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Michael Wilde

Researcher at Argonne National Laboratory

Publications -  132
Citations -  7060

Michael Wilde is an academic researcher from Argonne National Laboratory. The author has contributed to research in topics: Workflow & Scripting language. The author has an hindex of 36, co-authored 131 publications receiving 6613 citations. Previous affiliations of Michael Wilde include University of Chicago & United States Department of Energy.

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Proceedings ArticleDOI

Big Data Remote Access Interfaces for Light Source Science

Justin M. Wozniak, +5 more
TL;DR: A typical x-ray science workflow is used to illustrate the use of two recently developed techniques for data movement, archiving, and tagging, including a scalable catalog for referencing, managing, and performing remote operations on distributed data and a novel remote object interface for structured data set manipulation and visualization.
Journal ArticleDOI

Parallel workflows for data-driven structural equation modeling in functional neuroimaging.

Sarah Kenny, +6 more
- 20 Oct 2009 - 
TL;DR: A computational framework suitable for a data-driven approach to structural equation modeling (SEM) is presented and several workflows for modeling functional magnetic resonance imaging (fMRI) data within this framework are described.
Journal ArticleDOI

New science on the Open Science Grid

Ruth Pordes, +17 more
TL;DR: The Open Science Grid includes work to enable new science, new scientists, and new modalities in support of computationally based research.
Journal ArticleDOI

Database-managed Grid-enabled analysis of neuroimaging data: The CNARI framework

Steven L. Small, +5 more
- 01 Jul 2009 - 
TL;DR: The Computational Neuroscience Applications Research Infrastructure (CNARI) incorporates novel methods for maintaining, serving, and analyzing massive amounts of fMRI data and believes that these advanced computational approaches will fundamentally change the future shape of cognitive brain imaging with fMRI.
Proceedings ArticleDOI

Evaluating Distributed Execution of Workloads

Matteo Turilli, +6 more
TL;DR: In this article, the authors compare the AIMES middleware and Swift workflow scripting language and runtime for distributed execution of workflows on Pilot-Jobs managed by the middleware, and use this insight to execute a multi-stage workflow across five production grade resources.