Presentation at WebEx Meeting June 15,

 Context  Challenge  Anticipated Outcomes  Framework  Timeline & Guidance  Comment and Questions 2

 Integrative and multi-scale  Not bound by organizational limits  Multi-disciplinary collaboration  Data Forests  Heterogeneous  Distributed, diverse  Central repositories  Data needs to be accessible and interoperable across funding agency and national boundaries 3

 Diverse sources and sizes.  Simulations, experiments, observations  From a variety of disciplines  Connected, distributed, or centralized.  Across a range of length and time scales, resolutions, and accuracies. 4

Multi-scale approaches 5

6  A modeling paradigm: solving complex nanostructures.  No single data set contains sufficient information by itself to constrain a unique solution. Experimental tools Computational tools Digital Data Digital Data

7 Leveraging internet models, to optimize the global “Physical Internet” for distribution of goods worldwide to minimize time, energy consumption and cost. Efficient real-time access, processing, interpretation and management of global sensory and event data is a fundamental element of this work at the (CELDi) Center for Excellence in Logistics and Distribution Universities: Arkansas, Clemson, Berkeley, Oklahoma, Missouri, Arizona State, Oklahoma State, Virginia Tech Members: Over 25 members from industry and government

 A charrette is a collaborative session in which a group of designers drafts a solution to a problem.  Often refers to activities that are focused on producing actionable plans for future funding.  The DataWay charrette is the beginning of a process.  To engage the community in developing strategies that identify and support the emergence of broadly useful ideas for a data infrastructure that facilitates and promotes efficient data utilization and management across the research communities. 8

 An engaged community, increasingly contributing to an interactive website.  A growing repository of white papers defining the elements of key issues.  Iterative discovery process leading to consensus on the best approaches.  Integrated and sustained connections among elements of the discovery process. 9

 Comprehensive and integrated CI  To transform research, innovation and education  Focus on computational and data-intensive science to address complex problems  Four major components  Data-enabled science  New computational infrastructure  Community research networks  Access and connections to CI facilities

 DataNet  Long-term preservation and access of data  Data Infrastructure Building Blocks (DIBBs)  Software Infrastructure for Sustained Innovation (SI 2 )  Cyber-Enabled Discovery and Innovation (CDI)  Data enabled science and engineering  Core Techniques and Technologies for Advancing Big Data Science & Engineering (BIGDATA)

12 Build strategies that support the development of infrastructure that will:

13 An infrastructure framework that supports dataAn infrastructure framework that supports data  Collection  Curation  Analysis  Visualization  Integration  Searching for data sets from experiments and simulations from many sources and at many scales. Salient issues includeSalient issues include  Validation  Annotation  Interoperability  Ontology

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 Support the development of integrated and interactive services that transcend fields and accelerate discovery in complex, multi-scale problems.  Create interoperable digital-access infrastructures, providing open, extensible and sustainable networks.  Foster collaborations and the sharing of observations, simulations, and other relevant scientific information.  Facilitate data transfer between individual researchers and data systems & applications.  Integrate research and education. 15 A common architecture is needed to produce an effective, interworking, sustainable system to: A common architecture is needed to produce an effective, interworking, sustainable system to:

Jun 2012Jul-Sept 2012Oct 2012Nov/Dec 2012Nov/11-Apr/13May 2013 DCL Released Two WebEx events Charrette Short-term enabling awards Proposed framework approaches developed 16

17 The charrette planning process is open to all.  We welcome a wide range of ideas and strategies  Participant selection will consider the collective:  Expertise in relevant cyberinfrastructure, data management, and software fields  Representation of a broad range of scientific domains

 NSF will work with the community to prepare for the charrette.  An emphasis on engaging and connecting research: both in communities where data-enabled science is already a focus, as well as other communities on the cusp of data science.  We seek participation from other government agencies (e.g. DOE, NIH, NASA) and the scientific communities they support.  We seek participation from other countries, particularly in Europe and Asia, and projects they support, especially those that need to share data across countries.  Expected outcomes from the charrette include multiple enabling awards to design framework(s) and build community involvement. 18

 A DataWay website will provide information on the charrette.  Guidance for the community  Preparation instructions  FAQs  The charrette will be in October.  A final date will be announced on the website and in a Dear Colleague Letter.  A second webinar will be held in August.  Questions/comments to 19

Where discoveries begin 20

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