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Title: Building Trust in Earth Science Findings through Data Traceability and Results Explainability
To trust findings in computational science, scientists need workflows that trace the data provenance and support results explainability. As workflows become more complex, tracing data provenance and explaining results become harder to achieve. In this paper, we propose a computational environment that automatically creates a workflow execution’s record trail and invisibly attaches it to the workflow’s output, enabling data traceability and results explainability. Our solution transforms existing container technology, includes tools for automatically annotating provenance metadata, and allows effective movement of data and metadata across the workflow execution. We demonstrate the capabilities of our environment with the study of SOMOSPIE, an earth science workflow. Through a suite of machine learning modeling techniques, this workflow predicts soil moisture values from the 27 km resolution satellite data down to higher resolutions necessary for policy making and precision agriculture. By running the workflow in our environment, we can identify the causes of different accuracy measurements for predicted soil moisture values in different resolutions of the input data and link different results to different machine learning methods used during the soil moisture downscaling, all without requiring scientists to know aspects of workflow design and implementation.  more » « less
Award ID(s):
2103836 2103845
PAR ID:
10382231
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
IEEE Transactions on Parallel and Distributed Systems
ISSN:
1045-9219
Page Range / eLocation ID:
1 to 14
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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