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Data management strategies for multinational large-scale systems biology projects

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...

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Detalles Bibliográficos
Autores principales: Wruck, Wasco, Peuker, Martin, Regenbrecht, Christian R.A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3896927/
https://www.ncbi.nlm.nih.gov/pubmed/23047157
http://dx.doi.org/10.1093/bib/bbs064
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author Wruck, Wasco
Peuker, Martin
Regenbrecht, Christian R.A.
author_facet Wruck, Wasco
Peuker, Martin
Regenbrecht, Christian R.A.
author_sort Wruck, Wasco
collection PubMed
description 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.
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spelling pubmed-38969272014-01-21 Data management strategies for multinational large-scale systems biology projects Wruck, Wasco Peuker, Martin Regenbrecht, Christian R.A. Brief Bioinform Papers 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. Oxford University Press 2014-01 2012-10-09 /pmc/articles/PMC3896927/ /pubmed/23047157 http://dx.doi.org/10.1093/bib/bbs064 Text en © The Author 2012. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial reuse, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com.
spellingShingle Papers
Wruck, Wasco
Peuker, Martin
Regenbrecht, Christian R.A.
Data management strategies for multinational large-scale systems biology projects
title Data management strategies for multinational large-scale systems biology projects
title_full Data management strategies for multinational large-scale systems biology projects
title_fullStr Data management strategies for multinational large-scale systems biology projects
title_full_unstemmed Data management strategies for multinational large-scale systems biology projects
title_short Data management strategies for multinational large-scale systems biology projects
title_sort data management strategies for multinational large-scale systems biology projects
topic Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3896927/
https://www.ncbi.nlm.nih.gov/pubmed/23047157
http://dx.doi.org/10.1093/bib/bbs064
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