Best Practice Data Life Cycle Approaches for the Life Sciences, now on F1000Research
Following our very successful data life cycle workshops held in Melbourne in October 2016, the feedback, findings and further reflections are now published online at bioRxiv: http://www.biorxiv.org/content/early/2017/07/24/167619.
The paper has since been accepted for publication at F1000Research and open to peer review.
From the 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.
The paper was led by Pip Griffin and Vicky Schneider with a number of co-authors including our Key Area Coordinators Group and Activity Leads and workshop faculty (local and international) and participants.