Georgia Institute of Technology
From the SelectedWorks of Diana Hicks

Fall December 1, 2010

Systemic data infrastructure for innovation policy
Diana Hicks, Georgia Institute of Technology - Main Campus

Available at: https://works.bepress.com/diana_hicks/23/

Systemic data infrastructure for innovation
policyNSTC’s interagency task group on Science of Science Policy
Science of Science Measurement Workshop
Washington D.C., December 2-3, 2010

Diana Hicks
School of Public Policy
Georgia Institute of Technology
Atlanta, GA 30332-0345
dhicks@gatech.edu

November 20101

Introduction
Progress on the vision laid out in the Science of Science Policy Roadmap requires a move to systemlevel thinking and analysis in the study of technology development. This contribution builds on theinsight that this move should be discussed explicitly as it will be a change in practice for the academic
community and building a system level infrastructure will encounter foreseeable problems.
As the Roadmap details, agencies have in the past approached outcome measurement individually so avariety of approaches obtain. The interagency task group hopes to move agencies toward a morecoordinated approach. A national data infrastructure that could handle the heterogeneity between fields
and technologies would be a powerful support for agency coordination. In addition, it would allow
agency programs to be comparatively assessed within the broader national context. Therefore, it isworthwhile to think through the practical basis of systemic infrastructure in order to foresee thechallenges that lie ahead.
The sophisticated econometric models used in economic policy making are based on an expensive,
comprehensive data infrastructure built and maintained over many decades by the Federal government.
Using these databases, economists became used to systemic, i.e. macro, thinking. They also became
used to acknowledging that although systemic data are not perfect (for example the informal economy
and housework are absent) they are useful. Lacking this kind of “Cadillac” data infrastructure, the studyof innovation outcomes has involved laborious construction of highly prized, career making datasets,
disparaging of systemic resources as not nearly as perfect as bespoke data and case study theorizing. As
close reading of the recent RAND review of outcome studies reveals, even the largest of analyses ofinnovation and impact have been case studies of at most a few fields or inventions1.
The theoretical principle underpinning the approach taken in this paper has been articulated by Bruno
Latour:An invisible agency that makes no difference, produces no transformation, leaves no trace,and enters no account is NOT an agency. Period. 2
In other words, if a person says they are doing science, and gets money to do science, but nothing is
produced, we are not interested. The paper discusses infrastructures that capture advances in science orinnovation by indexing evidence of those advances. Thus the focus is not so much on cataloging thepeople or the money dedicated to the S&T enterprise, or inputs. Rather I focus on outputs.
The infrastructure advocated here would:

1

S. Marjanovic, S. Hanney, S. Wooding. 2009. A historical reflection on research evaluation studies, their recurrent themes
and challenges. Santa Monica CA: RAND Corporation.
2

B. Latour. 2007. Reassembling the Social: An Introduction to Actor-Network-Theory (Clarendon Lectures in Management
Studies). USA: Oxford University Press, p. 53.

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provide a foundation for almost any quantitative analytical method: modeling, visualization,
network analysis, mapping and indicator production.
support both advancing the understanding of the research and innovation ecosystem and analysis
of agency programs.
provide time series rather than snapshots
include evidence of linkages that reflect some part of the cumulative, networked enterprise thatcreates advances in knowledge and technology.
be accessible to all interested parties, ideally free at the point of use to encourage a large anddiverse community of practice.

Although the U.S. used to lead in this area, recent history suggests that other countries have surpassed
the US in developing and using systemic infrastructures in S&T policy.3 This paper will review such
efforts. The paper describes an infrastructure constructed in the U.S. for tracing research funding
through scientific papers to patents across a decade in time and all areas of science and technology.
Also examined are national research documentation systems used in systemic evaluations, a structured
CV data system, the Community Innovation Survey in Europe and the NRC national dataset on graduate
programs. The paper begins by positing a vision of an ideal infrastructure and then assesses theseexamples of infrastructure against the ideal.

