TWC Document

Printer-friendly version

Addressing Scientific Rigor in Data Analytics using Semantic Workflows

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.

History

DateCreated ByLink
Download

Related Projects:

CHEAR Project LogoChild Health Exposure Analysis Repository (CHEAR)
Principal Investigator: Deborah L. McGuinness
Co Investigator: Kristin Bennett
Description: Child Health Exposure Analysis Repository Data Science Semantics
DataONE Semantics LogoDataONE Semantics (D1-Semantics)
Principal Investigator: Deborah L. McGuinness
Co Investigator: Matt Jones, Ben Leinfelder, Xixi Luo, and Mark Schildhauer
Description: Semantic search on measurements will enable precise data discovery by helping users identify relevant content from the massive and heterogeneous catalog in DataONE, thereby improving efficiency and opportunities for researchers and other data consumers.
SemNExT LogoSemantic Numeric Exploration Technology (SemNExT)
Principal Investigator: Kristin Bennett and Deborah L. McGuinness
Description: SemNExT combines numeric analysis of data with semantic understanding and explanation technologies to provide a holistic means of exploring robust datasets.

Related Research Areas:

Health Informatics
Lead Professor: Deborah L. McGuinness
Description:

Health informatics is "the interdisciplinary study of the design, development, adoption and application of IT-based innovations in healthcare services delivery, management and planning." Procter, R. Dr. (Editor, Health Informatics Journal, Edinburgh, United Kingdom). (From the U.S. National Library of Medicine)


Concepts: None.
Knowledge Provenance
Lead Professor: Deborah L. McGuinness
Description: Knowledge Provenance
Concepts: Provenance, Semantic Web
Semantic Foundations
Lead Professor: Deborah L. McGuinness
Description: Semantic Foundations
Concepts: Semantic Web
Semantic eScience
Lead Professor: Peter Fox
Description:
Science has fully entered a new mode of operation. E-science, defined as a combination of science, informatics, computer science, cyberinfrastructure and information technology is changing the way all of these disciplines do both their individual and collaborative work.
As semantic technologies have been gaining momentum in various e-Science areas (for example, W3C's new interest group for semantic web health care and life science), it is important to offer semantic-based methodologies, tools, middleware to facilitate scientific knowledge modeling, logical-based hypothesis checking, semantic data integration and application composition, integrated knowledge discovery and data analyzing for different e-Science applications.
Partially influenced by the Artificial Intelligence community, the Semantic Web researchers have largely focused on formal aspects of semantic representation languages or general-purpose semantic application development, with inadequate consideration of requirements from specific science areas. On the other hand, general science researchers are growing ever more dependent on the web, but they have no coherent agenda for exploring the emerging trends on the semantic web technologies. It urgently requires the development of a multi-disciplinary field to foster the growth and development of e-Science applications based on the semantic technologies and related knowledge-based approaches.

Concepts: