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650 results for “workflows”
Supplementary material 1: Animation of BrainBox's workflow from: Open Neuroimaging Laboratory - Research Ideas and Outcomes 2: e9113 (08 May 2016) https://doi.org/10.3897/rio.2.e9113
Animation showing the different functionalities of BrainBox: opening a Magnetic Resonance Imaging volume, viewing it, and editing it collaboratively online.
Data and code for the publication of a surge-specific DEM workflow on ASTER DEMs.
<p>This repository is associated to the publication submitted with the title "Glacier surge monitoring from temporally dense elevation time series: application to an ASTER dataset over the Karakoram region".<br>It contains elevation change maps produced by the workflow, the Python script of the workflow, and vector outlines used in the study.</p> <p>_______________________________<br>Content of the data repository:</p> <p>1) Elevation change maps (raster <em>**.tif</em> files)<br> Regional maps of the elevation changes over 3 years periods, interpolated results in metre (m).<br> Name: <em>dh_[date1]_[date2].tif</em><br> <br>2) Surge-affected areas (vector <em>**.gpkg </em>files)<br> Surge-affected areas drawn manually of four selected glacier surges, divided into reservoir and receiving areas.<br> <br>3) Python script <em>dem_processing_publi.py</em><br> Script with the implementation of the workflow presented in this study.</p>
Dataset for "Bacterial genome annotation" and "AMR gene detection" workflows
<p>This dataset is associated with the workflows "Bacterial genome annotation" and "AMR gene detection in an assembled bacterial genome".</p>
Eddy covariance data processing workflow example utilizing openeddy and REddyProc R packages
<p>The example dataset is provided within the folder structure required by the workflow files (version 2025-04-27; amended on 2025-07-31) related to the R package openeddy version 0.0.0.9009. Only files needed for successful processing are included. It is shared here as part of a data processing example at <a href="https://github.com/lsigut/EC_workflow">https://github.com/lsigut/EC_workflow</a> to overcome the file size limitation of GitHub.</p>
Roadmap for Developing a Dynamic and Reproducible Research Article with ARTE workflow
<p>The figures illustrates a roadmap for developing a dynamic and reproducible research article using <strong>ARTE (Article Reproducibility Template & Environment) </strong>workflow. The process is categorized into three levels of reproducibility: <strong>Minimal, Proper, and Full</strong>. Each level integrates specific tools and practices to enhance the reproducibility of the research.</p> <p>This proposal is published in the following <strong>OSF project</strong>: <a title="OSF" href="https://osf.io/njdq5/" target="_blank" rel="noopener">https://osf.io/njdq5/</a><br>Shared in the following <strong>GitHub repository</strong>: <a title="GitHub" href="https://github.com/phdpablo/article-template" target="_blank" rel="noopener">https://github.com/phdpablo/article-template</a><br>Exemplified in the following <strong>URL address</strong>: <a title="Article Example" href="https://phdpablo.github.io/article-template/" target="_blank" rel="noopener">https://phdpablo.github.io/article-template/</a></p> <h1>Minimal Reproducibility</h1> <p><strong>1. Use this template</strong>: Start by utilizing the provided template, which is pre-configured with the <strong>TIER Protocol 4.0</strong>. This protocol helps organize research projects in a systematic manner.</p> <p><strong>2. Edit READMEs</strong>: Customize the README files to reflect the details and conclusions of your research. These README files help document the project structure and contents.</p> <p><strong>3. Share on OSF</strong>: Share the project on the <strong>Open Science Framework (OSF)</strong> to ensure accessibility and transparency. This can be done at the beginning, during, or at the end of the research process.</p> <h1>Proper Reproducibility</h1> <p>In addition to the steps mentioned above, the following steps are added:</p> <p><strong>4. Quarto settings:</strong> Adjust the Quarto configuration to fit the needs of your project. This includes modifying the <em>_quarto.yml</em> file for different themes and output formats.</p> <p><strong>5. Develop your narrative</strong>: Write the research narrative using <em>Quarto’s .qmd files</em> within RStudio. This narrative forms the main body of your article and integrates text, code, and outputs seamlessly.