NSF’S CYBERINFRASTRUCTURE VISION FOR 

21ST CENTURY DISCOVERY 

NSF Cyberinfrastructure Council

 

National Science Foundation 

January 20, 2006 

Version 5.0
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ACRONYMS 

CCSDS Consultative Committee for Space Data Standards 

CI Cyberinfrastructure 

CODATA National Committee on Data for Science and Technology 

CPU Central Processing Unit 

CSNET Computer Science Network 

DARPA Defense Advanced Research Projects Agency 

DOD Department of Defense 

DOE Department of Energy 

ETF Extensible Terascale Facility 

FLOPS Floating point operations/sec 

HPC High Performance Computing 

HPCMOD DOD’s High-Performance Computing Modernization program 

HPCS DARPA’s High Productivity Computing Systems program 

HPCC High-Performance Computing and Communications 

GEON Geosciences Network 

GriPhyN Grid Physics Network 

ICPSR Inter-university Consortium for Political and Social Research 

ICSU International Council for Science 

ICTSI International Council for Scientific and Technical Information 

IRIS Incorporated Research Institutions for Seismology 

ISO International Organization of Standardization 

IT Information Technology 

ITR Information Technology Research 

IVDGL International Virtual Data Grid Laboratory 

MPI Message Passing Interface 

NARA National Archives and Record Administration 

NASA National Aeronautics and Space Administration 

NIH National Institutes of Health 

NITRD Networking and Information Technology Research and Development 

NNSA National Nuclear Security Administration 

NSFNET NSF Network 

NRC National Research Council 

NSB National Science Board 

NSF National Science Foundation 

OAIS Open Archival Information System 

OS Operating System 

PACI Partnership for Advanced Computational Infrastructure 

PITAC President’s Information Technology Advisory Committee 

RLG Research Library Group 

SciDAC Scientific Discovery through Advanced Computing 

SSP Software Services Provider 

TFLOPS Teraflops: Trillion floating point operations/sec 

WDC World Data Center 
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Acronyms

TABLE OF CONTENTS 

CHAPTER 1 Call to Action.....................................................................................4 

I. Drivers and Opportunities .....................................................................4 

II. Vision, Mission, and Principles ........................................................... 5 

III. Goals and Strategies ...................................... .................................. 7 

IV. Strategic Planning for Cyberinfrastructure Components......................... ....9 

CHAPTER 2 Strategic Plan for High Performance Computing (2006-2010).....................10 

 I. What Does High Performance Computing Offer Science and Engineering? ..10 

II. The Next Five Years: Creating a High Performance Computing 

Environment for Petascale Science and Engineering ...............................11 

CHAPTER 3 Strategic Plan for Data, Data Analysis and Visualization (2006-2010)... ......16 

I. A Wealth of Scientific Opportunities Afforded by Digital Data ...... ....... .....16 

II. Definitions ...................................... .................................. ........ .....17 

III. Developing a Data Cyberinfrastructure in a Complex, Global Context ..... ....17 

IV. Plan of Action ...................................... .................................. ........ .19 

V. Conclusion......................................................... ............................ 24 

CHAPTER 4 Strategic Plan for Collaboratories, Observatories and Virtual 

Organizations (2006-2010) ...............25 

CHAPTER 5 Strategic Plan for Education and Workforce (2006-2010) ............. ............26 

Appendix A Representative Reports and Workshops ...............................................27 

Appendix B Chronology of NSF IT Investments.......................................................30 

Appendix C Management of Cyberinfrastructure........................................... .........32 
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CHAPTER 1 

CALL TO ACTION 

I. DRIVERS AND OPPORTUNITIES 

How does a protein fold? What happens to space-time when two black holes collide? What 

impact does species gene flow have on an ecological community? What are the key factors 

that drive climate change? Did one of the trillions of collisions at the Large Hadron Collider 

produce a Higgs boson, the dark matter particle or a black hole? Can we create an 

individualized model of each human being for targeted healthcare delivery? How does major 

technological change affect human behavior and structure complex social relationships? What 

answers will we find – to questions we have yet to ask – in the very large datasets that are 

being produced by telescopes, sensor networks, and other experimental facilities? 

These questions – and many others – are only now coming within our ability to answer because 

of advances in computing and related information technology. Once used by a handful of elite 

researchers in a few research communities on select problems, advanced computing has 

become essential to future progress across the frontier of science and engineering. Coupled 

with continuing improvements in microprocessor speeds, converging advances in networking, 

software, visualization, data systems and collaboration platforms are changing the way research 

and education is accomplished. 

Today’s scientists and engineers need access to new information technology capabilities, such 

as distributed wired and wireless observing network complexes, and sophisticated simulation 

tools that permit exploration of phenomena that can never be observed or replicated by 

experiment. Computation offers new models of behavior and modes of scientific discovery that 

greatly extend the limited range of models that can be produced with mathematics alone, for 

example, chaotic behavior. Fewer and fewer researchers working at the frontiers of knowledge 

can carry out their work without cyberinfrastructure of one form or another. 

While hardware performance has been growing exponentially – with gate density doubling every 

18 months, storage capacity every 12 months, and network capability every 9 months – it has 

become clear that increasingly capable hardware is not the only requirement for computationenabled discovery. Sophisticated software, visualization tools, middleware and scientific 

applications created and used by interdisciplinary teams are critical to turning flops, bytes and 

bits into scientific breakthroughs. It is the combined power of these capabilities that is 

necessary to advance the frontiers of science and engineering, to make seemingly intractable 

problems solvable and to pose profound new scientific questions. 

The comprehensive infrastructure needed to capitalize on dramatic advances in information 

technology has been termed cyberinfrastructure. Cyberinfrastructure integrates hardware for 

computing, data and networks, digitally-enabled sensors, observatories and experimental 

facilities, and an interoperable suite of software and middleware services and tools. 

Investments in interdisciplinary teams and cyberinfrastructure professionals with expertise in 
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algorithm development, system operations, and applications development are also essential to 

exploit the full power of cyberinfrastructure to create, disseminate, and preserve scientific data, 

information, and knowledge. 

For four decades, NSF has provided leadership in the scientific revolution made possible by 

information technology (Appendices A and B). Through investments ranging from 

supercomputing centers and the Internet to software and algorithm development, information 

technology has stimulated scientific breakthroughs across all science and engineering fields. 

Most recently, NSF’s Information Technology Research (ITR) priority area sowed the seeds of 

broad and intensive collaboration between the computational, computer and domain research 

communities that sets the stage for this “Call to Action.” 

NSF is the only agency within the U.S. government that funds research and education across all 

disciplines of science and engineering. Over the past five years, NSF has held community 

workshops, commissioned blue-ribbon panels and carried out extensive internal planning 

(Appendix A.) Thus, it is strategically placed to leverage, coordinate and transition 

cyberinfrastructure advances in one field to all fields of research. 

Other Federal agencies, the Administration and Congress, the private sector, and other nations 

are aware of the growing importance of cyberinfrastructure to progress in science and 

engineering. Other Federal agencies have planned improved capabilities for specific 

disciplines, and in some cases to address interdisciplinary challenges. Other countries have 

also been making significant progress in scientific cyberinfrastructure. Thus, the U.S. must 

engage in and actively benefit from cyberinfrastructure developments around the world. 

Not only is the time ripe for a coordinated investment in cyberinfrastructure, progress at the 

science and engineering frontiers depends upon it. Our communities are in place and are 

poised to respond to such an investment. 

Working with the science and engineering research and education communities and partnering 

with other key stakeholders, NSF is ready to lead. 

II. VISION, MISSION, AND PRINCIPLES 

A. Vision 

NSF will play a leadership role in the development and support of a 

comprehensive cyberinfrastructure essential to 21st century advances in science 

and engineering research and education. 

B. Mission 

NSF’s mission for cyberinfrastructure (CI) is to: 

• Develop a human-centered CI that is driven by science and engineering research and 

education opportunities; 
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• Provide the science and engineering communities with access to world-class CI tools 

and services, including those focused on: high performance computing; data, data 

analysis and visualization; collaboratories, observatories and virtual organizations; and, 

education and workforce development; 

• Promote a CI that serves as an agent for broadening participation and strengthening the 

Nation’s workforce in all areas of science and engineering; 

• Provide a sustainable CI that is secure, efficient, reliable, accessible, usable, and 

interoperable, and which evolves as an essential national infrastructure for conducting 

science and engineering research and education; and 

• Create a stable CI environment that enables the research and education communities to 

contribute to the agency’s statutory mission. 

C. Principles 

 

The following principles will guide NSF’s actions. 

