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650 results for “Workflow”
Demonstration of Scientific Workflow Reproducibility with Hyperflow Workflow Management System
<p>This package contains the Experiment Digital Object that allows to reproduce the workflow execution experiment.</p> <p>The content of the package:<br> - Information about the experimental workflow (below).<br> - Experiment execution traces in the form of a dataframe (csv file) with description of the format (below).<br> - Visualization of the execution (png file).<br> - Python script for analysis of the execution trace (generates the visualization). <br> - Instructions describing how to reproduce the experiment (below).</p>
IPBES Data Management Tutorials - Session 4.2: Recommendations for workflow establishment and data management
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The <em>Data management of active research data </em>chapter provides an introduction for IPBES experts on how to manage data while actively being used, analyzed, and produced to fulfill the criteria of the IPBES data management policy.</p> <p>This session,<em> Recommendations for workflow establishment and data management</em>, provides recommendations for data management including the generation of workflows and data storage. </p>
Code-Level Model Checking in the Software Development Workflow -- Replication Package
<p>This experience report describes a style of applying symbolic model checking developed over the course of four years at Amazon Web Services (AWS). Lessons learned are drawn from proving properties of numerous C-based systems, e.g., custom hypervisors, encryption code, boot loaders, and an IoT operating system. Using our methodology, we find that we can prove the correctness of industrial low-level C-based systems with reasonable effort and predictability. Furthermore, AWS developers are increasingly writing their own formal specifications. All proofs discussed in this paper are publicly available on GitHub. All proofs and specifications described in the paper are available, under the Apache 2.0 license, on the GitHub repository located at <a href="https://github.com/awslabs/aws-c-common/">https://github.com/awslabs/aws-c-common/</a> This is the master repository for AWS C Common library, and is in active use by the AWS C Common development team. The description of the contents of this repository are based off commit <code>b0ea9f35df8934f9e03fc3bab3919d55efd69b88</code>, although they are not expected to change significantly in the future.</p>
WDS-RDA Publishing Data Workflows Working Group Analysis sheet
<p>The information was further refined by adding color: pink indicates “project” (4 entries), blue shows “repository” (14 entries), yellow is “journal” (7 entries) and green for “hybrid” (1 entry). Future versions of the spreadsheet are planned, which can be filtered via other categories, for example: discipline-specific vs discipline-agnostic, funding model, level of editing/intervention, etc.</p> <p>The collection and analysis of data took place between 1 February and 30 June 2015</p>
myExperiment workflow types
<p>A JSON document containing the list of workflow types available on http://myexperiment.org, and the number of workflows available of each type.</p>
WDS-RDA-F11 Publishing Data Workflows WG Synthesis FINAL CORRECTED
<p>Final, revised version of http://dx.doi.org/10.5281/zenodo.33413 (published 6 November 2015)</p>
WDS-RDA Publishing Data Workflows Working Group Analysis sheet FINAL
<p><strong>NB: This dataset is superseded by:</strong></p> <p>Murphy, Fiona et al.. (2015). WDS-RDA-F11 Publishing Data Workflows WG Synthesis FINAL CORRECTED. Zenodo. 10.5281/zenodo.33899</p> <p>Data Publishing Workflows collected and analysed between 1 February - 30 June 2015. Fields were populated using a combination of consultation and desk research. This is a refined version of the previous spreadsheet also lodged in Zenodo: </p> <p>Murphy, Fiona et al.. (2015). WDS-RDA Publishing Data Workflows Working Group Analysis sheet. Zenodo. 10.5281/zenodo.19107</p> <p>Publication date: 29 June 2015</p> <p>Keyword(s): <strong>data publishing, workflows, journals, repositories, research data</strong></p>
Imperial College London Library RDM Workflow
<p>Workflow diagram for Imperial College London RDM Service.</p>
In-memory integration of existing software components for parallel adaptive unstructured mesh workflows: PHASTA 3D dam break
<p>Input mesh, solution field, configuration files, and job scripts for running the adaptive PHASTA-Chef 3D dam break case.</p> <p>Data stream, POSIX file, and ramdisk results are also included. The python scripts used to generate the bandwidth and open/close time plots are included.</p>
Incorporation of QC samples within the experimental workflow
<p>A liquid chromatography - mass spectrometry experimental workflow can incorporate QC samples (blue) through various combinations with the biological samples (black).</p>
Liquid chromatography - mass spectrometry workflow
<p>A typical LC-MS experiment consists of a sample preparation, a liquid chromatography, a mass spectrometry, and a bioinformatics stage. The sample preparation includes the proteolytic digestion of proteins into peptides. Next, consecutively the peptides are separated through liquid chromatography and measured through mass spectrometry. Finally, the acquired spectra are interpreted through bioinformatics means.</p>
Explainable few-shot learning workflow for detecting invasive and exotic tree species
