Conference paper Open Access

I'll take that to go: Big data bags and minimal identifiers for exchange of large, complex datasets

Chard, Kyle; D'Arcy, Mike; Heavner, Ben; Foster, Ian; Kesselman, Carl; Madduri, Ravi; Rodriguez, Alexis; Soiland-Reyes, Stian; Goble, Carole; Clark, Kristi; Deutsch, Eric W.; Dinov, Ivo; Price, Nathan; Toga, Arthur


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    <dct:description>&lt;p&gt;&lt;em&gt;Big data workflows&lt;/em&gt; often require the assembly and exchange of complex, multi-element datasets. For example, in biomedical applications, the input to an analytic pipeline can be a dataset consisting thousands of images and genome sequences assembled from diverse repositories, requiring a description of the contents of the dataset in a concise and unambiguous form. Typical approaches to creating datasets for big data workflows assume that all data reside in a single location, requiring costly data marshaling and permitting errors of omission and commission because dataset members are not explicitly specified.&lt;/p&gt; &lt;p&gt;We address these issues by proposing simple methods and tools for assembling, sharing, and analyzing large and complex datasets that scientists can easily integrate into their daily workflows. These tools combine a simple and robust method for describing data collections (&lt;strong&gt;BDBags&lt;/strong&gt;), data descriptions (&lt;strong&gt;Research Objects&lt;/strong&gt;), and simple persistent identifiers (&lt;strong&gt;Minids&lt;/strong&gt;) to create a powerful ecosystem of tools and services for big data analysis and sharing.&lt;/p&gt; &lt;p&gt;We present these tools and use biomedical case studies to illustrate their use for the rapid assembly, sharing, and analysis of large datasets.&lt;/p&gt;</dct:description>
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