• Science and engineering research and education are foundational drivers of CI.

• NSF has a unique leadership role in formulating and implementing a national CI agenda 

focused on advancing science and engineering. 

• Inclusive strategic planning is required to effectively address CI needs across a broad 

spectrum of organizations, institutions, communities and individuals, with input to the 

process provided through public comments, workshops, funded studies, advisory 

committees, merit review and open competitions.

• Strategic investments in CI resources and services are essential to continued U.S. 

leadership in science and engineering. 

• The integration and sharing of cyberinfrastructure assets deployed and supported at 

national, regional, local, community, and campus levels represent the most effective way 

of constructing a comprehensive CI ecosystem suited to meeting future needs. 

• National and international partnerships, public and private, that integrate CI users and 

providers and benefit NSF’s research and education communities are also essential for 

enabling next-generation science and engineering. 

• Existing strengths, including research programs and CI facilities, serve as a foundation 

upon which to build a CI designed to meet the needs of the broad science and 

engineering community. 

• Merit review is essential for ensuring that the best ideas are pursued in all areas of CI 

funding. 

• Regular evaluation and assessment tailored to individual projects is essential for 

ensuring accountability to all stakeholders. 

• A collaborative CI governance structure that includes representatives who contribute to 

basic CI research, development and deployment, as well as those who use CI, is 

essential to ensure that CI is responsive to community needs and empowers research at 

the frontier. 
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III. GOALS AND STRATEGIES 

NSF’s vision and mission statements need well-defined goals and strategies to turn them into 

reality. The goals underlying these statements are provided below, with each goal followed by a 

brief description of the strategy to achieve the goal. 

Across the CI landscape, NSF will: 

• Provide communities addressing the most computationally challenging problems 

with access to a world-class high performance computing (HPC) environment 

through NSF acquisition and through exchange-of-service agreements with other 

entities, where possible. 

NSF’s investment strategy in the provision of HPC resources and services will be linked to 

careful requirements analyses of the computational needs of research and education 

communities. Our investments will be coordinated with those of other agencies in order to 

maximize access to these capabilities and to provide a range of representative high 

performance architectures. 

• Broaden access to state-of-the-art computing resources, focusing especially on 

institutions with less capability and communities where computational science is 

an emerging activity. 

Building on the achievements of current CI service providers and other NSF investments, the 

agency will work to make necessary computing resources more broadly available, paying 

particular attention to emerging and underserved communities. 

• Support the development and maintenance of robust systems software, 

programming tools, and applications needed to close the growing gap between 

peak performance and sustained performance on actual research codes, and to 

make the use of HPC systems, as well as novel architectures, easier and more 

accessible. 

NSF will build on research in computer science and other research areas to provide science and 

engineering applications and problem-solving environments that more effectively exploit 

innovative architectures and large-scale computing systems. NSF will continue and build upon 

its existing collaborations with other agencies in support of the development of HPC software 

and tools. 

• Support the continued development, expansion, hardening and maintenance of 

middleware that permits the integration and sharing of digital resources by 

communities of researchers and educators, as well as new middleware and 

applications needed to take advantage of advances in connectivity made possible 

by Internet2, the National Lambda Rail and other emerging networks. 

These investments will build on the middleware products of current and former programs, and 

will leverage work in core computer science research and development efforts supported by 

NSF and other federal agencies. 
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• Support the development of the computing professionals, interdisciplinary teams 

and new organizational structures, such as virtual communities, needed to 

achieve the scientific breakthroughs made possible by advanced CI, paying 

particular attention to the opportunities to broaden the participation of 

underrepresented groups. 

NSF will continue to invest in understanding how participants in its research and education 

communities, as well as the scientific workforce, can use CI. For example, virtual organizations 

empower communities of users to interact, exchange information and access and share 

resources through tailored interfaces. Some of NSF’s investments will focus on appropriate 

mechanisms or structures for use, while others will focus on how best to train future users of CI. 

NSF will take advantage of the emerging communities associated with CI that provide unique 

and special opportunities for broadening participation in the science and engineering enterprise. 

• Support state-of-the-art innovation in data management and distribution systems, 

including digital libraries and educational environments that are expected to 

contribute to many of the scientific breakthroughs of the 21st century. 

NSF will foster communication between forefront data management and distribution systems, 

digital libraries and other education environments sponsored in its various directorates. NSF 

will ensure that its efforts take advantage of innovation in large data management and 

distribution activities sponsored by other agencies and international efforts as well. These 

developments will play a critical role in decisions that NSF makes about long-lived data. 

• Support the design and development of the CI needed to realize the full scientific 

potential of NSF’s investments in tools and large facilities, from observatories and 

accelerators to sensor networks and remote observing systems. 

NSF’s large facilities and other tools investments require new types of CI such as wireless 

control of networks of sensors in hostile environments, rapid distribution of petascale data sets 

around the world, adaptive knowledge-based control and sampling systems, and innovative 

visualization systems for collaboration. NSF will ensure that these projects invest appropriately 

in CI capabilities and that its CI programs serve the needs of these projects. 

• Support the development and maintenance of the increasingly sophisticated 

applications needed to achieve the scientific goals of research and education 

communities. 

The applications needed to produce cutting-edge science and engineering have become 

increasingly complex. They require teams, even communities, to develop and sustain wide and 

long-term applicability. NSF’s investments in applications will involve its directorates, which 

support domain-specific science and engineering. Special attention will be paid to the crossdisciplinary nature of much of the work. 

• Invest in the high-risk/high-gain basic research in computer science, computing 

and storage devices, mathematical algorithms and the human/CI interfaces that 

are critical to powering the future exponential growth in all aspects of computing, 

from hardware speed, storage, connectivity and scientific productivity. 
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NSF’s investments in operational CI must be coupled with vigorous research programs in the 

directorates that will ensure operational capabilities continue to expand and extend in the future. 

Important among these are activities to understand how humans adopt and use CI. NSF is 

especially placed to foster collaborations among computer scientists, social, behavioral and 

economic scientists, and other domain scientists and engineers to understand how humans can 

best use CI, both in research and education environments.

• Provide a framework that will sustain reliable, stable resources and enable the 

integration of new technologies and research developments with a minimum of 

disruption to users. 

NSF will minimize disruption to users by means of pre-planned arrangements for alternative CI 

availabilities during competitions, changeovers and upgrades to production operations and 

services, cooperative arrangements with other agencies, and additional evolving flexibility 

afforded by the realization of a comprehensive cyberinfrastructure created in an interoperable, 

open architecture format. 

A strategy common to achieving all of these goals is partnering nationally and internationally, 

with other agencies, the private sector, and with universities to achieve a worldwide CI that is 

interoperable, flexible, efficient, evolving and broadly accessible. In particular, NSF will take a 

lead role in formulating and implementing a national CI strategy. 

IV. STRATEGIC PLANNING FOR CYBERINFRASTRUCTURE COMPONENTS

To implement its cyberinfrastructure vision, NSF will develop detailed strategic plans for each of 

the following CI components: 

• High Performance Computing; 

• Data, Data Analysis, and Visualization; 

• Collaboratories, Observatories, and Virtual Organizations; and, 

• Education and Workforce Development. 

Others may be added at a later date. 

The strategies will be reviewed annually and will evolve over time, paced by the considerable 

rate of innovation in computing and the growing needs of the science and engineering 

community for state-of-the-art CI capabilities. Through their simultaneous implementation, 

NSF’s vision will become reality. 

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CHAPTER 2 

STRATEGIC PLAN FOR HIGH PERFORMANCE COMPUTING 

 (2006-2010) 

I. WHAT DOES HIGH PERFORMANCE COMPUTING OFFER SCIENCE AND 

ENGINEERING? 

What are the three-dimensional structures of all of the proteins encoded by the human genome 

and how does structure influence their function in a human cell? What patterns of emergent 

behavior occur in models of very large societies? How do massive stars explode and produce 

the heaviest elements in the periodic table? What sort of abrupt transitions can occur in Earth’s 

climate and ecosystem structure? How do these occur and under what circumstances? If we 

could design catalysts atom-by-atom, could we transform industrial synthesis? What strategies 

might be developed to optimize management of complex infrastructure systems? What kind of 

language processing can occur in large assemblages of neurons? Can we enable integrated 

planning and response to natural and man-made disasters that prevent or minimize the loss of 

life and property? These are just some of the important questions that researchers wish to 

answer using contemporary tools in a state-of-the-art High Performance Computing (HPC) 

environment. 

