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Best practice data life cycle approaches for the life sciences [version 2; referees: 2 approved]

Authors :
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
Author Affiliations :
<relatesTo>1</relatesTo>EMBL Australia Bioinformatics Resource, The University of Melbourne, Parkville, VIC, 3010, Australia<br /><relatesTo>2</relatesTo>Melbourne Bioinformatics, The University of Melbourne, Parkville, VIC, 3010, Australia<br /><relatesTo>3</relatesTo>NIHR BioResource, University of Cambridge and Cambridge University Hospitals NHS Foundation Trust Hills Road, Cambridge , CB2 0QQ, UK<br /><relatesTo>4</relatesTo>Australian National Data Service, Monash University, Malvern East , VIC, 3145, Australia<br /><relatesTo>5</relatesTo>Lawrence Berkeley National Laboratory, Environmental Genomics and Systems Biology Division, Berkeley, CA, 94720, USA<br /><relatesTo>6</relatesTo>European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Cambridge, CB10 1SD, UK<br /><relatesTo>7</relatesTo>School of BioSciences, The University of Melbourne, Parkville, VIC, 3010, Australia<br /><relatesTo>8</relatesTo>Metabolomics Australia, School of BioSciences, The University of Melbourne, Parkville, VIC, 3010, Australia<br /><relatesTo>9</relatesTo>Australian Genome Research Facility Ltd, Parkville, VIC, 3052, Australia<br /><relatesTo>10</relatesTo>Monash Bioinformatics Platform, Monash University, Clayton, VIC, 3800, Australia<br /><relatesTo>11</relatesTo>Queensland Cyber Infrastructure Foundation and the University of Queensland Research Computing Centre, St Lucia, QLD, 4072, Australia<br /><relatesTo>12</relatesTo>School of Biological Sciences, Monash University, Clayton, VIC, 3800, Australia<br /><relatesTo>13</relatesTo>Agriculture Victoria, AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources (DEDJTR), Bundoora, VIC, 3083, Australia<br /><relatesTo>14</relatesTo>Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC, 3010, Australia<br /><relatesTo>15</relatesTo>The University of Melbourne, Parkville, VIC, 3010, Australia<br /><relatesTo>16</relatesTo>Faculty of Science and Engineering, Federation University Australia, Mt Helen , VIC, 3350, Australia<br /><relatesTo>17</relatesTo>Bioinformatics Core Research Group & Centre for Integrative Ecology, Deakin University, Geelong, VIC, 3220, Australia<br /><relatesTo>18</relatesTo>School of Agriculture, Food and Wine, University of Adelaide, Glen Osmond, SA, 5064, Australia<br /><relatesTo>19</relatesTo>Department of Biochemistry and Molecular Biology, Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, VIC, 3010, Australia
Source :
F1000Research. 6:1618
Publication Year :
2018
Publisher :
London, UK: F1000 Research Limited, 2018.

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.

Subjects

Subjects :
Opinion Article
Articles
Data Sharing
data sharing
data management
open science
bioinformatics
reproducibility

Details

ISSN :
20461402
Volume :
6
Database :
F1000Research
Journal :
F1000Research
Notes :
Revised Amendments from Version 1 In Version 2 of this article we have addressed the comments of the two reviewers, and included more detail about integrating datasets, workflows, authentication and privacy considerations. We have also included a second figure (Figure 2), a flowchart showing how the data life cycle considerations might be applied to an example research project., , [version 2; referees: 2 approved]
Publication Type :
Academic Journal
Accession number :
edsfor.10.12688.f1000research.12344.2
Document Type :
opinion-article
Full Text :
https://doi.org/10.12688/f1000research.12344.2
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