Data management strategies for multinational large-scale systems biology projects

• Published in: Briefings in bioinformatics Vol. 15; no. 1; pp. 65 - 78
• Main Authors: Wruck, Wasco, Peuker, Martin, Regenbrecht, Christian R A
• Format: Journal Article
• Language: English
• Published: England Oxford Publishing Limited (England) 01.01.2014; Oxford University Press
• Subjects: Access to Information; Accessibility; Biology; Computational Biology; Data Collection; Data management; Database Management Systems; Incentives; Information Dissemination; Information sharing; Internationality; Open source software; Policies; Raw materials; Software; Source code; Strategy; Systems Biology - statistics & numerical data; systems biology; open access; data management; data sharing; data citation
• ISSN: 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbs064

Good accessibility of publicly funded research data is essential to secure an open scientific system and eventually becomes mandatory [Wellcome Trust will Penalise Scientists Who Don't Embrace Open Access. The Guardian 2012]. By the use of high-throughput methods in many research areas from physics to systems biology, large data collections are increasingly important as raw material for research. Here, we present strategies worked out by international and national institutions targeting open access to publicly funded research data via incentives or obligations to share data. Funding organizations such as the British Wellcome Trust therefore have developed data sharing policies and request commitment to data management and sharing in grant applications. Increased citation rates are a profound argument for sharing publication data. Pre-publication sharing might be rewarded by a data citation credit system via digital object identifiers (DOIs) which have initially been in use for data objects. Besides policies and incentives, good practice in data management is indispensable. However, appropriate systems for data management of large-scale projects for example in systems biology are hard to find. Here, we give an overview of a selection of open-source data management systems proved to be employed successfully in large-scale projects.