<p>This is the supporting dataset of research work: <a href="Link"><strong>Explainable few-shot learning workflow for detecting</strong></a> <a href="Link"><strong>invasive and exotic tree species</strong></a>. (Link to be added after the publication) In this research, we presents a workflow that tackles both challenges by proposing an explainable few-shot learning workflow for detecting invasive and exotic tree species in the Atlantic Forest of Brazil using Unmanned Aerial Vehicle (UAV) images. By integrating a Siamese network with explainable AI (XAI), the workflow enables the classification of tree species with minimal labeled data while providing visual, case-based explanations for the predictions.</p> <p>The workflow is accessible in <a href="Link">this GitHub repository</a> (Link to be added after the publication). The required dataset of this workflow in provided in this Zenodo repository.</p> <p>This dataset repository has the following contents</p> <ul> <li> <p>uav_img.zip: the UAV orthomosaic image (.tif) of the study area used in this research, with related metadata</p> </li> <li>tree_labels.zip: the labels of trees created by expert, available in .shp and .gpkg</li> <li> <p>cutouts.zip: tree cutouts used in this study. They are two sub-directories:</p> <ul> <li>all_cutouts: all the candidated cutouts from three sources. See the README.md file insisde this folder for more information</li> <li>selected cutout: the manually selected cutouts from all cutouts used for training.</li> </ul> </li> <li> <p>training_pairs_20000.zarr.zip: training data created for base network traning. It is created by pairing the selected cutouts.</p> </li> <li>netflora.zip: Netflora workflow prediction results</li> <li>optimized_models.zip: Optimized base models (shallow and deep) and refined models with different shots/fold setup.</li> <li>n_fold_x_validation.zip: data pairs for refinement traing, with n fold and x valiation setup.</li> </ul>
AnalyzAIRR: A user-friendly guided workflow for AIRR data analysis: example data and analysis source-code
<p>This repository contains:</p> <ul> <li>Annotated TCR-seq data files named <em>tripod-XX-XXXX</em></li> <li>The metadata corresponding to the annotated files</li> <li>The RepSeqExperiment object, which integrates the annotated files and the metadata and was used in the analysis pipeline</li> <li>The analysis script to generate the plots of the different figures</li> </ul>
Datasets associated with the manuscript "Differential detection workflows for multi-sample single-cell RNA-seq data"
<p>In this Zenodo repository, we share the data that is required to reproduce all the analyses from our publication "Differential detection workflows for multi-sample single-cell RNA-seq data".</p> <p>This repository includes all* input data, intermediate results and final outputs that are represented in our manuscript. For a more elaborate description of the data, we refer to the companion GitHub. https://github.com/statOmics/DD_benchmarks for the benchmarks and https://github.com/statOmics/DD_cases for the case studies, respectively.</p>
Snakemake workflow for bacterial assembly QC update v1.0.1
<p>This repository contains a Snakemake bacterial assembly QC workflow to perform quality control on assembly files. It will use Quast (metrics), CheckM2 (completedness and contamination), Busco (completedness), skANI (taxonomic assignment against GTDB with ANI). It will also produce a Busco plot summary, a beeswarm plot of N50 and number of contigs, and an excel file with Quast, CheckM2 and skANI summaries. It will also output PDF and HTML reports with a summary and plots of all tools in the pipeline for all samples. The latest version of the scripts can be found at https://gitlab.ilvo.be/stevebaeyen/bacterial-assembly-qc-snakemake.</p>
Example of Workflow Run RO-Crate Output in Sapporo
<p>This is an archive of the contents under <a href="https://github.com/sapporo-wes/sapporo-service/tree/main/tests/ro-crate/ro-crate_dir">https://github.com/sapporo-wes/sapporo-service/tree/main/tests/ro-crate/ro-crate_dir</a> as of <a href="https://github.com/sapporo-wes/sapporo-service/releases/tag/1.5.1">version 1.5.1 of GitHub - sapporo-wes/sapporo-service</a>. For more details, please refer to <a href="https://github.com/sapporo-wes/sapporo-service/blob/main/tests/ro-crate/README.md">https://github.com/sapporo-wes/sapporo-service/blob/main/tests/ro-crate/README.md</a>.</p>
Research Data Management Workflow
<p>This diagram illustrates essential ideas and useful tools during the RDM cycle.</p>
Data for BY-COVID Pathways to MINERVA Analysis Workflow
<p>Source data (<a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE182152">GSE182152</a>) was analysed with <a href="https://workflowhub.eu/workflows/688">WFHub:688</a> to generate these datasets</p>
Machine Learning Classification Workflow and Datasets for Ionospheric VLF Data Exclusion
<p><span>This data includes the pre-processed dataset, along with a novel workflow that utilizes the PyCaret library and a post-processing workflow. The code and data serve educational purposes in the interdisciplinary field of machine learning and ionospheric physics science, as well as being useful to other researchers for diverse objectives. </span></p> <p><span><span>Acknowledgements:</span></span></p> <p><span><span>The WALDO database (<a href="https://waldo.world"><span>https://waldo.world</span></a>, accessed on October 1, 2023) provides VLF data. It is run collaboratively by the University of Colorado Denver and the Georgia Institute of Technology, utilizing data gathered from Stanford University and those two institutions. It has been made possible by numerous grants from the Department of Defense, NASA, and the NSF.<span> </span></span></span></p> <p> </p> <p> </p>
Resource Usage and Optimization Opportunities in Workflows of GitHub Actions - Artifact
<p>This package contains the data, the code used for collection and the analysis notebooks used to obtain the results presented in the paper: "Resource Usage and Optimization Opportunities in Workflows of GitHub Actions" published at ICSE2024.</p>
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.