Some of the keys of the CWLProv manifest are explained below:
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Some of the keys of the CWLProv manifest are explained below:
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{
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},
"https://w3id.org/bundle/context"
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In this workshop we will explore recent advancements in Research Objects and publishing of research data.
Scholarly Communication has evolved significantly in recent years, with an increasing focus on Open Research, FAIR data sharing and community-developed open source methods. The concepts of authorship and citation are changing, as researchers are increasingly reusing and evolving common software tools and datasets. Yet with a growing amount of cloud compute power and open platforms available, reproducibility of computational analyses becomes more challenging, and not yet commonly included in peer review. While recent advances in scientific workflows and provenance capture systems have improved on this situation, a question remains on how to publish, archive, explore and understand digital research outputs, as academic authors and publishers remain focused on PDFs and the occasional CSV file, with the Web and Open Research often left to “best effor
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<title>The HTML5 Herald</title> | |
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<head><title>Conservancy Welcomes the Common Workflow Language as a Member Project</title></head> | |
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<h1>Conservancy Welcomes the Common Workflow Language as a Member Project</h1> | |
<em>2018-04-10. For immediate release.</em> | |
<p>Software Freedom Conservancy welcomes the <a href="http://www.commonwl.org/">Common Workflow Language (CWL) project</a> as Conservancy's newest member project. The project develops and maintains a specification for describing data analysis workflows and tools in a way that makes them portable and scalable across a variety of software and hardware environments, as well as a supporting reference implementation.</p> | |
<p>The Common Workflow Language project follows the <a href="https://open-stand.org/about-us/principles/">OpenStand principles for collaborative open standards development</a> to openly evolve the |
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Stian Soiland-Reyes, The University of Manchester; Apache Software Foundation
Scientific Workflow systems are well established for computational analysis in all science domains, following the rapid development of workflow technology and community practices spanning the two recent decades, the eScience era. Workflow systems have gained traction in the era of Big Data Science due to their “ASAP" properties”: Automation over repetitive pipelines and simulation sweep campaigns; Scaling over computational infrastructure & handle large data; Abstraction to shield users and programs from complexity and incompatibilities and Provenance to auto-document execution logs and data
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