Abstract
New NIH grants require establishing scientific rigor, i.e. applicants must provide evidence of strict application of the scientific method to ensure robust and unbiased experimental design, methodology, analysis, interpretation and reporting of results. Researchers must transparently report experimental details so others may reproduce and extend findings. Provenance can help accomplish these objectives; analytical workflows can be annotated with sufficient information for peers to understand methods and reproduce the intended results. We aim to produce enhancements to the ontology space including links between existing ontologies, terminology gap analysis and ontology terms to address gaps, and potentially a new ontology aimed at integrating the higher level data analysis planning concepts. We are developing a collection of techniques and tools to enable workflow recipes or plans to be more clearly and consistently shared, improve understanding of all analysis aspects and enable greater reuse and reproduction. We aim to show that semantic workflows can improve scientific rigor in data analysis and to demonstrate their impact in specific research domains.
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Notes
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W3C PROV-O refers to these as “plans.” http://www.w3.org/TR/prov-o/#Plan.
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“Prospective provenance” refers to a workflow’s “plan” or “recipe.” See [12].
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We know of no ontology that enables scripted workflow processes accomplishing semantically similar tasks to be annotated in the same way using the same vocabulary.
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References
NIH Grants: Funding: Rigor and Reproducibility. https://grants.nih.gov/reproducibility/index.htm
Repetitive flaws: Strict guidelines to improve the reproducibility of experiments are a welcome move, Nature Editorial. http://www.nature.com/news/repetitive-flaws-1.19192
Challenges In Irreproducible Research: Nature News & Comment Special. http://www.nature.com/news/reproducibility-1.17552
Bowers, S., Ludäscher, B.: Actor-oriented design of scientific workflows. In: Conceptual Modeling ER 2005: Proceedings of the 24th International Conference on Conceptual Modeling, Klagenfurt, Austria, October 2005
Oinn, T., et al.: Taverna: a tool for the composition and enactment of bioinformatics workflows. Bioinformatics 20(17), 3045–3054 (2004)
Gil, Y., et al.: Wings: intelligent workflow-based design of computational experiments. IEEE Intell. Syst. 26(1), 62–72 (2011)
Davidson, S.B., Freire, J., J.: Provenance and scientific workflows: challenges and opportunities. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1345–1350. ACM Press, New York (2008)
Murta, L., Braganholo, V., Chirigati, F., Koop, D., Freire, J.: noWorkflow: capturing and analyzing provenance of scripts. In: Ludaescher, B., Plale, B. (eds.) IPAW 2014. LNCS, vol. 8628, pp. 71–83. Springer, Heidelberg (2015)
McPhillips, T., et al.: YesWorkflow: a user-oriented, language-independent tool for recovering workflow information from scripts. Int. J. Digit. curation 10, 298–313 (2015)
McPhillips, T., et al.: Retrospective provenance without a runtime provenance recorder. In: USENIX Workshop on Theory and Practice of Provenance (2015)
DataONE Scientific Workflows, Provenance Working Group: ProvONE: a PROV extension data model for scientific workflow provenance. W3C unofficial draft, 27 March 2014. http://vcvcomputing.com/provone/provone.html
Lim, C., Lu, S., Chebotko, A., Fotouhi, F.: Prospective and retrospective provenance collection in scientific workflow environments. In: 2010 IEEE International Conference on Services Computing (2010)
Acknowledgements
Thanks to T. McPhillips of UIUC and B. Ludäscher of UC-Davis for help with YesWorkflow, D. Garijo and V. Ratnakar of USC ISI for help with WINGS, and NSF Grant No. 1331023.
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Erickson, J.S., Sheehan, J., Bennett, K.P., McGuinness, D.L. (2016). Addressing Scientific Rigor in Data Analytics Using Semantic Workflows. In: Mattoso, M., Glavic, B. (eds) Provenance and Annotation of Data and Processes. IPAW 2016. Lecture Notes in Computer Science(), vol 9672. Springer, Cham. https://doi.org/10.1007/978-3-319-40593-3_18
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DOI: https://doi.org/10.1007/978-3-319-40593-3_18
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