reproducibility of computational research: methods to avoid madness (session introduction)

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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

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Introduction on the session "Reproducibility of computational research: methods to avoid madness" held Wednesday, September 17, during ICSB 2014 in Melbourne, Australia, 2014.

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Page 1: 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

Page 2: Reproducibility of computational research: methods to avoid madness (Session introduction)

So, what’s this about reproducibility?

Page 3: Reproducibility of computational research: methods to avoid madness (Session introduction)
Page 4: Reproducibility of computational research: methods to avoid madness (Session introduction)

“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).

Page 5: Reproducibility of computational research: methods to avoid madness (Session introduction)
Page 6: Reproducibility of computational research: methods to avoid madness (Session introduction)

“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).

Page 7: Reproducibility of computational research: methods to avoid madness (Session introduction)

What is the focus of this session?

Page 8: Reproducibility of computational research: methods to avoid madness (Session introduction)

Facets of reproducibility

Methodologicalissues

Reproducibility issues

Culturalissues

Motivations Policies

Incentives Funding

MethodsStandardsAlgorithmsInfrastructure…

Page 9: Reproducibility of computational research: methods to avoid madness (Session introduction)

Facets of reproducibility

Methodologicalissues

Reproducibility issues

Culturalissues

Motivations Policies

Incentives Funding

MethodsStandardsAlgorithmsInfrastructure…

Reproducibility issues

Culturalissues

Motivations Policies

Incentives Funding

Page 10: Reproducibility of computational research: methods to avoid madness (Session introduction)

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

Page 11: Reproducibility of computational research: methods to avoid madness (Session introduction)

“… 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)

Page 12: Reproducibility of computational research: methods to avoid madness (Session introduction)

“… 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

Page 13: Reproducibility of computational research: methods to avoid madness (Session introduction)

What will be covered in this session?

Page 14: Reproducibility of computational research: methods to avoid madness (Session introduction)

Can’t cover all potential topics

Page 15: Reproducibility of computational research: methods to avoid madness (Session introduction)

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