The ideal infrastructure – a vision
In an ideal world we would be able to seamlessly connect disparate data sources to enableknowledgeable users to assess the relative strength and impact of an institution or agency portfolio overtime and within the context of the nation and world. This resource would also serve scholars, enablingthem to build and test multivariate models, advance understanding of networks and develop
visualization tools to aid comprehension of the ecosystem. Because there is heterogeneity between
fields and over time, and users want to compare across fields and time, normalized metrics should bebuilt into the system. Because government interest spans all fields of science and technology, coverage
should be comprehensive. We do not want to introduce error, so the infrastructure should be built
carefully, by skilled experts.
The requirements for this to happen are many:
1. Researchers have to produce output, i.e. some sort of text. Ideally, their texts contain referencesthat link their work to predecessors. Somebody, not us, has to index that output for some otherpurpose.
2. We need access to the complete index, and permission to put it into our infrastructure and makeit accessible for all users who want to undertake systemic analysis. Not a problem with public
sources like US patents, but a chronic problem with the core of the scientific literature whose
indices are owned by Thomson-Reuters and Elsevier. Though frankly, the Department ofCommerce’s NTIS database does not seem that open either.
3

It is also worth noting that the proposed Japanese funding initiative for science, technology and innovation policy includes
plans to build a data infrastructure.

33. We need to identify the people and institutions associated with each record – a daunting andexpensive task whose complexity is easily underestimated.
4. The databases need to be linked through thesauri of people’s names and institutional names.
5. Processes 4 and 5 must be ongoing so that the infrastructure is always current.
Points 3 through 5 highlight the need for a curator. This crucial work is always undervalued, whichmakes it challenging to obtain long term resources and set up the institution required.
In this ideal world we would link up texts that reported research output and texts that signified theinfluence of research on society and technology.
Texts related to research:
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Agency records on each grant awarded
Papers published and their references
Patents applied for and issued and their references in text and front page
Technical reports and their references(Excluded – working papers, blogs and other forms lacking independent review before
publication)4

Texts related to influence and outcome:
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Patent references (again)
Policy white papers and their references
The press, extracting any references made to scholars and researchers
New regulations and the references made in those texts
Congressional testimony of researchers
Evidence based reviews of best medical practice
New product introductions
Download counts
Social media mentions (Blogs, Facebook, Twitter etc.) to gauge impact on fast moving social
discourse
Etc.

With the vision in place, we can assess a selection of real infrastructures against this ideal. We can also
explore the challenges inherent in mounting such an infrastructure.

Patent-to-paper citation database
Throughout the 1980’s and 1990’s agencies had access to an infrastructure that did go some way
towards addressing outcomes. This infrastructure was built by CHI Research, a research consulting
company run by Dr. Francis Narin. CHI produced the bibliometrics for NSF’s Science & Engineering
Indicators, and as part of that contract, Narin built an analytically useful database infrastructure that
4

The mores of science are unlikely to change, so to be taken seriously the infrastructure should focus on texts that haveundergone some kind of review or that demonstrate the interest of people beyond the original research group.

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allowed tracing from funding acknowledgments in papers, through to the organizations whose
technology built on the funded research, i.e. whose patents referenced funded papers. The infrastructure
was comprehensive across the US patent database and the Science Citation Index. Therefore, analyses
were normalized for differences in citation rates across fields and over time. Analyses were also
comparative, and so could establish how well an agency or institution was doing in comparison withothers.
The steps involved in building and maintaining this infrastructure were these:
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Obtain access to the base databases, USPTO and SCI. This was unproblematic in the case of the
public USPTO database, but difficult, expensive and contentious in relation to ThomsonReuter’s SCI. The base USPTO data is now even easier to obtain as it can be downloaded from
data.gov. Thomson-Reuters is slightly more forthcoming since the advent of competition in theform of Scopus.
Clean up the institutional affiliations so that the corporate owner of each patent and theinstitution(s) producing each paper were identified. The challenges here are keeping up withchanging corporate affiliations as companies merge or fail and working at the departmental levelin universities.
Code patents and papers by geography so that analysis by Congressional district could be
produced.
Manually examine each non-patent reference in US patents, identify those that were to journal
articles, standardize the reference and match to the SCI. The references also could be matched toother paper databases.
Obtain funding acknowledgments. At the time, this was done by sending students to the library.
Web of Science now includes this information in its records.

