S

Shawn Bowers

Gonzaga University

ORCID: 0000-0002-2972-0197

Publishes on Scientific Computing and Data Management, Distributed and Parallel Computing Systems, Semantic Web and Ontologies. 145 papers and 3.9k citations.

145Publications
3.9kTotal Citations

Is this you? Claim your profile.

Add your photo, update your bio, and get notified when your ranking changes.

Top publicationsby citations

The New Bioinformatics: Integrating Ecological Data from the Gene to the Biosphere
Matthew B. Jones, Mark Schildhauer, O. J. Reichman et al.|Annual Review of Ecology Evolution and Systematics|2006
Cited by 226

Bioinformatics, the application of computational tools to the management and analysis of biological data, has stimulated rapid research advances in genomics through the development of data archives such as GenBank, and similar progress is just beginning within ecology. One reason for the belated adoption of informatics approaches in ecology is the breadth of ecologically pertinent data (from genes to the biosphere) and its highly heterogeneous nature. The variety of formats, logical structures, and sampling methods in ecology create significant challenges. Cultural barriers further impede progress, especially for the creation and adoption of data standards. Here we describe informatics frameworks for ecology, from subject-specific data warehouses, to generic data collections that use detailed metadata descriptions and formal ontologies to catalog and cross-reference information. Combining these approaches with automated data integration techniques and scientific workflow systems will maximize the value of data and open new frontiers for research in ecology.

Provenance in Scientific Workflow Systems
Susan B. Davidson, Sarah Cohen‐Boulakia, Anat Eyal et al.|IEEE Data(base) Engineering Bulletin|2007
Cited by 167

The automated tracking and storage of provenance information promises to be a major advantage of scientific workflow systems. We discuss issues related to data and workflow provenance, and present techniques for focusing user attention on meaningful provenance through “user views,” for managing the provenance of nested scientific data, and for using information about the evolution of a workflow specification to understand the difference in the provenance of similar data products.