With HPC tools, researchers study the properties of minerals at the extreme temperatures and 

pressures that occur deep within the Earth. They simulate the development of structure in the 

early Universe. They probe the structure of novel phases of matter such as the quark-gluon 

plasma. HPC capabilities enable the modeling of life cycles that capture interdependencies 

across diverse disciplines and multiple scales to create globally competitive manufacturing 

enterprise systems. And they examine the way proteins fold and vibrate after they are 

synthesized inside an organism. In fact, sophisticated numerical simulations permit scientists 

and engineers to perform a wide range of in silico experiments that would otherwise be too 

difficult, too expensive or impossible to perform in the laboratory. 

HPC systems and services are also essential to the success of research conducted with 

sophisticated experimental tools. For example, without the waveforms produced by numerical 

simulation of black hole collisions and other astrophysical events, gravitational wave signals 

cannot be extracted from the data produced by the Laser Interferometer Gravitational Wave 

Observatory; high-resolution seismic inversions from the higher density of broad-band seismic 

observations furnished by the Earthscope project are necessary to determine shallow and deep 

Earth structure; simultaneous integrated computational and experimental testing is conducted 

on the Network for Earthquake Engineering Simulation to improve seismic design of buildings 

and bridges; and HPC is essential to extracting the signature of the Higgs boson and 

supersymmetric particles – two of the scientific drivers of the Large Hadron Collider – from the 

petabytes of data produced in the trillions of particle collisions. 

Science and engineering research and education enabled by state-of-the-art HPC tools have a 

direct bearing on the Nation’s competitiveness. If investments in HPC are to have long-term 
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impact on problems of national need, such as bioengineering, critical infrastructure protection 

(for example, the electric power grid), health care, manufacturing, nanotechnology, energy, and 

transportation, then HPC tools must deliver high performance capability to a wide range of 

science and engineering applications. 

II. THE NEXT FIVE YEARS: CREATING A HIGH PERFORMANCE COMPUTING 

ENVIRONMENT FOR PETASCALE SCIENCE AND ENGINEERING 

NSF’s five-year HPC goal is to enable petascale science and engineering through the 

deployment and support of a world-class HPC environment comprising the most capable 

combination of HPC assets available to the academic community. The petascale HPC 

environment will enable investigations of computationally challenging problems that require 

computers operating at sustained speeds on actual research codes of 1015 floating point 

operations per second (petaflops) or that work with extremely large data sets on the order of 

1015 bytes (petabytes). 

Petascale HPC capabilities will permit researchers to perform simulations that are intrinsically 

multi-scale or that involve multiple simultaneous reactions, such as modeling the interplay 

between genes, microbes, and microbial communities and simulating the interactions between 

the ocean, atmosphere, cryosphere and biosphere in Earth systems models. In addition to 

addressing the most computationally challenging demands of science and engineering, new and 

improved HPC software services will make supercomputing platforms supported by NSF and 

other partner organizations more efficient, more accessible, and easier to use. 

NSF will support the deployment of a well-engineered, scalable, HPC infrastructure designed to 

evolve as science and engineering research needs change. It will include a sufficient level of 

diversity, both in architecture and scale of deployed HPC systems, to realize the research and 

education goals of the broad science and engineering community. 

The following principles will guide the agency’s FY 2006 through FY 2010 investments. 

• Science and engineering research and education priorities will drive HPC investments.

• Collaborative activities involving science and engineering researchers and private sector 

organizations are needed to ensure that HPC systems and services are optimally configured 

to support petascale scientific computing. 

• Researchers and educators require access to reliable, robust, production-quality HPC 

resources and services.

• HPC-related research and development advances generated in the public and private 

sectors, both domestic and foreign, must be leveraged to enrich HPC capabilities. 

• The development, implementation and annual update of an effective multi-year HPC. 

strategy is crucial to the timely introduction of research and development outcomes and 

innovations in HPC systems, software and services. 

NSF’s implementation plan to create a petascale environment includes the following three 

interrelated components: 

 
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1). Specification, Acquisition, Deployment and Operation of Science-Driven HPC 

Systems Architectures

An effective computing environment designed to meet the computational needs of a range of 

science and engineering applications will include a variety of computing systems with 

complementary performance capabilities. By 2010, the petascale computing environment 

available to the academic science and engineering community is likely to consist of: (i) a 

significant number of systems with peak performance in the 1-50 teraflops range, deployed and 

supported at the local level by individual campuses and other research organizations; (ii) 

multiple systems with peak performance of 100+ teraflops that support the work of thousands of 

researchers nationally; and, (iii) at least one system in the 1-10 petaflops range that supports a 

more limited number of projects demanding the highest levels of computing performance. All 

NSF-deployed systems will be appropriately balanced and will include core computational 

hardware, local storage of sufficient capacity, and appropriate data analysis and visualization 

capabilities. Chapters 3 and 4 in this document describe the complementary investments 

necessary to provide effective data analysis and visualization capabilities, and to integrate HPC 

resources into a comprehensive national CI environment to improve both accessibility and 

usability. 

Over the FY 2006-2010 period, NSF will focus on HPC system acquisitions in the 100 teraflops 

to 10 petaflops range, where strategic investments on a national scale are necessary to ensure 

international leadership in science and engineering. Since different science and engineering 

codes may achieve optimal performance on different HPC architectures, it is likely that by 2010 

the NSF-supported HPC environment will include both loosely-coupled and tightly coupled 

systems, with several different memory models. 

To address the challenge of providing the research community with access to a range of HPC 

architectures within a constrained budget, a key element of NSF’s strategy is to participate in 

resource-sharing with other federal agencies. A strengthened interagency partnership will 

focus, to the extent practicable, on ensuring shared access to federal leadership-class 

resources with different architectures, and on the coordination of investments in HPC system 

acquisition and operation. The Department of Energy’s Office of Science and National Nuclear 

Security Administration have very active programs in leadership computing. The Department of 

Defense’s (DOD’s) High Performance Computing Modernization Office (HPCMOD) provisions 

HPC resources and services for the DOD science and engineering community, while NASA is 

deploying significant computing systems also of interest to NSF PIs. To capitalize on these 

common interests, NSF will work toward the creation of a Leadership Computing Council as 

proposed by Simon et al.1

, to include representatives from all federal agencies with a stake in 

science and engineering-focused HPC. As conceived, the Leadership Computing Council will 

make coordinated and collaborative investments in science-driven hardware architectures, will 

increase the diversity of architectures of leadership class systems available to researchers and 

educators around the country, will promote sharing of lessons learned, and will provide a richer 

HPC environment for the user communities supported by each agency. 

Strong partnerships involving universities, industry and government are also critical to success. 

In addition to leveraging the promise of Phase III of the DARPA-sponsored High Productivity 

 

1 Simon et al., “Science-Driven System Architecture: A New Process for Leadership Class Computing,” Journal of the Earth 

Simulator, pages 1-9, Vol. 2, January 2005.
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Computing Systems (HPCS) program2 in which NSF is a mission partner, the agency will 

establish a discussion and collaboration forum for scientists and engineers - including 

computational and computer scientists and engineers - and HPC system vendors, to ensure that 

HPC systems are optimally configured to support state-of-the-art scientific computing. On the 

one hand, these discussions will keep NSF and the academic community informed about new 

products, product roadmap and technology challenges at various vendor organizations. On the 

other, they will provide HPC system vendors with insights into the major concerns and needs of 

the academic science and engineering community. These activities will lead to better alignment 

between applications and hardware both by influencing algorithm design and by influencing 

system integration. 

NSF will also promote resource sharing between and among academic institutions to optimize 

the accessibility and use of HPC assets deployed and supported at the campus level. This will 

be accomplished through development of a shared governance structure that includes relevant 

HPC stakeholders. 

2). Development and Maintenance of Supporting Software: New Design Tools, 

Performance Modeling Tools, Systems Software, and Fundamental Algorithms. 

Many of the HPC software and service building blocks in scientific computing are common to a 

number of science and engineering applications. A supporting software and service 

infrastructure will accelerate the development of the scientific application codes needed to solve 

challenging scientific problems, and will help insulate these codes from the evolution of future 

generations of HPC hardware. 

Supporting software services include the provision of intelligent development and problemsolving environments and tools. These tools are designed to provide improvements in ease of 

use, reusability of modules, and portable performance. Tools and services that are similar 

across different HPC platforms will greatly reduce the time-to-solution of computationallyintensive research problems by permitting local development of research codes that can then be 

rapidly transferred to larger production environments or shared with colleagues. Applications 

scientists and engineers will also benefit from the development of new tools and approaches to 

debugging, performance analysis, and performance optimization. 

