Abstract
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.
Highlights
Technological data production capacity is revolutionising biology[1], but is not necessarily correlated with the ability to efficiently analyse and integrate data, or with enabling long-term data sharing and reuse
Throughout this article, we present a researcher-focused data life cycle framework that has commonalities with other published frameworks [e.g. the DataONE Data Life Cycle, the US geological survey science data lifecycle model and ], 11,14–15 but is aimed at life science researchers (Figure 1)
A few participants had put intensive individual effort into developing custom online lab notebook approaches, but the majority had little awareness of this as a useful goal. This suggests a gap between modern biological research as a field of data science, and biology as it is still mostly taught in undergraduate courses, with little or no focus on computational analysis, or project or data management
Summary
1. Johannes Starlinger , Charité – Universitätsmedizin Berlin, Berlin, Germany Humboldt-Universität zu Berlin, Berlin, Germany. Any reports and responses or comments on the article can be found at the end of the article. This article is included in the Research on Research, Policy & Culture gateway. This article is included in the International Society for Computational Biology Community Journal gateway. This article is included in the EMBL-EBI collection
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