EarthCube Monthly Community Webinar- Nov. 22, 2013


Published on

This webinar features project overviews of all EarthCube Awards (Building Blocks, Research Coordination Networks, Conceptual Designs, and Test Governance), followed by a call for involvement, and a Q&A session.

EarthCube Awards – Project Overviews
1.. EarthCube Web Services (Building Block)
2. EC3: Earth-Centered Community for Cyberinfrastructure (RCN)
3. GeoSoft (Building Block)
4. Specifying and Implementing ODSIP (Building Block)
5. A Broker Framework for Next Generation Geoscience (BCube) (Building Block)
6. Integrating Discrete and Continuous Data (Building Block)
7. EAGER: Collaborative Research (Building Block)
8. A Cognitive Computer Infrastructure for Geoscience (Building Block)
9. Earth System Bridge (Building Block)
10. CINERGI – Community Inventory of EC Resources for Geoscience Interoperability (BB)
11. Building a Sediment Experimentalist Network (RCN)
12. C4P: Collaboration and Cyberinfrastructure for Paleogeosciences (RCN)
13. Developing a Data-Oriented Human-centric Enterprise for Architecture (CD)
14. Enterprise Architecture for Transformative Research and Collaboration (CD)
15. EC Test Enterprise Governance: An Agile Approach (Test Governance)

A Call for Involvement!

Published in: Technology, Education
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

EarthCube Monthly Community Webinar- Nov. 22, 2013

  1. 1. EARTHCUBE COMMUNITY UPDATES Monthly Webinar November 22, 2013 Organized by the EarthCube Test Enterprise Governance Project
  2. 2. Webinar Agenda ∗ EarthCube Awards – Project Overviews ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ EarthCube Web Services (Building Block) EC3: Earth-Centered Community for Cyberinfrastructure (RCN) GeoSoft (Building Block) Specifying and Implementing ODSIP (Building Block) A Broker Framework for Next Generation Geoscience (BCube) (Building Block) Integrating Discrete and Continuous Data (Building Block) EAGER: Collaborative Research (Building Block) A Cognitive Computer Infrastructure for Geoscience (Building Block) Earth System Bridge (Building Block) CINERGI – Community Inventory of EC Resources for Geoscience Interoperability (BB) Building a Sediment Experimentalist Network (RCN) C4P: Collaboration and Cyberinfrastructure for Paleogeosciences (RCN) Developing a Data-Oriented Human-centric Enterprise for Architecture (CD) Enterprise Architecture for Transformative Research and Collaboration (CD) EC Test Enterprise Governance: An Agile Approach (Test Governance) ∗ A Call for Involvement!
  3. 3. EarthCube Web Services Building Block By Tim Ahern Director of Data Services, IRIS IRIS, CUAHSI, IEDA @ LDEO, UNAVCO, Unidata, GPlates-Caltech, RAMADDA
  4. 4. Overview The objective of the Web Services Building Block is to provide access to scientific data from a variety of domains using relatively simple service interfaces. These interfaces will be as consistent as possible and service as data sources to the Brokering Building Block. The are intended to assist in data discovery, access, and usability across multiple domains.
