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.
44
datasets available to search
ShareScore release 0.9.0
Dataset results
44 results for “research workflow”
Open Research Skills Workshops - GitHub collaborative workflows
<p>This is the fourth workshop on GitHub collaborative workflows in a series of workshop about Open Research Skills.</p><p>This workshop covers:</p><p>- Introduction to version control</p><p>- How to fork a repository</p><p>- Forking exercises</p><p>- How to work in a team and create and merge branches</p><p>- Branching exercises</p><p><strong>List of training workshops in Open Research Skills:</strong></p><ul><li>24th February 2023 - Open access publishing</li><li>24th March 2023 - Using repositories</li><li>21st April 2023 - GitHub basics</li><li><strong>28th April 2023 - GitHub collaborative workflows</strong></li><li>26th May 2023 - Standard vocabularies and ontologies</li><li>30th June 2023 - FAIR data</li></ul><p><strong>Project overview:</strong></p><p>Our project aims to upskill participants in open research skills to increase the quality and reusability of phytolith research and related disciplines such as archaeology, palaeosciences and plant sciences. We will run six hands-on training workshops on open access publishing and research outputs, using repositories, ontologies and standard vocabularies, implementation of FAIR Guidelines for phytolith research, and two workshops on Github basic and advanced skills. The materials from all workshops will be archived as self-study courses on our website (<a href="https://open-phytoliths.netlify.app/">https://open-phytoliths.netlify.app/</a>). We will also provide translation during workshops and training materials into multiple languages. </p><p>Github is a collaborative, project management tool used to run reproducible research projects with version control. In these videos, you will learn how to use version control, how to branch and fork a repository, how to pull a request and how to collaborate as part of a team on GitHub.</p>
RDA Publishing Workflows_ Research Workflows (Responses)
<p>Responses to online questionnaire / call for examples from Research Data Alliance Working Group on Publishing Research Data Workflows.</p> <p>These relate to the article 'Connecting data publication to the research workflow: a preliminary analysis' by the same authors, submitted to the International Digital Curation Conference, 2017</p>
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.
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>
Dataset of "Key Aspects in Designing High-Throughput Workflows in Electrocatalysis Research: A Case Study on IrCo Mixed-Metal Oxidese"
<p>With the growing interest of the electrochemical community in high-throughput (HT) experimentation as a powerful tool in accelerating materials discovery, the implementation of HT methodologies and the design of HT workflows has gained traction. We identify 6 aspects essential to HT workflow design in electrochemistry and beyond to ease the incorporation of HT methods in the community’s research and to assist in their improvement. We study IrCo mixed-metal oxides (MMOs) for the oxygen evolution reaction (OER) in acidic media using the mentioned aspects to provide a practical example of possible workflow design pitfalls and strategies to counteract them. </p>
W2Share Case Study: Workflow Research Object (WRO)
<p>Case Study - Molecular Dynamics</p> <p>Our case study is based on a molecular dynamics simulation defined in the following article:</p> <p>Silveira, R.L. and Skaf, M. S. Molecular Dynamics Simulations of Family 7 Cellobiohydrolase Mutants Aimed at Reducing Product Inhibition. J. Phys. Chem. B 119, 9295-9303 (2015). DOI: <a href="https://doi.org/10.1021/jp509911m">https://doi.org/10.1021/jp509911m</a></p>
Market Research - What's become... of new entrants in research workflows and scholarly communication? ('long list')
