### Subcommittee on Research Business Models

#### Federal Register Notice Comments requested

**Question F. Research Support. How can public funding mechanisms and
policies encourage or discourage innovative approaches to research? Does
the current process for research funding encourage or discourage
innovative research? How do support mechanisms influence the mix of
investigators supported (e.g. principal investigators, research
scientists, postdoctoral scholars, graduate students, or technicians):
How can changes in the conduct of science and engineering necessitate
modified funding models? Are data available to help decide these
questions?**

# **Policy**/Encourage innovative Approaches to Research

We recommend that the Federal Demonstration Partnership (FDP) continue
to be expanded and enhanced to enroll more institutions and heighten the
visibility of this valuable initiative. FDP is both a policy tool and
delivery mechanism that has streamlined the efficiency of sponsored
research delivery by standardizing terms and conditions across Federal
agencies, simplifying the prior approval process, and streamlining the
award distribution process. In summary, FDP has made federally sponsored
research more accessible to the researcher and easier to manage for the
administrator.

# **Mechanisms**/Encourage Innovative Approaches to Research

We recommend that electronic proposal and progress report submission
systems continue to be developed and, most importantly, standardized as
much as possible. The NSF Fastlane system is the prime example of an
efficient electronic submission system; it is accessible to principal
investigators, staff support personnel, and administrators. We must
caution against the development of numerous, competing electronic
submissions systems that may create significant confusion and jeopardize
timely submission.

# Mix of Investigators Supported

The FDP and electronic submissions systems have encouraged researchers
from various fields, backgrounds and institutions to pursue
collaborative sponsored research because these systems make the
administrative aspect of research uniform. Expansion of successful
electronic systems (e.g., NSF FastLane) to other sponsoring agencies
should enhance these types of collaborations. Another potential way to
encourage collaboration of a broader mix of researchers is to improve
upon centralized repositories that list available funding opportunities.
Heightening the visibility of Requests for Proposals and Applications
will encourage those who do not have an established relationship with
program officers from various funding agencies to be exposed to, and to
apply for additional funding opportunities. At UC Davis, not unlike
similarly situated institutions, the research funding is largely skewed
toward those researchers in the physical and biological sciences with a
long history of extramurally funded projects. Creating a highly visible,
centralized repository of funding opportunities may encourage those in
the social sciences, as well as the less experienced researchers to
apply for federal funds.

# Data to Address the Above Issues

There are many sources of data available to illustrate the impact of the
FDP and NSF Fastlane. Both the volume and dollar amounts of research
awards by Federal agencies could be tabulated to measure the extent to
which these initiatives have permeated the research community.

A main objective for science and technology policy should be to help
eliminate disciplinary boundaries that have evolved over centuries of
academic tradition; contemporary complex research efforts in, for
example, nanotechnology, biotechnology, and biomedical engineering
require scientists in different disciplines to work together. Science
and technology policy can define the structure and \"make-up\" of the
21st-century university, where cross-disciplinary research will be the
standard mode for advancement in many areas of scientific inquiry in
science and engineering fields.

The NSF Science and Technology Center (STC) and Engineering Research
Center (ERC) programs are centered on the idea of conducting research
(and complementary educational efforts) in a collaborative Center
setting. Typically, diverse faculty teams and collaborators from
industry and national laboratories from multiple disciplines work
jointly with graduate students, post-docs, and researchers to
investigate inherently complicated problems. Research Center
environments emphasize co-supervision of students and researchers,
active visitors and exchange programs and, typically, application-driven
research. Science and technology policy should consider the various
benefits that arise from such Center-based efforts, and increase further
funding for research Centers, especially in society-relevant areas.

Research Center-based efforts are becoming increasingly important
components of the research and education focuses of science and
engineering departments. Recognizing this trend, science and technology
policy must take into consideration that larger research centers are
more likely to be owned by a university at large, where Centers report
to a central oversight unit, not just one department. Considering the
desired broad definition of Center-based research, in many cases maximal
research prosperity can be achieved only if reporting and oversight
issues are treated centrally, and not, for example, within the
boundaries of a specific college or department. We discuss some examples
in the following paragraphs.

Computational approaches have become increasingly important over the
past two decades to solve society-relevant problems. Computational
science and engineering (CSE) is now an established scientific
discipline, which is conducted ideally in close symbiotic collaborations
involving scientists from applied mathematics, computer science, and
others in the driving applications areas such as physics, chemistry, or
biology. CSE is a field driven by the needs of the scientific research
community and applications throughout science and engineering
disciplines. Strong ties and interactions between users and developers
of computational technology are crucial for the success of CSE in the
future. Academic boundary conditions must enable and support
multi-disciplinary research collaborations, coupled with desirable
curricular innovations for the training of next-generation students in
relevant CSE approaches. In the context of CSE, for example, science and
technology policy can catalyze the desirable stronger integration of
science and engineering research and graduate education efforts by
further increasing emphasis on multi-disciplinary, multi-investigator,
and even multi-institutional funding opportunities.

Large-scale computing-centered research requires substantial investments
in basic supporting infrastructure, including costs for maintenance and
administrative staff. Academic institutions are typically not in a
position to support large-scale multi-investigator Center-like efforts
with sufficient base support, concerning equipment and administrative
needs. It is desirable to increase the number of funding mechanisms and
programs that would provide a relatively larger number academic
institutions access to funding dedicated to supporting infrastructure
needs of campus-wide, cross-disciplinary computing-centered efforts.

