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Conclusion

In this tutorial, we have demonstrated some key features of CyVerse that enable reproducible science at scale. Key functionality areas included:

1) Data upload: Data can be imported and uploaded to the CyVerse Data Store. Our tutorial dataset and analyses total 80 GB of disk space. CyVerse supports terabyte-scale datasets for active analysis with appropriate justification and documentation.

2) Data sharing: These datasets can be shared with fine-grained permissions to other CyVerse users (by username) nearly instantaneously.

3) Metadata: Metadata can be applied to files (either following a template, or by designing a spreadsheet of arbitrary attributes). Once applied, these metadata can be used to search rapidly (via elasticsearch). The Data Store documentation also details how metadata can be directly edited in the Discovery Environment (or by command line through the iCommands interface), and how filters and other features can be used to automate the organization of your files.

4) Reproducible analyses: Software tools used in the Discovery Environment are containerized (Docker) versions of open source software, making it possible to select the desired versions of software and reproduce previous analyses. The DE’s analyses functions keep detailed histories of analyses and parameters.

5) Interactive analyses: Through the DE’s VICE platform, interactive sessions such as RStudio and R Shiny are used to directly interact with and analyze data.

6) Computational capacity: Although not directly highlighted, all applications make use of the underlying CyVerse compute infrastructure. Additionally, some applications in the DE catalog directly make use of XSEDE supercomputing resources.

Taken together, these features provide a high level of functionality that is tailor-made to support data-intensive research and collaboration, all in one place.


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