Specific applications depend on a broad class of numerical and non-numerical algorithms that 

are widely used by many applications; for example, linear algebra, fast spectral transforms, 

optimization algorithms, multi-grid methods, adaptive mesh refinement, symplectic integrators, 

and sorting and indexing routines. To date, improved or new algorithms have been important 

contributors to performance improvements in science and engineering applications, the 

development of multi-grid solvers for elliptic partial differential equations being a prime example. 

Innovations in algorithms will have a significant impact on the performance of applications 

software. The development of algorithms for different architectural environments is an essential 

component of the effort to develop portable, scalable, applications software. Other important 

software services include libraries for communications services, such as MPI and OpenMP. 

 

2 The DARPA High Productivity Computing Systems is focused on providing a new generation of economically viable high 

productivity computing systems. HPCS program researchers have initiated a fundamental reassessment of how performance, 

programmability, portability, robustness and ultimately, productivity in the HPC domain are defined and measured.
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The development and deployment of operating systems and compilers that scale to hundreds of 

thousands of processors are also necessary. They must provide effective fault-tolerance and 

must effectively insulate users from parallelization, latency management and thread 

management issues. To test new developments at large scales, operating systems and kernel 

researchers and developers must have access to the infrastructure necessary to test their 

developments at scale. 

NSF will support Software Services Providers (SSPs) to develop this supporting software 

infrastructure. SSPs will be individually and collectively responsible for: applied research and 

development of supporting technologies; harvesting promising supporting software technologies 

from the research communities; performing scalability/reliability tests to explore software 

viability; developing, hardening and maintaining software where necessary; and facilitating the 

transition of commercially viable software into the private sector. SSPs will also support general 

software engineering consulting services for science and engineering applications, and will 

provide software engineering consulting support to individual researchers and to research and 

education teams as necessary. 

SSPs will be responsible for ensuring software interoperability with other components of the 

cyberinfrastructure software stack, such as those generated to provide Data, Data Analysis and 

Visualization services, and Collaboratories, Observatories and Virtual Organization capabilities 

– see Chapters 3 and 4 in this document. This will be accomplished through the creation and 

utilization of appropriate software test harnesses and will ensure that sufficient configuration 

controls are in place to support the range of HPC platforms used by the research and education 

community. The applications community will identify needed improvements in supporting 

software and will provide input and feedback on the quality of services provided by SSPs. 

To guide the evolution of the SSP program, NSF will establish an HPC Software Services 

Council that includes representatives from academe, federal agencies and private sector 

organizations, including 3rd party and system vendors. The HPC Software Services Council will 

provide input on the strengths, weaknesses, opportunities and gaps in the software services 

currently available to the science and engineering research and education communities. 

 

To minimize duplication of effort and to optimize the value of HPC services provided to the 

science and engineering community, NSF’s investments will be coordinated with those of other 

agencies. DOE currently invests in software infrastructure centers through the Scientific 

Discovery through Advanced Computing (SciDAC) program, while DARPA’s investments in the 

HPCS program contribute significant systems software and hardware innovations. NSF will 

seek to leverage and add value to ongoing DOE and DARPA efforts in this area. 

3). Development and Maintenance of Portable, Scalable Applications Software

Today’s microprocessor-based terascale computers place considerable demands on our ability 

to manage parallelism, and to deliver large fractions of peak performance. As the agency seeks 

to create a petascale computing environment, it will embrace the challenge of developing or 

converting key application codes to run effectively on new and evolving system architectures. 

Over the FY 2006 through 2010 period, NSF will make significant new investments in the 

development, hardening, enhancement and maintenance of scalable applications software, 

including community models, to exploit the full potential of current terascale and future 
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petascale systems architectures. The creation of well-engineered, easy-to-use software will 

reduce the complexity and time-to-solution of today’s challenging scientific applications. NSF 

will promote the incorporation of sound software engineering approaches in existing widely-used 

research codes and in the development of new research codes. Multidisciplinary teams of 

researchers will work together to create, modify and optimize applications for current and future 

systems using performance modeling tools and simulators. 

Since the nature and genesis of science and engineering codes varies across the research 

landscape, a successful programmatic effort in this area will weave together several strands. A 

new activity will be designed to take applications that have the potential to be widely used within 

a community or communities, to harden these applications based on modern software 

engineering practices, to develop versions for the range of architectures that scientists wish to 

use them on, to optimize them for modern HPC architectures, and to provide user support. 

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CHAPTER 3 

STRATEGIC PLAN FOR DATA, DATA ANALYSIS, AND 

VISUALIZATION (2006-2010)

I. A WEALTH OF SCIENTIFIC OPPORTUNITIES AFFORDED BY DIGITAL DATA 

Science and engineering research and education have become increasingly data-intensive, as a 

result of the proliferation of digital technologies and pervasive networks through which data are 

collected, generated, shared and analyzed. Worldwide, scientists and engineers are producing, 

accessing, analyzing, integrating and storing terabytes of digital data daily through 

experimentation, observation and simulation. Moreover, the dynamic integration of data 

generated through observation and simulation is enabling the development of new scientific 

methods that adapt intelligently to evolving conditions to reveal new understanding. The 

enormous growth in the availability and utility of scientific data is increasing scholarly research 

productivity, accelerating the transformation of research outcomes into products and services, 

and enhancing the effectiveness of learning across the spectrum of human endeavor. 

New scientific opportunities are emerging from increasingly effective data organization, access 

and usage. Together with the growing availability and capability of tools to mine, analyze and 

visualize data, the emerging data cyberinfrastructure is revealing new knowledge and 

fundamental insights. For example, analyses of DNA sequence data are providing remarkable 

insights into the origins of man, are revolutionizing our understanding of the major kingdoms of 

life, and are revealing stunning and previously unknown complexity in microbial communities. 

Sky surveys are changing our understanding of the earliest conditions of the universe and 

providing comprehensive views of phenomena ranging from black holes to supernovae. 

Researchers are monitoring socio-economic dynamics over space and time to advance our 

understanding of individual and group behavior and their relationship to social, economic and 

political structures. Using combinatorial methods, scientists and engineers are generating 

libraries of new materials and compounds for health and engineering, and environmental 

scientists and engineers are acquiring and analyzing streaming data from massive sensor 

networks to understand the dynamics of complex ecosystems. 

In this dynamic research and education environment, science and engineering data are 

constantly being collected, created, deposited, accessed, analyzed and expanded in the pursuit 

of new knowledge. In the future, U.S. international leadership in science and engineering will 

increasingly depend upon our ability to leverage this reservoir of scientific data captured in 

digital form, and to transform these data into information and knowledge aided by sophisticated 

data mining, integration, analysis and visualization tools. 

This chapter sets forth a framework in which NSF will work with its partners in science and 

engineering – public and private sector organizations both foreign and domestic representing 

data producers, scientists, engineers, managers and users alike – to address data acquisition, 

access, usage, stewardship and management challenges in a comprehensive way.
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II. DEFINITIONS 

A. Data, Metadata and Ontologies 

In this document, “data” and “digital data” are used interchangeably to refer to data and 

information stored in digital form and accessed electronically. 

• Data. For the purposes of this document, data are any and all complex data entities 

from observations, experiments, simulations, models, and higher order assemblies, 

along with the associated documentation needed to describe and interpret the data. 

• Metadata. Metadata are a subset of data, and are data about data. Metadata 

summarize data content, context, structure, inter-relationships, and provenance 

(information on history and origins). They add relevance and purpose to data, and 

enable the identification of similar data in different data collections. 

• Ontology. An ontology is the systematic description of a given phenomenon, often 

includes a controlled vocabulary and relationships, captures nuances in meaning and 

enables knowledge sharing and reuse.

B. Data Collections 

This document adopts the definition of data collection types provided in the NSB report on LongLived Digital Data Collections3

, where data collections are characterized as being one of three 

functional types: 

• Research Collections. Authors are individual investigators and investigator teams. 

Research collections are usually maintained to serve immediate group participants only 

for the life of a project, and are typically subjected to limited processing or curation. 

Data may not conform to any data standards.

• Resource Collections. Resource Collections are authored by a community of 

investigators, often within a domain of science or engineering, and are often developed 

with community-level standards. Budgets are often intermediate in size. Lifetime is 

between the mid- and long-term.

• Reference Collections. Reference collections are authored by and serve large segments 

of the science and engineering community, and conform to robust, well-established, 

comprehensive standards, which often lead to a universal standard. Budgets are large 

and often derived from diverse sources with a view to indefinite support. 

Boundaries between the types are not rigid and collections originally established as research 

collections may evolve over time to become resource and/or reference collections. In this 

document, the term data collection is construed to include one or more databases and their 

relevant technological implementation. Data collections are managed by organizations and 

individuals with the necessary expertise to structure them and to support their effective use. 

