Automated Research Workflows for Accelerated Discovery Closing the Knowledge Discovery Loop (2022) / Chapter Skim
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5 Overcoming Barriers to Wider Use of Automated Research Workflows
5 Overcoming Barriers to Wider Use of Automated Research Workflows
Pages 73-88
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From page 73... ...
Beyond technical challenges, discussion at the March 2020 workshop and other information indicates that the same conditions that slow or prevent change in other aspects of the research enterprise are in play here as well. These conditions include the tendency to maintain academic silos and a focus of research funders on investigator-led projects rather than underlying infrastructure.
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From page 74... ...
Some experts believe that the use of bibliometric measures such as the Journal Impact Factor in evaluation leads students and early career researchers to focus their efforts on publishing articles in the most prestigious journals and to choose currently fashionable topics where articles are likely to be highly cited, rather than to engage in riskier fundamental studies (Lawrence, 2007; Alberts, 2013; Ioannidis et al., 2014)
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From page 75... ...
OVERCOMING BARRIERS IN THE RESEARCH CULTURE Cultural changes in the research enterprise are necessary for effective adoption and use of ARWs. It will be important to develop these processes in a way that promotes ARWs as tools that can support both reliability and innovation in discovery, rather than falling into the trope of "machines replacing humans." This inaccurate representation has been seen extensively with the advent of AI in medicine, including articles in the popular press about whether "AI will replace doctors," and it has hampered progress.
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From page 76... ...
This will require integrating domain science training with data science training and relevant software engineering into academic programs across all disciplines at both the undergraduate and graduate levels. In addition, research teams will need additional specialized expertise from research software engineers, computational scientists, and data stewards.
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From page 77... ...
For example, research software engineers are key players in the development of ARWs and other research workflows, with their own career paths. Organizations such as the United States Research Software Engineer Association,1 the Society of Research Software Engineering,2 and the Campus Research Computing Consortium3 are working to build community among research computing and data professionals.
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From page 78... ...
The concept of translational research -- meaning the conversion of basic knowledge into products or processes that meet critical real-world needs -- emerged several decades ago in the biomedical domain. Computational scientists have begun to conceive of priorities within their own discipline, including workflow management systems, as examples of translational research (Deelman et al., 2020)
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From page 79... ...
Investment Priorities to Advance ARWs For several of the use cases discussed in Chapter 3, the development of tools and technologies constitutes a key enabler for accelerating progress. For example, materials researchers examined existing research workflow management systems and ended up building their own due to the need for a system that enables dynamic rerouting, facilitates constant communication among researchers, incorporates error management capability, and is flexible.
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From page 80... ...
One of the workshop speakers cited digital music as an analogy; to implement ARWs, communities need to move to shared data resources in the cloud that are available for a myriad of uses, similar to music streaming services. Creating and sustaining community data resources involves many challenges, including funding, deciding which data sets should be stored and maintained, and facilitating interoperability between them.
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From page 81... ...
Examples of language that funders can use to encourage sharing of data and other research products such as software, methods, and samples were discussed as part of a recent National Academies workshop and resulting proceedings on Developing a Toolkit for Fostering Open Science Practices (NASEM, 2021)
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From page 82... ...
federal government providing shared resources that have allowed research communities to harness information technologies to significantly advance their work. Examples include the establishment of national supercomputer centers in the 1980s by NSF in partnership with academic institutions, the development of GenBank and other digital data resources in the life sciences by NIH and the National Library of Medicine starting in the 1980s, and NSF's advanced cyberinfrastructure program launched in the early 2000s.
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From page 83... ...
. Further, the "down-sampled data are usually written into proprietary file formats, which impede and sometimes even preclude access to data and metadata, complicate long-term archiving, obstruct sharing, and fracture the scientific communities along file formats" (Somnath et al., 2019)
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From page 84... ...
In response to concerns about the security and use of personal data -- exacerbated by well-publicized examples such as the data breach at credit reporting firm Equifax in 2017 and the 2018 exposure of the use of Facebook data for political purposes by Cambridge Analytica -- policy makers and public interest groups have pushed to allow individuals to have greater control over the use, storage, and reuse of their data. The European Union's General Data Protection Regulations, put into effect in 2018, are intended to protect personal data by placing strong regulations on the entities that collect, process, and use data (EU, 2018)
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From page 85... ...
, an ML platform using federated learning to allow participating organizations to use proprietary data to speed drug discovery while data owners retain control of those data (IMI, 2021)
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From page 86... ...
. Research and its associated data production and use or reuse is also international, making the effectiveness of a single national government on shaping global policy challenging.
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From page 87... ...
43) In lab-based science, an intervention to embed research ethics training was evaluated through a randomized trial conducted by the Center for Open Science (COS)
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