The base databases add records constantly, so if the infrastructure is to remain current, this work must becontinuous. Every week CHI staff standardized between six and ten thousand non-patent references(NPRs) for US and EP patents, extracting the year, journal, author, and page from the free form text ofthe front-page non-patent references. The US linkage backfile covered patents from 1983 onwards andin 2001 contained 667,000+ patents with 3.78 million non-patent references, of which two million werereferences to scientific journal articles.
Agencies who requested studies of their impact paid for the time and effort needed to compile their data,
produce an analysis and write a report. The database was built and maintained on the NSF S&EI
contract and with overhead. Basic reports could be produced in one to two months. Francis Narin has
since retired, and with his departure the unique vision of the value of this kind of infrastructure has
faded. This infrastructure is high cost but of limited value to private sector clients, therefore it no longer
exists.
There have been other efforts to mount a similar infrastructure. DOE attempted to collect and link thepatents and paper abstracts of its PI’s, but this project ended after a departmental reorganization.
Academics have been somewhat more successful. NBER has produced two versions of a cleaned patent
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database although this has never been as current, accurate in institutional affiliation or inclusive of
paper-linkages as the CHI databases. The current Fleming data contains the patent-paper linkageelement, but is not comprehensive and was built on an NSF grant, which suggests it will disappear after
a short time. None of this should come as a surprise because database curation is expensive and does
not fit well into the incentive system faced by professors or into agency definitions of “research
activity”5.
To compare this genre to the ideal, we examine the strongest of these efforts, the CHI databases. These
fell short of the ideal along several dimensions. Because the link to funding was made fromacknowledgements on papers, the data were incomplete and were at the agency level. Thus analyses
could be used for advocacy, where assessing the strength of the agency as a whole is helpful but not for
program management which requires program level data. The problem here would be obtaining
government-wide data on grants (the experience of Radius being relevant). Once an open, accurate,functional database of government R&D grants and contracts was built, individual people would need to
be uniquely identified and matched to authors and inventors. For completeness, the NTIS database oftechnical reports would need to be opened up, and the same cleaning of meta-data conducted.
On the outcome side, the situation is even worse. CHI used patents at the firm level which is convincing
because one can say that the firm’s products are built on the firm’s technology, which is more or lessdocumented in its patents, and patents document some of the links to research in their references topapers. In an ideal world we would have documentation of links between research and individual
products. This only exists in pharmaceuticals because FDA regulations require that product applications
be supported by extensive textual evidence with a scientific aura – i.e. references.
To truly embrace the full spectrum of outcomes, we would need to incorporate many more types of text:
regulations, best practice recommendations, press, Congressional testimony, policy white papers,
mentions in social media etc. All of these would need to be scraped for mentions of researchers which
would be catalogued and linked, again by researcher name, to the database of research grants andoutputs.

National Research Documentation Systems
The difficulty in constructing an evaluation infrastructure is that Thomson-Reuters or Elsevier own thecomprehensive databases of journal articles and citations.6 To build an evaluation infrastructure, one
needs access to all the records in a database, and then one needs to do cleaning and indicator
construction. This is not a model private database providers support. As a result, CHI faced chronic
difficulties in constructing its infrastructure. In the medical area, NIH spends a great deal of money tobuild and maintain PubMed which as a public database is free at the point of use. Fleming’s data uses
PubMed. There is no equivalent for the rest of science, the social sciences or the humanities. Ironically,5

P.N. Schofield et al., 29 October 2010, Sustaining the Data and Bioresource Commons, Science, 330: 592-593.

6This discussion is simplified for the sake of brevity. A complete discussion would probe the strengths and weaknesses ofother indexes such as Inspec, ChemAbstracts, Google Scholar, Xarchiv, CiteSeer, etc.

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countries that have felt excluded from Web of Science have moved ahead with their own electronic
publication databases using open access and so may be better able to build evaluation infrastructures on
top of these databases. In Latin America there is SciELO, Scientific Electronic Library Online, afederation of electronic journal infrastructures that meet a centrally defined standard of excellence injournal publishing (scielo.org). SciELO’s site not only provides access to 250,000 articles from 660
journals, but also offers basic bibliometric statistics. Similarly, in Africa there is African Journals
Online (ajol.info) hosting 46,000 articles from 396 peer reviewed journals. China has built the CSCI,
and CSSCI (Chinese versions of the SCI and SSCI), administered by CAS National Science Library andNanjing University respectively.
An alternate route to a similar end is a national research documentation system. These have beencreated in several smaller countries – Norway, Australia and most recently Denmark. These
infrastructures have emerged to support national, metrics-based evaluation of university research. In
these countries it is possible for the national government to mandate university participation as acondition of receiving their block grant. In the U.S. this is not possible. However, the model may haverelevance for agency intra-mural institutions.
In national research documentation systems, universities submit bibliographic records of their
publications and are responsible for data quality. In the Norwegian system, the agency validates andstandardizes bibliographic records submitted by universities. This involves creating and updating an
authority file of allowed publication channels. Data from Thomson Reuters and the Norwegian national
library are imported to verify and standardize records. The authority file standardizes names ofpublication channels, document types, and institutional affiliations of authors. The work by the agency
recognizes and addresses known accuracy problem in submitted data. Publications are differentiatedaccording to a 2-4 level classification of the quality of the publication venue. Weighted publication
counts or publication distributions across the levels are then produced. The system is somewhat costly.
Full cost includes that born by universities in submission and by the agency in validation.
The base model contains no impact measures – i.e. citations - but the Australian version extends thesystem. Australia will buy data from Scopus, and for papers published in journals indexed in Scopus,
citation counts will be produced. This will serve as an additional dimension in the evaluation in addition
to the distribution of papers across journal level.
A key limitation of this model is that it does not serve to capture the connection to grants or outcomes economic, social, health and environmental benefits or technological developments. These governments
first sought to evaluate and increase scientific quality, and the infrastructure serves that purpose. One
could imagine an analogous system that might go some way to assessing outcomes. For example, intra
mural laboratories could be required to submit not only information on journal articles, but also patent
information, information on impact on the “enlightenment” literature, i.e publications that reach industry
and decision makers (Wall Street Journal, New York Times, trade press etc.) and mentions in social
media. Or end of grant reporting could be changed from narrative texts to entry of structured data on
outputs, enlightenment literature, patents, people employed etc. (It would probably not be ideal torequire structured data entry in addition to traditional narrative reports.)
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Lattes
One way to move toward this goal is to work with CVs. Brazil has built an open access web-based CV
infrastructure that serves as a model for investigator level data collection. The Brazilian Lattes website
provides the tools for researchers and students to register and build or update a CV at any time7. The
system structures the entry process to produce usable information. The Lattes CV system wasdeveloped for the National Council for Scientific and Technological Development (CNPq), and is usedby the MCT [Science and Technology Ministry], FINEP [Projects and Studies Financing], CAPES/MEC[Personal Improvement Coordination/Ministry of Education], and the Brazilian scientific community.
In the CNPq, the information is used to evaluate candidates for scholarships and/or research support;
select consultants, members of committees and advising groups; and evaluate Brazilian post-graduate
education and research programs. Universities are allowed access to the data for their own indicators
and analysis. Information scientists develop tools to mine the data and a small amount of bibliometricwork has been done using the data. Researchers can use the Lattes CV as their website (though if Lattes
mounted working papers for download it would be more useful for researchers).
As the system matured, it has developed. First, Lattes CVs became mandatory in 2002 for all
researchers and students that deal with the Council. Absence of a Lattes CV causes impediments topayments and grant renewals. More recently, CNPq has improved data validation. CNPq has boughtThomson Reuters data so that CVs have online access to the number of citations in Web of Science forthe articles registered in Lattes with their digital document identifiers (DOI). Researchers can also see
who has cited them. To retrieve the number of citations, researchers must ensure that the DOI, thejournal, volume, issue and pages (first and last) are registered properly. CNPq also uses data fromFederal Revenue of Brazil to validate personal data such as the Individual Taxpayer Registry (CPF)
document, name, date or birth and affiliations, preventing the creation of fake new CVs. This validationis new and is not applied retroactively to CVs already in existence.
The strength of the Lattes system is its focus on the individual. S&T data infrastructures have largely
functioned at the organisational level. This is true of the CHI databases, national researchdocumentation systems and the CIS (below). Such infrastructures are useful for producing S&T
indicators, which are largely comparisons of nations. Organizational level data can go some way tosupport understanding of the ecosystem and study of policy effects. They also serve governments
deciding how to divide block grants among universities. As was mentioned above, working at the firmlevel helps link patents to products and economic success since firms have a patent oeuvre, a productportfolio and performance metrics. So the organizational level is extremely useful.
Today there are pressures to move to the lowest possible level, the individual investigator. These
pressures arise both in the scholarly community and among government agencies. Methodologicalinterest in agent based modelling and network analysis generate a need to create infrastructures thattrack the contributions of individual investigators. Federal funding agencies do not fund institutions,7