III. DEVELOPING A COHERENT DATA CYBERINFRASTRUCTURE IN A COMPLEX, 

GLOBAL CONTEXT 

Since data and data collections are owned or managed by a wide range of communities, 

organizations and individuals around the world, NSF must work in an evolving environment 

 

3

 Long-Lived Digital Data Collections: Enabling Research and Education in the 21st Century, NSB-05-40 
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constantly being shaped by developing international and national policies and treaties, 

community-specific policies and approaches, institutional-level programs and initiatives, 

individual practices, and continually advancing technological capabilities. 

At the international level, a number of nations and international organizations have already 

recognized the broad societal, economic, and scientific benefits that result from open access to 

science and engineering digital data. In 2004 more than thirty nations, including the United 

States, declared their joint commitment to work toward the establishment of common access 

regimes for digital research data generated through public funding4

. Since the international 

exchange of scientific data, information and knowledge promises to significantly increase the 

scope and scale of research and its corresponding impact, these nations are working together 

to define the implementation steps necessary to enable the global science and engineering 

system. 

The U.S. community is engaged through the National Committee on Data for Science and 

Technology (CODATA). CODATA is working with its international partners, including the 

International Council for Science (ICSU), the International Council for Scientific and Technical 

Information (ICTSI), the World Data Centers (WDCs) and others, to create a Global Information 

Commons for Science. As currently conceived, this online “open-access knowledge space” will: 

promote the promise of easy access to and use of scientific data and information; promote wider 

adoption of successful methods and models for providing open availability on a sustainable 

basis; facilitate reuse of publicly-funded scientific data and information, as well as cooperative 

sharing of research materials and tools among researchers; and, encourage and coordinate the 

efforts of many stakeholders in the world’s diverse science and engineering community to 

achieve these objectives. 

A number of international science and engineering communities have also been developing 

data management and curation approaches for reference and resource collections. For 

example, the international Consultative Committee for Space Data Standards (CCSDS) defined 

an archive reference model and service categories for the intermediate and long-term storage of 

digital data relevant to space missions. This effort produced the Open Archival Information 

System (OAIS), now adopted as the “de facto” standard for building digital archives, and 

evidence that a community-focused activity can have much broader impact than originally 

intended. In another example, the Inter-University Consortium for Political and Social Research 

(ICPSR) - a membership-based organization with over 500 member colleges and universities 

around the world - maintains and provides access to a vast archive of social science data. 

ICPSR serves as a content management organization, preserving relevant social science data 

and migrating them to new storage media as technology changes, and also provides user 

support services. ICPSR recently announced plans to establish an international standard for 

social science documentation. Similar activities in other communities are also underway. 

Clearly, NSF must maintain a presence in, support, and add value to these ongoing 

international discussions and activities. 

Activities on an international scale are complemented by activities within nation states. In the 

United States, a number of organizations and communities of practice are exploring 

mechanisms to establish common approaches to digital data access, management and 

curation. For example, the Research Library Group (RLG – a not for profit membership 

organization representing libraries, archives and museums) and the U.S. National Archives and 

 

4 http://www.oecd.org/document/0,2340,en_2649_34487_25998799_1_1_1_1,00.html
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Records Administration (NARA – a sister agency whose mission is to provide direction and 

assistance to Federal agencies on records management) are producing certification 

requirements for establishing and selecting reliable digital information repositories. RLG and 

NARA intend their results to be standardized via the International Organization of 

Standardization (ISO) Archiving Series, and may impact all data collections types. The NIH 

National Center for Biotechnology Information plays an important role in the management of 

genome data at the national level, supporting public databases, developing software tools for 

analyzing data, and disseminating biomedical information. 

At the institutional level, colleges and universities are developing approaches to digital data 

archiving, curation, and analysis. They are sharing best practices to develop digital libraries that 

collect, preserve, index and share research and education material produced by faculty and 

other individuals within their organizations. The technological implementations of these systems 

are often open-source and support interoperability among their adopters. University-based 

research libraries and research librarians are positioned to make significant contributions in this 

area, where standard mechanisms for access and maintenance of scientific digital data may be 

derived from existing library standards developed for print material. These efforts are 

particularly important to NSF as the agency considers the implications of not just making all data 

generated with NSF funding broadly accessible, but of also promoting the responsible 

organization and management of these data such that they are widely usable. 

IV. PLAN OF ACTION 

Motivated by a vision in which science and engineering digital data are routinely deposited in 

well-documented form, are regularly and easily consulted and analyzed by specialists and nonspecialists alike, are openly accessible while suitably protected, and are reliably preserved, 

NSF’s five-year goal is twofold: 

• To catalyze the development of a system of science and engineering data collections 

that is open, extensible and evolvable; and 

• To support development of a new generation of tools and services facilitating data 

mining, integration, analysis, and visualization essential to turning data into new 

knowledge and understanding. 

The resulting national digital data framework will consist of a range of data collections and 

managing organizations, networked together in a flexible technical architecture using standard, 

open protocols and interfaces, and designed to contribute to the emerging global information 

commons. It will be simultaneously local, regional, national and global in nature, and will evolve 

as science and engineering research and education needs change and as new science and 

engineering opportunities arise. Widely accessible tools and services will permit scientists and 

engineers to access and manipulate these data to advance the science and engineering 

frontier. 

In print form, the preservation process is handled through a system of libraries and other 

repositories throughout the country and around the globe. Two features of this print-based 

system make it robust. First, the diversity of business models deriving support from a variety of 

sources means that no single entity bears sole responsibility for preservation, and the system is 

resilient to changes in any particular sector. Second, there is overlap in the collections, and 

redundancy of content reduces the potential for catastrophic loss of information. 
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The national data framework is envisioned to provide an equally robust and diverse system for 

digital data management and access. It will: promote interoperability between data collections 

supported and managed by a range of organizations and organization types; provide for 

appropriate protection and reliable long-term preservation of digital data; deliver computational 

performance, data reliability and movement through shared tools, technologies and services; 

and accommodate individual community preferences. The agency will also develop a suite of 

coherent data policies that emphasize open access and effective organization and management 

of digital data, while respecting the data needs and requirements within science and 

engineering domains. 

The following principles will guide the agency’s FY 2006 through FY 2010 investments. 

• Science and engineering research and education opportunities and priorities will 

motivate NSF investments in data cyberinfrastructure. 

• Science and engineering data generated with NSF funding will be readily accessible and 

easily usable, and will be appropriately, responsibly and reliably preserved. 

• Broad community engagement is essential in the prioritization and evaluation of the 

utility of scientific data collections, including the possible evolution between research, 

resource and reference collection types. 

• Continual exploitation of data in the creation of new knowledge requires that 

investigators have access to the tools and services necessary to locate and access 

relevant data, and understand its structure sufficiently to be able to interpret and 

(re)analyze what they find. 

• The establishment of strong, reciprocal, international, interagency and public-private 

partnerships is essential to ensure all stakeholders are engaged in the stewardship of 

valuable data assets. 

• Mechanisms will be created to share data stewardship best practices between nations, 

communities, organizations and individuals. 

• In light of legal, ethical and national security concerns associated with certain types of 

data, mechanisms essential to the development of both statistical and technical ways to 

protect privacy and confidentiality will be supported. 

A. A Coherent Organizational Framework - Data Collections and Managing Organizations 

To date, challenges associated with effective stewardship and preservation of scientific data 

have been more tractable when addressed through communities of practice that may derive 

support from a range of sources. For example, NSF supports the Incorporated Research 

Institutions for Seismology (IRIS) consortium to manage seismology data. Jointly with NIH and 

DOE, the agency supports the Protein Data Bank to manage data on the three-dimensional 

structures of proteins and nucleic acids. Multiple agencies support the University Consortium 

for Atmospheric Research, an organization that has provided access to atmospheric and 

oceanographic data sets, simulations, and outcomes extending back to the 1930s through the 

National Center for Atmospheric Research. 

Existing collections and managing organization models reflect differences in culture and practice 

within the science and engineering community. As community proxies, data collections and 

their managing organizations can provide a focus for the development and dissemination of 

appropriate standards for data and metadata content and format, guided by an appropriate 

community-defined governance approach. This is not a static process, as new disciplinary 
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fields and approaches, data types, organizational models and information strategies inexorably 

emerge. This is discussed in detail in the Long-Lived Digital Data Collections report of the 

National Science Board. 

