Building a scientific basis for research evaluation
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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

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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

  • Final thoughts


Outline

  • Science of science policy

  • A proposed conceptual framework

  • Empirical approaches:

    • NSF Engineering Dashboard

    • ASTRA – Australia

    • HELIOS – France

  • Final thoughts


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?

  • 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


The timing is right:


A conceptual framework for a science of science policy


Getting the right framework matters

  • What you measure is what you get

    • Poor incentives

    • Falsification

  • Usefulness

  • Effectiveness


A proposed conceptual framework

Adapted from Ian Foster, University of Chicago


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


Challenge – The data infrastructure didn’t exist

However, some of the data do exist


Empirical Approaches

Leveraging existing data to begin describing results of the scientific enterprise


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

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

  • 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 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


Outline

  • Science of science policy

  • A proposed conceptual framework

  • Empirical approaches:

    • NSF Engineering Dashboard

    • ASTRA – Australia

    • HELIOS – France

  • Final thoughts


Outline

  • Science of science policy

  • A proposed conceptual framework

  • Empirical approaches:

    • NSF Engineering Dashboard

    • ASTRA – Australia

    • HELIOS – France

  • Final thoughts


Linking administrative and grant funding datain Australia


Outline

  • Science of science policy

  • A proposed conceptual framework

  • Empirical approaches:

    • NSF Engineering Dashboard

    • ASTRA – Australia

    • HELIOS – France

  • Final thoughts


Describing public-private partnerships in France

People

People


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

    • ??


Final thoughts


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

  • 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


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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