Reproducibility in Scientific Data Analysis - BioScience Seminar
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The document discusses the importance of reproducibility in scientific data analysis, highlighting that it ensures reliability despite complexities in computing environments. It outlines key concerns, such as the challenges posed by software and data versions, and proposes actionable steps like maintaining detailed records and using open standards. The organization pharmb.io promotes open data and standards to enhance reproducibility in research.
Reproducibility in Scientific Data Analysis - BioScience Seminar
- 1. Reproducibility in Scientific Data Analysis Samuel Lampa @smllmp PhD Student Pharmaceutical Bioinformatics at pharmb.io with Assoc. Prof. Ola Spjuth @ola_spjuth @ Dept. of Pharm. Biosci. / Uppsala University Farmbio BioScience Seminar – Dec 16 2016
- 3. Structure of this talk Reproducibility in Scientific Data Analysis … ● What is it? ● Why is it important? ● Why is it a problem? ● What can we do about it? ● What does pharmb.io do about it?
- 4. What is it? “it” = reproducibility in scientific data analysis
- 7. Why is it important? “it” = reproducibility in scientific data analysis
- 8. Why is it important? ● More and more data generation automated → More and more focus on data analysis ● Culture of replicability not (yet) as established in computational as in classical disciplines ● “it is the only thing that an investigator can guarantee about a study” simplystatistics.org/2014/06/06/the-real-reason-reproducible-research-is-important
- 9. Why is it a problem? “it” = reproducibility in scientific data analysis
- 10. wet lab data analysis?
- 11. Why is it a problem? ● Complexity of computing environment – Software versions, Data versions ... ● More black box components ● Assumptions on computing environment often left out ● Manual steps often left out
- 12. What can we do about it? “it” = reproducibility in scientific data analysis
- 13. What can we do about it? Utopia: Infrastructure for all data and computations to be inspected and re-run with other data and parameters by anyone But: We can’t wait for that In the meanwhile: Even small steps towards reproducibility will help. Start today!
- 14. General themes Know exactly what data and results mean Know exactly how results were obtained Be able to get same result independently
- 15. More concretely ... Know exactly what data and results mean – Open standards, Ontologies, Data formats Know exactly how results were obtained – Keeping track of manual steps, parameters, versions of software and data ... – Version control – Automation (scripts) Be able to get same result independently – code, data, and scripts … make it all available!
- 16. Sandve GK, Nekrutenko A, Taylor J, Hovig E. Ten Simple Rules for Reproducible Computational Research. PLoS Comput Biol. 2013;9(10):1-4. dx.doi.org/10.1371/journal.pcbi.1003285
- 17. FAIR Principles for data and meta data F - Findable A - Accessible I - Interoperable R – Reusable Wilkinson MD, Dumontier M, Aalbersberg IjJ, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016;3:160018. doi:10.1038/sdata.2016.18.
- 18. What does pharmb.io do about it? “it” = reproducibility in scientific data analysis
- 19. What does pharmb.io do about it? ● Open data, open source, open standards Promoting and using as much as possible ● BioImg.org Store Virtual Machines & Containers ● Semantic Data Technologies Machine readability - Avoiding ambiguity ● Re-runnable computational experiments Via workflows, containers, infrastructure as code
- 20. O’Boyle NM, Guha R, Willighagen EL, et al. Open Data, Open Source and Open Standards in chemistry: The Blue Obelisk five years on. J Cheminform. 2011;3(10):1-16. doi:10.1186/1758-2946-3-37
- 21. BioImg.org Dahlö M, Haziza F, Kallio A, Korpelainen E, Bongcam-Rudloff E, Spjuth O. BioImg.org: A catalog of virtual machine images for the life sciences. Bioinform Biol Insights. 2015;9(Vmi):125-128. doi:10.4137/BBI.S28636. Martin Dahlö
- 22. Semantic Data Technologies Lampa S, Willighagen E, Kohonen P, King A, Vrandečić D, Grafström R, Spjuth O. RDFIO: Extending Semantic MediaWiki for interoperable biomedical data management. J Biomed Sem. Submitted.
- 23. Re-runnable experiments via containers (and infrastructure as code) Marco Capuccini github.com/kubenow/KubeNow github.com/mcapuccini/SparkNow
- 25. Lampa S, Alvarsson J, Spjuth O. Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles. J Cheminform. 2016;8(1):67. doi:10.1186/s13321-016-0179-6.
- 26. Lampa S, Alvarsson J, Spjuth O. Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles. J Cheminform. 2016;8(1):67. doi:10.1186/s13321-016-0179-6.