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Science automation in practice: Performance data farming in workflows., , , , , , and . ETFA, page 1-4. IEEE, (2016)Reproducibility of execution environments in computational science using Semantics and Clouds, , , , , and . Future Generation Computer Systems, (2017)Pegasus, a workflow management system for science automation., , , , , , , , , and 1 other author(s). Future Generation Comp. Syst., (2015)Reproducibility of execution environments in computational science using Semantics and Clouds., , , , , and . Future Generation Comp. Syst., (2017)Pegasus: A framework for mapping complex scientific workflows onto distributed systems., , , , , , , , , and 3 other author(s). Scientific Programming, 13 (3): 219-237 (2005)Dynamic computational workflows: Discovery, optimisation and scheduling., and . Scientific Programming, 15 (4): 191-192 (2007)Simulating Spatially Explicit Problems on High Performance Architectures., and . J. Parallel Distrib. Comput., 62 (3): 446-467 (2002)Examining the Challenges of Scientific Workflows., , , , , , , , , and . IEEE Computer, 40 (12): 24-32 (2007)Custom Execution Environments with Containers in Pegasus-enabled Scientific Workflows., , , , , , , , , and . CoRR, (2019)Provenance: The Bridge Between Experiments and Data., , , , , and . Computing in Science and Engineering, 10 (3): 38-46 (2008)