</p> <p><strong>6. Environment control:</strong> Implement environment control using the <em>renv package</em>. This ensures that the R environment is consistent and reproducible. The <em>renv.lock</em> file captures the exact versions of R packages used in the project.</p> <p><strong>7. Share dynamic article:</strong> Render and share the dynamic document via GitHub Pages. The Quarto-generated HTML files (docs folders) are hosted on GitHub Pages, making the research accessible and interactive.</p> <h1>Full Reproducibility</h1> <p>Building on the proper reproducibility steps, full reproducibility adds:</p> <p><strong>8. Use Docker:</strong> Employ Docker for operating system-level environment control. A Docker container encapsulates the entire project environment, ensuring that the research can be replicated exactly, regardless of the local machine setup.</p> <h2>Tools Utilized</h2> <ul> <li><strong>TIER Protocol 4.0</strong>: Provides a framework for organizing and documenting research projects.</li> <li><strong>OSF:</strong> A platform for sharing research outputs and ensuring open science practices.</li> <li><strong>Quarto:</strong> A tool for creating dynamic documents that integrate text, code, and outputs.</li> <li><strong>RStudio:</strong> An integrated development environment (IDE) for R, facilitating data analysis and reproducible research.</li> <li><strong>Git/GitHub:</strong> Version control systems that track changes and manage project versions.</li> <li><strong>renv: </strong>An R package for managing and reproducing consistent R environments.</li> <li><strong>GitHub Pages:</strong> A service for hosting static websites directly from a GitHub repository.</li> <li><strong>Docker:</strong> A platform for containerizing applications to ensure consistent environments across different systems.</li> </ul> <h2>Summary</h2> <p>This template guides researchers through creating a reproducible and dynamic article using ARTE (Article Reproducibility Template & Environment) workflow. It starts with basic project setup and documentation, progresses through developing the research narrative with environment control, and culminates in full reproducibility with Docker. This structured approach ensures that research is well-documented, versioned, and easily shareable, promoting open science practices.</p>
Genome annotation workflow for Effrenium voratum rt-383
<p>Scripts of complete genome annotation workflow for Effrenium voratum rt-383, associated with the key genome paper (Shah et al., 2024, Massive genome reduction predates the divergence of Symbiodiniaceae dinoflagellates, under review in <em>ISME Journal</em>). An earlier preprint of this manuscript is available at <em>bioRxiv</em>: <a href="https://doi.org/10.1101/2023.03.24.534093" target="_blank" rel="noopener">https://doi.org/10.1101/2023.03.24.534093</a>.</p> <p>See <strong>README_Evrt383.txt</strong> for more detail.</p>
Genome annotation workflow for Effrenium voratum CCMP421
<p>Scripts of complete genome annotation workflow for Effrenium voratum CCMP421, associated with the key genome paper (Shah et al., 2024, Massive genome reduction predates the divergence of Symbiodiniaceae dinoflagellates, under review in <em>ISME Journal</em>). An earlier preprint of this manuscript is available at <em>bioRxiv</em>: <a href="https://doi.org/10.1101/2023.03.24.534093" target="_blank" rel="noopener">https://doi.org/10.1101/2023.03.24.534093</a>.</p> <p>See <span><strong>README_EvCCMP421.txt</strong> </span>for more detail.</p>
AMIRIS demand response workflow input
<p>This upload contains the <strong>input data</strong> necessary to run a workflow applying the agent-based power market model <strong><a href="https://gitlab.com/dlr-ve/esy/amiris/amiris">AMIRIS</a> </strong>in order to study the impact of power tariffs design on <strong>demand response</strong> profitability.</p> <h2>Usage</h2> <p>The data has to be copied into the "./inputs/data/" folder of the <a href="https://github.com/jokochems/demand_response_analyses_workflow"><em>demand response analyses workflow</em></a> and unpacked there. See the description of the workflow on the dependencies how to execute the model.</p> <p>Important note: You will need a version of AMIRIS that is not yet open source and contains the demand response implementation. Feel free to contact the author of this data set in order to request it. Also, you will need a solver, such as Gurobi or CPLEX for instance.</p> <h2>Background</h2> <p>Data has been obtained from previous <a href="https://github.com/pommes-public/pommesinvest"><em>pommesinvest </em></a>model runs.</p> <p>It has been put together by executing a data <a href="https://github.com/pommes-public/pommesevaluation/blob/main/amiris_converter.ipynb">converter script</a> from the <a href="https://github.com/pommes-public/pommesevaluation/">pommesevaluation </a>repository that compiles the inputs into a format understood by AMIRIS, thus allowing for a soft model coupling (sequential execution) with full input harmonization.</p>
