Addressing Scientific Rigor in Data Analytics Using Semantic Workflows View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2016

AUTHORS

John S. Erickson , John Sheehan , Kristin P. Bennett , Deborah L. McGuinness

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. More... »

PAGES

187-190

References to SciGraph publications

  • 2015. noWorkflow: Capturing and Analyzing Provenance of Scripts in PROVENANCE AND ANNOTATION OF DATA AND PROCESSES
  • 2005. Actor-Oriented Design of Scientific Workflows in CONCEPTUAL MODELING – ER 2005
  • Book

    TITLE

    Provenance and Annotation of Data and Processes

    ISBN

    978-3-319-40592-6
    978-3-319-40593-3

    Author Affiliations

    From Grant

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-40593-3_18

    DOI

    http://dx.doi.org/10.1007/978-3-319-40593-3_18

    DIMENSIONS

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