# Presentation: 026017

## Cyber-enabledDiscovery and Innovation (CDI)‏

***Objective:***
*Enhance American competitiveness by enabling innovation through the use of computational thinking*

## Cyber-Enabled Discovery and Innovation

- Multi-disciplinary research seeking contributions to more than one area of science or engineering, by innovation in, or innovative use of computational thinking
- Computational thinking refers to computational...
- ...Concepts
- ...Methods
- ...Models
- ...Algorithms
- ...Tools

## CDI is Unique within NSF

- five-year initiative
- all directorates, programmatic offices involved
- to create *revolutionary* science and engineering research outcomes
- made possible by innovations and advances in computational thinking
- emphasis on bold, multidisciplinary activities
- radical, paradigm-changing science and engineering outcomes through computational thinking

## CDI Philosophy

- “Business as usual” need not apply
***“******Projects that make straightforward use of existing computational concepts, methods, models, algorithms and tools to significantly advance only one discipline should be submitted to an appropriate program in that field instead of to CDI.”****  *
- No place for incremental research
- Untraditional approaches and collaborations welcome

## NSF Review Criteria

- Intellectual Merit
- Broader Impacts
- New on Transformative Research: to what extent does the proposed activity suggest and explore creative, original, or potentially transformative concepts?

## Additional CDI Review Criteria

- The proposal should define a bold multidisciplinary research agenda that, through computational thinking, promises paradigm-shifting outcomes in more than one field of science and engineering.
- The proposal should provide a clear and compelling rationale that describes how innovations in, and/or innovative use of, computational thinking will lead to the desired project outcomes.
- The proposal should draw on productive intellectual partnerships that capitalize upon knowledge and expertise synergies in multiple fields or sub-fields in science or engineering and/or in multiple types of organizations.
- potential for extraordinary outcomes, such as,
- revolutionizing entire disciplines,
- creating entirely new fields, or
- disrupting accepted theories and perspectives
- ... as a result of taking a fresh, multi-disciplinary approach.
- Special emphasis will be placed on proposals that promise to enhance competitiveness, innovation, or safety and security in the United States.

## Slide 7

## Slide 8

## Long-term Funding for Cyber-enabled Discovery and Innovation 

- All NSF directorates are participating in this activity (*subject to budget approval*); estimated $750M investment in 5 years:

## Three CDI Themes

- CDI seeks transformative research in the following general themes, via innovations in, and/or innovative use of, computational thinking:
**From Data to Knowledge:** *enhancing human cognition and generating new knowledge from a wealth of heterogeneous digital data;*
**Understanding Complexity in Natural, Built, and Social Systems:** *deriving fundamental* *insights on* *systems comprising multiple interacting elements;*  and
**Building Virtual Organizations:** *enhancing discovery and innovation by* *bringing people and resources together across institutional, geographical and cultural boundaries*.

## From Data to Knowledge

- Knowledge extraction, noise, statistics
- Modeling, data assimilation, inverse problems
- Validation; model/cyber/domain feedbacks
- Algorithms for analysis of large data sets, dimension reduction
- Visualization, pattern recognition

## Understanding Complexity in Natural, Built, and Social Systems

- Identifying general principles and laws that characterize complexity and capture the essence of complex systems
- Attaining the breakthroughs, to overcome these challenges, requires transformative ideas in the following areas:
- Simulation and Computational Experiments
- Methods, Algorithms, and Tools
- Nonlinear couplings across multiple scales

## Virtual Organizations (VOs)‏

- Design, development, and assessment of VOs
- Bringing domain needs together with algorithm development, systems operations, organizational studies, social computing, and interactive design
- Flexible boundaries, memberships, and lifecycles, tailored to particular research problems, users and learner needs or tasks of any community, providing opportunities for:
- Remote access
- Collaboration
- Education and training

## Types of Projects

- CDI defines research modalities
- Project size not measured by $$
- Projects classified by magnitude of effort
- Three types are defined: Types I, II, and III
- Type III, center-scale efforts, will not be supported in the first year of CDI

## Type I Projects

- focused aims that tackle discrete, high-risk problems that, once resolved, may enable transformative breakthroughs in multiple fields of science or engineering through computational thinking
- research and education efforts roughly comparable to that of up to two investigators with summer support, two graduate students, and their research needs (e.g., materials, supplies, travel), for a duration of three years

