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650 results for “Workflow”
CWL run of RNA-seq Analysis Workflow (CWLProv 0.5.0 Research Object)
<p>This workflow adapts the approach and parameter settings of <a href="https://github.com/heliumdatacommons/TOPMed_RNAseq_CWL">Trans-Omics for precision Medicine (TOPMed)</a>. The <a href="https://w3id.org/cwl/view/git/018d344b12e9e1b888e21e0819096f9b337d371d/topmed-workflows/TOPMed_RNAseq_pipeline/rnaseq_pipeline_fastq.cwl">RNA-seq pipeline</a> originated from the Broad Institute. There are in total five steps in the workflow starting from:</p> <ol> <li>Read alignment using <strong>STAR</strong> which produces aligned BAM files including the Genome BAM and Transcriptome BAM.</li> <li>The Genome BAM file is processed using <strong>Picard MarkDuplicates.</strong> producing an updated BAM file containing information on duplicate reads (such reads can indicate biased interpretation).</li> <li><strong>SAMtools index</strong> is then employed to generate an index for the BAM file, in preparation for the next step.</li> <li>The indexed BAM file is processed further with <strong>RNA-SeQC</strong> which takes the BAM file, human genome reference sequence and Gene Transfer Format (GTF) file as inputs to generate transcriptome-level expression quantifications and standard quality control metrics.</li> <li>In parallel with transcript quantification, isoform expression levels are quantified by <strong>RSEM</strong>. This step depends only on the output of the STAR tool, and additional RSEM reference sequences.</li> </ol> <p>For testing and analysis, the workflow author provided example data created by down-sampling the read files of a TOPMed public access data. <em>Chromosome 12</em> was extracted from the <em>Homo Sapien Assembly 38</em> reference sequence and provided by the workflow authors. The required GTF and RSEM reference data files are also provided. The workflow is well-documented with a detailed set of instructions of the steps performed to down-sample the data are also provided for transparency. The availability of example input data, use of containerization for underlying software and detailed documentation are important factors in choosing this specific CWL workflow for CWLProv evaluation.</p> <p>This dataset folder is a <strong>CWLProv Research Object</strong> that captures the Common Workflow Language execution provenance, see <a href="https://w3id.org/cwl/prov/0.5.0">https://w3id.org/cwl/prov/0.5.0</a> or use <a href="https://pypi.org/project/cwl">https://pypi.org/project/cwl</a></p> <p><strong>Steps to reproduce</strong></p> <p>To build the research object again, use Python 3 on macOS. Built with:</p> <ul> <li>Processor 2.8GHz Intel Core i7</li> <li>Memory: 16GB</li> <li>OS: macOS High Sierra, Version 10.13.3</li> <li>Storage: 250GB</li> </ul> <ol> <li> <p>Install <strong>cwltool</strong></p> <pre><code class="language-bash">pip3 install cwltool==1.0.20180912090223</code></pre> </li> <li> <p>Install <strong>git lfs</strong><br> The data download with the git repository requires the installation of Git lfs:<br> <a href="https://www.atlassian.com/git/tutorials/git-lfs#installing-git-lfs">https://www.atlassian.com/git/tutorials/git-lfs#installing-git-lfs</a></p> </li> <li> <p>Get the data and make the analysis environment ready:</p> <pre><code class="language-bash">git clone https://github.com/FarahZKhan/cwl_workflows.git cd cwl_workflows/ git checkout CWLProvTesting ./topmed-workflows/TOPMed_RNAseq_pipeline/input-examples/download_examples.sh</code></pre> </li> <li> <p>Run the following commands to create the CWLProv Research Object:</p> <pre><code class="language-bash">cwltool --provenance rnaseqwf_0.6.0_linux --tmp-outdir-prefix=/CWLProv_workflow_testing/intermediate_temp/temp --tmpdir-prefix=/CWLProv_workflow_testing/intermediate_temp/temp topmed-workflows/TOPMed_RNAseq_pipeline/rnaseq_pipeline_fastq.cwl topmed-workflows/TOPMed_RNAseq_pipeline/input-examples/Dockstore.json zip -r rnaseqwf_0.5.0_mac.zip rnaseqwf_0.5.0_mac sha256sum rnaseqwf_0.5.0_mac.zip > rnaseqwf_0.5.0_mac_mac.zip.sha256</code></pre> </li> </ol> <p>The <a href="https://github.com/FarahZKhan/cwl_workflows">https://github.com/FarahZKhan/cwl_workflows</a> repository is a frozen snapshot from <a href="https://github.com/heliumdatacommons/TOPMed_RNAseq_CWL">https://github.com/heliumdatacommons/TOPMed_RNAseq_CWL</a> commit <a href="https://github.com/heliumdatacommons/TOPMed_RNAseq_CWL/tree/027e8af41b906173aafdb791351fb29efc044120">027e8af41b906173aafdb791351fb29efc044120</a></p>
