TY - JOUR T1 - Best Practice Data Life Cycle Approaches for the Life Sciences JF - bioRxiv DO - 10.1101/167619 SP - 167619 AU - Philippa C. Griffin AU - Jyoti Khadake AU - Kate S. LeMay AU - Suzanna E. Lewis AU - Sandra Orchard AU - Andrew Pask AU - Bernard Pope AU - Ute Roessner AU - Keith Russell AU - Torsten Seemann AU - Andrew Treloar AU - Sonika Tyagi AU - Jeffrey H. Christiansen AU - Saravanan Dayalan AU - Simon Gladman AU - Sandra B. Hangartner AU - Helen L. Hayden AU - William W. H. Ho AU - Gabriel Keeble-Gagnère AU - Pasi K. Korhonen AU - Peter Neish AU - Priscilla R. Prestes AU - Mark F. Richardson AU - Nathan S. Watson-Haigh AU - Kelly L. Wyres AU - Neil D. Young AU - Maria Victoria Schneider Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/07/24/167619.abstract N2 - 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. ER -