<p>Market Research - What’s become... of new entrants in research workflows and scholarly communication?</p> <p>Full report to be published as preprint on OSF Preprints shortly.</p> <p>To systematically create a list of the new entrants in research workflows and scholarly communication that I’ve seen over the years, I used a range of resources, listed hereunder:</p> <ul> <li>400+ Tools and innovations in scholarly communication (charting the <a href="http://bit.ly/innoscholcomm-list">creation and availability</a> (supply side))<a href="#_edn1">[i]</a>. This database continues to build on the 2015-2016 survey by Bianca Kramer & Jeroen Bosman (both at Utrecht University Library), who are interested in the way information is created, shared, and processed in academia<a href="#_edn2">[ii]</a>. From this source I pulled 683 players. From the answers to the open and closed questions in the 2015-2016 survey I found 225 players.</li> <li>Presenters at STM events<a href="#_edn3">[iii]</a> from 2010-2017. This contributed 53 players.</li> <li>Outsell<a href="#_edn4">[iv]</a> ‘Companies to watch’, as per their annual Information Industry Outlook (2009-2017). This source provided 29 players.</li> <li>Pitches at APE (Academic Publishing in Europe) Conferences<a href="#_edn5">[v]</a> between 2011-2017. Here I found 44 players.</li> <li>Nominees for the ALPSP Award for Innovation in Publishing. This source contributed 10 players from 2014-2017.<a href="#_edn6">[vi]</a>.</li> </ul> <p>This file contains the 'long list' per 31 December 2018.</p> <p><a href="#_ednref1">[i]</a> https://docs.google.com/spreadsheets/d/1KUMSeq_Pzp4KveZ7pb5rddcssk1XBTiLHniD0d3nDqo/edit#gid=0</p> <p><a href="#_ednref2">[ii]</a> https://101innovations.wordpress.com/</p> <p><a href="#_ednref3">[iii]</a> https://www.stm-assoc.org/events/?previous</p> <p><a href="#_ednref4">[iv]</a> https://www.outsellinc.com/</p> <p><a href="#_ednref5">[v]</a> https://www.ape2019.eu/ape-literature</p> <p><a href="#_ednref6">[vi]</a> https://www.alpsp.org/Awards</p>
Market Research - What's become... of new entrants in research workflows and scholarly communication? ('sample V2')
<p>Market Research - What’s become... of new entrants in research workflows and scholarly communication?</p> <p>Full report to be published as preprint on OSF Preprints shortly.</p> <p>To systematically create a list of the new entrants, a range of approaches and sources were used. This resulted in a long list (see: <a href="https://zenodo.org/record/2530048#.XDnfnFxKjIV">https://zenodo.org/record/2530048#.XDnfnFxKjIV</a>).</p> <p>The ‘long list’ was shortened to a sample of 120 independent for-profit startups through various filtering exercises, described in the full report. For the sample, three questions were investigated: 1. Did they still exist (independently) in 2018? 2. If so, how were they funded and how were they doing? 3. If they were acquired by 2018, by whom and when were they taken over?</p> <p>This file contains the ‘sample’ and answers to the research questions per 31 December 2018.</p>
Research Data Management Workflow
<p>This diagram illustrates essential ideas and useful tools during the RDM cycle.</p>
CWL run of Somatic Variant Calling Workflow (CWLProv 0.5.0 Research Object)
<p>The somatic variant calling workflow included in this case study is designed by <a href="http://bcb.io/">Blue Collar Bioinformatics (bcbio)</a>, a community-driven initiative to develop best-practice pipelines for variant calling, RNA-seq and small RNA analysis workflows. According to the documentation, the goal of this project is to facilitate the automated analysis of high throughput data by making the resources quantifiable, analyzable, scalable, accessible and reproducible.</p> <p>All the underlying tools are containerized, facilitating software use in the workflow. The somatic variant calling workflow defined in CWL is available on GitHub and equipped with a well defined test dataset.</p> <p>This dataset folder is a CWLProv Research Object 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/cwlprov/">https://pypi.org/project/cwlprov/</a> to explore</p> <p><strong>Steps to reproduce</strong></p> <p>To build the research object again, use Python 3 on macOS. Built on:</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> <p>To run the workflow:<br> </p> <pre><code class="language-bash">pip3 install cwltool==1.0.20180912090223 git clone https://github.com/FarahZKhan/bcbio_test_cwlprov cd bcbio_test_cwlprov/somatic/somatic-workflow/ cwltool --provenance somaticwf_0.5.0_mac main-somatic.cwl main-somatic-samples.json</code></pre> <p>To package the research object:<br> </p> <pre><code class="language-bash">zip -r somaticwf_0.5.0_mac.zip somaticwf_0.5.0_mac/ sha256sum somaticwf_0.5.0_mac.zip > somaticwf_0.5.0_mac.zip.sha256</code></pre> <p>The <a href="https://github.com/FarahZKhan/bcbio_test_cwlprov">cloned git repository</a> is a fork of <a href="https://github.com/bcbio/test_bcbio_cwl">https://github.com/bcbio/test_bcbio_cwl</a>. It was obtained using:</p> <pre><code class="language-bash">wget -O test_bcbio_cwl.tar.gz https://github.com/bcbio/test_bcbio_cwl/archive/master.tar.gz</code></pre> <p>The content is from an archived version from the documentation here: <a href="https://bcbio-nextgen.readthedocs.io/en/latest/contents/cwl.html#install-bcbio-vm-with-containers">https://bcbio-nextgen.readthedocs.io/en/latest/contents/cwl.html#install-bcbio-vm-with-containers</a></p>
