Difference between revisions of "Template:Article of the week"
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Cassim AfricanJLabMed2020 9-2.jpg|240px]]</div> | <!--<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Cassim AfricanJLabMed2020 9-2.jpg|240px]]</div> //--> | ||
'''"[[Journal: | '''"[[Journal:Making data and workflows findable for machines|Making data and workflows findable for machines]]"''' | ||
[[Research]] data currently face a huge increase of data objects, with an increasing variety of types (data types, formats) and variety of [[workflow]]s by which objects need to be managed across their lifecycle by data infrastructures. Researchers desire to shorten the workflows from data generation to [[Data analysis|analysis]] and publication, and the full workflow needs to become transparent to multiple stakeholders, including research administrators and funders. This poses challenges for research infrastructures and user-oriented data services in terms of not only making data and workflows findable, accessible, interoperable, and reusable ([[Journal:The FAIR Guiding Principles for scientific data management and stewardship|FAIR]]), but also doing so in a way that leverages machine support for better efficiency. One primary need yet to be addressed is that of findability, and achieving better findability has benefits for other aspects of data and workflow management. In this article, we describe how machine capabilities can be extended to make workflows more findable, in particular by leveraging the Digital Object Architecture, common object operations, and [[machine learning]] techniques. ('''[[Journal:Making data and workflows findable for machines|Full article...]]''')<br /> | |||
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''Recently featured'': | ''Recently featured'': | ||
{{flowlist | | {{flowlist | | ||
* [[Journal:Timely delivery of laboratory efficiency information, Part I: Developing an interactive turnaround time dashboard at a high-volume laboratory|Timely delivery of laboratory efficiency information, Part I: Developing an interactive turnaround time dashboard at a high-volume laboratory]] | |||
* [[Journal:Advanced engineering informatics: Philosophical and methodological foundations with examples from civil and construction engineering|Advanced engineering informatics: Philosophical and methodological foundations with examples from civil and construction engineering]] | * [[Journal:Advanced engineering informatics: Philosophical and methodological foundations with examples from civil and construction engineering|Advanced engineering informatics: Philosophical and methodological foundations with examples from civil and construction engineering]] | ||
* [[Journal:Explainability for artificial intelligence in healthcare: A multidisciplinary perspective|Explainability for artificial intelligence in healthcare: A multidisciplinary perspective]] | * [[Journal:Explainability for artificial intelligence in healthcare: A multidisciplinary perspective|Explainability for artificial intelligence in healthcare: A multidisciplinary perspective]] | ||
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Revision as of 18:46, 13 December 2021
"Making data and workflows findable for machines"
Research data currently face a huge increase of data objects, with an increasing variety of types (data types, formats) and variety of workflows by which objects need to be managed across their lifecycle by data infrastructures. Researchers desire to shorten the workflows from data generation to analysis and publication, and the full workflow needs to become transparent to multiple stakeholders, including research administrators and funders. This poses challenges for research infrastructures and user-oriented data services in terms of not only making data and workflows findable, accessible, interoperable, and reusable (FAIR), but also doing so in a way that leverages machine support for better efficiency. One primary need yet to be addressed is that of findability, and achieving better findability has benefits for other aspects of data and workflow management. In this article, we describe how machine capabilities can be extended to make workflows more findable, in particular by leveraging the Digital Object Architecture, common object operations, and machine learning techniques. (Full article...)
Recently featured:
- Timely delivery of laboratory efficiency information, Part I: Developing an interactive turnaround time dashboard at a high-volume laboratory
- Advanced engineering informatics: Philosophical and methodological foundations with examples from civil and construction engineering
- Explainability for artificial intelligence in healthcare: A multidisciplinary perspective