Very little information was found on the Lattes system, this section is basically taken from the Lattes website:
http://lattes.cnpq.br/english/index.htm

8they fund investigators, and to understand the impact their programs are having, they need to be able to
track the contributions of people, not institutions.
The strength of the Lattes approach is the individual basis of the data. In other systems, accurate
identification of the individual responsible for or mentioned in a text is extremely challenging. People
are tricky: a lot of them have the same name; they change names; they change jobs; they misspell thenames of others; they make errors when entering names in databases; they choose different conventions
for representing names when setting up different databases, etc. The SciSIP community is actively
investigating solutions to these problems. Although doing the topic justice would require a review initself, suffice it to say that some suggest scholars adopt an ID number and associate it with all their
output while others work on algorithmic solutions. Requiring researchers to enter all their own data is
the ultimate solution to this problem, though it brings with it another set of challenges.
The challenges of the Lattes system lie in linkages and data quality. CVs do not naturally contain muchlinkage information. The system could overcome some of this by requiring co-authors with Lattes CVs
to be identified when a paper is entered. This provides some network information. The pure Lattes
system also lacks impact indicators, but a Lattes-Web of Science/Scopus hybrid can solve this problem
and this is the direction Brazil is taking the system. The Lattes system could also go some way toward
incorporating societal impact measures if a section on public dissemination were built.
Data quality is also a challenge for a Lattes system. If the system is used for assessment, there will be
incentive to falsify and inflate the CV which must be countered in the design and operation of thesystem. Validating identity against IRS records would be a gold standard. Presumably institutional
affiliation could be validated through cross-checks with systems such as Fastlane. Journal article entries
could be validated by cross-checking with Web of Science or Scopus data. Another method ofcontrolling what is listed as a journal article is only allowing recognized peer reviewed journals to be
included (the authority list approach used in national research documentation systems).
Strangely for a vehicle that contains as much information as does a CV, context is lacking and thiscompromises the utility of the data. For example, if a researcher gets a grant and this supports work
leading to some papers, several conference presentations, testimony to Congress and a patent, each ofthese items will be on the CV, but they will be in different sections and the funding agency will not beable to determine which items on the CV are associated with their grant8. Contrast this with working
with funding acknowledgements in papers.
Other limits of a Lattes system are the potential holes, the national basis, and the shallow content . Not
every active researcher is engaged with a Federal agency, or is currently engaged. This will lead toabsent or outdated material. For example, coverage of humanities and some areas of social science willbe sparse. Further a system driven by national funding agencies will be domestic, eliminating the

8

Fábio Mascarenhas Silva and Johanna Wilhelmina Smit, (2009), Information organization in open electronic systems of
Scientific and Technological Information: analysis of the Lattes Database, Perspect. ciênc. inf. vol.14 no.1 Belo Horizonte
Jan./Apr., doi: 10.1590/S1413-99362009000100007

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potential for international comparison inherent in comprehensive infrastructures based oncomprehensive databases (also true of national research documentation systems, NRC and CIS – see
below). A Lattes system is also limited by having only the title of a paper. Even having an abstract
increases the sophistication of the analysis, and full text databases allow much more sophisticatedanalysis based on content (including references, acknowledgements etc.).