DataverseNO curation workflow used by the Research data team at the University Library of Bergen
<p>DataverseNO curation workflow used by the Research data team at the University Library of Bergen.</p> <p>Original Google Slides are available here: https://docs.google.com/presentation/d/1fxpAnvca7jisqjb0oqD1L3N3Kxj4hrpn_ywxSEl6-RU/edit?usp=sharing</p>
Dataset for "Bacterial genome assembly" workflow
<p>This dataset is associated with the workflow "Bacterial genome assembly for paired end data".</p>
Hypothetical Git workflows for simple scientific collaboration
<p>These diagrams illustrate three common workflow scenarios of a relatively simple empirical study with a maximum of three authors using Git/Github versioning tools. Their design was informed by Ram's (2013) body of work. The first scenario is the same as the three-author hypothesis proposed by Ram (2013).</p> <p>In the other two hypothetical scenarios, a single author's workflow is examined. In the first, he coordinates and manages changes with the remote repository from the start of the study, and in the second, he only does this when it comes time to publish the research's materials, data, and history of changes.</p> <p>Six .JPG files, in English (_EN) and Brazilian Portuguese (_PT), depict these three potential scenarios. The repository also contains a .PPTX file that can be edited if the reader wants to utilize any of the figures, either as a basis for additional diagrams or to change the figure's colors.</p>
AsaruSim: a single-cell and spatial RNA-Seq Nanopore long-reads simulation workflow
Open the record for dataset details and reuse information.
Synthetic log file recording the workflow of a smart factory.
<p>This file is a synthetic log file recording the workflow of a smart factory.</p>
Simulated paired-end reads for "Swimming downstream" workflow (1)
<p>Simulated paired-end reads for "Swimming downstream" workflow</p>
Simulated quantification files for "Swimming downstream" workflow
<p>Simulated quantification files for "Swimming downstream" workflow</p>
Simulated paired-end reads for "Swimming downstream" workflow (3)
<p>Simulated paired-end reads for "Swimming downstream" workflow</p>
Simulated paired-end reads for "Swimming downstream" workflow (2)
<p>Simulated paired-end reads for "Swimming downstream" workflow</p>
Data and analysis pipelines used in Increasing workflow development speed and reproducibility with Vectools
<p>The methods, pipelines, and data used for the examples shown in the Vectools manuscript.</p>
Simulated paired-end reads for "Swimming downstream" workflow - uniform coverage (10-12)
<p>Simulated paired-end reads for "Swimming downstream" workflow</p>
Simulated paired-end reads for "Swimming downstream" workflow - uniform coverage (7-9)
<p>Simulated paired-end reads for "Swimming downstream" workflow</p>
Capturing and Reporting Provenance Information of Simulation Studies Based on an Artifact-Based Workflow Approach - Appendix Provenance Graph
<p>Provenance graph for a fictional ecological simulation study</p>
Test data for running snakePipes : WGBS workflow
<p><strong>Test files for running snakePipes workflows</strong></p> <p><strong>snakePipes</strong> are pipelines built using snakemake and python for the analysis of epigenomic datasets. Please refer to <a href="https://snakepipes.readthedocs.io/en/latest/">this link</a> for further information on snakePipes.</p> <p>This folder contains test files that can be used to run the WGBS workflow under snakePipes. To test the workflow, follow the following steps : </p> <ul> <li>Download or prepare genome fasta, indices and annotations for the mouse (<strong>GRCm38</strong>) genome.</li> <li>Download and install snakePipes via `conda create -n snakePipes -c mpi-ie -c bioconda -c conda-forge snakePipes`</li> <li>Update <a href="https://snakepipes.readthedocs.io/en/latest/content/running_snakePipes.html#genome-configuration-file">Genome configuration file</a> with path to indices and annotations.</li> <li>Move to this repository and run the example <strong>command.sh</strong></li> </ul>
Test data for running snakePipes : RNA-seq workflow