ATS workflow dataset
<p>This is the supplemental dataset used in the ats-workflow examples: https://pinshuai.github.io/ats-workflow/intro.html. It allows all notebook examples to run successfully regardless of changes made to the upstream data sources.</p>
GRMHD Workflow
<p>This repository contains LaTeX and TikZ source codes for a diagram depicting the inner workings of a General Relativistic Magneto Hydrodynamics (GRMHD)code.</p> <p>In particular, it details the process of filling the Right-hand Side (RHS) of the evolution equations for hydrodynamical variables. It also shows how this coupled with the evolution of the spacetime metric tensor.</p> <p>This diagram was created with the original intent to be included in a paper detailing AsterX, a GRMHD code for the Einstein Toolkit, as well as to be included in a springer chapter about GRMHD and the Einstein Toolkit.</p> <p> </p> <p>See README.md for further information</p>
Architectural Design Decisions for the Machine Learning Workflow: Dataset and Code
<p><strong>Title:</strong> Architectural Design Decisions for the Machine Learning Workflow: Dataset and Code</p> <p><strong>Authors:</strong> Stephen John Warnett; Uwe Zdun</p> <p><strong>About:</strong> This is the dataset and code artifact for the article entitled "Architectural Design Decisions for the Machine Learning Workflow".</p> <p><strong>Contents:</strong> The "_generated" directory contains the generated results, including latex files with tables for use in publications and the Architectural Design Decision model in textual and graphical form. "Generators" contains Python applications that can be run to generate the above. "Metamodels" contains a Python file with type definitions. "Sources_coding" contains our source codings and audit trail. "Add_models" contains the Python implementation of our model and source codings. Finally, "appendix" contains a detailed description of our research method.</p> <p><strong>Article Abstract: </strong>Bringing machine learning models to production is challenging as it is often fraught with uncertainty and confusion, partially due to the disparity between software engineering and machine learning practices, but also due to knowledge gaps on the level of the individual practitioner. We conducted a qualitative investigation into the architectural decisions faced by practitioners as documented in gray literature based on Straussian Grounded Theory and modeled current practices in machine learning. Our novel Architectural Design Decision model is based on current practitioner understanding of the topic and helps bridge the gap between science and practice, foster scientific understanding of the subject, and support practitioners via the integration and consolidation of the myriad decisions they face. We describe a subset of the Architectural Design Decisions that were modeled, discuss uses for the model, and outline areas in which further research may be pursued.</p> <p><strong>Objective:</strong> This article aims to study current practitioner understanding of architectural concepts associated with data processing, model building, and Automated Machine Learning (AutoML) within the context of the machine learning workflow.</p> <p><strong>Method:</strong> Applying Straussian Grounded Theory to gray literature sources containing practitioner views on machine learning practices, we studied methods and techniques currently applied by practitioners in the context of machine learning solution development and gained valuable insights into the software engineering and architectural state of the art as applied to ML.</p> <p><strong>Results:</strong> Our study resulted in a model of Architectural Design Decisions, practitioner practices, and decision drivers in the field of software engineering and software architecture for machine learning.</p> <p><strong>Conclusions:</strong> The resulting Architectural Design Decisions model can help researchers better understand practitioners' needs and the challenges they face, and guide their decisions based on existing practices. The study also opens new avenues for further research in the field, and the design guidance provided by our model can also help reduce design effort and risk. In future work, we plan on using our findings to provide automated design advice to machine learning engineers.</p>