  5. 5. EarthCube Building Block Partners RAMADDA Long Tail Data GGP NEON IRIS UTEP Gravity Inter Magnet Structural Geology UNAVCO NGDC WSBB CUAHSI SDSC Caltech GPlates OOI Columbia IEDA BCube Unidata CINE RGY WOVODAT
  6. 6. Strategy Goal: Create web services that are as uniform as possible and support the most fundamental space-time discovery and access criteria. Two solutions depending on data type: 1. Simple Web Services REST-like services offering data in cross-domain formats (text, NetCDF) with common or similar query parameters and patterns. 2. 3. RAMADDA An existing data management system that will be extended to support simple web service access. Simple Clients Another benefit is that the envisioned simple services enable simple clients to be developed by scientists
  7. 7. There is no better place to have these conversations than in the field EC3—Earth-Centered Communication for Cyberinfrastructure: Challenges of field Yosemite/Owen’s Valley Summer 2014 field trip: data collection, management, and integration Why Concentrate on Field-based disciplines of the Geosciences? Initiate relationships and collaborations challenges field-based to between with regards Common set of geoscientists and computer digitizing our data and making those data Summer 2015 field trip: TBA scientists available through community databases. Applications to participate in fieldtrips: Steering Committee Membership: Richard Allmendinger, Cornell U; Jim Bowring, College of Form available in Chan, U of Utah; Amy Ellwein, Rocky Mountain Bio Charleston; Marjorie December, 2013 Deadline: provides essential information Lab; Yolanda Gil, U of Southern CA; Paul Harnik, Franklin and Marshall College; Eric FieldworkTBA Kirby, Penn State U; Ali Kooshesh, Sonoma State U; Matty Mookerjee, Sonoma State U; Rick about the long-term Inc; Terry Pavlis, theTexas, El Paso; Shanan Morrison, Comprehend Systems history of U of Earth’s Peters, U of Wisc, Madison; Bala Ravikumar, Sonoma State U; Paul Selden, U of atmosphere, oceans, and tectonic cycles. Kansas; Thomas Shipley, Temple U; Frank Spear, Rensselaer Poly. Inst; Basil Tikoff, U of Wisc, Madison; Douglas Walker, U of Kansas; Mike Williams, U of Mass., Amherst
  8. 8. Software Stewardship for Geosciences Principal Investigators: NSF ICER-1343800 Christopher J. Duffy Department of Civil and Environmental Engineering, Penn State University Yolanda Gil Information Sciences Institute, University of Southern California Department of Computer Science, University of Southern California James D. Herbsleb Institute for Software Research, Carnegie Mellon University Chris A. Mattmann NASA Jet Propulsion Laboratory Department of Computer Science, University of Southern California Scott D. Peckham Department of Hydrologic Sciences, University of Colorado Erin Robinson Foundation for Earth Science
  9. 9. The Importance of Geosciences Software • EarthCube aims to enable scientists solve challenging problems that span diverse geoscience domains – This requires not only data sharing but new forms of knowledge sharing • The focus of our project is on helping scientists to share knowledge concerning the software they develop
  10. 10. Problems: Software Cost – “Scientists and engineers spend more than 60% of their time just preparing the data for model input or data-model comparison” (NASA A40) “Common Motifs in Scientific Workflows: An Empirical Analysis.” Garijo, D.; Alper, P.; Belhajjame, K.; Corcho, O.; Gil, Y.; and Goble, C. Future Generation Computer Systems, 2013.
  11. 11. Problems: Reproducibility
  12. 12. The Importance of Geosciences Software • The focus of our project is on helping scientists to share knowledge concerning the software they develop – Software implements models (ecology, hydrology, climate,...) • Embodies very sophisticated knowledge about those models – Other software implements data reformatting, QC, etc. • Estimated to take 60-80% of effort in most research projects • There are repositories of model software (CSDMS, CGI, ESMF,…) • There are no shared repositories for other geosciences software
  13. 13. GeoSoft: Software Stewardship for Geosciences • An on-line community for sharing knowledge about geosciences software – Intelligent assistance to describe new software: how to use it appropriately, what kinds of data, how it relates to other software – Sophisticated search capabilities to find software for their needs – Interactive advice on open source software, forming successful developer communities, and other software sharing topics • Project involves: geoscientists, social scientists expert in on-line communities, and computer scientists expert in knowledge capture, open source software, and software reuse
  14. 14. ODSIP: The Open Data-Services Invocation Protocol Building Blocks for EarthCube Dave Fulker (OPeNDAP, Inc), PI November-2013
  15. 15. OPeNDAP Mission: Reduce Data-Usage Impedance
  16. 16. Data Access as a Service In the ideal, WebServices building blocks ⇒ Distance (users to data) does not matter
  17. 17. But Distance Does Matter... When combined with factors such as Large data volumes
  18. 18. Large Volumes May Be OK With services for creating subsets (OPeNDAP’s forte since ~1994)
  19. 19. But Subsetting May Be Insufficient Especially for multi-instrument, x-disciplinary studies Users require data that (prior to delivery) have been transformed
  20. 20. ODSIP Building Blocks: Data acquisition protocol + rich algebra of preretrieval ops Statistical summarization Binning Remapping Feature extraction ...
  21. 21. Summary: the ODSIP Project Will Develop a service-invocation paradigm Extend the well-used OPeNDAP protocol Offer an algebra of pre-retrieval operations Prototype its use in 3 (hard!) geo contexts Climate-model downscaling - native-Hawaiian use Storm surge prediction - coastal NC emergencies SST front analysis/synthesis - from satellite images Engage the EarthCube community...