Since data are held by many Federal agencies, commercial and non-profit organizations, and 

international entities, NSF will foster the establishment of interagency, public-private and 

international consortia charged with providing stewardship for digital data collections to promote 

interoperability across data collections. The agency will work with the broad community of 

science and engineering producers, managers, scientists and users to develop a common 

conceptual framework. A full range of mechanisms will be used to identify and build upon 

common ground across domain communities and managing organizations, engaging all 

stakeholders. Activities will include: the support of new projects; development and 

implementation of evaluation and assessment criteria that, amongst other things, reveal lessons 

learned across communities; support of community and inter-community workshops; and the 

development of strong partnerships with other stakeholder organizations. Stakeholders in these 

activities include data authors, data managers, data scientists and engineers, and data users 

representing a diverse range of communities and organizations, including universities and 

research libraries, government agencies, content management organizations and data centers, 

and industry. 

To identify and promote lessons learned across managing organizations, NSF will continue to 

promote the coalescence of appropriate collections with overlapping interests, approaches, and 

services. This reduces data-driven fragmentation of science and engineering domains. 

Progress is already being made in some areas. For example, NSF has been working with the 

environmental science and engineering community to promote collaboration across disciplines 

ranging from ecology and hydrology to environmental engineering. This has resulted in the 

emergence of common cyberinfrastructure elements and new interdisciplinary science and 

engineering opportunities. 

B. Developing A Flexible Technological Architecture 

From a technological perspective, the national data framework must provide for reliable 

preservation, access, analysis, interoperability, and data movement, possibly using a web or 

grid services distributed environment. The architecture must use standard open protocols and 

interfaces to enable the broadest use by multiple communities. It must facilitate user access, 

analysis and visualization of data, addressing issues such as authentication, authorization and 

other security concerns, and data acquisition, mining, integration, analysis and visualization. It 

must also support complex workflows enabling data discovery. Such an architecture can be 

visualized as a number of layers providing different capabilities to the user, including data 

management, analysis, collaboration tools, and community portals. The connections among 

these layers must be transparent to the end user, and services must be available as modular 

units responsive to individual or community needs. The system is likely to be implemented as a 

series of distributed applications and operations supported by a number of organizations and 

institutions distributed throughout the country. It must provide for the replication of data 

resources to reduce the potential for catastrophic loss of digital information through repeated 

cycles of systems migration and all other causes since, unlike printed records, the media on 

which digital data are stored and the structures of the data are relatively fragile. 

High quality metadata, which summarize data content, context, structure, inter-relationships, 

and provenance (information on history and origins), are critical to successful information 
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management, annotation, integration and analysis. Metadata take on an increasingly important 

role when addressing issues associated with the combination of data from experiments, 

observations and simulations. In these cases, product data sets require metadata that describe, 

for example, relevant collection techniques, simulation codes or pointers to archived copies of 

simulation codes, and codes used to process, aggregate or transform data. These metadata 

are essential to create new knowledge and to meet the reproducibility imperative of modern 

science. Metadata are often associated with data via markup languages, representing a 

consensus around a controlled vocabulary to describe phenomena of interest to the community, 

and allowing detailed annotations of data to be embedded within a data set. Because there is 

often little awareness of markup language development activities within science and 

engineering communities, energy is expended reinventing what could be adopted or adapted 

from elsewhere. Scientists and engineers therefore need access to tools and services that help 

ensure that metadata are automatically captured or created in real-time. 

Effective data analysis tools apply computational techniques to extract new knowledge through 

a better understanding of the data, its redundancies and relationships, by filtering extraneous 

information and by revealing previously unseen patterns. For example, the Large Hadron 

Collider at CERN generates such massive data sets that the detection of both expected events, 

such as the Higgs boson, and unexpected phenomena requires the development of new 

algorithms, both to manage data and to analyze it. Algorithms and their implementations must 

be developed for statistical sampling, for visualization, to enable the storage, movement and 

preservation of enormous quantities of data, and to address other unforeseen problems certain 

to arise. 

Scientific visualization, including not just static images but also animation and interaction, leads 

to better analysis and enhanced understanding. Currently, many visualization systems are 

domain or application-specific and require a certain commitment to understand or learn to use. 

Making visualization services more transparent to the user lowers the threshold of usability and 

accessibility, and makes it possible for a wider range of users to explore or use a data 

collection. Analysis of data streams also introduces problems in data visualization and may 

require new approaches for representing massive, heterogeneous data streams. 

Deriving knowledge from large data sets presents specific scaling problems due to the sheer 

number of items, dimensions, sources, users, and disparate user communities. The human 

ability to process visual information can augment analysis, especially when analytic results are 

presented in iterative and interactive ways. Visual analytics, the science of analytical reasoning 

enabled by interactive visual interfaces, can be used to synthesize the information content and 

derive insight from massive, dynamic, ambiguous, and even conflicting data. Suitable fully 

interactive visualizations help us absorb vast amounts of data directly, to enhance our ability to 

interpret and analyze otherwise overwhelming data. Researchers can thus detect the expected 

and discover the unexpected, uncovering hidden associations and deriving knowledge from 

information. As an added benefit, their insights are more easily and effectively communicated to 

others. 

Creating and deploying visualization services requires new frameworks for distributed 

applications. In common with other cyberinfrastructure components, visualization requires 

easy-to-use, modular, extensible applications that capitalize on the reuse of existing technology. 

Today’s successful analysis and visualization applications use a pipeline, component-based 

system on a single machine or across a small number of machines. Extending to the broader 

distributed, heterogeneous cyberinfrastructure system will require new interfaces and work in 
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fundamental graphics and visualization algorithms that can be run across remote and distributed 

settings. 

To address this range of needs for data tools and services, NSF will work with the broad 

community to identify and prioritize needs. In making investments, NSF will complement private 

sector efforts, for example, those producing sophisticated indexing and search tools and 

packaging them as data services. NSF will support projects to: conduct applied research and 

development of promising, interoperable data tools and services; perform scalability/reliability 

tests to explore tool viability; develop, harden and maintain software tools and services where 

necessary; and, harvest promising research outcomes to facilitate the transition of 

commercially-viable software into the private sector. Data tools created and distributed through 

these projects will include not only access and ease-of-use tools, but tools to assist with data 

input, tools that maintain or enforce formatting standards, and tools that make it easy to include 

or create metadata in real time. Clearinghouses and registries from which all metadata, 

ontology, and markup language standards are provided, publicized, and disseminated must be 

developed and supported, together with the tools for their implementation. Data accessibility 

and usability will also be improved with the support of means for automating cross-ontology 

translation. Collectively, these projects will be responsible for ensuring software interoperability 

with other components of the cyberinfrastructure, such as those generated to provide High 

Performance Computing capabilities and to enable the creation of Collaboratories, 

Observatories and Virtual Organizations. 

 

The user community will work with tool providers as active collaborators to determine 

requirements and to serve as early users. Scientists, educators, students and other end users 

think of ways to use data and tools that the developers didn’t consider, finding problems and 

testing recovery paths by triggering unanticipated behavior. Most importantly, an engaged set 

of users and testers will also demonstrate the scientific value of data collections. The value of 

repositories and their standards-based input and output tools arises from the way in which they 

enable discoveries. Testing and feedback are necessary to meet the challenges presented by 

current datasets that will only increase in size, often by orders of magnitude, in the future. 

Finally, in addition to promoting the use of standards, tool and service developers will also 

promote the stability of standards. Continual changes to structure, access methods, and user 

interfaces, mitigate against ease of use, and against interoperability. Instead of altering a 

standard for a current need, developers will adjust their implementation of that need to fit within 

the standard. This is especially important for resource-limited research and education 

communities. 

C. Developing and Implementing Coherent Data Policies 

In setting priorities and making funding decisions, NSF is in a powerful position to influence data 

policy and management at research institutions. NSF’s policy position on data is 

straightforward: all science and engineering data generated with NSF funding must be made 

broadly accessible and usable, while being suitably protected and preserved. Through a suite 

of coherent policies designed to recognize different data needs and requirements within 

communities, NSF will promote open access to well-managed data recognizing that this is 

essential to continued U.S. leadership in science and engineering. 
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In addition to addressing the technological challenges inherent in the creation of a national data 

framework, NSF’s data policies will be redesigned to overcome existing sociological and cultural 

barriers to data sharing and access. Two actions are critical. NSF will conduct an inventory of 

existing policies, to bring them into accord across programs and to ensure coherence. This will 

lead to the development of a suite of harmonized policy statements supporting data open 

access and usability. NSF’s actions will promote a change in culture such that the collection 

and deposition of all appropriate digital data and associated metadata become a matter of 

routine for investigators in all fields. This change will be encouraged through an NSF-wide 

requirement for data management plans in all proposals. These plans will be considered in the 

merit review process, and will be actively monitored post-award. 

