Best practice data life cycle approaches for the life sciences

Philippa C Griffin; Jyoti Khadake; Kate S LeMay; Suzanna E Lewis; Sandra Orchard; Andrew Pask; Bernard Pope; Ute Roessner; Keith Russell; Torsten Seemann; Andrew Treloar; Sonika Tyagi; Jeffrey H Christiansen; Saravanan Dayalan; Simon Gladman; Sandra B Hangartner; Helen L Hayden; William W H Ho; Gabriel Keeble-Gagnère; Pasi K Korhonen; Peter Neish; Priscilla R Prestes; Mark F Richardson; Nathan S Watson-Haigh; Kelly L Wyres; Neil D Young; Maria Victoria Schneider

doi:10.12688/f1000research.12344.2

Best practice data life cycle approaches for the life sciences

F1000Res. 2017 Aug 31:6:1618. doi: 10.12688/f1000research.12344.2. eCollection 2017.

Authors

Philippa C Griffin^{1

2}, Jyoti Khadake³, Kate S LeMay⁴, Suzanna E Lewis⁵, Sandra Orchard⁶, Andrew Pask⁷, Bernard Pope², Ute Roessner⁸, Keith Russell⁴, Torsten Seemann², Andrew Treloar⁴, Sonika Tyagi^{9

10}, Jeffrey H Christiansen¹¹, Saravanan Dayalan⁸, Simon Gladman¹, Sandra B Hangartner¹², Helen L Hayden¹³, William W H Ho⁷, Gabriel Keeble-Gagnère^{7

13}, Pasi K Korhonen¹⁴, Peter Neish¹⁵, Priscilla R Prestes¹⁶, Mark F Richardson¹⁷, Nathan S Watson-Haigh¹⁸, Kelly L Wyres¹⁹, Neil D Young¹⁴, Maria Victoria Schneider^{2

15}

Affiliations

¹ EMBL Australia Bioinformatics Resource, The University of Melbourne, Parkville, VIC, 3010, Australia.
² Melbourne Bioinformatics, The University of Melbourne, Parkville, VIC, 3010, Australia.
³ NIHR BioResource, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust Hills Road, Cambridge , CB2 0QQ, UK.
⁴ Australian National Data Service, Monash University, Malvern East , VIC, 3145, Australia.
⁵ Lawrence Berkeley National Laboratory, Environmental Genomics and Systems Biology Division, Berkeley, CA, 94720, USA.
⁶ European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Cambridge, CB10 1SD, UK.
⁷ School of BioSciences, The University of Melbourne, Parkville, VIC, 3010, Australia.
⁸ Metabolomics Australia, School of BioSciences, The University of Melbourne, Parkville, VIC, 3010, Australia.
⁹ Australian Genome Research Facility Ltd, Parkville, VIC, 3052, Australia.
¹⁰ Monash Bioinformatics Platform, Monash University, Clayton, VIC, 3800, Australia.
¹¹ Queensland Cyber Infrastructure Foundation and the University of Queensland Research Computing Centre, St Lucia, QLD, 4072, Australia.
¹² School of Biological Sciences, Monash University, Clayton, VIC, 3800, Australia.
¹³ Agriculture Victoria, AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources (DEDJTR), Bundoora, VIC, 3083, Australia.
¹⁴ Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC, 3010, Australia.
¹⁵ The University of Melbourne, Parkville, VIC, 3010, Australia.
¹⁶ Faculty of Science and Engineering, Federation University Australia, Mt Helen , VIC, 3350, Australia.
¹⁷ Bioinformatics Core Research Group & Centre for Integrative Ecology, Deakin University, Geelong, VIC, 3220, Australia.
¹⁸ School of Agriculture, Food and Wine, University of Adelaide, Glen Osmond, SA, 5064, Australia.
¹⁹ Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, VIC, 3010, Australia.

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.

Keywords: bioinformatics; data management; data sharing; open science; reproducibility.

Grants and funding

This publication was possible thanks to funding support from the University of Melbourne and Bioplatforms Australia (BPA) via an Australian Government NCRIS investment (to EMBL-ABR).