NRC exercise
In the U.S. the NRC gathered extensive data from universities via a survey in order to rank, or rather toavoid ranking, graduate programs. The NRC has built an accessible data infrastructure which includes
survey responses as well as bibliometric data purchased from Thomson-Reuters. As the NRC’s 1995
ranking, based on a reputational survey, was seen as too “soft”, the current effort collected quantitative
data. A small reputational survey was conducted, and a regression analysis used to identify a weighted
mix of quantitative variables that best predicted reputational judgments. The NRC required from the5000 participating programs at 212 universities submission of information on the 48 variables to be
included in the ranking formula. The result is almost 1/4 million data points. The 48 variables concern
institutional characteristics (i.e. total research expenditure, characteristics of library, childcare and healthinsurance availability, university housing for PhD students etc.); doctoral program characteristics (i.e.
size, time to degree, financial support, facilities for PhD students, test scores, support provided,
employment destinations etc.), and program faculty (size, demographics, awards, bibliometrics etc.)(NRC, 2004, Table 4.1). For the bibliometrics, the NRC compiled full bibliographies of SCI indexed
papers and their citations from Thomson-Scientific and used this information to calculate bibliometricvariables at the departmental level: publications/faculty, and citations/publication.
The NRC method was elaborate, and this had a cost. Planning for the exercise began in 2000; it was
originally scheduled for 2003-2004 and slated to cost $5 million (direct cost only); it was actuallyconducted in 2005-2006 for release in 2007. The results were released in September 2010. Questions
are being raised about the accuracy of the data and whether they are too dated to be informative.
Again, this data infrastructure does not index the origin of grants or outcomes such as economic, social,health and environmental benefits or technological developments from the work in U.S. graduate
programs. Presumably the survey method could be refocused to query on these items.

Community Innovation Survey (CIS)The European Community Innovation Survey or CIS provides a model of an outcome focused, surveybased data infrastructure.9 The survey is administered to a random, stratified sample of firms. The CIS
began in 1993 and has been conducted five times, the latest being CIS-5 in 2007. The survey isconducted by 30 countries, with Eurostat working to harmonize the questionnaire and methodology.

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The CIS discussion is based upon: Arundel, A. & C. Bordoy, P. Mohnen and K. Smith, 2008, Innovation surveys andpolicy: lessons from the CIS, Chapter 1 in C. Nauwelaers and R. Wintjes, Innovation Policy in Europe: Measurement andStrategy (Edward Elgar: Cheltenham, UK, 2008), pp. 3-28.

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The CIS collects information about product, process, organizational and marketing innovation during thethree years prior. Questions cover new or significantly improved goods or services or implementation of
new or significantly improved processes, logistics or distribution methods. The survey provides a basis
for statistics on the occurrence of innovation across Europe by types of firms, geographic location, typeof innovation (process, product, organizational etc.), innovative activities (R&D, acquisition ofadvanced equipment or software, training, marketing etc.), amount spent on innovation, and effects ofinnovation on goods or services. Questions are asked about public support and public information
sources, but we learn only what types of support or institutions were helpful. To be useful for agency
analysis, firms would have to be asked which agencies and which institutions were supportive.
CIS data is widely used in European studies of innovation. The survey now provides time series
indicators of innovation, though panel data is not possible since different firms are surveyed in eachround. Descriptive analysis of CIS data has established that innovation is spread widely in the economy,
beyond the relatively few firms with substantial R&D expenditure. Formal R&D expenditure accounts
for just 20% of expenditure on innovation. Diffusion of new technology, through purchase of advanced
equipment for example, has been shown to be a very important route for firms to update their products
and processes. Multivariate analysis has been used to examine, for example, the effects of innovation
strategies on performance. The survey data have enabled quite a bit of analyses of the relationshipbetween government support for innovation and innovation outcomes. Both the incidence of innovation
with government support and the additionality of government support have been examined.
There are several obstacles to greater impact of CIS work. First, the analytical value of the data islimited because any access to micro data requires project-by-project approval of every country whose
data is used. Second, academics have not seriously engaged with policy makers and their agendas.
Third, constraints on length of questionnaire limit the depth of data that is collected, for example,
agencies providing support are not named. Constraints to do with anonymity make it difficult to link thefirm level data with other sources providing information on business performance. The lack of panel
data is also a serious limitation with regard to policy relevant analysis.
In 2010 NSF released the results from the first comparable US survey – the 2008 BRDIS.10 BRDIS is a
joint effort of NSF and the U.S. Census Bureau. BRDIS has been designed to collect a wide range ofdata on business R&D and innovation activities in the United States, including on topics that were notaddressed by its predecessor, the Survey of Industrial Research and Development. The sample ofcompanies for BRDIS is selected to represent all for-profit companies in the United States with five or
more domestic employees, publicly or privately held. The resulting sample provides statistical estimates
for the population of companies that perform or fund R&D or engage in innovative activities in the
United States. For the 2008 BRDIS survey, 39,553 companies were sampled, representing 1,926,012
companies in the population. When these preliminary data were tabulated, the overall response rate was
77%, and the response rate for the top 500 domestic R&D-performing companies was 93%.

