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Award Detail

Doing Business As Name:University of Illinois at Urbana-Champaign
  • Jodi Schneider
  • (217) 333-2187
Award Date:07/27/2021
Estimated Total Award Amount: $ 599,963
Funds Obligated to Date: $ 172,572
  • FY 2021=$172,572
Start Date:08/01/2021
End Date:07/31/2026
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.075
Primary Program Source:040100 R&RA ARP Act DEFC V
Award Title or Description:CAREER: Using network analysis to assess confidence in research synthesis
Federal Award ID Number:2046454
DUNS ID:041544081
Parent DUNS ID:041544081
Program:Science of Science
Program Officer:
  • Brian Humes
  • (703) 292-7284

Awardee Location

Street:1901 South First Street
Awardee Cong. District:13

Primary Place of Performance

Organization Name:University of Illinois at Urbana-Champaign
Street:506 S. Wright Street
Cong. District:13

Abstract at Time of Award

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). The best available science is an important factor that informs policy in areas such as conservation, energy, healthcare, and sustainable. Determining the best available science requires synthesizing multiple scientific results to gauge both the level of scientific consensus and the reliability of the research. However, on some policy-relevant topics, different syntheses come to incompatible conclusions. Such inconsistency in the synthesis of evidence wastes money, generates misleading results, and can lead to poor decisions impacting large numbers of people. Through research, education, and outreach, this CAREER project aims to develop and test a novel framework of tools and workflows that will reveal potential sources of bias in expert literature. The framework will enable stakeholders to quickly understand which individuals, institutions and funders contributed to the creation of the evidence. It will assess other factors that create risk of bias as well as the degree of confidence an expert community has in the evidence presented. Research outcomes could facilitate data-driven decision-making in a broad range of areas. Examples include topics in energy and environmental sciences and health sciences, like the carbon footprint of various forms of food production, herd immunity, and vaccine effectiveness. This project will also help diversify the science workforce by employing student assistants from underserved populations and by developing two policy-relevant STEM university courses and a middle school career video to attract underrepresented students. This project explores how to improve the assessment of confidence in research at scale. It will enable evidence-seekers to quickly understand the level of consensus within a body of literature, along with risk factors that might impact reliability of research, providing a key resource for robustness and reproducibility. This framework can be applied to any bibliography, including manuscripts under peer review, published articles, and database search results. Project outputs will be beneficial for identifying risks in literature reviews, such as sponsor bias or the avoidance of citation of contradictory evidence, which will help reduce the spread of misinformation. This project is made possible by recent advances in network science and text mining methods, as well as the availability of abstracts, affiliation, citations, and funding data under suitable licenses for data science. The work is novel in bringing together complementary approaches that have not previously been combined: argumentation theory and the study of controversies; approaches for synthesizing evidence; and bibliometric and scientometric approaches for looking structurally at a field. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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