Eu descrevo em detalhe uma abordagem científica para medir os resultados dos investimentos em ciência. O modelo é baseado em uma abordagem sócio científica, ao invés de bibliométrica para descrever o empreendimento científico. Isso significa estudar e explicar a criação, transmissão e adoção de ideias científicas, ao invés de descrever e classificar documentos. As ideias são geradas dentro das redes sociais (tanto científicas quanto econômicas); o financiamento da ciência funciona, em parte, ao permitir que estas redes existam e se expandam. Como Kahneman salientou “o primeiro grande avanço em nossa compreensão do mecanismo de associação foi uma melhoria no método de medição”, e a chave para melhores medições científicas são melhores dados. Eu descrevo os princípios e metodologia de um amplo espectro de dados que descrevem o processo de pesquisa e as redes de pesquisa que impulsionam este processo. Eu discuto a abordagem para a construção de uma poderosa nova infraestrutura de dados, que facilitará a integração destes dados permitindo, assim, uma análise do papel do financiamento para estimular a criação, transmissão e adoção de ideias através destas redes. I describe in detail a science-based approach for measuring the results of science investments. The framework is based on a social scientific, rather than a bibliometric approach to describing the scientific enterprise. This means studying and explaining the creation, transmission and adoption of scientific ideas, rather than describing and classifying documents. The ideas are generated within social (both scientific and economic) networks; science funding works in part by enabling those networks to exist and expand. As Kahneman has pointed out, “the first big breakthrough in our understanding of the mechanism of association was an improvement in a method of measurement,” and the key to better scientific measurements is better data. Since the key to better scientific measurements is better data. I describe the methodical and principled capture of a broad spectrum of data describing the research process and the research networks that drives that process. I discuss the approach to building a powerful new data infrastructure that will enable the integration of this data and thus permit analysis of the role of funding in stimulating the creation, transmission and adoption of ideas through those networks. Describo en detalle un enfoque basado en la ciencia para medir los resultados de las inversiones científicas. El marco es un enfoque basado en las ciencias sociales más que un enfoque bibliométrico para describir la empresa científica. Esto significa estudiar y explicar la creación, transmisión y adopción de las ideas científicas, en lugar de describir y clasificar los documentos. Las ideas se generan dentro de las redes sociales (tanto científicas como económicas); la financiación de las ciencia opera en parte al permitir que las redes existan

1. A scientific framework to
measure results of research
investmentsInstitutes of Research,
Julia Lane, American
University of Strasbourg and University of
Melbourne
And many colleagues

2. Key ideas
• Need sensible scientific framework which:
– Is theoretically driven
– Uses appropriate unit of analysis
– Is generalizable and replicable
• Need sensible empirical framework which
– Uses 21st Century technology to collect data
– Uses 21st Century technology to link activities
• Need framework which can be international

3. Outline
•
•
•
•
Motivation
Conceptual Framework
Empirical Frameworks
Next steps

4. Motivation
The President recently asked his Cabinet to
carry out an aggressive management agenda
for his second term that delivers a smarter,
more innovative, and more accountable
government for citizens. An important
component of that effort is strengthening
agencies' abilities to continually improve
program performance by applying existing
evidence about what works, generating new
knowledge, and using experimentation and
innovation to test new approaches to
program delivery.

5. Motivation
How much should a nation spend on science? What kind of science?
How much from private versus public sectors? Does demand for
funding by potential science performers imply a shortage of funding or
a surfeit of performers?......A new “science of science policy” is
emerging, and it may offer more compelling guidance for policy
decisions and for more credible advocacy

6. We spend a lot on research: What’s the
impact?

7. Classic Questions for Measuring
Impact
• What is the impact or causal effect
of a program on outcome of
interest?
• Is a given program effective
compared to the absence of the
program?
• When a program can be
implemented in several ways, which
one is the most effective?

8. Classic Example: Measuring Impact
Illustration of swan-necked flask experiment used by Louis Pasteur to test the hypothesis of
spontaneous generation

9. Classic Challenge: Theory of
Change

10. Key ideas
• Need sensible scientific framework which:
– Is theoretically driven (theory of change)
– Uses appropriate unit of analysis (people)
– Is generalizable and replicable (open)

11. Outline
•
•
•
•
Motivation
Conceptual Framework
Empirical Frameworks
Next steps

12. The Theory of Change

13. Classic Challenge: Theory of
Change

14. Writing the Framework Down
• (1) Yit(1) = Yit(2)α + Xit(1)λ + εit
• (2) Yit(2) = Zitβ +Xit(2)μ + ηit
where the subscripts i and t denote project teams and quarters
ε and η stand for unobserved factors, serendipity and errors of
measurement and specification (and can possibly include individual
unobserved project teams’ characteristics).
The output variables are measured by Y(1) and research
collaboration variables by Y(2).
Both are determined by a set of control variables X(1) and X(2) that
can overlap and be truly exogenous or predetermined variables of
key interest Z (funding).

