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Editorial

Ten Simple Rules for Effective Computational Research

  • James M. Osborne mail,

    James.Osborne@cs.ox.ac.uk

    Affiliations: Computational Biology Group, Department of Computer Science, University of Oxford, Wolfson Building, Oxford, United Kingdom, Computational Science Laboratory, Microsoft Research, Cambridge, United Kingdom

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  • Miguel O. Bernabeu,

    Affiliations: CoMPLEX, Mathematical and Physical Sciences, University College London, Physics Building, London, United Kingdom, Centre for Computational Science, Department of Chemistry, University College London, London, United Kingdom

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  • Maria Bruna,

    Affiliations: Computational Biology Group, Department of Computer Science, University of Oxford, Wolfson Building, Oxford, United Kingdom, Computational Science Laboratory, Microsoft Research, Cambridge, United Kingdom

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  • Ben Calderhead,

    Affiliation: CoMPLEX, Mathematical and Physical Sciences, University College London, Physics Building, London, United Kingdom

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  • Jonathan Cooper,

    Affiliation: Computational Biology Group, Department of Computer Science, University of Oxford, Wolfson Building, Oxford, United Kingdom

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  • Neil Dalchau,

    Affiliation: Computational Science Laboratory, Microsoft Research, Cambridge, United Kingdom

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  • Sara-Jane Dunn,

    Affiliation: Computational Science Laboratory, Microsoft Research, Cambridge, United Kingdom

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  • Alexander G. Fletcher,

    Affiliation: Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Oxford, United Kingdom

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  • Robin Freeman,

    Affiliations: Computational Science Laboratory, Microsoft Research, Cambridge, United Kingdom, CoMPLEX, Mathematical and Physical Sciences, University College London, Physics Building, London, United Kingdom

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  • Derek Groen,

    Affiliation: Centre for Computational Science, Department of Chemistry, University College London, London, United Kingdom

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  • Bernhard Knapp,

    Affiliation: Department of Statistics, University of Oxford, Oxford, United Kingdom

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  • Greg J. McInerny,

    Affiliations: Computational Biology Group, Department of Computer Science, University of Oxford, Wolfson Building, Oxford, United Kingdom, Computational Science Laboratory, Microsoft Research, Cambridge, United Kingdom

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  • Gary R. Mirams,

    Affiliation: Computational Biology Group, Department of Computer Science, University of Oxford, Wolfson Building, Oxford, United Kingdom

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  • Joe Pitt-Francis,

    Affiliation: Computational Biology Group, Department of Computer Science, University of Oxford, Wolfson Building, Oxford, United Kingdom

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  • Biswa Sengupta,

    Affiliation: The Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, United Kingdom

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  • David W. Wright,

    Affiliations: CoMPLEX, Mathematical and Physical Sciences, University College London, Physics Building, London, United Kingdom, Centre for Computational Science, Department of Chemistry, University College London, London, United Kingdom

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  • Christian A. Yates,

    Affiliation: Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Andrew Wiles Building, Radcliffe Observatory Quarter, Oxford, United Kingdom

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  • David J. Gavaghan,

    Affiliation: Computational Biology Group, Department of Computer Science, University of Oxford, Wolfson Building, Oxford, United Kingdom

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  • Stephen Emmott,

    Affiliation: Computational Science Laboratory, Microsoft Research, Cambridge, United Kingdom

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  • Charlotte Deane

    Affiliation: Department of Statistics, University of Oxford, Oxford, United Kingdom

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Reader Comments (2)

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Another interesting article on developing software

Posted by JamesOsborne on 04 Apr 2014 at 08:11 GMT

The following article has some other good advice on developing software.

http://www.ploscompbiol.o...

In particular the following passage

"The key to effective use of programming time is to put a high value on your time. As a guide, it can be helpful to put a value on your time based on your salary, stipend, or personal goals. If you determine your programming time to be worth $100 an hour, is it reasonable to take the time to re-implement a statistical method in code if you can purchase a commercial software library that can provide it for $50? Is it reasonable to spend weeks to optimize an algorithm if $2,000 in additional computing hardware will accomplish the same performance gains?"

No competing interests declared.