A New Vision for Center-Based Engineering Research (2017) / Chapter Skim
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2 A Vision for Convergent Engineering Research
2 A Vision for Convergent Engineering Research
Pages 20-30
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From page 20... ...
Use of information technology-enabled tools for collaboration is now the norm, including artificial intelligence and "big data," allowing teams -- both large and small -- to work more effectively. The world also faces a complex set of global challenges: threats to the environment, threats to national security, disruptive changes in the workforce, new diseases and health risks, and a rapidly changing world economy and competitive landscape.
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One source for such technologies could be the recently identified six "research big ideas" and the three "process ideas" 4 National Academyof Engineering, 2008, Changing the Conversation: Messages for Improving Public Understanding of Engineering, The National Academies Press, Washington, D.C.
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There are many other opportunities and guiding themes for complex engineering and societal problems that could be appropriate for future NSF engineering centers. Examples include the 15 global challenges identified by the Millennium Project5 or the health and development grand challenges identified by the Bill and Melinda Gates Foundation.6 Other entities, such as the President's Council of Advisors on Science and Technology (PCAST)
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There appears to be an emerging consensus among international centers that future university-industry research centers should be more challenge-focused -- that is, a greater fraction of centers addressing "needs pull" challenges, rather than just tackling "science push" opportunities.11 BEST PRACTICES OF TEAM RESEARCH AND VALUE CREATION The "big problem" orientation described above should help to inspire future center personnel, but it complicates the tasks of assembling the right research team, managing it efficiently, and maintaining its focus on center goals. These challenges highlight the importance of the systematic use of the best practices of team research and value creation in the centers to give them the best opportunity to succeed.
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Examples include the following: • Team composition influences team effectiveness; in particular, task-relevant diversity is critical and has a positive influence on team effectiveness; • Team professional development training improves team processes and outcomes; and • Geographically dispersed science teams and groups face more challenges in communicating and developing trust than do face-to-face teams and groups. Best practices of team research include those listed in Box 2.4.
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Most of the systems mentioned in Box 2.5 are oriented toward delivering economic value for industry -- the more commonly accepted definition of value creation -- but the principles can easily be adapted to the broader goal of delivering societal value. THE CURRENT SITUATION According to NSF, "the goal of the ERC program is to integrate engineering research and education with technological innovation to transform national prosperity, health, and security."15 The idea that the Engineering Research Center (ERC)
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, and there are significant opportunity costs associated with participating in a center. At the same time, the centers perceive that annual reporting requirements and bureaucratic oversight have increased.18 A STRATEGIC NEW DIRECTION Here and in later chapters the committee articulates a strategic new direction for future engineering centers.
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The speed at which any given scientific discovery advances will depend on how well its researchers collaborate with one another, and with technologists, in areas of e-science such as databases, workflow management, visualization, and cloud-computing technologies.24 It is anticipated that the application of data science methods and approaches will affect advances in many fields of engineering, including machine translation, speech recognition, robotics, search engines, the digital economy, as well as the biological sciences, medical informatics, health care, social sciences, and the humanities. From an engineering perspective, data science has fostered competitive intelligence, a newly emerging field that encompasses a number of activities, such as data mining and data analysis.25,26 CERCs could uniquely benefit from incorporating methods and approaches from this emerging field to help further gain insights about trends and patterns in data that will foster research breakthroughs.
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This more complex undertaking could require more resources for supporting roles such as project, cross-project, and cross-center management and/or emerging integrative roles, such as interdisciplinary scientists and data science professionals.29 The funding levels of a number of international center programs are growing and, in some cases, appear to be higher than that of NSF ERCs.30 For example, centers in Singapore, which are modeled on NSF centers, often receive $10 million to $15 million per year.31 The committee makes no recommendation on absolute funding levels but notes that $3 million to $5 million for an ERC in 1985 would translate to between $7 million and $11 million in 2016, accounting for inflation. In the committee's view, the alternative model of tackling big problems with a larger number of smaller, more focused centers would be a recipe for higher overhead and transaction costs.
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Although there is significant variation in practice from program to program in terms of reporting on progress, a number of international center directors interviewed as part of this study quickly volunteered that their annual reporting requirements and midterm reviews are not too onerous. It was also suggested by some of those interviewed that management information tools and IT systems were reducing the burden of annual reporting, making it easier to collect and collate journal articles, conference papers, patents, and so on, and to gather information about outreach and impact activities.33 While these concerns are not new, as part of its re-visioning effort, NSF should review its accountability procedures and minimize bureaucratic reporting requirements, with an eye to identifying what outcomes are essential to report, what might be nice to know, and what is unnecessary.
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Leverage the emerging fields of data science and analytics to inform research directions and enhance team research; 4. Create new engineering platforms and tools upon which others will build, accelerating the pace of research and innovation; 5.
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