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BRDIS discussion taken from: NSF Releases New Statistics on Business Innovation, NSF 11-300, October 2010 http://www.nsf.gov/statistics/infbrief/nsf11300/

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In comparing the CIS and BRDIS, note that in contrast with CIS emphasis, NSF states that one of theclearest findings in the BRDIS data is companies with R&D (either performing R&D or funding others
to perform R&D) exhibit far higher rates of innovation than do non-R&D companies. There will be
fewer obstacles to analytical use of the BRDIS than there are to use of CIS data. Presumably, BRDIS
data will be housed in Census Data Centers which will allow qualified scholars access to the full data set
and the opportunity to link it with other business data.
As infrastructure the CIS/BRDIS approach excels where the grant-paper-patent approach is weak – it isoutcome focused, comprehensive and encompasses non-R&D based innovation including that inservices, organization, marketing etc. Very valuable time series indicators can be produced, akin to
traditional economic indicators. However, this approach is not the best one for enabling agencies totrack the impact of individual R&D programs. The approach also may fall prey to standard weaknessesof surveys – declining response rates and doubts as to whether everybody filling in the survey canprovide accurate answers to questions such as:
whether during the three years prior the enterprise co-operated on any innovation activity with
one or more of seven types of institutions including “Government or public research institutes”
in: a) their country, b) other Europe, c) U.S., d) other countries (question 6.3, CIS 4th
harmonised survey questionnaire).

Classification in S&T Infrastructures
Information infrastructures require classification systems so that individual records can be aggregatedfor analysis. Classification systems emphasize continuity and so have trouble keeping up with reality,
especially when they are used to examine an activity such as research that seeks to invent entirely new
categories. What are deemed useful categories varies with the users’ perspectives, so coordinating
among agencies will mean developing a consensus classification system, and the difficulty of doing thisshould not be underestimated. Different classification systems and issues are involved in each of theinfrastructures reviewed here.
CHI Research used NSF’s classification of scientific fields for papers and a custom aggregation ofpatent classes. Patent offices classify patents based on the “art” involved. Everyone else is interested in
the industry represented by the technology. Therefore innovation analysts who works with patents must
develop a thesaurus to translate from patent classes based on “art”, to the industrial classification scheme- NAICS (formerly SIC). Patent classification schemes developed for analysis usually have about 30
categories and correspond roughly to industries at the NAICS 3 digit level.
The design of a research documentation system begins with a consultative design process to specify theclassification systems (fields and journal quality levels) and the system’s boundaries (journal list). Thiscan be a contentious process and reaching inter-agency consensus would be difficult, but necessary if
coordination and comparison were desired.
Lattes is challenged to fit everybody into a single set of boxes so that counts might be undertaken. The
system includes field classifications, and at a high level researchers are presented with a list of choices.
At the more specialist level, researchers can use predetermined categories or enter their own.
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Researcher invented categories produce a lot of scatter in field identification at the specialist level,
limiting analytical potential. A similar set of issues would apply to institutional and departmental
identification. If researchers can be forced to pick a department and institution from a closed list, ananalytically useful dataset will be created. If freedom is allowed in entering affiliation, a large amountof data cleaning will be needed prior to any analysis.
The NRC used a 62 category field taxonomy that included established fields as well as those designatedas emerging (feminist & gender studies, film, rhetoric, information science, nuclear engineering etc.).
The NRC scheme has categories for French, German and Spanish and so would have no way toaccommodate a department of modern languages. International affairs or the emerging area of digital
media/gaming studies do not appear, even among emerging fields. Because older fields are secure intaxonomies, traditionally defined departments are advantaged in evaluation/rankings in comparison tonewer, interdisciplinary areas. To the extent that university administrators and students make decisions
based on the NRC rankings, newer departments will be hurt.
The CIS asked respondents to classify their firms into the European equivalent of NAICS at the 4-digit
level, that is into one of 500 categories. Choosing a fine-grained category in a traditional industrial
classification might present problems for very innovative firms. In the beginning new industries are too
small to warrant their own category within an economy-wide classification system that has been in
existence for many decades and seeks to maintain continuity with the past. For example, business model
innovation is reshaping our economy at an even more fundamental level than technological innovation
as goods and services are sold together, tasks become tradable in addition to products and the focus
shifts to markets and consumers rather than producers. The conceptual basis of the NAICS industrial
classification scheme reified the agriculture OR manufacturing OR service framework and has great
difficulty adapting.
Systemic infrastructure will require hidden and underappreciated foundations - classification systems
such as NAICS or NSF’s scientific field classification. Entities that fall between categories will bedisadvantaged. The UK RAE was criticized for penalizing interdisciplinary departments. Nonneoclassical economists have struggled in evaluation systems in which the “economics” category isdefined by neoclassical economists. Firms that don’t fit neatly into categories have been shown to bedisadvantaged in credit scoring11. These problems are of the same type that led the government to allow
people to check multiple race/ethnicity categories on their Census forms.
Of classification schemes, Bowker and Star commented “architecture becomes archaeology overtime”.12 As the NRC example suggested, category systems can easily exclude the most innovative work.
In a fast moving economy subject to fundamental change, the study of innovation will be hampered byschemes that privilege areas prominent when they were first devised. Although revisions take place, it