CWL run of Alignment Workflow (CWLProv 0.6.0 Research Object)
<p>This dataset folder is a <strong>CWLProv Research Object</strong> that captures the <a href="http://commonwl.org/">Common Workflow Language</a> execution provenance, see <a href="https://w3id.org/cwl/prov/0.6.0">CWLProv 0.6.0</a> or use the <a href="https://pypi.org/project/cwlprov/">cwlprov Python tool</a> to explore.</p> <p> </p> <p>The CWL <a href="https://w3id.org/cwl/view/git/923ae325fa3a6d3ee3871539c65afa06ac86ac54/aligner/sbg-alignment-cwl/topmed-alignment.cwl">alignment workflow</a> included in this case study is designed by <a href="https://github.com/DataBiosphere">Data Biosphere</a>. It adapts the alignment pipeline originally developed at Abecasis Lab, The University of Michigan. This <a href="https://github.com/FarahZKhan/topmed-workflows/tree/cwlprov_testing">workflow</a> is part of NIH Data Commons initiative and comprises of four stages.</p> <p>First step, <strong><em>Pre-align</em></strong><em>,</em> accepts a <em>Compressed Alignment Map</em> (CRAM) file (a compressed format for BAM files developed <em>by European Bioinformatics Institute</em> (EBI)) and human genome reference sequence as input and using underlying software utilities of <strong>SAMtools</strong> such as <em>view</em>, <em>sort</em> and <em>fixmate</em> returns a list of fastq files which can be used as input for the next step.</p> <p>The next step <strong><em>Align</em></strong> also accepts the human reference genome as input along with the output files from <em>Pre-align</em> and uses <strong>BWA-mem</strong> to generate aligned reads as BAM files. <strong>SAMBLASTER</strong> is used to mark duplicate reads and <strong>SAMtools</strong> view to convert read files from SAM to BAM format.</p> <p>The BAM files generated after <em>lign</em> are sorted with <strong><em>SAMtool sort</em></strong>'.</p> <p>Finally, these sorted alignment files are merged to produce single sorted BAM file using SAMtools merge in <strong><em>Post-align</em></strong> step.</p> <p> </p> <p><strong>Steps to reproduce</strong></p> <p>This analysis was run using a 16-core Linux cloud instance with 64GB RAM and pre-installed docker.</p> <ol> <li> <p>Install <strong>gsutils</strong><br> </p> <pre><code class="language-bash">export CLOUD_SDK_REPO="cloud-sdk-$(lsb_release -c -s)" echo "deb http://packages.cloud.google.com/apt $CLOUD_SDK_REPO main" | \ sudo tee -a /etc/apt/sources.list.d/google-cloud-sdk.list curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | \ sudo apt-key add - sudo apt-get update && sudo apt-get install google-cloud-sdk</code></pre> <p> </p> </li> <li> <p>Get the data and make the analysis environment ready:<br> </p> <pre><code class="language-bash">git clone https://github.com/FarahZKhan/topmed-workflows.git cd topmed-workflows git checkout cwlprov_testing cd aligner/sbg-alignment-cwl # this is a custom script download google bucket files from json files and create a local json # it needs gsutil to be installed though git clone https://github.com/DailyDreaming/fetch_gs_frm_json.git # Wait... this should download ~18Gb. python2.7 fetch_gs_frm_json/dl_gsfiles_frm_json.py topmed-alignment.sample.json </code></pre> <p> </p> </li> <li> <p>Run the following commands to create the CWLProv Research Object:</p> <pre><code class="language-bash">time cwltool --no-match-user --provenance alignmnentwf0.6.0 --tmp-outdir-prefix=/CWLProv_workflow_testing/intermediate_temp/temp --tmpdir-prefix=/CWLProv_workflow_testing/intermediate_temp/temp topmed-alignment.cwl topmed-alignment.sample.json.new zip -r alignment_0.6.0_linux.zip alignment_0.6.0_linux sha256sum alignment_0.6.0_linux.zip > alignment_0.6.0_linux.zip.sha25</code></pre> <p> </p> </li> </ol>
Research Workflows and Open Science - Data Set
