, and address climate-change caused biotic and abiotic stress concerns more effectively.</p>
A computational neuroscience framework for quantifying warning signals
<p>Animal warning signals show remarkable diversity, yet subjectively appear to share certain visual features that make defended prey stand out and look different from more cryptic palatable species. For example, many (but far from all) warning signals involve high contrast elements, such as stripes and spots, and often involve the colours yellow and red. How exactly do aposematic species differ from non-aposematic ones in the eyes (and brains) of their predators?</p> <p>Here we develop a novel computational modelling approach, to quantify prey warning signals and establish what visual features they share. First, we develop a model visual system, made of artificial neurons with realistic receptive fields, to provide a quantitative estimate of the neural activity in the first stages of the visual system of a predator in response to a pattern. The system can be tailored to specific species. Second, we build a novel model that defines a 'neural signature', comprising quantitative metrics that measure the strength of stimulation of the population of neurons in response to patterns. This framework allows us to test how individual patterns stimulate the model predator visual system.</p> <p>For the predator-prey system of birds foraging on lepidopteran prey, we compared the strength of stimulation of a modelled avian visual system in response to a novel database of hyperspectral images of aposematic and undefended butterflies and moths. Warning signals generate significantly stronger activity in the model visual system, setting them apart from the patterns of undefended species. The activity was also very different from that seen in response to natural scenes. Therefore, to their predators, lepidopteran warning patterns are distinct from their non-defended counterparts, and stand out against a range of natural backgrounds.</p> <p>For the first time, we present an objective and quantitative definition of warning signals based on how the pattern generates population activity in a neural model of the brain of the receiver. This opens new perspectives for understanding and testing how warning signals have evolved, and, more generally, how sensory systems constrain signal design.</p>
Data for "Impact of Ligand Substitution and Metal Node Exchange in the Electronic Properties of Scandium Terephthalate Frameworks"
<p>The AiiDA archives of the high-throughput calculations presented in the paper "Impact of Ligand Substitution and Metal Node Exchange in the Electronic Properties of Scandium Terephthalate Frameworks".</p><p>The file "MOF_workflows.aiida" contains the actual calculation data and the files with suffix "*.yaml" contain configuration files of the workflows.</p>
Computationally directed manipulation of cross-linked covalent organic frameworks for membrane applications - PCCP
<p>The dataset uploaded herein is associated with the paper published under the title "<i>Computationally directed manipulation of cross-linked covalent organic frameworks for membrane applications</i>" with the Royal Society of Chemistry - Physical Chemistry Chemical Physics Journal. This dataset includes the .vasp files for all modeled structures, an example dftb_in.hsd file, which is the instructional file for geometry optimization with DFTB+, an Excel spreadsheet with atom number densities and total energy values for all modeled geometries, and, finally, a Python script that was used to calculate the Enthalpy of Formation and Cohesive Energies for all structures. This data has been made available to the scientific community in the interest of open-source and accessible data. The authors request that you please cite the associated paper and Zenodo dataset if used.</p><p><strong>Abstract</strong></p><p>Two-dimensional covalent organic frameworks (2D-COFs) exhibit characteristics ideal for membrane applications, such as high stability, tunability and porosity along with well-ordered nanopores. However, one of the many challenges with fabricating these materials into membranes is that membrane wetting can result in layer swelling. This allows molecules that would be excluded based on pore size to flow around the layers of the COF, resulting reduced separation. Cross-linking between these layers inhibits swelling to improve the selectivity of these membranes. In this work, computational models were generated for a quinoxaline-based COF cross-linked with oxalyl chloride (OC) and hexafluoroglutaryl chloride (HFG). Enthalpy of formation and cohesive energy calculations from these models show that formation of these COFs is thermodynamically favorable and the resulting materials are stable. The cross-linked COF with HFG was synthesized and characterized with Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), thermogravimetric analysis with differential scanning calorimetry (TGA-DSC), and water contact angles. Additionally, these frameworks were fabricated into membranes for permeance testing. The experimental data supports the presence of cross-linking and demonstrates that varying the amount of HFG used in the reaction does not change the amount of cross-linking present. Computational models indicate that the effect of varying cross-linking concentration on the framework stability is negligible and less cross-linking still results in stable materials. This work sheds light on the nature of the cross-linking in these 2D-COFs and their application in membrane separations.</p>
Dataset of the HazardNet: A Thermal Hazard Prediction Framework for Datacenters
<p>This dataset entry showcases a comprehensive collection obtained from the Tier-0 supercomputer, Marconi A2, hosted at CINECA (<a href="https://www.hpc.cineca.it/">https://www.hpc.cineca.it/</a>). The dataset records inlet and outlet temperatures along with power consumption data from 3312 computing nodes, spanning from January 14, 2019, to December 31, 2019. The data is generated through ExaMon, a sophisticated monitoring datacenter infrastructure. The primary objective of this dataset is to support the research and development of HazardNet, an innovative thermal hazard prediction framework tailored specifically for datacenters. HazardNet integrates a comprehensive pipeline of machine-learning models. Researchers and enthusiasts interested in exploring our work further can find the complete set of codes and machine-learning models at our GitHub repository: <a href="https://github.com/MSKazemi/HazardNet">https://github.com/MSKazemi/HazardNet</a></p>
Source Data for Manuscript: "Retrievals Applied To A Decision Tree Framework Can Characterize Earth-like Exoplanet Analogs"
<p>This dataset accompanies the manuscript entitled: "Retrievals Applied To A Decision Tree Framework Can Characterize Earth-like Exoplanet Analogs", which was accepted for publication in the Planetary Science Journal. Included are the source files for all figures included in the paper.</p>
Tomography Data for: Three-dimensional Nanoscale Metal, Metal Oxide and Semiconductor Frameworks through DNA-programmable Assembly and Templating
<p>This is data collected at the 3-ID Hard X-ray Nanoprobe beamline. This repository supports the following research article: </p><p>Data provided is the aligned dataset and reconstruction using a FISTA algorithm. Angles Collected -90 to +45 at 1 degree steps. </p><p><strong>Three-dimensional Nanoscale Metal, Metal Oxide and Semiconductor Frameworks through DNA-programmable Assembly and Templating</strong></p><p>By Aaron Michelson. </p><p> </p>
Dataset for the paper "A framework for robotic excavation and dry stone construction using on-site materials"
<p>Stone data from the <i>Science Robotics</i> paper "A framework for robotic excavation and dry stone construction using on-site materials" containing:</p><ul><li>Mesh files of 1,100 stones (quarried boulders, erratics, and concrete debris) that were digitized by the autonomous excavator HEAP<ul><li><a href="https://zenodo.org/api/records/10038881/draft/files/1100%20Unprocessed%20Stone%20Meshes.zip/content">1100 Unprocessed Stone Meshes.zip: </a>Raw mesh files directly from the poisson reconstruction of accumulated LiDAR points, containing some artifacts and floating geometries</li><li><a href="https://zenodo.org/api/records/10038881/draft/files/1100%20Closed%20Stone%20Meshes.zip/content">1100 Closed Stone Meshes.zip: </a>Clean, closed, downsampled meshes</li><li><a href="https://zenodo.org/api/records/10038881/draft/files/Stone_Shape_Properties.csv/content">Stone_Shape_Properties.csv: </a>Properties file with a list of the stone IDs (IDs in the 1xxx and 3xxx range typically correspond to concrete elements) and select shape properties</li></ul></li><li>A dataset of candidate placements from automatically generated stone walls. The candidate placement data zip files contain:<ul><li><a href="https://zenodo.org/api/records/10038881/draft/files/Candidate_Placement_Data-npy.zip/content">Candidate_Placement_Data-npy.zip: </a>SDF (.npy) representation of each candidate, with three channels of 32x32x32 for distances to the stone, the already-placed stones, and the target wall</li><li><a href="https://zenodo.org/api/records/10038881/draft/files/Candidate_Placement_Data-pcd.zip/content">Candidate_Placement_Data-pcd.zip: </a>Point cloud (.pcd) representations of each candidate, with separate files for the placed stone, target wall (search volume), and already-placed stones (where they exist)</li></ul></li><li><a href="https://zenodo.org/api/records/10038881/draft/files/sdf_classifier.zip/content">sdf_classifier.zip: </a>Python examples:<ul><li>Rendering the three channel SDF data to mesh geometry using marching cubes and libigl</li><li>Candidate SDF classification using the pretrained model</li></ul></li><li>Candidate attributes and labels<ul><li><a href="https://zenodo.org/api/records/10038881/draft/files/candidate_attributes_labels.csv/content">candidate_attributes_labels.csv: </a>CSV file containing a list of UUID's corresponding to each candidate placement in the dataset. For each candidate, additional information is included about the dimensions and location of the solution, together with the (subjectively) hand-labelled binary value for placement viability. </li></ul></li><li><a href="https://zenodo.org/api/records/10038881/draft/files/README.md/content">README.md: </a>An additional readme with some details on the attributes file</li></ul><p>If you use this data in your research, please cite the <a href="https://www.science.org/doi/10.1126/scirobotics.abp9758">journal article</a>.</p><p> </p>
agroBRIDGES Multi-Actor Framework Engagement Data Analysis
<p>The multi-actor framework employed in agroBRIDGES foresees the creation of a regional Multi-Actor Platform (MAP) in the 12 focal European regions and countries of the project (Beacon Regions) as well as a Stakeholder Reference Group (SRG) at European level with a view to engaging stakeholders in the project’s activities.</p> <p>In this way, this dataset contains the engagement analysis of the MAP and the SRG members that have been selected as active participants in the activities carried out in the frame of the <a href="https://www.agrobridges.eu/">agroBRIDGES</a> H2020 project, focused on building bridges between producers and consumers, thanks to the <a href="https://www.agrobridges.eu/toolbox/">agroBRIDGES Toolbox</a> developed and other supporting activities organized throughout the project. To achieve project objectives, results from MAPs and SRG management have been tracked during the first half of the project, and will continue to be monitored until the end of the project, in collaboration with all Beacon Region Leaders and the SRG Manager.</p> <p>In this first round of monitoring, with respect to the SRG, 20 stakeholders have been engaged, of which approximately 50% have had a high engagement, 30% a medium engagement, and 20% a low-to-none engagement. As part of the planned engagement analysis and based on the data provided by each of the Beacon Region Leaders, the initial and long-term number of MAP members, as well as the evolution in their level of engagement, have been monitored., getting a total of 179 MAP members currently engaged in the project. In a first evaluation, high engagement was generally achieved in approximately 80% of cases, with less engagement from some educational bodies and consumers.</p> <p>In the second round of monitoring, the MAPs were expanded to a total of 199 stakeholders in 12 countries, while the SRG synthesis remained unchanged. The activities of the project supported higher engagement of the MAPs, while collaboration with the SRG continued in an ad hoc basis, as pan-European activities were less frequently organised than regional ones.</p> <ul> <li>The countries involved are: <ul> <li>Denmark</li> <li>Finland</li> <li>France</li> <li>Greece</li> <li>Ireland</li> <li>Italy</li> <li>Latvia</li> <li>Lithuania</li> <li>Netherlands</li> <li>Poland</li> <li>Spain</li> <li>Turkey</li> </ul> </li> </ul> <p>The dataset contains:</p> <ul> <li><strong>agroBRIDGES_StakeholderEngagement_2022.12.27_v1</strong>: Spreadsheet in .xlsx format, containing a table with all MAP members, classified by type of organization, country, and including the level of engagement of each of them.</li> <li><strong>agroBRIDGES_StakeholderEngagement_2022.12.27_v1</strong>: Spreadsheet in .xlsx format, containing a table with all MAP members, classified by type of organization, country, and including the level of engagement of each of them.</li> <li><strong>agroBRIDGES_StakeholderEngagement_2023.12.19_v2</strong>: Spreadsheet in .xlsx format, containing a table with all MAP members, classified by type of organization, country, and including the level of engagement of each of them. The data is updated up to December 2023.</li> <li><strong>agroBRIDGES_MAP-Engagement_2023.12.19_v2:</strong> Spreadsheet in .xlsx format, where all MAP Engagement data are collected, classified by country. This file also includes a tab with indicators of how the level of engagement has been assigned. The data is updated up to December 2023.</li> <li><strong>agroBRIDGES_SRG-Engagement_2022.12.27_v1</strong>: Spreadsheet in .xlsx format, where all data concerning SRG Engagement is collected. This file also includes a tab with indicators of how the level of engagement has been assigned.</li> </ul>
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.