A local platform for user-friendly FAIR data management and reproducible analytics

Florian Wieser, Sarah Stryeck, Konrad Lang, Christoph Hahn, Gerhard G. Thallinger, Julia Feichtinger, Philipp Hack, Manfred Stepponat, Nirav Merchant, Stefanie Lindstaedt, Gustav Oberdorfer

Research output: Contribution to journalArticlepeer-review

Abstract

Collaborative research is common practice in modern life sciences. For most projects several researchers from multiple universities collaborate on a specific topic. Frequently, these research projects produce a wealth of data that requires central and secure storage, which should also allow for easy sharing among project participants. Only under best circumstances, this comes with minimal technical overhead for the researchers. Moreover, the need for data to be analyzed in a reproducible way often poses a challenge for researchers without a data science background and thus represents an overly time-consuming process. Here, we report on the integration of CyVerse Austria (CAT), a new cyberinfrastructure for a local community of life science researchers, and provide two examples how it can be used to facilitate FAIR data management and reproducible analytics for teaching and research. In particular, we describe in detail how CAT can be used (i) as a teaching platform with a defined software environment and data management/sharing possibilities, and (ii) to build a data analysis pipeline using the Docker technology tailored to the needs and interests of the researcher.

Original languageEnglish (US)
Pages (from-to)43-50
Number of pages8
JournalJournal of Biotechnology
Volume341
DOIs
StatePublished - Nov 20 2021

Keywords

  • Bioinformatics
  • Cyberinfrastructure
  • CyVerse
  • FAIR
  • Research data management
  • Teaching

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Applied Microbiology and Biotechnology

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