  22. 22. BCubeBrokering Building Blocks for EarthCube 1st EarthCube Community Webinar
  23. 23. Project Goals • Demonstrate both a process and a technology for addressing critical needs of EarthCube – A process for understanding and working with scientists and educators while developing infrastructure – To develop cyberinfrastructure components that ease the discovery, understanding, use and reuse of data and knowledge
  24. 24. Technical Facets of BCube • Advance brokering capabilities – Mediating new types of resources, including real time data, model components, and workflows – Enhancing semantic mediation capabilities – Developing public APIs to broker components • Establish a testbed in the cloud – For purposes of test and evaluation • Build and test interfaces with major data repositories • Demonstrate ability to find new data and knowledge resources by crawling the web
  25. 25. Social Science Facets of BCube • Develop innovative methods to capture data practices and needs, and then incorporate these into broker development in an iterative, continuous (i.e. agile) development cycle – Observe scientists as they execute scenarios – Interview project scientists and developers
  26. 26. Educational Facets of BCube • Explore ways in which brokering can enhance learning and improve access to data and models by novices – Exploit capabilities of broker to interface with social networks • Foster interest among early career scientists in cyberinfrastructure development and utilization
  27. 27. BCube Organizational Structure
  28. 28. Learn more about our vision for EarthCube
  29. 29. Stay Informed! Subscribe to: by visiting and bookmark (coming soon)
  30. 30. EarthCube Building Blocks: Integrating Discrete and Continuous Data David Maidment, Dan Ames, Alva Couch, Ethan Davis EarthCube Community Webinar, Nov 22, 2013
  31. 31. Hydrologic domain: Discrete Spatial Objects Observations GIS • Discrete observations • Features • In situ sensors E.g. : Waterflow, Streamflow, Discharge
  32. 32. Atmospheric Science Domain: Continuous space-time arrays • Arrays of multi-dimensional data • Coverages • Satellite observations E.g. Precipitation maps, Remote Sensing Rain flux, Soil moisture grids, Wind direction/magnitude/elevation Climate
  33. 33. Digital Barriers Observations and Time Series Climate Observations and Grids Geographic Information Systems (GIS) Remote Sensing
  34. 34. Breaking Down Digital Barriers • Cataloging and discovery • Common Data/Information Model • Data access web services, data encodings • Data access, analysis, and visualization applications/tools • Outreach to the community
  35. 35. Leveraging Semantics and Linked Data for Geoscience Data Sharing and Discovery A wide spectrum of maturing methods and tools, collectively characterized as Semantic Web Technologies, enables machines to complete tasks automatically. For the Geosciences, Semantic Web Technologies will vastly improve the integration, analysis and dissemination of research data and results. This EarthCube project will conduct exploratory research applying state-of-the-art Semantic Web Technologies to support data representation, discovery, analysis, sharing and integration of datasets from the global oceans, and related resources including meeting abstracts and library holdings.
  36. 36. Leveraging Semantics and Linked Data for Geoscience Data Sharing and Discovery • A key contribution will be semantically-enabled cyberinfrastructure components capable of automated data integration across distributed repositories. Woods Hole Oceanographic Institution • Cynthia Chandler • Lisa Raymond • Adam Shepherd • Peter Wiebe Lamont-Doherty Earth Observatory • Robert Arko • Suzanne Carbotte University of Maryland, Baltimore County • Tim Finin • Tom Narock Wright State University • Pascal Hitzler • Michelle Cheatham • Adila Krisnadhi
  37. 37. Peters File
  38. 38. Earth System Bridge An NSF funded EarthCube Building Block Scott Peckham, CU-Boulder, PI Co-PIs Cecilia Deluca (NOAA, CIRES), David Gochis (NCAR), Rocky Dunlap (GA Tech), Anna Kelbert (OSU), Gary Egbert (OSU), Eunseo Choi (Memphis), Jennifer Arrigo (CUAHSI)
  39. 39. The Science Goal: Improving Environmental Modeling Predictions ∗ Mission-Driven agencies providing predictions ∗ Efficient data and computational enterprise ∗ Information to protect life and property “Bridging the Gap” to Enable Research-to-Operations Operations-to-Research ∗ Academic Enterprise ∗ Geoscientists advancing the science ∗ Computer scientists advancing the technology ∗ Scientific inquiry and hypothesis testing