Heatwave breaks down the linearity between sun-induced fluorescence and gross primary production. Reproducible workflow
<p>Dataset for manuscript entitled "Heatwave breaks down the linearity between sun-induced fluorescence and gross primary production" accepted for publication in New Phytologist. The dataset was obtained for the site Majadas del Tietar, Spain, between June/2018 and August/2018. It consists of eddy covariance data, sun-induced fluorescence data and active fluorescence data. </p>
Enhanced Protein Isoform Characterization Through Long-Read Proteogenomics - Workflow Results
<pre> </pre> <p>The detection of physiologically relevant protein isoforms encoded by the human genome is critical to biomedicine. Mass spectrometry (MS)-based proteomics is the preeminent method for protein detection, but isoform-resolved proteomic analysis relies on accurate reference databases that match the sample; neither a subset nor a superset database is ideal. Long-read RNA sequencing (e.g. PacBio, Oxford Nanopore) provides full-length transcript sequencing, which can be used to predict full-length proteins. Here, we describe a long-read proteogenomics approach for integrating matched long-read RNA-seq and MS-based proteomics data to enhance isoform characterization. We introduce a classification scheme for protein isoforms, discover novel protein isoforms, and present the first protein inference algorithm for the direct incorporation of long-read transcriptome data in protein inference to enable detection of protein isoforms that are intractable to MS detection. We have released an open-source Nextflow pipeline that integrates long-read sequencing in a proteomic workflow for isoform-resolved analysis.</p> <p>Companion Repositories:</p> <ol> <li><a href="https://doi.org/10.5281/zenodo.5920817">Long-Read-Proteogenomics Workflow GitHub Repository Release</a></li> <li><a href="https://doi.org/10.5281/zenodo.5920847">Long-Read-Proteogenomics Analysis GitHub Repository Release</a></li> </ol> <p>Companion Datasets</p> <ol> <li><a href="https://zenodo.org/deposit/5703754">Long-Read-Proteogenomics Workflow Sample and Reference Data</a></li> <li><a href="https://doi.org/10.5281/zenodo.5234651">TEST Data for Long-Read-Proteogenomics Workflow GitHub Actions</a></li> </ol> <p>This Repository contains the complete output from the execution of the <a href="https://doi.org/10.5281/zenodo.5920817">Long-Read-Proteogenomics Workflow</a>, using the input from <a href="https://zenodo.org/deposit/5703754">Jurkat Samples and Reference Data</a>. </p> <p>The file <em>jurkat.flnc.bam </em>was 6.5 GB had to be split into 13 separate files and for use should be rejoined -- here are the steps that were used to split the file up. </p> <p>1. Convert <em>jurkat.flnc.bam</em> (binary format) to sam file (text format) without header: <em>samtools view jurkat.flnc.bam > jurkat.flnc.sam</em></p> <p>2. Capture the header: <em>samtools view -H jurkat.flnc.bam > jurkat.flnc.header.sam</em></p> <p>3. Split <em>jurkat.flnc.sam</em> into smaller files (aim to get final size under 2GB): <em>split -l 400000 jurkat.flnc.sam jurkat.flnc.chunk.</em></p> <p>4. Convert each of these files back to bam for uploading: <em>samtools view -b jurkat.flnc.chunk.a* -o jurkat.flnc.chunk.a*.bam (*=a,b,c,d,e,f,g,h,i,j,k,l,m)</em></p> <p>After downloading, reverse this process including using the header file which is found in the LRPG-Manuscript-Results-results-results-jurkat-isoseq3-companion-files.tar.gz file></p> <p>1. Convert the bam files back to sam files: <em>samtools view jurkat.flnc.chunk.a*.bam > jurkat.flnc.chunk.a*.sam (*=a,b,c,d,e,f,g,h,i,j,k,l,m)</em></p> <p>2. Combine the header together with the sam files: <em>cat jurkat.flnc.chunk.a*sam > jurkcat.flnc.sam (</em>verified the same number of lines of the sam files is identical to the number of lines of the original without header: 4,956,761. Header file is 13 lines.</p> <p>3. Convert to bam files if desired: <em>samtools view -b jurkat.flnc.sam -o jurkat.flnc.bam</em></p> <p>4. Rehead with the header file: <em>samtools reheader -P -i jurkat.flnc.header.sam jurkat.flnc.bam</em></p>
Elements of Style in Reproducible Workflow Creation and Analysis: An INCLUDE Training Event