Traditional funding policy, and academic tradition, assumes that
scientists conduct their research in either the theory-simulation or the
experimental-observational setting. Science and technology policy can
foster the highly desirable integration of research efforts in these
still rather separated settings. The driving objective behind more
integrative efforts should be to make possible a much more effective,
team-oriented process to gain new fundamental scientific insight and
transfer this insight quickly into technological domains.

Scientific disciplines that just a few years ago were rather
\"data-poor\" have become, and will become even more so in the future,
\"data-rich.\" Advances in ultra-high-resolution imaging, sensor-network
technology, and increasing supercomputing simulation power are creating
data sets so large that individual scientists no longer are able to
fully mine the generated data. To ensure that scientific data sets are
explored fully it is imperative to share massive data sets with the
scientific community at large, and to establish funding and
collaborative research mechanisms supporting the data sharing over
high-speed networks. Science and technology policy must fully recognize
the basic paradigm shift affecting the very essence of the discovery and
design process in science, engineering, and medicine; funding policy
must recognize the need for a \"digital cyber-infrastructure\" that
scientists will have to rely on to effectively utilize vast, rapidly
expanding digital data repositories. It is paramount to recognize that
the needed technology has to be defined, implemented, and tested in
close collaborations involving domain scientists and technologists.

Scientific data management - including data formats, data storage, data
conversion, data transfer, data-query technology, etc. -- is becoming
increasingly important. Formerly disparate sub-fields in science or
engineering domains begin to integrate, and thus diverse data sets must
be combined for collaborative scientific discovery and engineering.
Integrated \"data science\" technology is in its infancy, and the gap
between our ability to generate multiple data sets describing a highly
complicated phenomenon and our access to appropriate data analysis tools
is widening. Revolutionary progress is needed in distributed and
network-supported scientific database and information systems technology
to allow the scientific community to analyze data repositories more
comprehensively.

Especially in engineering and high-technology disciplines, multiple
system development efforts are ongoing concurrently and competitively,
with little emphasis placed on \"harvesting\" the individual results for
the potential user community at large, and with hardly any efforts
dedicated to integrating partial results obtained form diverse efforts
going on in parallel at multiple institutions. Research in computer and
computational science and engineering in academia is typically driven by
the need of developing a system prototype that suffices to demonstrate
an idea in a conference or journal publication; centralized efforts to
utilize and integrate the vast amounts of prototype software hardly
exists. Science and technology policy can play a significant role in
fostering the establishment of \"integration centers\" whose mission
would be to identify and integrate the latest and most powerful in
computer and computational science and engineering technology. Ideally,
such centers would be co-funded and co-administered by industry partners
or consortia, with a main objective being rapid technology transfer into
the private sector.

### Question G. Multidisciplinary/collaborative research. Are any funding organizations, either inside or outside of government, employing funding mechanisms or strategies that are particularly effective in encouraging multidisciplinary work, collaborative activities, and other innovative approaches? Are there any data available relevant to these questions?

There exists an increased need to communicate scientific and
technological capabilities more quickly and more effectively to the
(potential) user community, especially the industrial sector. In
general, individual scientists and research engineers in academia cannot
be expected to be aware of the needs of and trends in all industrial
applications directly related to their research domains. Effective
\"knowledge and technology transfer\" is needed. Science and technology
policy can support this goal by mandating and supporting at a
much-increased level technology transfer-driven components in federally
funded research. Novel mechanisms must be defined that will allow
scientists and engineers from academia to communicate findings to
non-traditional groups, especially the industrial and policy sectors.
For example, designated conference centers or national laboratories
could serve the purpose of communicating our scientists\' latest
findings to the broad potential user communities.

[NSF Collaborative Proposal Mechanisms]{.underline} - NSF's
collaborative research opportunities and Fastlane system make the
collaborative submission process simple. Prior to linking proposals from
various institutions for a collaborative project, NSF requires that the
approval of the relevant program officer be secured. Once such approval
is granted, the collaborating institutions are allowed to submit their
respective proposals, which are electronically linked through the
Fastlane system. The collaborating institutions each receive separate
research funds, i.e., no subcontracting is involving. However the
projects remain programmatically linked. Under this system, the
administrative burden is significantly lighter for the participating
institutions, while the investigators/institutions clearly benefit from
the collaboration.

[University of California's Industry-University Collaborative Research
Program]{.underline} - This UC program invites the researcher to find an
industry partner to sponsor a research project. If selected, the
program, using State of California funds, will match (1:2) the industry
partner's contribution to the direct costs of the project. The goal is
for the university is to partner with industry to facilitate research
and potentially commercialize any technology that may arise from the
research results.

[University of California's MEXUS Program]{.underline} - This UC program
invites the researcher to find a Mexican researcher to partner on a
collaborative project. If the project is selected, the MEXUS program
issues an award to the Mexican and UC institution, separately, with
common and uncomplicated terms and condition. Similar to the NSF
Collaborative projects, the MEXUS program provides administrative ease,
i.e., no subcontracting and straightforward terms and conditions, and
facilitates collaboration.

## Data to Address this Issue

The above programs have volume and dollar totals that can be used to
summarize the impact of these mechanisms on the research community.

**Question H. Research Infrastructure. What information is available to
examine policies at the Federal, State, local or institutional level
that affect research infrastructure and the costs of building,
maintaining and/or operating the research infrastructure' What factors
influence performers' investments in research infrastructure? What data
are available to demonstrate that? What information is available on the
mix of sources used to finance research infrastructure?**

Research infrastructure policies vary at the Federal, State, local and
institutional level; thus we have no comment on this question.