  40. 40. Building the Bridge ∗ ∗ ∗ ∗ ∗ ∗ ∗ Framework Definition Language (FDL) ∗ ∗ Metadata specification Application Architecture Protocols for interaction Mechanics and Implementation Build a series of bridges Semantic Frameworks new services to improve the integration of inter-agency, fourdimensional databases with more heterogeneous academic databases
  41. 41. Initial Groups for Demonstration ∗ ESMF- Earth System Modeling Framework ∗ NUOPC - National Unified Operational Prediction Capability – Layer to enhance interoperability FEDERAL ∗ CSDMS - Community ACADEMIC Surface Dynamics Modeling System ∗ Pyre -Python Framework for Coupling CIG Models ∗ CUAHSI data services ∗ NCAR/UCAR resources ∗ WRF ∗ CESM ∗ CSS-Wx
  42. 42. Community Inventory of EarthCube Resources for Geoscience Interoperability dataset discovery is the most often cited issue in executive summaries on the EarthCube web site
  43. 43. Goals  Inventory of EarthCube information resources  datasets, catalogs, vocabularies, information     models, services, process models, etc. resources for interoperability links between resources, people, publications, models, etc. cross domain data applications and collaborations community contribution to resource descriptions.
  44. 44. Inventory resource scope Activity Person Organization Community Vocabulary Record Collection Crosswalk Software Model Workflow Repository Facility Specification
  45. 45. Starting Point: Queries          Is there a defined information exchange for topic X Find example implementations of specification X Find resources with subject X (theme, geographic area...) Find datasets with subject Y using query concept expansion Find people who have worked in Domain X, dataset Y, location L Find repositories that will accept my data Find resources annotated by person X Find geographic region having data of type Z Find datasets that contain property X
  46. 46. Approach Build on  Compile metadata for as many resources as we can (collect recommendations at AGU, harvest existing catalogs)  Expose through simple search interface  Use off the shelf technology: Geoportal, ISO metadata, CSW  Make it accessible through 
  47. 47. Then add features Links to organizations, researchers, other systems  Validation Services  Deep registration of datasets/databases  Data search capabilities  Quality/interop readiness assessment  Annotation system 
  48. 48. Development Team  San Diego Supercomputer Center/UCSD  Ilya Zaslavsky, David Valentine, Tom Whitenack  Amarnath Gupta, Jeff Grethe (NIF project)  Lamont /Columbia Univ./IEDA  Kerstin Lehnert, Leslie Hsu  Arizona Geological Survey  Stephen Richard  University of Chicago  Tanu Malik  Open Geospatial Consortium  Luis Bermudez Community Partners • Anthony Aufdenkampe: Critical Zone Observatories • Shanan Peters: stratigraphy • Bernhard Peucker-Ehrenbrink: Global River Observatories • RCN projects that plan to organize community resources • Test Enterprise Governance • Building Blocks projects working on web services, brokering solutions • Agencies • International
  49. 49. RCN: Building a Sediment Experimentalist Network (SEN) Wonsuck Kim (UT Austin) Leslie Hsu (LDEO) Brandon McElroy (U Wyoming) Raleigh Martin (UCLA) meanders channels deltas ripples floods mountains
  50. 50. Motivation: Sediment experiment data and facilities increasing and improving rapidly, but the rate of science communication, data discovery, and data re-use is not keeping pace. Goal: More accessible data, guidelines, and procedures, leading to better-equipped investigators. Archived, re-usable data for our own and other related research communities. EarthCube RCN: Building a Sediment Experimentalist Network
  51. 51.  Component 1  SEN Knowledge Base (SEN-KB): a centralized place for sharing knowledge and data    a data repository leveraging and building on the existing National Center for Earth-surface Dynamics (NCED) Data Repository synthesizes research activities of experimentalists by continuously aggregating existing and newly-collected experimental data. make data discoverable to those outside our immediate community – e.g. modelers and field geomorphologists EarthCube RCN: Building a Sediment Experimentalist Network 58
