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Reproducibility of computational research: methods to avoid madness (Session introduction)
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Transcript of Reproducibility of computational research: methods to avoid madness (Session introduction)
Reproducibility of computational research: methods to avoid madness
Chair: Michael Hucka, Ph.D. Department of Computing and Mathematical Sciences
California Institute of Technology Pasadena, CA, USA
ICSB 2014, Melbourne, Australia, September 2014
Session introduction
So, what’s this about reproducibility?
“In biomedical science, at least one thing is apparently reproducible: a steady stream of studies that show the irreproducibility of many important experiments …”
— Wadman. (2013). Nature, 500(7460).
“We find it utterly unexpected that, overall, it is only a minority of articles that properly describe (in a reproducible way) the computational research performed …”
— Hübner, Sahle & Kummer. (2011). FEBS Journal 278(16).
What is the focus of this session?
Facets of reproducibility
Methodologicalissues
Reproducibility issues
Culturalissues
Motivations Policies
Incentives Funding
…
MethodsStandardsAlgorithmsInfrastructure…
Facets of reproducibility
Methodologicalissues
Reproducibility issues
Culturalissues
Motivations Policies
Incentives Funding
…
MethodsStandardsAlgorithmsInfrastructure…
Reproducibility issues
Culturalissues
Motivations Policies
Incentives Funding
…
Computational research should be easier to get rightHave greater control of what is done, and how it’s done
⇒ Greater potential for making our work reproducible
Assertion:
The methodological issues are amenable to practical interventions
Some examples:
• Define and adopt standards for data formats, ontologies, protocols
• Develop better methods for analysis, simulation, comparison
• Develop effective resources for sharing & communicating research
“… reproducibility in computational biology is aspired to, but rarely achieved. This is unfortunate since the quantitative nature of the science makes reproducibility more obtainable than in cases where experiments are qualitative and hard to describe explicitly.”
— Garijo et al. (2013), PLoS One 8(11)
“… reproducibility in computational biology is aspired to, but rarely achieved. This is unfortunate since the quantitative nature of the science makes reproducibility more obtainable than in cases where experiments are qualitative and hard to describe explicitly.”
— Garijo et al. (2013), PLoS One 8(11)
currently
What will be covered in this session?
Can’t cover all potential topics
Speaker Subject Relevance
Hucka standard formats accurate communication of models
Lovell data analysis appropriate inferences from data
Kuperstein data curation & visualization software reconciling data from multiple sources
Nahid workflow software recreating data analysis procedures