<p><a href="https://github.com/NIH-NICHD/Elements-of-Style-Workflow-Creation-Maintenance/blob/main/README.md">Elements of Style Workflow Creation and Maintenance</a>: An INCLUDE Training Event</p> <p>The <a href="https://includedcc.org/">INCLUDE Data Hub</a> is a new resource that securely hosts human clinical, genomic, transcriptomic, proteomic, and other data providing a wealth of opportunities to study conditions that affect individuals with Down syndrome. Today, the approach to answering new scientific questions with these data often uses cloud-based methods accessible through web browsers.</p> <p>During a three-hour virtual training, users learn the know-how to ask scientific questions with these data using cloud platforms and workflows. Users will learn how to build and share processes that assure reproducibility, repurposablility regardless of the computational environment. While many things are possible, the user will be oriented to approaching their work in a modular, testable fashion. </p>
CCG Programme overview: workflow
<p>Schematic representation of the CCG programme workflow across Output Areas (OAs).</p>
Test data for jga-analysis per-sample workflow
<p>Test data for jga-analysis per-sample workflow.</p> <p>Please see:</p> <p>- <a href="https://github.com/biosciencedbc/jga-analysis">https://github.com/biosciencedbc/jga-analysis</a></p> <p>- <a href="https://github.com/biosciencedbc/jga-analysis/blob/main/per-sample/Workflows/per-sample.cwl">https://github.com/biosciencedbc/jga-analysis/blob/main/per-sample/Workflows/per-sample.cwl</a></p>
Traces of scientific workflow instances with and without memoization
<p>This is a collection of anonymized traces describing the execution of workflows on a POWER and x86-64 cluster.</p> <p>This dataset contains the measurements we used to evaluate our workflow memoization system in:</p> <pre><code>Vassiliadis, V., Johnston, A. M., McDonagh, L. J. "Fast, Transparent, and High-Fidelity Memoization Cache-Keys for Computational Workflows." 2022 IEEE International Conference on Services Computing (SCC). IEEE, 2022.</code></pre> <p> </p>
Integration of an event-driven Timepix3 hybrid pixel detector into a cryo-EM workflow
<p><strong>Abstract</strong></p> <p>The development of direct electron detectors has played a key role in low-dose electron microscopy imaging applications. Monolithic active-pixel sensor (MAPS) detectors are currently widely applied for cryogenic electron microscopy (cryo-EM); however, they have best performance at 300~kV, have relatively low read-out speed and only work in imaging mode. Hybrid pixel detectors (HPDs) can operate at any energy, have a higher DQE at lower voltage, have unprecedented high time resolution, and can operate in both imaging and diffraction modes. This could make them well-suited for novel low-dose life-science applications, such as cryo-ptychography, iDPC, and liquid cell imaging. Timepix3 is not frame-based, but truly event-based, and can record individual hits with 1.56~ns time resolution. Here, we present the integration of such a detector into a cryo-EM workflow and demonstrate that it can be used for automated data collection on biological specimens. The performance of the detector in terms of MTF and DQE has been investigated at 200~kV and we studied the effect of deterministic blur. We describe a single-particle analysis structure of \SI{3}{\angstrom} resolution and compare it with Falcon3 data collected under the same microscope. These studies could pave the way toward more efficient low-dose single-particle techniques.</p> <p><strong>Data description</strong></p> <p>Data has been split up in several different directories. In general: each directory contains individual READMEs</p> <p><strong>Flat fields</strong></p> <p>Collected on both TImepix3 and Falcon3 at 200 kV using a Tecnai Arctica microscope. These data have been used for calculating NPS, ToT correction and gain correction. </p> <p><strong>Knife edge</strong></p> <p>Collected on both TImepix3 and Falcon3 at 200 kV using a Tecnai Arctica microscope. These data have been used for calculating MTF.</p> <p><strong>ToT correction calibration file</strong></p> <p>This calibration file has been used to correct all raw Timepix3 data. Including micrographs deposited in EMPIAR.</p> <p><strong>Gain correction</strong></p> <p>Gain correction files calculated from flat field data for several different image formation methods of the Timepix3. The Python script for calculating the gain has been included.</p> <p><strong>Software</strong></p> <p>The software tpx3HitParser, tpx3EventViewer and the MTF-NPS-DQE scripts have listed as related identifiers to this entry.</p> <p> </p>
The FloRes Database: A floral resources trait database for pollinator habitat-assessment generated by a multistep workflow