## Type II projects

- multiple major aims that tackle complementary facets of complex solutions for advancing multiple fields of science and engineering through computational thinking.
- several intellectual leaders, multidisciplinary teams
- significant education component
- likely to be distributed collaborative projects with more extensive project coordination needs
- greater effort than in Type I, and, for example, roughly comparable to that of up to three investigators with summer support, three graduate students, one or two other senior personnel (post-doctoral researchers, staff), and their research needs (e.g., materials, supplies, travel), for a duration of four years

## Type III Projects

- collaborative research, potentially distributed across several institutions
- may involve center-type activities, demanding substantial coordination efforts
- greater effort than in Type II in terms of scope and in the order of magnitude of expected outcomes
- Type III projects will not be supported in FY08, but in the future years, subject to the availability of funds

## Broadening Participation

- diversity of sciences and engineering, academic departments
- underrepresented minorities in STEM
- collaborations with industry in order to match
- scientific insights with
- technical insights

## International Collaborations

**International Collaborations**

- involve true intellectual partnership in which successful outcomes depend on the unique contributions of all partners, U.S. and foreign
- engage junior researchers and students in the collaboration, taking advantage of cyber environments to prepare a globally-engaged workforce
- in conducting research in all of the major components of the CDI
- create more systematic knowledge about the intertwined social and technical issues of effective VOs, changing both the practice and the outcomes of science and engineering research and education.

- NSF awards are, in principle, limited to support of the U.S. side of an
- international collaboration. In almost all cases, international partners
- should obtain their own funding for participation.

## Examples

- Cyber-enabled discovery and innovation in any field of science or engineering is appropriate for the CDI program.
- Examples illustrate desired outcomes of potential successful CDI projects.
- Note:  included for purposes of illustration only; it is neither exhaustive, nor indicative of preference regarding research areas.
- The listed examples represent contributions in one or more CDI themes, via multidisciplinary approaches that hinge on innovations in, or innovative use of, computational concepts, methods, models, algorithms, and tools.
- http://www.nsf.gov/crssprgm/cdi/

## Key Dates:

- Letters of Intent (required) due: 	**Nov 30, 07**** **
- Preliminary Proposals due:
- 		**Jan 8, 08**
- Full proposals due:
- 	 **April 29, 08**
- Full proposals by invitation only!
- Awards: no later than October 2008

## More Information on CDI:

- Contact members of CDIIT.
- Contact the CDI Co-chairs Sirin Tekinay (CISE), Tom Russell (MPS), Eduardo Misawa(ENG) or members of the team listed in the solicitation
- _[cdi@nsf.gov](mailto:cdi@nsf.gov)_ ; (703) 292-8080
- http://www.nsf.gov/crssprgm/cdi/

## Questions?   Comments?

## Example:

- Systems with many interacting parts on multiple scales require major advances in modeling to limit the interactions to the essential ones, in algorithms to solve the models efficiently and accurately, in software implementation of the algorithms, and in analysis of the massive generated data if the challenges of length and time scales are to be overcome.
- Examples. Researchers have visualized the changing atomic structure of a simple, plant-infecting virus and revealed key physical properties by calculating how each of the virus' one million atoms interacted with each other every femtosecond.  A few additional examples of other domains with analogous challenges: in chemistry, in analyses of molecular structure and the dynamics of excited states; in astronomy, in modeling of galactic interactions; and condensed-matter physics and materials engineering, in custom design and synthesis of tailor-made molecules for new materials such as fibers, coatings, ceramics, and electronic materials.
- Simulation results may in turn suggest that the theory underlying a model requires revision; the Einstein equations of general relativity may be an example.  The challenges are compounded by stochastic behavior, which may arise because of the fundamental nature of a system at some scale (for example, a quantum mechanical model), data uncertainties or noise, or incorporation of effective small-scale phenomena into large-scale models.
- Themes: Complexity, Data to Knowledge.
- Domains: all fields of science and engineering.

