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Software engineering for scientific big data analysis
Univ Freiburg, Dept Comp Sci, Bioinformat Grp, Georges Koehler Allee 106, D-79110 Freiburg, Germany;Univ Freiburg, Ctr Biol Syst Anal ZBSA, Habsburgerstr 49, D-79104 Freiburg, Germany.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Stockholm Univ, Natl Bioinformat Infrastruct Sweden, Sci Life Lab, Dept Biochem & Biophys, Svante Arrhenius Vag 16C, S-10691 Solna, Sweden.ORCID iD: 0000-0001-6740-9212
Univ Bergen, Dept Clin Sci, KG Jebsen Ctr Diabet Res, Postboks 7804, N-5020 Bergen, Norway;Haukeland Hosp, Ctr Med Genet & Mol Med, Postboks 7804, N-5020 Bergen, Norway.
Cleveland Clin, Lerner Res Inst, Genom Med Inst, 9500 Euclid Ave NE50, Cleveland, OH 44106 USA.ORCID iD: 0000-0002-6833-9049
2019 (English)In: GigaScience, ISSN 2047-217X, E-ISSN 2047-217X, Vol. 8, no 5, article id giz054Article, review/survey (Refereed) Published
Abstract [en]

The increasing complexity of data and analysis methods has created an environment where scientists, who may not have formal training, are finding themselves playing the impromptu role of software engineer. While several resources are available for introducing scientists to the basics of programming, researchers have been left with little guidance on approaches needed to advance to the next level for the development of robust, large-scale data analysis tools that are amenable to integration into workflow management systems, tools, and frameworks. The integration into such workflow systems necessitates additional requirements on computational tools, such as adherence to standard conventions for robustness, data input, output, logging, and flow control. Here we provide a set of 10 guidelines to steer the creation of command-line computational tools that are usable, reliable, extensible, and in line with standards of modern coding practices.

Place, publisher, year, edition, pages
Oxford University Press, 2019. Vol. 8, no 5, article id giz054
Keywords [en]
software development, big data, workflow, standards, data analysis, coding, software engineering, scientific software, integration systems, computational tools
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:uu:diva-390339DOI: 10.1093/gigascience/giz054ISI: 000474856100022PubMedID: 31121028OAI: oai:DiVA.org:uu-390339DiVA, id: diva2:1341613
Funder
EU, Horizon 2020, 654241Available from: 2019-08-09 Created: 2019-08-09 Last updated: 2019-08-09Bibliographically approved

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