<p><strong>Background</strong></p> <p>The decline of pollinating insects in agricultural landscapes proceeds due to intensive land use and the associated loss of habitat and food sources. The feeding of those insects depends on the spatial and temporal distribution of nectar and pollen as food resources. Hence, to protect insect biodiversity a spatio-temporal assessment of food quantity of their habitats is necessary. Therefore, sufficient data on traits of floral resources are required.</p> <p><strong>New information</strong></p> <p>Because floral resources' traits of plants are important to quantify food availability, we present two databases, the FloRes Database (Floral Resources Database) and the raw database, where FloRes was derived from. Both databases contain the plant traits (1) flowering period, (2) floral-unit density per day, (3) nectar volume per floral unit per day, (4) sugar content per floral unit, (5) sugar concentration in nectar, (6) pollen mass or volume per floral unit and per day, (7) protein content of pollen and (8) corolla depth. All traits are sampled from literature and online databases. The raw database consists of 702 specified plant species, 138 unspecified species 37 species (spec., sp), 22 species <em>pluralis </em>(spp) and for 79 only the genus was identified) and two species complexes (agg.). Those 842 taxa belong to 488 genera and 102 families. Finally, only 27 taxa have a complete set of traits, too less for a sufficient assessment of spatio-temporal availability of floral food resources.</p> <p>Because information of floral resources is scattered throughout many publications with different units, we also present our multistep workflow implemented in five consecutive R-scripts. The multistep workflow standardizes the trait units of the raw database to comparable entities with identical units and aggregates them on a reasonable taxonomic level into the second application database, the FloRes Database. Finally, the FloRes Database contains aggregated information of traits for 42 taxa and, when corolla depth is excluded, for 70 taxa.</p> <p>This is the first attempt to gather these eight traits from different literature sources in one database with a multistep workflow. The publication of the multistep workflow enables the users to extend the FloRes Database on their own demands with other literature data or newly gathered data to improve the quantification of food resources. Especially, the combination of pollen, nectar, and open flowers per square meter is, as far as we know, a novelty.</p> <p>The FloRes Database can be used to evaluate the quantity of food-resource habitats available for pollinators, e.g., to compare seed mixtures of agri-environmental measures, such as flower strips, considering flower phenology on a daily basis.</p>
PSnpBind: A database of mutated binding site protein-ligand complexes constructed using a multithreaded virtual screening workflow
<p>A key concept in drug design is how natural variants, especially the ones occurring in the binding site of drug targets, affect the inter-individual drug response and efficacy by altering binding affinity. These effects have been studied on very limited and small datasets while, ideally, a large dataset of binding affinity changes due to binding site single-nucleotide polymorphisms (SNPs) is needed for evaluation. However, to the best of our knowledge, such a dataset does not exist. Thus, a reference dataset of ligands binding affinities to proteins with all their reported binding sites’ variants was constructed using a molecular docking approach. Having a large database of protein-ligand complexes covering a wide range of binding pocket mutations and a large small molecules’ landscape is of great importance for several types of studies. For example, developing machine learning algorithms to predict protein-ligand affinity or a SNP effect on it requires an extensive amount of data. In this work, we present PSnpBind: A large database of mutated binding site protein-ligand complexes constructed using a multithreaded virtual screening workflow. It provides a web interface to explore and visualize the protein-ligand complexes and a REST API to programmatically access the different aspects of the database contents. PSnpBind is freely available at <a href="https://psnpbind.org">https://psnpbind.org</a>.<strong> </strong>The source code of the tools used in constructing PSnpBind is available on <a href="https://github.com/ammar257ammar/PSnpBind-Build">GitHub</a>.</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.