15. Source: Jason Owen Smith

16. Outline
•
•
•
•
Approach: Doing an Evaluation
Conceptual Framework
Empirical Framework
Next steps

17. STAR METRICS approach
• Level 1: Document the levels and trends in the
scientific workforce supported by federal
funding.
• Level 2: Develop an open automated data
infrastructure and tools that will enable the
documentation and analysis of a subset of the
inputs, outputs, and outcomes resulting from
federal investments in science.

18. STAR
Pilot
Project
Institution
STAR
Agency Budget
Award
Acquisition
And Analysis
Agency
Record
Direct
Benefit
Analysis
Award
State
Funding
Institution
Endowment
Funding
Papers
Financial System
Disbursement
Intellectual
Property
Benefit
Analysis
Research
Project
Patents
HR System
Procurement
System
Subcontracting
System
Personnel
Vendor
Contractor
Buy
Start-Up
Engage
Hire
Jobs,
Purchases,
Contracts
Benefit
Analysis
Detailed
Characterization
and
Summary
Existing
Institutional
Reporting
Download
State
Innovation
Analysis

19. Automated Data Construction
• Most data efforts focus on hand-curated data
• Scalable, Low cost / burden: Algorithmically
link researchers to their support (grants) 
scientific output (publications and citations)
 technological products (patents and drug
approvals)  Impacts (Health, economy,
productivity)
• Link to linked employee / employer data
• Probabilistic matches

20. The Theory of Change

21. Key ideas
• Need sensible empirical framework which
– Uses 21st Century technology to collect data
(cybertools..and SCIELO like activities)
– Uses 21st Century technology to link activities
(disambiguation; ORCID)

22. Example in practice: CalTech Project
• Funded by Sloan Foundation
• Goals
– Use STAR METRICS Level I data to examine production
of science at project, PI and lab level
– Interview Caltech PIs to get qualitative grounding
– Begin to build STAR METRICS Level 2 data linking
PEOPLE to results: publications, patents, altmetrics,
dissertations, and Census data on student placements,
firm startups etc
– Make source code and database infrastructure
available to all STAR METRICS institutions

23. Award Funding for one researcher
12
10
8
Ongoing awards
6
New awards
Ongoing awards
New awards
4
2
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
0

24. Lab staffing
120
100
Undergraduate
80
Technician / Staff scientist
Research
60
Research Analyst
Faculty
40
Post-Doc
Graduate Students
20
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
0

25. Vendor Expenditures on one project
Industry
Expenditures
Number of transactions
3386.36
121
36
1
896.12
4
Commercial Banking
4616
2
Testing Laboratories
8312.92
100
Pharmaceutical Preparation Manufacturing
629.63
12
Biological Product (except Diagnostic) M
2480.45
37
Electrometallurgical Ferroalloy Product
189.8
8
Electronic Computer Manufacturing
6831.41
49
Semiconductor and Related Device Manufac
3672.51
73
Analytical Laboratory Instrument Manufac
61464.87
49
Scheduled Passenger Air Transportation
5892.79
19
Passenger car rental
1015.28
8
Research and development in the physical
1654.88
38
Colleges, Universities, and Professional
-110.88
1
Other Professional Equipment and Supplie
Rail transportation
Scenic and Sightseeing Transportation, L

26. 2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
Publications of researcher
12
10
8
6
4
2
0

27. 0
2…
2…
2…
2…
2…
2…
2…
2…
2…
2…
2…
2…
2…
N. of Theses
PHD Theses Supervised
6
5
4
3
2
1

28. Patents for same researcher
USPTO Patents
EPO Patents
n_pat_uspto
n_pat_uspto
n_pat
n_pat
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
0
2001
0
2000
0.5
2012
0.5
2011
1
2010
1
2009
1.5
2008
1.5
2007
2
2006
2
2005
2.5
2004
2.5
2003
3
2002
3
2001
3.5
2000
3.5

29. New research: Exploratory
regressions

30. Y (outputs) can be expanded
• Currently Y is just publications, patents, PhD
students
• Census interest suggests we can develop
additional economic outcomes:
– Wages and career trajectories for postdocs/grad.
Students
– Firm startups, growth and productivity
• And..substantial competence in SciSIP community
in building out science and social outcomes