**Notes:**
For the first time, researchers have visualized the changing atomic structure of a simple, plant-infecting virus and revealed key physical properties by calculating how each of the virus' one million atoms interacted with each other every femtosecond (10-15 s).  Systems with many interacting parts on multiple scales, such as this, require major advances in modeling to limit the interactions to the essential ones, in algorithms to solve the models efficiently and accurately, in software implementation of the algorithms, and in analysis of the massive generated data if the challenges of length and time scales are to be overcome.  Chemistry, in analyses of molecular structure and the dynamics of excited states; astronomy, in modeling of galactic interactions; and condensed-matter physics and materials engineering, in custom design and synthesis of tailor-made molecules for new materials such as fibers, coatings, ceramics, and electronic materials are just a few examples of other domains with analogous challenges.  Simulation results may in turn suggest that the theory underlying a model requires revision; the Einstein equations of general relativity may be an example.  The challenges are compounded by stochastic behavior, which may arise because of the fundamental nature of a system at some scale (for example, a quantum mechanical model), data uncertainties or noise, or incorporation of effective small-scale phenomena into large-scale models.  

Themes: Complexity, Data to Knowledge.  

Domains: all fields of science and engineering.

## Example

- The semiconductors and magnetic memory materials from which today’s computers are built, and the nanoscale physical processes used to fabricate them, are the results of fundamental research in the physical sciences.  This research has fueled Moore’s law.  Moreover, new materials, like photonic bandgap materials and higher density memories, hold great promise.  Even more striking are the completely new approaches to computer design on the horizon, for example, quantum computing, molecular computing, and spintronics.  These offer the possibility of revolutionary advances, and constitute the best hope for continuing, or accelerating, Moore’s law of hardware into the future.  Novel large-scale simulations based on advances in computational models, methods, and algorithms play a key role in implementing these approaches, through fundamental understanding of the nanoscale and of the emergence of macroscopic properties.  This creates a virtuous circle: cyber-enabled discovery that, in turn, enables cyber.
- Themes: Complexity, Data to Knowledge.
-  Domains: physical sciences, engineering, computer science, mathematical sciences.

**Notes:**
The semiconductors and magnetic memory materials from which today’s computers are built, and the nanoscale physical processes used to fabricate them, are the results of fundamental research in the physical sciences.  This research has fueled Moore’s law.  Moreover, new materials, like photonic bandgap materials and higher density memories, hold great promise.  Even more striking are the completely new approaches to computer design on the horizon, for example, quantum computing, molecular computing, and spintronics.  These offer the possibility of revolutionary advances, and constitute the best hope for continuing, or accelerating, Moore’s law of hardware into the future.  Novel large-scale simulations based on advances in computational models, methods, and algorithms play a key role in implementing these approaches, through fundamental understanding of the nanoscale and of the emergence of macroscopic properties.  This creates a virtuous circle: cyber-enabled discovery that, in turn, enables cyber.  

Themes: Complexity, Data to Knowledge. 

 Domains: physical sciences, engineering, computer science, mathematical sciences.

## Example

- Physical, electrical and cyber infrastructures, such as drinking water and wastewater treatment facilities; electrical energy generation, transmission, and distribution systems; chemical production and distribution systems; communications networks; transportation systems; agriculture and food production; and public health networks, are critical to the nation's welfare, security, and ability to compete in a global economy.  Research to date has separately considered the issues of resiliency, sustainability, and interdependence.  Complexity issues include cyber-enabled methodologies to analyze and forecast how infrastructures grow, self-organize, interact, renew, and operate as interdependent resilient and sustainable systems.  Interdisciplinary, geographically diverse, virtually connected, nonlinear dynamic networks that predict and control changes across multiple infrastructures, length and time scales, with fidelity and the ability to handle huge volumes of data could involve a large number of disciplines and organizations.
- CDI themes: Data to Knowledge, Complexity, Virtual Organizations.
- Domains: engineering, computer science, social sciences, physical sciences, biological sciences.

**Notes:**
Physical, electrical and cyber infrastructures, such as drinking water and wastewater treatment facilities; electrical energy generation, transmission, and distribution systems; chemical production and distribution systems; communications networks; transportation systems; agriculture and food production; and public health networks, are critical to the nation's welfare, security, and ability to compete in a global economy.  Research to date has separately considered the issues of resiliency, sustainability, and interdependence.  Complexity issues include cyber-enabled methodologies to analyze and forecast how infrastructures grow, self-organize, interact, renew, and operate as interdependent resilient and sustainable systems.  Interdisciplinary, geographically diverse, virtually connected, nonlinear dynamic networks that predict and control changes across multiple infrastructures, length and time scales, with fidelity and the ability to handle huge volumes of data could involve a large number of disciplines and organizations.  

CDI themes: Data to Knowledge, Complexity, Virtual Organizations.  