Policy and management issues in data handling occur at every level, and there is an urgent 

need for rational agency, national and international strategies for sustainable access, 

organization and use. Discussions at the interagency level on issues associated with data 

policies and practices will be supported by a new interagency working group on digital data 

recently proposed by NSF under the auspices of the Committee on Science of the National 

Science and Technology Council. This group will consider not only data challenges and 

opportunities discussed throughout this chapter, but especially the issues of cross-agency 

funding and access, the provision and preservation of data to and for other agencies, and 

monitoring agreements as agency imperatives change with time. Formal policies must be 

developed to include data quality and security, ethical and legal requirements, and technical and 

semantic interoperability issues, throughout the complete process from collection and 

generation to analysis and dissemination. 

As already noted, many large science and engineering projects are international in scope, 

where national laws and international agreements directly affect data access and sharing 

practices. Differences arise over privacy and confidentiality, from cultural attitudes to ownership 

and use, in attitudes to intellectual property protection and its limits and exceptions, and 

because of national security concerns. Means by which to find common ground within the 

international community must continue to be explored. 

V. CONCLUSION 

NSF is in a unique position to influence the science and engineering communities throughout 

the country, emphasizing the needs and importance of standardized straightforward access to 

and use of digital data collections. NSF will promote data management practices and the 

development of new tools and new algorithms that encourage appropriate levels of openness 

and sharing. NSF’s merit review process will allow communities to select which of their 

collections should be long-lived, and to trade data preservation costs against new research 

costs in the way that most benefits their discipline. NSF’s strong working relations with the 

community, with other agencies, and with its international partners, will engender the 

widespread existence of collaboratively supported, community-run data collections. By adopting 

persuasive strategies during the review and funding of proposals and projects, while respecting 

the diverse and disparate communities it serves, NSF has the opportunity to stimulate a cultural 

change that will greatly accelerate the pace of discovery. Twenty-first century science and 

engineering research and education deserve no less. 

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CHAPTER 4 

STRATEGIC PLAN FOR COLLABORATORIES, 

OBSERVATORIES AND VIRTUAL ORGANIZATIONS 

(2006-2010) 

Under development. 

Collaboratories, observatories and virtual organizations – IT-enabled knowledge environments 

made possible by contemporary communication and networking technologies - allow scientists 

and engineers to pursue their research and education goals without regard to geographical 

location. In these environments, individuals will be able to access experimental and 

computational tools, interact with their colleagues, and share data, information and knowledge. 

This chapter will focus on the tools and services necessary to create these highly-interactive, 

widely-accessible environments to promote progress in science and engineering. 
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CHAPTER 5 

STRATEGIC PLAN FOR EDUCATION & WORKFORCE 

 (2006-2010) 

Under development. 

NSF recognizes that cyberinfrastructure will have a profound impact on the practice of science 

and engineering research and education, enabling individuals, groups and organizations to 

advance science and engineering in ways that revolutionize what they can do, how they do it, 

and who can participate. To harness the full power of cyberinfrastructure and the promise it 

portends for discovery, learning and innovation across and within all areas of science and 

engineering requires focused investments in the preparation of a science and engineering 

workforce with the knowledge and requisite skills needed to create, advance and exploit 

cyberinfrastructure over the long-term. This chapter will describe NSF’s approach to doing so.
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APPENDIX A: REPRESENTATIVE REPORTS AND WORKSHOPS 

Building a Cyberinfrastructure for the Biological Sciences; workshop held July 14-15, 2003; 

information available at http://research.calit2.net/cibio/archived/CIBIO_FINAL.pdf and 

http://research.calit2.net/cibio/report.htm 

CHE Cyber Chemistry Workshop; workshop held October 3-5, 2004; information available at 

http://bioeng.berkeley.edu/faculty/cyber_workshop

Commission on Cyberinfrastructure for the Humanities and Social Sciences; sponsored by the 

American Council of Learned Societies; seven public information-gathering events held in 2004; 

report in preparation; information available at http://www.acls.org/cyberinfrastructure/cyber.htm

Community Climate System Model Strategic Business Plan (2003), 28pp; information available 

at http://www.ccsm.ucar.edu/management/busplan2004-2008.pdf

Community Climate System Model Science Plan 2004-2008 (2003),” 76pp; information available 

at http://www.ccsm.ucar.edu/management/sciplan2004-2008.pdf

Computation as a Tool for Discovery in Physics; report by the Steering Committee on 

Computational Physics: information available at 

http://www.nsf.gov/pubs/2002/nsf02176/start.htm

Cyberinfrastructure for the Atmospheric Sciences in the 21st Century; workshop held June 2004; 

information available at http://netstats.ucar.edu/cyrdas/report/cyrdas_report_final.pdf

Cyberinfrastructure for Engineering Research and Education; workshop held June 5 – 6, 2003; 

information available at http://www.nsf.gov/eng/general/Workshop/cyberinfrastructure/index.jsp

Cyberinfrastructure for Environmental Research and Education (2003); workshop held October 

30 – November 1, 2002; information available at http://www.ncar.ucar.edu/cyber/cyberreport.pdf

CyberInfrastructure (CI) for the Integrated Solid Earth Sciences (ISES) (June 2003); workshop 

held on March 28-29, 2003;, June 2003; information available at 

http://tectonics.geo.ku.edu/ises-ci/reports/ISES-CI_backup.pdf

Cyberinfrastructure and the Social Sciences (2005); workshop held March 15-17, 2005; 

information available at http://www.sdsc.edu/sbe/

Cyberinfrastructure needs for environmental observatories; information available at 

http://www.orionprogram.org/office/NSFCyberWkshp.html

Cyberlearning Workshop Series; workshops held Fall 2004 – Spring 2005 by the Computing 

Research Association (CRA) and the International Society of the Learning Sciences (ISLS); 

information available at http://www.cra.org/Activities/workshops/cyberlearning

Data Management for Marine Geology and Geophysics: Tools for Archiving, 

Analysis, and Visualization (2001); information available at 

http://humm.whoi.edu/DBMWorkshop/data_mgt_report.hi.pdf
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Environmental Cyberinfrastucture Needs For Distributed Sensor Networks; workshop held 

August 12-14, 2003; information available at http://www.lternet.edu/sensor_report

Federal Plan for High-End Computing (2004); 72 pp; available at: 

http://www.ostp.gov/nstc/html/HECRTF-FINAL_051004.pdf

Geoinformatics: Building Cyberinfrastructure for the Earth Sciences (2004); workshop held May 

14 – 15, 2003; Kansas Geological Survey Report 2004-48; information available at 

http://www.geoinformatics.info

Geoscience Education and Cyberinfrastructure, Digital Library for Earth System Education, 

(2004); workshop held April 19-20, 2004; information available at 

http://www.dlese.org/documents/reports/GeoEd-CI.pdf

Getting Up to Speed: The Future of Supercomputing (2004). 308pp; available at: 

http://www.nap.edu/books/0309095026/html/ or 

http://www.sc.doe.gov/ascr/Supercomputing%20Prepub-Nov9v4.pdf ) 

High-Performance Computing Requirements for the Computational Solid Earth Sciences (2005); 

96 pp; available at: http://www.geo-prose.com/computational_SES.html. 

