Abstract

Management of data to produce scientific knowledge is a key challenge for biological research in the 21st century. Emerging high-throughput technologies allow life science researchers to produce big data at speeds and in amounts that were unthinkable just a few years ago. This places high demands on all aspects of the workflow: from data capture (including the experimental constraints of the experiment), analysis and preservation, to peer-reviewed publication of results. Failure to recognise the issues at each level can lead to serious conflicts and mistakes; research may then be compromised as a result of the publication of non-coherent protocols, or the misinterpretation of published data. In this report, we present the results from a workshop that was organised to create an ontological data-modelling framework for Laboratory Protocol Standards for the Molecular Methods Database (MolMeth). The workshop provided a set of short- and long-term goals for the MolMeth database, the most important being the decision to use the established EXACT description of biomedical ontologies as a starting point.

Published in

New Biotechnology
2013, Volume: 30, number: 2, pages: 109-113
Publisher: ELSEVIER SCIENCE BV

SLU Authors

• Klingström, Tomas

  • Department of Animal Biosciences, Swedish University of Agricultural Sciences
    

• Bongcam Rudloff, Erik

  • Department of Animal Biosciences, Swedish University of Agricultural Sciences
    
  • Uppsala University

UKÄ Subject classification

Bioinformatics (Computational Biology)


Publication identifier

DOI: https://doi.org/10.1016/j.nbt.2012.05.019

Permanent link to this page (URI)

https://res.slu.se/id/publ/52340