11

Ruef, M., and K. Patterson, 2009. Credit and classification: the impact of industry boundaries in nineteenth-century
America. Administrative Science Quarterly 54, 486–520.
12

Geoffery Bowker and Susan Leigh Star. 2000. Sorting Things Out. Boston, MA: MIT Press.

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can be argued that they take care of certain usual suspects, semiconductors and biotechnology forexample, upon which we then build our entire understanding of innovation, to the neglect of obviously
innovative areas such as photonics and gaming which are hidden in current classifications. On the other
hand, allowing a free-for-all data entry system results in data that cannot be analyzed. True systemic
understanding will need to address this problem.

Summary
A truly systemic database infrastructure built to track research and innovation and their societal
outcomes would be valuable both for agencies managing programs and for academics advancing
fundamental understandings of the science and technology ecosystem. Here we have imagined an ideal
system, as well as noted the development of parts of that ideal system.
There is a potential alternative to the approaches here that gets very close to outcomes, and has neverbeen tried. The RAND report on outcomes makes clear that relationships are essential for research
results to transition to application. The problem is that relationships do not always leave traces in
written records, so to analyze them we must find a way to surface them. Noting that the flip side of a
productive relationship with industry is a conflict of interest, Toby Smith of the AAU points out thatmandatory conflict of interest reporting, implemented through a standardized, structured data entry
interface could create traces of outcome related relationships that could be analyzed. There are privacy
concerns, so perhaps Census Data Centers would need to house the resulting data and manage its
analysis.
The examples reviewed here exhibit a full range of funding and institutional mechanisms, including
grants (Fleming), long term intramural institutional funding (PubMed), agency budgets (national
research documentation systems, Lattes) and were produced by government agencies, libraries,
universities, non-profits and firms. An ideal system would combine the long term commitment andlevel of resources marshaled by NIH’s PubMed, with a mandate to encompass all of science andtechnology (like Web of Science, Scopus or Lattes), in an independent non-profit focused on building
and curating an open infrastructure for the use of scholars and government agencies (somewhat like
ICPSR, though that is housed in a university). Such an entity could pay high enough salaries to hire themost talented programmers as well as employing data cleaners and curators in tasks that bring no
academic glory.
There are of course substantial challenges in building any of these infrastructures – data owners refusing
access, researchers resisting mandatory data entry requirements, lack of a social science research funderable to commit substantial resources guaranteed over the long term, lack of interest in the social science
research community accustomed to case study level work. Any method chosen will be imperfect, andthis will be obvious to traditionalists seeking to block an initiative. Nevertheless, there are encouraging
shifts. Large scale databases are becoming more accepted in social sciences, and cheaper to mount andmanage. The popularity of network analyses leads naturally to consideration of entire systems. And
interest among agencies advances the conversation.

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