Identifying Major Scientific Challenges in the Mathematical and Physical Sciences and their 

CyberInfrastructure Needs, workshop held April 21, 2004; information available at 

http://www.nsf.gov/attachments/100811/public/CyberscienceFinal4.pdf

Improving the effectiveness of U.S. Climate modeling, Commission on Geosciences, 

Environment and Resources (2001). National Academy Press, Washington, D.C., 144pp; 

information available at http://www.nap.edu/books/0309072573/html/

An Information Technology Infrastructure Plan to Advance Ocean Sciences (2002). 80 pp. 

available at http://www.geo-prose.com/oiti/index.html

Materials Research Cyberscience enabled by Cyberinfrastructure; workshop held June 17 – 19, 

2004; information available at http://www.nsf.gov/mps/dmr/csci.pdf

Multi-disciplinary Workshop at the Interface of Cyber infrastructure, and Operations Research, 

with “Grand Challenges” in Enterprise-wide Applications in Design, Manufacturing and Services; 

workshop held August 31 - September 1, 2004; information available at 

https://engineering.purdue.edu/PRECISE/CI-OR/index.html

Multiscale Mathematics Initiative: A Roadmap; workshops held May 3-5, July 20-22, September 

21-23, 2004; information available at 

www.sc.doe.gov/ascr/mics/amr/Multiscale%20Math%20Workshop%203%20-

%20Report%20latest%20edition.pdf

NIH/NSF Spring 2005 Workshop on Visualization Research Challenges; workshop held on May 

2-3, 2005; information available at http://www.sci.utah.edu/vrc2005/index.html
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An Operations Cyberinfrastructure: Using Cyberinfrastructure and Operations Research to 

Improve Productivity in American Enterprises"; workshop held August 30 – 31, 2004; 

information available at http://www.optimization-online.org/OCI/OCI.doc; 

http://www.optimization-online.org/OCI/OCI.pdf 

Planning for Cyberinfrastructure Software (2005); workshop held October 5 – 6, 2004; 

information available at www.nsf.gov/cise/sci/ci_workshop/index.jsp 

Preparing for the Revolution: Information Technology and the Future of the Research University 

(2002); NRC Policy and Global Affairs, 80 pages; information available at 

http://www.nap.edu/catalog/10545.html

Polar Science and Advanced Networking: workshop held on April 24 - 26, 2003; sponsored by 

OPP/CISE; information available at http://www.polar.umcs.maine.edu

Recurring Surveys: Issues and Opportunities: workshop held March 28-29, 2003; information 

available at www.nsf.gov/sbe/ses/mms/nsf04_211a.pdf (2004) 

Research Opportunities in CyberEngineering/CyberInfrastructure; workshop held April 22 - 23, 

2004; information available at http://thor.cae.drexel.edu/~workshop/

Revolutionizing Science and Engineering Through Cyberinfrastructure: report of the National 

Science Foundation Blue-Ribbon Advisory Panel on Cyberinfrastructure; Daniel E. Atkins 

(Chair), January 2003; information available at http://www.nsf.gov/cise/sci/reports/atkins.pdf

Roadmap for the Revitalization of High-End Computing (200?); available at 

http://www.hpcc.gov/hecrtf-outreach/20040112_cra_hecrtf_report.pdf

Science-Based Case for Large-Scale Simulation; workshop held June 24-25, 2003; information 

available at http://www.pnl.gov/scales/docs/volume1_72dpi.pdf; 

http://www.pnl.gov/scales/docs/SCaLeS_v2_draft_toc.pdf 

Supplement to the President’s Budget for FY 2006; Report by the Subcommittee on Networking 

and Information Technology Research and Development (NITRD), February 2005; information 

available at http://www.nitrd.gov

Trends in IT Infrastructure in the Ocean Sciences (2004); workshop held May 21-23, 2003; 

information available at http://www.geo-prose.com/oceans_iti_trends/oceans_iti_trends_rpt.pdf 
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APPENDIX B: CHRONOLOGY OF NSF IT INVESTMENTS

NSF’s early investments in what has now become known as cyberinfrastructure date back 

almost to the agency’s inception. In the 1960’s and 1970’s, the agency supported a number of 

campus-based computing facilities. As computational methodologies became increasingly 

essential to the research endeavor, the science and engineering community began to call for 

NSF investments in specialized, higher capability computing facilities that would meet the 

computational needs of the broad national community. As a consequence, NSF’s 

Supercomputer Centers program was initiated in 1985 through the agency’s support of five 

academic-based supercomputer centers. 

During the 1980’s, academic-based networking activities also flourished. Networking 

technologies were expected to improve the effectiveness and efficiency of researchers and 

educators, providing enhanced, easier access to computer resources and more effective 

transfer and sharing of information and knowledge. After demonstrating the potential of 

CSNET in linking computer science departments, NSF moved on to develop the high-speed 

backbone, called NSFNET, with the five supercomputer centers supported under the 

Supercomputer Centers program and the National Center for Atmospheric Research becoming 

the first nodes on the backbone. NSF support also encouraged the development of regional 

networks to connect with the backbone NSFNET, thereby speeding the adoption of networking 

technologies on campuses around the country. In 1995, in partnership with MCI, NSF catalyzed 

support of the vBNS permitting advanced networking research and the development of novel 

scientific applications. A few years later, we established the NSF Middleware Initiative, focused 

on the development of advanced networking services to serve the evolving needs of the science 

and engineering community.

In the early to mid-1990’s, informed by both the Branscomb and the Hayes Reports, NSF 

consolidated its support of national computing facilities in the establishment of the Partnerships 

for Advanced Computational Infrastructure (PACI) program. Two partnerships were established 

in 1997, together involving nearly 100 partner institutions across the country in efforts to make 

more efficient use of high-end computing in all areas of science and engineering. The 

partnerships have been instrumental in fostering the maturation of cyberinfrastructure and its 

widespread adoption by the academic research and education community, and by industry. 

Also in the early 1990’s, NSF as part of the U.S. High-Performance Computing and 

Communications (HPCC) program, began to support larger-scale research and educationfocused projects pursuing what became known as "grand challenges." These HPCC projects 

joined scientists and engineers, computer scientists and state-of-the-art cyberinfrastructure 

technologies to tackle important problems in science and engineering whose solution could be 

advanced by applying cyberinfrastructure techniques and resources. First coined by the HPCC 

program, the term “grand challenge” has been widely adopted in many science and engineering 

fields to signify an overarching goal that requires a large-scale, concerted effort. 

During the 1990’s, the penetration of increasingly affordable computing and networking 

technologies on campuses was also leading to the creation of what would become missioncritical, domain-specific cyberinfrastructure. For example, in the mid 1990’s the earthquake 

engineering community began to define what would become the Network for Earthquake 

Engineering Simulation, one of many significant cyberinfrastructure projects in NSF’s portfolio 

today. 
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In 1999, the President’s Information Technology Advisory Committee (PITAC) released the 

seminal report ITR-Investing in our Future, prompting new and complementary NSF 

investments in CI projects, such the Grid Physics Network (GriPhyN) and international Virtual 

Data Grid Laboratory (iVDGL) and the Geosciences Network, known as GEON. Informed by 

the PITAC report, NSF also created an MREFC project entitled Terascale Computing Systems 

that began its construction phase in FY 2000 and ultimately created the Extensible Terascale 

Facility – now popularly known as the Teragrid. Teragrid entered its production phase in 

October 2004 and represents one of the largest, fastest, most comprehensive distributed 

cyberinfrastructures for science and engineering research and education. 

In 2001, NSF charged an Advisory Committee for Cyberinfrastructure under the leadership of 

Dr. Dan Atkins, to evaluate the effectiveness of PACI and to make recommendations for future 

NSF investments in cyberinfrastructure. The Atkins Committee, as it became popularly known, 

recommended support for the two Partnership lead sites through the end of their original PACI 

cooperative agreements. In October 2004, following merit review, the National Science Board 

(NSB) endorsed funding of those sites through the end of FY 2007. 

Through 2005, in addition to the groups already cited, a number of prestigious groups have 

made recommendations that continue to inform the agency’s cyberinfrastructure planning 

including the High-End Computing Revitalization Task Force, the PITAC Subcommittee on 

Computational Science, and the NRC Committee on the Future of Supercomputing. 
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APPENDIX C: MANAGEMENT OF CYBERINFRASTRUCTURE 

NSF has nurtured the growth of what is now called cyberinfrastructure for a number of decades. 

In recent years, the Directorate for Computer and Information Science and Engineering (CISE) 

has been responsible for the provision of national supercomputing infrastructure for the 

academic community. In addition, the Directorate was instrumental in the creation of what 

ultimately became known as the Internet. During this incubation period, the management of CI 

was best provided by those also responsible for the research and development of related CI 

technologies. 

Over the years, the penetration and impact of computing and networking on campuses has 

been extensive, and has led to the creation of many disciplinary-specific or community-specific 

CI projects and activities. Today, CI projects are supported by all NSF Directorates and Offices. 

Because of the growing scope of investment and variability in needs among users in the broad 

science and engineering community, it has become clear that effective CI development and 

deployment now requires the collective leadership of NSF senior management. This leadership 

will be provided by a Cyberinfrastructure Council chaired by the NSF Director and comprised of 

the NSF Deputy Director, the Assistant Directors of NSF’s Directorates (BIO, CISE, GEO, EHR, 

ENG, MPS, and SBE) and the Heads of the Office of International Science and Engineering, 

Office of Polar Programs, and the recently established Office of Cyberinfrastructure (OCI). The 

Cyberinfrastructure Council has been meeting regularly since May 2005, and OCI was 

established in the Office of the Director on July 22, 2005. 

CISE will continue to be responsible for a broad range of programs that address the 

Administration’s priorities for fundamental research and education in computing, representing 

more than 85% of the overall federal investment in university-based basic research.