<p>Data set accompanying the report "Research Workflows and Open Science", a systematic study of open science research workflows.</p> <p>The data set summarises the open science characteristics exhibited by the analysed workflows. The first two columns ‘<strong>workflow ID</strong>’ and ‘<strong>URL</strong>’ are dedicated to the ID we used to identify each workflow and to the publications related to the workflows respectively.</p> <p>The remaining columns are dedicated to the characteristics exhibited by the analysed workflows and are named The remaining columns are dedicated to the characteristics exhibited by the analysed workflows and are named following the different categories identified:</p> <ul> <li> <p>'<strong>used/open science infrastructure/virtual</strong>'</p> <ul> <li> <p>If a workflow relies on a virtual open infrastructure (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open science infrastructure/physical</strong>'</p> <ul> <li> <p>If a workflow relies on a physical open infrastructure (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open scientific knowledge/open source software</strong>'</p> <ul> <li> <p>If a workflow relies on open source software (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open scientific knowledge/open hardware</strong>'</p> <ul> <li> <p>If a workflow relies on open hardware (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open scientific knowledge/open research data</strong>'</p> <ul> <li> <p>If a workflow (re)uses open research data (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>used/open scientific knowledge/open educational resources</strong>'</p> <ul> <li> <p>If a workflow (re)uses open educational resources (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>produced/open scientific knowledge/(open access) scientific publication</strong>'</p> <ul> <li> <p>If a workflow envisages the release of a scientific publication (e.g. papers, reports, data management plans, preprints, study designs) under an open access licence (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>produced/open scientific knowledge/open source software</strong>'</p> <ul> <li> <p>If a workflow envisages the release of software (e.g. code, analysis scripts) under an open access licence (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>produced/open scientific knowledge/open research data</strong>'</p> <ul> <li> <p>If a workflow envisages the release of open research data (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>produced/open scientific knowledge/open educational resources</strong>'</p> <ul> <li> <p>If a workflow envisages the release of open educational resources (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>transparency/transparency type</strong>'</p> <ul> <li> <p>degree of transparency of a workflow, defined in terms of which research products are openly shared and when in order to document the research processes (‘built-in’ if transparent, ‘enabled’ if capable of being transparent, ‘opaque’ otherwise)</p> </li> </ul> </li> <li> <p>'<strong>transparency/sharing type</strong>'</p> <ul> <li> <p>workflow categories based on when the research products are shared (‘end’ for sharing at the end of the workflow, mixed for sharing part of the research products during the workflow and the rest at the end of it, ‘iterative’ for sharing iteratively during or at the end of the related workflow phase, and ‘user-dependent’, where it is ultimately up to the researcher to decide when to share the research products since the workflow offers different paths to follow while imposing no sharing constraint.)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/collaboration implementation</strong>'</p> <ul> <li> <p>If a workflow implements collaborative practices (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open engagement of societal actors/crowdfunding</strong>'</p> <ul> <li> <p>If a workflow envisages crowdfunding (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open engagement of societal actors/crowdsourcing</strong>'</p> <ul> <li> <p>If a workflow envisages crowdsourcing (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open engagement of societal actors/scientific volunteering</strong>'</p> <ul> <li> <p>If a workflow envisages scientific volunteering (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open engagement of societal actors/citizen and participatory science</strong>'</p> <ul> <li> <p>If a workflow envisages citizen and participatory science (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open dialogue with other knowledge systems/indigenous peoples</strong>'</p> <ul> <li> <p>If