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FAIR Computational Workflows

Sarah COHEN-BOULAKIA


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        <foaf:name>Sarah COHEN-BOULAKIA</foaf:name>
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            <foaf:name>Université Paris-Sud</foaf:name>
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    <dct:title>FAIR Computational Workflows</dct:title>
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    <dct:description>&lt;p&gt;Computational workflows describe the complex multi-step methods that are used for data collection, data preparation, analytics, predictive modelling, and simulation that lead to new data products. They can inherently contribute to the &lt;strong&gt;FAIR data principles&lt;/strong&gt;: by processing data according to established metadata; by creating metadata themselves during the processing of data; and by tracking and recording data provenance. These properties aid data quality assessment and contribute to secondary data usage. Moreover, workflows are digital objects in their own right.&lt;/p&gt; &lt;p&gt;This is a presentation of the paper &lt;a href="https://doi.org/10.1162/dint_a_00033"&gt;FAIR Computational Workflows&lt;/a&gt;, published in &lt;em&gt;Data Intelligence&lt;/em&gt;. The paper argues that FAIR principles for workflows need to address their specific nature in terms of their composition of executable software steps, their provenance, and their development.&lt;/p&gt; &lt;p&gt;Presented at &lt;a href="https://eccb2020.info/programme-at-a-glance/"&gt;ECCB 2020&lt;/a&gt; Workshop on &lt;a href="http://FAIR Computational Workflows"&gt;FAIR Computational Workflows&lt;/a&gt;.&lt;/p&gt;</dct:description>
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