Automated Research Workflows for Accelerated Discovery Closing the Knowledge Discovery Loop (2022) / Chapter Skim
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Computation and automation of data acquisition allow automated systems to analyze data and extract knowledge, to create predictive models, and to use those models to guide the acquisition of additional data, closing and automating the loop in a typical research workflow. The committee uses the term automated research workflow (ARW)
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Simultaneously, ARWs provide a way to satisfy pressing demands across fields to increase interoperability, reproducibility, replicability, and trustworthiness by better tracking results, recording data, establishing provenance, and creating more consistent metadata than even the most dedicated researchers can provide themselves. To explore the benefits and challenges, as well as to suggest opportunities to move forward, the National Academies of Sciences, Engineering, and Medicine's Board on Research Data and Information, in collaboration with the Board on Mathematical Sciences and Analytics and the Computer Science and Telecommunications Board, launched a study aimed at examining current efforts to develop advanced and automated workflows to accelerate research progress.
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These include designing and conducting experiments, analyzing data, and observing natural phenomena. These improvements can be realized at scale by implementing infrastructure and practices that facilitate the application of artificial intelligence and machine learning and related technologies to research.
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An additional trend offering an on-ramp to ARWs is the use of interactive computational laboratory notebooks, such as Jupyter, that allow researchers to capture a set of discrete analysis steps and track them with a single user interface. There were about 9.7 million Jupyter notebooks stored on GitHub when this was written in November 2020, with the number growing by about 8,500 per day (Project Jupyter, 2020)
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• ARWs should facilitate the effective use of artificial intelligence (AI) and machine learning (ML)
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. FINDING C: RESEARCH ENTERPRISE Realizing the potential of automated research workflows (ARWs)
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Although providing sustained support for the development and operation of shared cyberinfrastructure across multiple disciplines has been challenging in the United States, there are numerous historical success stories such as the National Science Foundation (NSF) National Supercomputer Centers, the development of GenBank and other digital data resources in the life sciences, and NSF's advanced cyberinfrastructure program.
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RECOMMENDATION 3: Human Resources Research funders, higher education, research institutions, and scientific and professional societies should support the development and implementation of educational programs and career pathways aimed at building the workforce needed to develop and utilize automated research workflows (ARWs) , including the creation of career tracks that support ARW capabilities.
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• Pursuing international collaboration when possible in order to ac celerate progress toward implementing the above changes at scale. Misalignment of the incentives and priorities of researchers, research institutions, and research funders with the actions and efforts needed to effectively develop and implement ARWs was a major theme of the March 2020 workshop discussion and manifested itself in a variety of ways.
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FINDING D: LEGAL AND POLICY ISSUES In addition to barriers to progress that exist within the research process itself, there are legal and policy issues that affect implementation of automated research workflows in specific domains that will require international multistakeholder efforts to address. Data collected for use in ARWs will increasingly include data generated outside of a traditional research setting, such as personal health data collected from wearables and medical visits or behavioral data collected online from social media.
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. RECOMMENDATION 5: Preserving Privacy Research enterprise funders, performers, publishers, and beneficiaries should work with governments, data privacy experts, and other entities to address the legal, policy, and associated technical barriers to implementing automated research workflows in specific domains.
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