  52. 52.  Component 2  SEN Education and Data Standards (SEN-ED): developing and promoting guidelines    workshops, special sessions, the NCED Summer Institute educate data management and experimental techniques facilitate development of suggested guidelines for data and metadata management and disseminate those to end users. EarthCube RCN: Building a Sediment Experimentalist Network 59
  53. 53.  Component 3  SEN Experimental Collaboratories (SEN-EC): a new form of research collaboration in our community:   broadcast physical laboratory experiments and interact with colleagues through a webinar system. foster greater communication among experimentalists and promote collaborations within and beyond the Earth-surface science community regardless of physical location. EarthCube RCN: Building a Sediment Experimentalist Network 60
  54. 54. EarthCube RCN: Cyberinfrastructure for Paleobioscience (C4P) Goals • Build new partnerships and collaborations among geoscientists and technologists • Survey and catalog existing resources • Build community around cyberscience & paleogeoinformatics • Advance development of common standards and semantic frameworks, working closely with biological community
  55. 55. EarthCube RCN: Cyberinfrastructure for Paleobioscience (C4P) Steering Committee Lehnert, Kerstin IEDA, Columbia University Anderson, David M. NOAA, National Climatic Data Center Fils, Douglas Consortium for Ocean Leadership Jenkins, Chris University of Colorado at Boulder Lenhardt, Christopher Renaissance Computing Institute Noren, Anders University of Minnesota Olszewski, Thomas Texas A&M University Smith, Dena University of Colorado at Boulder Uhen, Mark George Mason University Williams, Jack University of Wisconsin-Madison C4P
  56. 56. Challenge: Much fossil data is ‘dark’ – i.e. not easily accessible Neotoma DB C4P
  57. 57. Challenge: Many informatic efforts, little coordination or interoperability among efforts VertNet Neotoma DB Paleobio. DB Morphobank Neptune IEDA iDigBio Digimorph Macro-Strat TMI Tree of Life C4P
  58. 58. Planned RCN Activities ❖ Cataloging existing cyberinfrastructure resources in the paleobiological sciences ❖ Webinar series featuring other EarthCube projects and related geo- & bioinformatic activities (coming soon) ❖ Workshops: ➢ Paleobioinformatics (May/June 2014) ➢ Geochronology ➢ Synthesis ❖ Town Halls & Early Career Symposia at GSA, AGU, ESIP ❖ Social Media: Twitter, EarthCube Website, etc. C4P
  59. 59. EarthCube Conceptual Design: Developing a DataOriented Human-Centric Enterprise Architecture for EarthCube Chaowei Yang*, Chen Xu*, and Carol Meyer° October 15, 2013 * George Mason University ° Federation of Earth Science Information Parterns
  60. 60. Project Objective • This project seeks to design a conceptual architecture that can bring geoscientists, computing scientists, and social scientists together to collaborate on networks of data, technology, applications, business models, and stakeholders. 1. Design a data-oriented CI for achieving the goal of EarthCube as a knowledge management system and CI that integrates all geosciences data to transform the conduct of geoscience research and education. 2. Have geoscientists and domain experts at the center and facilitate them to communicate and collaborate using multiple layers of technology and information to achieve the sharing of data/information/knowledge.
  61. 61. Three-Layer Architecture Layer 2: Information Layer 3: Knowledge Layer 1: Model
  62. 62. EarthCube Architecture Report Template (TBD) VOLUME 1 VOLUME 2 Vision for EarthCube 1.1 Fit for Purpose Architecture 1.2 Goals and Objectives 2. Framework Overview 2.1 EarthCube Architecture Framework Defined 2.2 Purpose and Scope 3. Domains of Architecture (Strategic, Capability, and Solution) 4. Principles of Federation 4.1 FEAF, TOGAF, Zachman, and DoDAF 4.2 EarthCube Enterprise Architecture Overview 5. Customer Requirements 5.1 Support of Key Processes 5.2 The Program Tier 5.3 The Enterprise Tiers (Component, Research Projects, and Geoscience Domains) 6. Methodologies 6.1 Methodology Based Approach to Architecture 6.2 System – Component, Package, Deployment Diagrams 7. Presentation 7.1 EarthCube Enterprise Architecture Notional Structure 7.2 Generated Views (Reports) 8. Data Focus 8.1 Core Architecture Data Model (CADM) 8.2 Exchange