Domains: engineering, computer science, social sciences, physical sciences, biological sciences.

## Example

- Manufacturing in the U.S. is affected by globalization, environmental and safety restrictions, and competition from an improved foreign scientific workforce.  Some recent developments of interest to researchers in engineering include just-in-time production, assembly and/or delivery, shortened product life cycles, and demand for zero-tolerance operational incidents.  Simultaneously, analogies from the life sciences are motivating the design of self-assembling and self-repairing materials.  This could lead to the design and manufacture of new materials and devices, such as artificial skin and self-optimizing fuel cells.  Interaction between researchers in these and other potentially relevant fields would benefit from novel mathematical and computational thinking, from complexity analysis, and from geographically disparate virtual organizations.  Combining research in multi-scale dynamic modeling and simulation for synthesis, design, prediction and control; large scale optimization; product allocation; data interoperability; sensor networks; organic and inorganic chemistry; materials synthesis; and device fabrication are relevant CDI topics.  Research and education projects in some of these areas are ideally suited for industry/academic collaborations, which might be, but are not limited to, GOALI projects (_[http://www.nsf.gov/pubs/2007/nsf07522/nsf07522.htm](http://www.nsf.gov/pubs/2007/nsf07522/nsf07522.htm)_).
- CDI themes: Complexity, Virtual Organizations.
- Domains: engineering, materials science, mathematics, chemistry, biological sciences, computer science, education.

## Example

- Living systems function through the encoding, exchange, and processing of information. The discovery of genetic code and the ability to capture it in digital form has transformed biology by catalyzing the creation of databases and applications for understanding the meaning of genetic code, to compare it and to predict its function.  New research seeking similar understanding of the communication flowing at other systemic levels such as chemical pathways, cell signaling, mate selection, or ecosystem services feedback poses a challenge to information science to develop more advanced cyber tools for digitally representing and manipulating the increasingly complex data structures found in natural and social systems.
- Themes: Complexity, Data to Knowledge.
- Domains: information science, biological sciences, social sciences, physical sciences, mathematical sciences.

**Notes:**
Living systems function through the encoding, exchange, and processing of information. The discovery of genetic code and the ability to capture it in digital form has transformed biology by catalyzing the creation of databases and applications for understanding the meaning of genetic code, to compare it and to predict its function.  New research seeking similar understanding of the communication flowing at other systemic levels such as chemical pathways, cell signaling, mate selection, or ecosystem services feedback poses a challenge to information science to develop more advanced cyber tools for digitally representing and manipulating the increasingly complex data structures found in natural and social systems.  

Themes: Complexity, Data to Knowledge.  

Domains: information science, biological sciences, social sciences, physical sciences, mathematical sciences.

## Example

- Theoretical foundations offering tools for understanding, modeling, and analysis of large-scale, complex, heterogeneous networks of signaling, signal processing, computing, decision making, communicating, sensing, controlling nodes with multi-scale interactions need to be developed.  Network science, drawing from economic theory, multi-scale analysis, and network information theory, is currently in its infancy.  The Internet, with its billions of interfaces, and mobile, wireless devices, spanning from personal area networks to satellite communications, cuts across man-made, social, and natural systems.  Specifically, communication networks, wired and wireless, span the globe and have become an indispensable tool for modern society, including science and engineering.  New models and analysis tools are needed to understand spatial and temporal behavior of interactions in the electromagnetic medium, or in the routing and resource allocation level.  Another area is biological networks, whose understanding remains rudimentary.  New, realistic models of signal transduction pathways, incorporating interactions with other pathways and behavior under prolonged stimulus or lack thereof, are needed.  Other topics involving complex coupled networks include communication systems, the human brain, and social networks.  All of these cases call for better understanding of network structure, function, and evolution.
- This example spans all three CDI themes: massive sets of network data should produce knowledge of patterns across many temporal and spatial scales; networks, man-made, social, or natural, embodiments of complex systems of interaction; finally, VOs themselves consist of networks at different scales of interaction and, in turn, study networks.
- Domains: computer science, engineering, biological sciences, social sciences, physical sciences, mathematical sciences.

