Best practice data life cycle approaches for the life sciences.

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Griffin, PC; Khadake, J; LeMay, KS; Lewis, SE; Orchard, S; Pask, A; Pope, B; Roessner, U; Russell, K; Seemann, T; ...Date
2017Source Title
F1000ResearchPublisher
F1000 Research LtdUniversity of Melbourne Author/s
Korhonen, Pasi; Young, Neil; Wyres, Kelly; Neish, Peter; Pask, Andrew; Roessner, Ute; Griffin, Philippa; Seemann, Torsten; Pope, Bernard; Gladman, SimonAffiliation
Veterinary BiosciencesBiochemistry and Molecular Biology
Academic Services and Registrar
School of BioSciences
Microbiology and Immunology
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Journal ArticleCitations
Griffin, P. C., Khadake, J., LeMay, K. S., Lewis, S. E., Orchard, S., Pask, A., Pope, B., Roessner, U., Russell, K., Seemann, T., Treloar, A., Tyagi, S., Christiansen, J. H., Dayalan, S., Gladman, S., Hangartner, S. B., Hayden, H. L., Ho, W. W. H., Keeble-Gagnère, G. ,... Schneider, M. V. (2017). Best practice data life cycle approaches for the life sciences.. F1000Res, 6, pp.1618-. https://doi.org/10.12688/f1000research.12344.2.Access Status
Open AccessOpen Access at PMC
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6069748Abstract
Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a 'life cycle' view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain. Here we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on 'omics' datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices.
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