Standard: XML Schema Definitions (XSDs) 9. Process Performance Engineering & Metrics 9.1 Performance Engineering in Process Improvement 9.2 Metrics for Use in Testing Process Improvement 10. Analytics 10.1 Modeling and Simulation 10.2 Executable Architectures 11. Architecture Planning 11.1 Organizing the Architecture Effort 11.2 Architecture Management 12. Configuration Management (CM, Not Instantiated Architectures) 13.1 Configuration Control Board (CCB) 13.2 CADM (EarthCube Architecture Data Model) 13.3 Generic Process Models 13.4 EarthCube Enterprise Architecture Framework 1. Operations Architecture 1.1 Capabilities/Metrics 1.2 Process 1.3 Information Flow 1.4 Data Descriptions 1.5 Roles 2. Technology Architecture 2.1 Systems, Applications, Services, Interfaces 2.2 Infrastructure (Networks, CES, Computing, Spectrum) 2.3 Standards (DISR, profiles) 2.4 Data Exchange 3. Net-Centric Architecture Description 3.1 Net-Centric Perspective 3.2 Backward Compatibility with Legacy Architectures 3.3 Point to Point Connections 3.4 Support for SOA 1. VOLUME 3 1. EarthCube Enterprise Architecture Metamodel APPENDICIES (Applies to all 3 Volumes): A. GLOSSARY B. ACRONYMS C. REFERENCES D. TRANSITION FROM DODAF V1.0/1.5 TO V2.0
  63. 63. EarthCube Conceptual Design: Enterprise Architecture for Transformative Research and Collaboration Across the Geosciences A socio-technical system of systems: Based on Technology components to support • social network • research computation • data management. Technology-enabled feedback between users cultivates an emergent, self-organizing system
  64. 64. Approach  Challenge: adoption of research enterprise to 21st century technology  Larger community, new communication channels, more information  Issues are mostly reshaping social networks and attitudes Paradigm: Emergent, Self-organizing system  Requires more direct interaction between agents in the system 
  65. 65. Feedback loop People Models Data… Resources Usage Activities Log Analysis Recommendations Impact Publication Discussion Data use Data revision Annotation…
  66. 66. Design  Incorporate emerging social technology in research enterprise  Klout, PeerIndex,, Mendeley, Google Scholar, LinkedIn, ResearchGate… Metrics for impact, recommendations  Integrate with recognized cyberinfrastructure components:   Catalogs, data services, repositories, standards
  67. 67. Development Team  San Diego Supercomputer Center/UCSD: Ilya Zaslavsky, Amarnath Gupta, David Valentine   Arizona Geological Survey: Stephen Richard University of Chicago: Tanu Malik Advisory Team of Large Information System Architects • USGS • Academic Data Centers • National Research Facility • Geoscientists • Social organization experts • Mendeley • OGC • • • • IBM Microsoft Elsevier ESRI
  68. 68. EarthCube Test Enterprise Governance: An Agile Approach M. Lee Allison, PI Rachael Black, Anna Katz, Kate Krestchmann, Kim Patten, & Genevieve Pearthree November 5, 2013
  69. 69. EARTHCUBE ENTERPRISE TEST GOVERNANCE An agile approach to design a system that catalyzes the field and works for you How do we bring your tools, standards, and skills into EarthCube?
  71. 71. Governance timeline – Year 1 Stakeholders (Assembly) – governance ideas, testing Integrate stakeholder concepts crowdsource Synthesize and recommend to NSF Governance timeline – Year 2 Establish Test Governance Facilitate convergence on system design, data standards Evaluate results: basis for long term governance
  72. 72. STAKEHOLDER ENGAGEMENT (YEAR 1): EARTHCUBE ASSEMBLY Industry & FOSS: Instrumentation, Software, and Technology Developers EarthCube Portfolio Research Science Communities End-User Communities & Workshop Participants Data Facilities & Users IT/CS/ Information Scientists Professional Societies Assembly Advisory Council (10-12 members)
  73. 73. “Executive Branch” An unbiased, objective process to get your input and propose a solution Test Governance Project Secretariat “Legislative Body” Assembly & Assembly Advisory Council
  74. 74. EARTHCUBE.ORG The “Public Site” Portal The “Workspace” The Workspace
  75. 75. A CALL FOR YOUR INVOLVEMENT • Virtual Town Hall: November 22, 11:30 EST • Upcoming community workshops: Jan – March 2014 • All Hands Meeting, June 24-26, 2014, Washington, DC • Crowdsourcing: • Virtual table-top gaming exercises • Feedback on governance models • Social Media – Twitter, Facebook •