31. Use data to estimate production
functions at project level
VARIABLES
Pubs
Patents
PhDs
Patents
PhDs
0.057***
Award expenditures
Pubs
0.0018
0.0093**
Labor inputs
0.19***
0.056***
0.10***
0.12***
0.053***
0.089***
Share post-doc
0.43**
-0.071
-0.078
0.23
-0.077
-0.11
Share PhD
0.072
-0.023
0.27***
-0.14
-0.030
0.23***
Equipments
0.010
0.00055
0.0029
-0.015
-0.00024
-0.0011
Share computer
-0.36
-0.042
-0.25
-0.41
-0.044
-0.26
Share optics
-0.21
0.68**
0.22
0.016
0.68**
0.26
seniority
-0.0098***
-0.00081
0.00014
-0.010***
-0.00083
0.000030
Full Prof.
0.081
0.027
0.072**
0.054
0.026
0.068**
0.94***
-0.018
-0.10
0.71**
-0.026
-0.14
harvard
-0.026
-0.041
-0.0024
-0.069
-0.042
-0.0095
mit
0.065
0.092
-0.00068
0.051
0.091
-0.0030
caltech
0.23**
0.028
0.046
0.21**
0.027
0.043
physics
0.26***
-0.047
0.0047
0.22***
-0.048
-0.0017
chemistry
0.40***
0.064
0.17**
0.38***
0.063
0.17**
engineering
0.60***
0.030
0.22***
0.59***
0.030
0.22***
Calendar year dummies
yes
yes
yes
yes
yes
yes
Constant
0.11
-0.021
-0.16***
0.018
-0.024
-0.17***
Observations
2,590
2,590
2,590
2,590
2,590
2,590
R-squared
0.321
0.084
0.205
0.365
0.084
0.210
Share ARRA
Robust standard errors in parentheses
Note: Same approach as that used to derive widely accepted result that R&D generated more than
half of US productivity growth in the 1990’s; these data preliminary and not to be cited

33. Next example: CIC Activity Now
building out across multiple
universities and frames
Bruce Weinberg, OSU

34. •
•
•
•
•
•
•
•
•
•
•
•
•
•
•
University of Chicago
University of Illinois
Indiana University
University of Iowa
University of Maryland
University of Michigan
Michigan State University
University of Minnesota
University of Nebraska-Lincoln
Northwestern University
Ohio State University
Pennsylvania State University
Purdue University
Rutgers University
University of Wisconsin-Madison
The CIC

35. STEM Workforce Training:
A Quasi-Experimental Approach
Using the Effects of Research
Funding
Joint with Bruce Weinberg, Vetle Torvik, Lee
Giles and Chris Morphew

36. Overview and Goals
• The impact of research environment and
funding structures on the training and
outcomes of graduate students and post docs
• Build automated, extensible data
infrastructure
• Pilot for international community

37. Data Structure
CIC STAR METRICS Data
(Grants/Labs / Teams;
Sample)
Web,
Algorithmic
Disambiguation,
Microsoft
Academic
(Pubs, Patents,
Cites, Grants)
LEHD
(Employment,
wages w/in US)
SED
(Chars, Initia
l outcomes)

38. Econometric Models

39. Identification
• Relate outcomes to length of training, team, and
funding structure
• ARRA funding as “experiment” to shift length of
training
– Lightly Reviewed Grants
– Supplements to Existing Grants
– Payline Extension Granst
• Also, presumably, shift teams toward postdocs
• Get returns to time in training under different
team and funding structures

40. Probability
of
Funding
Figure 2. Research Design for Payline Extension.
Unlikely
to be
Funded
even with
ARRA
Proposed Project “Quality”
Likely
Funded
only
under
ARRA
Extended
ARRA
Payline
Likely
Funded
even
without
ARRA
NonARRA
Payline

41. Possible Analyses
• Estimate how training environment affects
retention in US, sector of employment, wages
• Estimate how flows of trainees to companies
affects productivity
• Measure impact on innovation by linking text
of patents to the research done in the labs
where people trained
• Open the knowledge transfer black box and
estimate returns to training

42. What are the results of research
(internationally)
ASTRA (Australia)
HELIOS (France)
CAELIS (Czech Republic)
NORDSJTERNEN (Norway)
STELLAR (Germany)
TRICS (UK)
SOLES (SPAIN)

43. Building new tools

44. We spend a lot on research: What’s the
impact?

45. Key ideas
• Need sensible scientific framework which:
– Is theoretically driven (theory of change)
– Uses appropriate unit of analysis (people)
– Is generalizable and replicable (open)
• Need sensible empirical framework which
– Uses 21st Century technology to collect data
(cybertools..and SCIELO like activities)
– Uses 21st Century technology to link activities
(disambiguation; ORCID)
• Need framework which can be international (develop
community of practice with common interests)

46. Thank you!
Julia Lane
www.julialane.org
www.cssip.org