**Notes:**
Theoretical foundations offering tools for understanding, modeling, and analysis of large-scale, complex, heterogeneous networks of signaling, signal processing, computing, decision making, communicating, sensing, controlling nodes with multi-scale interactions need to be developed.  Network science, drawing from economic theory, multi-scale analysis, and network information theory, is currently in its infancy.  The Internet, with its billions of interfaces, and mobile, wireless devices, spanning from personal area networks to satellite communications, cuts across man-made, social, and natural systems.  Specifically, communication networks, wired and wireless, span the globe and have become an indispensable tool for modern society, including science and engineering.  New models and analysis tools are needed to understand spatial and temporal behavior of interactions in the electromagnetic medium, or in the routing and resource allocation level.  Another area is biological networks, whose understanding remains rudimentary.  New, realistic models of signal transduction pathways, incorporating interactions with other pathways and behavior under prolonged stimulus or lack thereof, are needed.  Other topics involving complex coupled networks include communication systems, the human brain, and social networks.  All of these cases call for better understanding of network structure, function, and evolution.  

This example spans all three CDI themes: massive sets of network data should produce knowledge of patterns across many temporal and spatial scales; networks, man-made, social, or natural, embodiments of complex systems of interaction; finally, VOs themselves consist of networks at different scales of interaction and, in turn, study networks.  

Domains: computer science, engineering, biological sciences, social sciences, physical sciences, mathematical sciences.

## Example

- Develop techniques to forecast critical events in geophysics and predict their impact on society.  Central is the ability to adaptively configure the resolution of numerical models and real-time observing networks; to zoom in and follow important dynamic features (ocean eddies, earthquakes, volcanic eruptions, landslides, storms, flash floods, hurricanes, algal blooms, etc.); and to predict their impact on human society, infrastructure, and ecosystem services.  Capabilities such as the tracking of hurricanes necessarily involve uncertainty, due to the intrinsic nature of the dynamics, limited understanding of features such as the coupling between the ocean and the atmosphere, and constraints on resolution of practical computations; quantifying and managing the uncertainty is of critical importance.  Themes: Complexity, Data to Knowledge.  Domains: geosciences, ecology, mathematical sciences, social sciences, engineering.Model, simulate, analyze, and validate complex systems with large data sets.  Extraction of significant features and patterns from high-dimensional data, which can be noisy, is crucial in a great variety of settings.  Examples include the Earth system (geosciences), gravitational waves (physics), galaxy formation (astronomy), highly complex dynamical systems simulation, health monitoring, prediction, design and control (engineering), communication and network control and optimization (information technology), human and social behavior simulation (social sciences), disaster response simulation and anti-terrorism preparation (homeland defense), design of smart systems for mitigation of exogenous threats using autonomic response (homeland security), predictive understanding of ecological and evolutionary processes at multiple scales (biological sciences), software development (information technology), and risk analysis.  A key issue for some systems is understanding whether they will enter a fundamentally different mode of behavior when an input crosses a tipping point; examples include the Earth’s climate (due to atmospheric carbon dioxide) and the U.S. economy (due to the federal funds interest rate).
- Themes: Data to Knowledge, Complexity.
- Domains: all fields of science and engineering.

**Notes:**
Develop techniques to forecast critical events in geophysics and predict their impact on society.  Central is the ability to adaptively configure the resolution of numerical models and real-time observing networks; to zoom in and follow important dynamic features (ocean eddies, earthquakes, volcanic eruptions, landslides, storms, flash floods, hurricanes, algal blooms, etc.); and to predict their impact on human society, infrastructure, and ecosystem services.  Capabilities such as the tracking of hurricanes necessarily involve uncertainty, due to the intrinsic nature of the dynamics, limited understanding of features such as the coupling between the ocean and the atmosphere, and constraints on resolution of practical computations; quantifying and managing the uncertainty is of critical importance.  Themes: Complexity, Data to Knowledge.  Domains: geosciences, ecology, mathematical sciences, social sciences, engineering.Model, simulate, analyze, and validate complex systems with large data sets.  Extraction of significant features and patterns from high-dimensional data, which can be noisy, is crucial in a great variety of settings.  Examples include the Earth system (geosciences), gravitational waves (physics), galaxy formation (astronomy), highly complex dynamical systems simulation, health monitoring, prediction, design and control (engineering), communication and network control and optimization (information technology), human and social behavior simulation (social sciences), disaster response simulation and anti-terrorism preparation (homeland defense), design of smart systems for mitigation of exogenous threats using autonomic response (homeland security), predictive understanding of ecological and evolutionary processes at multiple scales (biological sciences), software development (information technology), and risk analysis.  A key issue for some systems is understanding whether they will enter a fundamentally different mode of behavior when an input crosses a tipping point; examples include the Earth’s climate (due to atmospheric carbon dioxide) and the U.S. economy (due to the federal funds interest rate).  

