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Building a Scientific Basis for Research Evaluation. Rebecca F. Rosen, PhD. Senior Researcher. Research Trends Seminar October 17, 2012. Outline. Science of science policy A proposed conceptual framework Empirical approaches: NSF Engineering Dashboard ASTRA – Australia HELIOS – France

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building a scientific basis for research evaluation

Building a Scientific Basis for Research Evaluation

Rebecca F. Rosen, PhD

Senior Researcher

Research Trends Seminar

October 17, 2012

outline
Outline
  • Science of science policy
  • A proposed conceptual framework
  • Empirical approaches:
    • NSF Engineering Dashboard
    • ASTRA – Australia
    • HELIOS – France
  • Final thoughts
outline1
Outline
  • Science of science policy
  • A proposed conceptual framework
  • Empirical approaches:
    • NSF Engineering Dashboard
    • ASTRA – Australia
    • HELIOS – France
  • Final thoughts
the emergence of a s cience of science policy
The emergence of a science of science policy
  • Jack Marburger’s challenge (2005)
  • Science of Science & Innovation Policy Program at the National Science Foundation (2007)
    • An emerging, highly interdisciplinary research field
  • Science of Science Policy Interagency Task Group publishes a “Federal Research Roadmap” (2008):
    • The data infrastructure is inadequate for decision-making
  • STAR METRICS (2010)
why a science of science policy
Why a science of science policy?
  • Evidence-based investments
    • Good metrics = good incentives
    • Science is networked and global
  • Build a bridge between researchers and policymakers
    • Researchers ask the right questions
  • The adjacent possible: leverage existing and new research and expertise
    • New tools to describe & measure communication
getting the right framework matters
Getting the right framework matters
  • What you measure is what you get
    • Poor incentives
    • Falsification
  • Usefulness
  • Effectiveness
a proposed conceptual framework
A proposed conceptual framework

Adapted from Ian Foster, University of Chicago

a framework to drive person centric data collection
A framework to drive person-centric data collection
  • WHO is doing the research
  • WHAT is the topic of their research
  • HOW are the researchers funded
  • WHERE do they work
  • With WHOM do they work
  • What are their PRODUCTS
empirical approaches
Empirical Approaches

Leveraging existing data to begin describing results of the scientific enterprise

an empirical approach
An empirical approach
  • Enhance the utility of enterprise data
  • Identify authoritative “core” data elements
  • Develop an Application Programming Interface (API)
    • Data platform that provides programmatic access to public (or private) agency information
  • Develop a tool to demonstrate value of API
topic modeling enhancing the value of existing data
Topic modeling: Enhancing the value of existing data

Automatically learned topics (e.g.):

t6. conflict violence war international military …

t7. model method data estimation variables …

t8. parameter method point local estimates …

t9. optimization uncertainty optimal stochastic …

t10. surface surfaces interfaces interface …

t11. speech sound acoustic recognition human …

t12. museum public exhibit center informal outreach

t13. particles particle colloidal granular material …

t14. ocean marine scientist oceanography …

NSF proposals

  • Topic Model:
  • Use words from
  • (all) text
  • Learn T topics

t49

t18

t114

t305

Topic tags for each and every proposal

David Newman - UC Irvine

stepwise empirical approach
Stepwise empirical approach
  • Enhance the utility of enterprise data
  • Identify authoritative “core” data elements
  • Develop an Application Programming Interface (API)
    • Data platform that provides flexible, programmatic access to public (or private) agency information
  • Develop a tool to demonstrate value of API
stepwise empirical approach1
Stepwise empirical approach
  • Enhance the utility of enterprise data
  • Identify authoritative “core” data elements
  • Develop an Application Programming Interface (API)
    • Data platform that provides programmatic access to public (or private) agency information
  • Develop a tool to demonstrate value of API
outline2
Outline
  • Science of science policy
  • A proposed conceptual framework
  • Empirical approaches:
    • NSF Engineering Dashboard
    • ASTRA – Australia
    • HELIOS – France
  • Final thoughts
outline3
Outline
  • Science of science policy
  • A proposed conceptual framework
  • Empirical approaches:
    • NSF Engineering Dashboard
    • ASTRA – Australia
    • HELIOS – France
  • Final thoughts
outline4
Outline
  • Science of science policy
  • A proposed conceptual framework
  • Empirical approaches:
    • NSF Engineering Dashboard
    • ASTRA – Australia
    • HELIOS – France
  • Final thoughts
what does getting it right mean
What does getting it right mean?
  • A community driven empirical data framework should be:
    • Timely
    • Generalizable and replicable
    • Low cost, high quality
    • The utility of “Big Data”:
    • Disambiguated data on individuals
      • Comparison groups
    • New text mining approaches to describe and measure communication
    • ??
policy makers can engage scisip communities
Policy makers can engage SciSIP communities:
  • Patent Network Dataverse; Fleming at Harvard and Berkeley
  • Medline-Patent Disambiguation; Torvik & Smalheiser at U Illinois)
  • COMETS (Connecting Outcome Measures in Entrepreneurship Technology and Science); Zucker & Darby at UCLA
the power of open research communities
The power of open research communities
  • Internet and data technology can transform effectiveness of science:
    • Informing policy
    • Communicating science to the public
    • Enabling scientific collaborations
  • Interoperability is key
  • Publishers are an important part of the community
slide32

THANK YOU!

Rebecca F. Rosen, PhD

E-Mail: [email protected]

1000 Thomas Jefferson Street NWWashington, DC 20007

General Information: 202-403-5000TTY: 887-334-3499

Website: www.air.org

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