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Second, the National Science and Technology to be answered. How robust are different taxonomies
Council’s Subcommittee on Social, Behavioral, and to different mapping algorithms? How robust are
Economic Sciences commissioned an Interagency the apparent relationships to different distance met-
Task Group (ITG) on Science of Science Policy. The rics? What do visual relationships among different
ITG published a road map (http://ostp.gov/cs/nstc/ scientific units of analysis mean? What do changes
documents_reports), “The Science of Science Policy: in the visual relationships mean? What statistical
A Federal Research Roadmap,” that represents the models can be applied to visualization algorithms to

The challenge
first organized description of the emergent field of validate relationships and predictability of how they
the science of science policy, outlining scientific the- are likely to evolve? How replicable and generalized
to statisticians
ories and defining terms that encompass efforts in are the results of visualization techniques?
is clear,
the field so far. It highlights the potential to greatly A necessary component for developing an evi-
increase the knowledge base and provide needed dence-based platform for science policy decision-
as the rapid
insights to improve the data, tools, and methods making is the development of an appropriate (i.e.,
that would enable a more rigorous and quantitative statistically valid) microdata infrastructure. The
advances
basis for science and technology policy. ITG identified four key areas in which such an
in cyber
infrastructure is necessary: measuring and tracking
Opportunities and Responsibilities
federal funding of science, measuring and tracking
infrastructure Some of the opportunities and responsibilities the scientific work force, measuring and tracking
mean new
for statisticians involve providing input into the
scientific outcomes, and measuring competitive-
appropriate use of existing sets of models and tools
ness. It also recognized the importance of providing
ways of
as identified by the ITG to address science policy
analytical access by researchers and federal govern-
questions. Others involve bringing statistical tools
ment agencies. The challenge to statisticians is clear,
collecting
to bear on new ways of analyzing and describing
as the rapid advances in cyber infrastructure mean
data exist.

complex relationships. Yet others involve provid-
new ways of collecting data exist. These include web
ing statistically valid approaches to collecting and
scraping, text and video data mining, and new uses
disseminating data.
of administrative data, but the analytical reliability
By way of illustration of the first set of oppor-
of such sources is relatively unknown.
tunities, the road map identified a set of models,
tools, and metrics most useful for federal agencies
Engagement of the Statistical
in addressing the scientific questions, particularly
Community
whether it is possible to “predict” scientific discov-
The statistical community can engage with the
ery or “predict” the impact of scientific discovery.
Science of Science Policy effort in a number of
These included many approaches familiar to the
ways. Statisticians are encouraged to submit propos-
statistical community, such as deterministic mod-
als to the NSF in the area of Science of Science and
els (econometric, risk modeling, options modeling,
Innovation Policy. The SoSP Interagency Group
cost benefit, cost effectiveness) and stochastic mod- established an electronic mailing list and wiki
els (agent-based modeling and system dynamics). to engage the SoSP community. The Science of
A few agencies, such as the Department of Energy Science Policy web site, http://scienceofsciencepolicy.
and National Institutes of Health, have begun to net, serves as the central point for data sharing and
experiment with dynamic modeling and options information dissemination and to communicate
modeling as ways to describe the effect of discovery. new events. Statisticians also can use the site to pro-
Statisticians could help provide guidance about the vide input via the polling and assessment tools or
quality and reliability of the resulting inferences. wiki once they have logged in. Those who are inter-
Other identified approaches that are less famil- ested in participating should send an email to Julia
iar included visual analytics, scientometrics, and Lane at jlane@nsf.gov for information about how to
network analysis. These approaches offer intriguing log in to the web site. Statisticians also can join the
possibilities for tracking the impact of investments electronic mailing list by sending a blank email to
in science. The possibilities range from tracing the subscribe-scisip@lists.nsf.gov and then replying to the
path from basic research discoveries to patents and automatic response email.
innovation to the changing structure of scientific To summarize, the burgeoning interest in creat-
disciplines, and from examining the importance of ing an evidence-based platform for science policy
social networks to the dispersion of scientific innova- decisions will require the input of the statistical
tions to comparators of international performance in community to ensure high-quality decisionmaking.
science. However, before such a vision is achieved, We look forward to the involvement and activity
many statistical questions about the robustness, participation of statisticians in developing the sci-
validity, and usability of the visualization tools remain ence of science policy. ■
30 AMSTAT NEWS FEBRUARY 2009
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