Themes: Data to Knowledge, Complexity.  

Domains: all fields of science and engineering.

## Example

- As hypotheses in the social, behavioral, and economic sciences have become more sophisticated, so have basic data needs.  Merging biomedical data with survey and administrative data is a relatively untested area, but it is becoming more crucial for understanding hypotheses emerging from behavioral economics and other fields.  Understanding human/environmental interactions requires the merging of data across multiple scales, such as remote sensing data, surveys of households, and ecological data.  The creation and use of these sophisticated data sets raises many issues.  For example, more and more of our data are geocoded.  This raises serious questions regarding data confidentiality.  How do researchers maintain the usability of data while protecting confidentiality when the identifying variables also are variables in the analysis?  Research in this area lends itself to potential advances in the social, behavioral, and economic sciences, computer science, and the mathematical sciences.
- CDI Theme: From Data to Knowledge.

**Notes:**
As hypotheses in the social, behavioral, and economic sciences have become more sophisticated, so have basic data needs.  Merging biomedical data with survey and administrative data is a relatively untested area, but it is becoming more crucial for understanding hypotheses emerging from behavioral economics and other fields.  Understanding human/environmental interactions requires the merging of data across multiple scales, such as remote sensing data, surveys of households, and ecological data.  The creation and use of these sophisticated data sets raises many issues.  For example, more and more of our data are geocoded.  This raises serious questions regarding data confidentiality.  How do researchers maintain the usability of data while protecting confidentiality when the identifying variables also are variables in the analysis?  Research in this area lends itself to potential advances in the social, behavioral, and economic sciences, computer science, and the mathematical sciences. 

CDI Theme: From Data to Knowledge.

## Example

- The introduction of cyberinfrastructure into formal and informal learning environments is already beginning to provide learners at all levels (K to grey) with the skills and literacies needed to operate effectively in those environments.  In order to take full advantage of the opportunity to learn in these environments, their design must be based on our best understanding of human cognitive and interactive styles and capacities.  That understanding, in turn, can be sharpened considerably by the data now becoming available from observations of students and teachers interacting with each other and with the cyber-environment.
- CDI themes: Data to Knowledge, Virtual Organizations.
- Domains: human-computer interaction, cognitive science, developmental and learning sciences.

**Notes:**
The introduction of cyberinfrastructure into formal and informal learning environments is already beginning to provide learners at all levels (K to grey) with the skills and literacies needed to operate effectively in those environments.  In order to take full advantage of the opportunity to learn in these environments, their design must be based on our best understanding of human cognitive and interactive styles and capacities.  That understanding, in turn, can be sharpened considerably by the data now becoming available from observations of students and teachers interacting with each other and with the cyber-environment.  

CDI themes: Data to Knowledge, Virtual Organizations.  

Domains: human-computer interaction, cognitive science, developmental and learning sciences.

## Example

- High school teachers and students can explore science through a virtual laboratory that gives them access to sophisticated modeling and simulation systems.  Impacts of global phenomena such as climate warming, and local ones such as earthquakes in susceptible communities, can be investigated.  They can also participate in simultaneous virtual experiments with classes at remote locations, underscoring how actions in one region impact another.  This innovative approach to science education depends on breakthroughs in secure virtual organizations for collaboration and shared control, models and simulations of natural and built complex systems that are accessible in real time and can be used and understood by students, and interdisciplinary approaches to complexity that help the public understand the relevance of science to daily life.
- Themes: Virtual Organizations, Complexity.
- Domains: education.

**Notes:**
High school teachers and students can explore science through a virtual laboratory that gives them access to sophisticated modeling and simulation systems.  Impacts of global phenomena such as climate warming, and local ones such as earthquakes in susceptible communities, can be investigated.  They can also participate in simultaneous virtual experiments with classes at remote locations, underscoring how actions in one region impact another.  This innovative approach to science education depends on breakthroughs in secure virtual organizations for collaboration and shared control, models and simulations of natural and built complex systems that are accessible in real time and can be used and understood by students, and interdisciplinary approaches to complexity that help the public understand the relevance of science to daily life.  

Themes: Virtual Organizations, Complexity.  

Domains: education.