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Automated Research Workflows for Accelerated Discovery Closing the Knowledge Discovery Loop (2022) / Chapter Skim
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6 Conclusion
Pages 89-94

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From page 89...
... The committee's exploration of ARWs illustrates that the research enterprise stands at an important inflection point. The scientific revolution of the 17th century ushered in an unprecedented era of human progress, leading directly to discoveries and innovations that have transformed tasks requiring the application of muscle or simple technologies into services performed by ever more effective machines.
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From page 90...
... These themes include the need to break down academic silos, provide incentives for greater collaboration among researchers, ensure greater interoperability across technologies, foster sharing of a broader range of research outputs, and address issues such as striking an appropriate balance between access to and protection of data. The committee's findings and recommendations point to promising areas of focus for the research enterprise in facilitating the effective implementation of ARWs.
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From page 91...
... Broader access to research workflows and results and the enhanced ability to uncover and correct errors can contribute to greater confidence in research findings and the research enterprise and reduce redundancy among research efforts. To be sure, issues such as dealing with large amounts of streaming data and complex computational approaches will continue to pose technical challenges to the design and implementation of ARWs.
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From page 92...
... 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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From page 93...
... 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. 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 use-inspired applications in specific domains and explore solutions to make the outputs available through privacy-preserving algorithms, federated learning approaches to using data, and other methods.
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Key Terms

  • automated research
  • machine learning
  • research funders
  • data science
  • specific domains

  • artificial intelligence
  • research process
  • funding support
  • research objects
  • research tools

  • research outputs
  • associated research
  • incentives research
  • disseminate research
  • mated research

  • research workflow
  • entire research
  • institutions enabling
  • arws illustrates
  • algorithm development

  • services performed
  • data literacy
  • pressing demands
  • supporting literature
  • enterprise stands

  • technical barriers
  • reduce redundancy
  • explore solutions
  • vastly increase
  • learning approaches


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