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Cyber-enabled Discovery and Innovation (CDI). Enhancing American competitiveness by enabling discovery and innovation through the use of computational thinking. CDI is Unique Within NSF. Five-year initiative All directorates, programmatic offices involved

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Cyber enabled discovery and innovation cdi

Cyber-enabledDiscovery and Innovation (CDI)

Enhancing American competitiveness by enabling

discovery and innovation through the use of computational thinking

Cdi is unique within nsf
CDI is Unique Within NSF

  • Five-year initiative

  • All directorates, programmatic offices involved

  • To create bold,revolutionary, radical, paradigm-changing, transformative science and engineering research outcomes

  • Multidisciplinary activities that significantly advance more than one field of science and engineering

  • Through innovations in, or innovative use of, computational thinking (concepts, methods, models, algorithms, tools)

The funding potential for cdi could be significant
The Funding Potential for CDI Could be Significant

  • All NSF directorates are participating in this activity (subject to budget approval)

Cdi philosophy
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
NSF Review Criteria

  • Intellectual Merit

  • Broader Impacts

  • New language on Transformative Research: to what extent does the proposed activity suggest and explore creative, original, orpotentially transformative concepts?

Cdi review criteria
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.

Cdi review criteria1
CDI Review Criteria

  • 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.

  • Three cdi themes
    Three CDI Themes

    • 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;

    • Virtual Organizations:enhancing discovery and innovation by bringing people and resources together across institutional, geographical, and cultural boundaries. 

    From data to knowledge
    From Data to Knowledge

    Improving our ability to gather, organize, analyze, model, and visualize large, multi-scale, heterogeneous data

    • Data aggregation and annotation

    • Modeling and algorithm development

    • Statistical analysis and stochastic simulation

    • Approaches to visualization and pattern recognition informed by knowledge of human cognition and perception

    • Data confidentiality, privacy, regulatory issues

    Understanding complexity in natural built and social systems
    Understanding Complexity in Natural, Built, and Social Systems

    Identifying general principles and laws that characterize complexity and capture the essence of complex systems is one of the major challenges of 21st century science.

    Attaining the breakthroughs to overcome these challenges requires transformative ideas in the following areas:

    • Simulation and computational experiments

    • Mathematical and statistical modeling and analysis, including agent-based modeling and neural networks

    • Nonlinear couplings across multiple scales

    Virtual organizations vos
    Virtual Organizations (VOs) Systems

    Creating systematic knowledge about the interwined social and technical issues of effective VOs.

    Advances in VOs bring together domain needs with computational thinking, including algorithm development, systems operations, organizational studies, social computing, and interactive design.

    CDI encourages:

    • Multidisciplinary and potentially international research and education teams advancing the design, development, and assessment of VOs

    • Exploring VOs as a primary vehicle for broadening participation in not just research but also education, with the potential to reach students at all levels and the public at large.

    Broadening participation
    Broadening Participation Systems

    • Diversity of sciences and engineering, academic departments

    • Junior researchers, students, and underrepresented minorities (especially in STEM)

    • Intellectual partnerships involving investigators from academe, industry, and/or other types of organizations, including international entities

      (NB: Generally speaking, for-profit entities and international partners must support their participation in CDI from other funding sources)

    Types of projects
    Types of Projects Systems

    • 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
    Type I Projects Systems

    • 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

    • For example, 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

    Type ii projects
    Type II projects Systems

    • Several intellectual leaders, larger 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

    • For example, multiple major aims that tackle complementary facets of complex solutions for advancing multiple fields of science and engineering through computational thinking.

    Type iii projects
    Type III Projects Systems

    • 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 will be supported in future years, subject to the availability of funds

    An sbe hypothetical example
    An SBE Hypothetical Example Systems

    • 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.

    Key dates
    Key Dates: Systems

    • Letters of Intent (required) due:

      Nov 30, 2007

    • Preliminary Proposals due:

      Jan 8, 2008

    • Full proposals due:

      April 29, 2008

      • Full proposals by invitation only!

    • Awards: no later than October 2008

    More information on cdi
    More Information on CDI Systems

    • CDI Solicitation:


    • CDI Overview, References, FAQ, Calendar of Events:


    • Contact members of CDIIT

      • Contact the CDIIT Co-chairs: Sirin Tekinay (CISE), Tom Russell (MPS), Eduardo Misawa (ENG)

      • SBE Reps: Terry Langendoen and Cheryl Eavey

    • [email protected] ; (703)292-8080