Automated Research Workflows for Accelerated Discovery Closing the Knowledge Discovery Loop (2022) / Chapter Skim
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Technological developments go hand in hand with scientific progress, and advances in computing and automation are no exception. Computing plays a central role throughout research workflows, from computerized models used for simulation and prediction, to control of equipment and data analysis, to publication.
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FIGURE 2-1 Knowledge discovery loop. NOTE: Automated research workflows can automate and close the loop of scientific discovery.
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That is, the edges of the graph in Figure 2-1 can be automated with methods that transcend individual disciplines, but the nodes -- data and models -- will remain domain specific. The loop remains open to human intervention, for example, to identify variables relevant to measurement and modeling, and to analyze serendipitous results.
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. Reproducibility and Replicability Reproducibility is obtaining consistent results using the same input data; computational steps, methods, and code; and conditions of analysis.
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2018. The frontiers of machine learning: 2017 Raymond and Beverly Sackler U.S.-U.K.
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BUILDING AUTOMATED RESEARCH WORKFLOWS: CURRENT STATE OF THE ART As outlined above, the confluence of several technological advancements is driving the development and implementation of ARWs. Fully realized ARWs are not common at present, and so this study examines how and where progress is being made in areas such as advanced computation, use of workflow management systems and notebooks, laboratory automation, and use of AI as a workflow component as well as in directing the "outer loop" of the research process.
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. Scientific workflow engines formalize a workflow construct, in which a user defines a set of steps and the dependencies between those steps through configuration files and code.
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. As the next generation of scientific workflow engines expands, automation of the scientific process can lead to a step change in the rate of discovery in many fields.
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