a workflow envisages the establishment of a dialogue with indigenous peoples (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open dialogue with other knowledge systems/marginalised scholars</strong>'</p> <ul> <li> <p>If a workflow envisages the establishment of a dialogue with marginalised scholars (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>collaboration/open dialogue with other knowledge systems/local communities</strong>'</p> <ul> <li> <p>If a workflow envisages the establishment of a dialogue with local communities (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>assessment</strong>'</p> <ul> <li> <p>If a workflow implements assessment processes for the evaluation of the research products created (yes/no)</p> </li> </ul> </li> <li> <p>'<strong>automation</strong>'</p> <ul> <li> <p>If a workflow includes automated processes (yes/no)</p> </li> </ul> </li> </ul>
Workflow for structured literature reviews using the Open Research Knowledge Graph (ORKG)
<p>Figure showing a workflow of making a structured literature review using the core features of the Open Research Knowledge Graph (ORKG). </p>
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>
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>
An ArcGIS Pro workflow to extract vegetation indices from aerial imagery of small‐plot turfgrass research
<p>Collection of multispectral imagery from an aerial sensor is a means to obtain plot-level vegetation index (VI) values; however, post-capture image processing and analysis remain a challenge for small-plot researchers. An ArcGIS Pro workflow of two task items was developed with established routines and commands to extract plot-level VI values (Normalized Difference VI, Ratio VI, and Chlorophyll Index-Red Edge) from multispectral aerial imagery of small-plot turfgrass experiments. Users can access and download task item(s) from the ArcGIS Online platform for use in ArcGIS Pro. The workflow standardizes the processing of aerial imagery to ensure repeatability between sampling dates and across site locations. A guided workflow saves time with assigned commands, ultimately allowing users to obtain a table with plot descriptions and index values within a .csv file for statistical analysis. The workflow was used to analyze aerial imagery from a small-plot turfgrass research study evaluating herbicide effects on St. Augustinegrass [<em>Stenotaphrum secundatum</em> (Walt.) Kuntze] grow-in. To compare methods, index values were extracted from the same aerial imagery by TurfScout, LLC and were obtained by handheld sensor. Index values from the three methods were correlated with visual percentage cover to determine the sensitivity (i.e., the ability to detect differences) of the different methodologies.</p>
An ArcGIS Pro workflow to extract vegetation indices from aerial imagery of small‐plot turfgrass research
Open the record for dataset details and reuse information.
Figure 1d from: Owen D, Livermore L, Groom Q, Hardisty A, Leegwater T, van Walsum M, Wijkamp N, Spasić I (2020) Towards a scientific workflow featuring Natural Language Processing for the digitisation of natural history collections. Research Ideas and Outcomes 6: e55789. https://doi.org/10.3897/rio.6.e55789
Figure 1d A range of sample specimens that demonstrate the wide taxonomic range of specimens encountered in collections. They also demonstrate the diversity of label types, which include handwritten, typed, and printed labels. Note the presence of various barcodes, rulers, and a colour chart in addition to labels describing the origin of the specimen and its identity. - Fossilised animal skin (Natural History Museum 2009)
Figure 1b from: Owen D, Livermore L, Groom Q, Hardisty A, Leegwater T, van Walsum M, Wijkamp N, Spasić I (2020) Towards a scientific workflow featuring Natural Language Processing for the digitisation of natural history collections. Research Ideas and Outcomes 6: e55789. https://doi.org/10.3897/rio.6.e55789
Figure 1b A range of sample specimens that demonstrate the wide taxonomic range of specimens encountered in collections. They also demonstrate the diversity of label types, which include handwritten, typed, and printed labels. Note the presence of various barcodes, rulers, and a colour chart in addition to labels describing the origin of the specimen and its identity. - Pinned insect specimen (Natural History Museum 2018)
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.