Data analysis pipelines are now established as an effective means for specifying and
executing bioinformatics data analysis and experiments. While scripting languages …

Research process automation–the reliable, efficient, and reproducible execution of linked
sets of actions on scientific instruments, computers, data stores, and other resources–has …

Scientific workflows have become integral tools in broad scientific computing use cases.
Science discovery is increasingly dependent on workflows to orchestrate large and complex …

The prevalence of scientific workflows with high computational demands calls for their
execution on various distributed computing platforms, including large-scale leadership-class …

Porting a scientific data analysis workflow (DAW) to a cluster infrastructure, a new software
stack, or even only a new dataset with some notably different properties is often challenging …

The past decade has seen tremendous breakthroughs in computation and there is no
indication that this will slow any time soon. Machine learning, large-scale computing …

Software systems enabling large-scale data analysis workflows (DAWs) are a key
technology for many scientific disciplines, as they allow extracting new insights from …

Abstract Workflow Management Systems used to automate the execution of scientific
workflow applications on parallel and distributed computing platforms must make scheduling …

Scientific workflow applications have gained significant importance, and their automated
and efficient execution on large-scale computing platforms has been the subject of extensive …

Background Scientific workflow systems are increasingly popular for expressing and
executing complex data analysis pipelines over large datasets, as they offer reproducibility …