<p><strong>Test files for running snakePipes workflows</strong></p> <p><strong>snakePipes</strong> are pipelines built using snakemake and python for the analysis of epigenomic datasets. Please refer to <a href="https://snakepipes.readthedocs.io/en/latest/">this link</a> for further information on snakePipes.</p> <p>This folder contains test files that can be used to run the RNA-seq workflow under snakePipes. To test the workflow, follow the following steps : </p> <ul> <li>Download or prepare genome fasta, indices and annotations for mouse (<strong>GRCm38</strong>) genome.</li> <li>Download and install snakePipes via `conda create -n snakePipes -c mpi-ie -c bioconda -c conda-forge snakePipes`</li> <li>Update <a href="https://snakepipes.readthedocs.io/en/latest/content/running_snakePipes.html#genome-configuration-file">Genome configuration file</a> with path to indices and annotations.</li> <li>Move to this repository and run the example <strong>command.sh</strong></li> </ul>
Test data for running snakePipes : HiC workflow
<p><strong>Test files for running snakePipes workflows</strong></p> <p><strong>snakePipes</strong> are pipelines built using snakemake and python for the analysis of epigenomic datasets. Please refer to <a href="https://snakepipes.readthedocs.io/en/latest/">this link</a> for further information on snakePipes.</p> <p>This folder contains test files that can be used to run the HiC workflow under snakePipes. To test the workflow, follow the following steps : </p> <ul> <li>Download or prepare genome fasta, indices and annotations for the mouse (mm9) genome.</li> <li>Download and install snakePipes via `conda create -n snakePipes -c mpi-ie -c bioconda -c conda-forge snakePipes`</li> <li>Update <a href="https://snakepipes.readthedocs.io/en/latest/content/running_snakePipes.html#genome-configuration-file">Genome configuration file</a> with path to indices and annotations.</li> <li>Move to this repository and run the example command in <strong>README</strong>.</li> </ul>
Test data for running snakePipes : ATAC-seq workflow
<p><strong>Test files for running snakePipes workflows</strong></p> <p><strong>snakePipes</strong> are pipelines built using snakemake and python for the analysis of epigenomic datasets. Please refer to <a href="https://snakepipes.readthedocs.io/en/latest/">this link</a> for further information on snakePipes.</p> <p>This folder contains test files that can be used to run the ATAC-seq workflow under snakePipes. To test the workflow, follow the following steps : </p> <ul> <li>Download or prepare genome fasta, indices and annotations for fruit fly (<strong>dm6</strong>) genome.</li> <li>Download and install snakePipes via `conda create -n snakePipes -c mpi-ie -c bioconda -c conda-forge snakePipes`</li> <li>Update <a href="https://snakepipes.readthedocs.io/en/latest/content/running_snakePipes.html#genome-configuration-file">Genome configuration file</a> with path to indices and annotations.</li> <li>Move to this repository and run the example <strong>command.sh</strong></li> </ul>
Test data for running snakePipes : ChIP-seq workflow
<p><strong>Test files for running snakePipes workflows</strong></p> <p><strong>snakePipes</strong> are pipelines built using snakemake and python for the analysis of epigenomic datasets. Please refer to <a href="https://snakepipes.readthedocs.io/en/latest/">this link</a> for further information on snakePipes.</p> <p>This folder contains test files that can be used to run the ChIP-seq workflow under snakePipes. To test the workflow, follow the following steps : </p> <ul> <li>Download or prepare genome fasta, indices and annotations for human (<strong>hg38</strong>) genome.</li> <li>Download and install snakePipes via `conda create -n snakePipes -c mpi-ie -c bioconda -c conda-forge snakePipes`</li> <li>Update <a href="https://snakepipes.readthedocs.io/en/latest/content/running_snakePipes.html#genome-configuration-file">Genome configuration file</a> with path to indices and annotations.</li> <li>Move to this repository and run the example <strong>command.sh</strong></li> </ul>
pyKNEEr: An image analysis workflow for open and reproducible research on femoral knee cartilage - Validation data
<p>Image data used in the paper introducing pyKNEEr. Explanations about these data are in the <a href="https://github.com/sbonaretti/pyKNEEr/tree/master/publication">GitHub</a> repository</p> <p>Changes in version 0.2.0: </p> <p>- Added inHouse images </p> <p>- Segmented images casted to int16 for smaller file size</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.