Community dialog

1. Community dialog White papers, user survey and website EarthCube Team: Eva Zanzerkia Jennifer Schopf Cliff Jacobs Robert Pennington Participating NSF Program Officers Gabrielle D. Allen Lisa Boush Milne, Peter J Eric DeWeaver Thomas Janecek, Barbara Ransom Ruth, Sarah LMark Suskin Laura Toran Thomas Torgersen BarisUz,

2. Overview • 540 members to the EarthCube website • 114 white paper submission • 185 respondents to user survey • Unknown number of hours of pro bono contributions by community • Unprecedented view of the pulse of the geoscience community

3. Requirements Survey EarthCube Vision Document White Papers

4. White Papers Some papers had more than one focus area

5. Where do you obtain your data?

6. Data • Do your require real-time data? • 34% Yes • 34% No • Do you require data or data products from outside your immediate discipline? • 52% Yes • 14% No • Is there a place for products of your own work to be archived and accessed? • 45% Yes • 25% No

7. Are there sufficient CI tools available to meet your needs?

8. Themes and Observations • The current research paradigm works, but clearly needs revision • Data is essential, but it can be problematic to use • Similarities abound, but dialog is lacking • Interesting solutions proposed, but silos are hard to break down • Complete designs proposed, but none were comprehensive • Community and NSF want increased productivity and new capabilities, but options must be narrowed

9. The Reach to Realize the EarthCube Vision • The Vision should be out of familiar zone • It is OK to be uncomfortable with attainment • Our conservative culture might not serve us well in exercise • It is OK if there are: • No Obvious path to attain the vision • Daunting barriers to overcome • No technological solutions current available • THIS IS A LONG TERM EFFORT - Think Big and Be Ambitious!

10. Thank You Questions and/or Comment

11. EarthCube should be guided by the following principles:  Community-driven, community-based governance EarthCube community members and staff — from across the spectrum of geoscience disciplines — must be encouraged to interact with each other at all levels of the organization.  Flexible and evolving designs based on the best ideas The EarthCube community should respond quickly and proactively to changes in the geoscience and technological landscapes.  Scalable and loosely-federated organization The EarthCube organization must recognize community members’ individual requirements and processes, while providing a framework to bring them together.  Preference for existing technologies EarthCube should have a preference for incorporating (and improving) technologies and processes that are already in widespread use.  Use of open standards EarthCube should display a strong preference for open standards, formats, and processes, which allow disparate systems to interact.  Strong strategic partnerships and collaborations EarthCube should strive to bring members (and other stakeholders) into partnerships in order to maximize development and avoid duplication of effort.  National center to provide community leadership EarthCube needs a central, unifying entity with long-term funding to coordinate and bring focus to the efforts of member groups WP18

12. Our design approach is built upon a distributed, internet-like paradigm that loosely couples existing geoscience data resources using a fundamental organizing principle – space-time – combined with semantic web approaches that work from the data up. WP39 EarthCube aims to essentially create the ultimate virtual environment for “Team Science”. Inherent to this goal is building bridges between data, resources and disciplines. To do this requires an “out of the box” approach that incorporates many of the “inside the box” technologies and methodologies that have been established by the different science communities. WP40 The vision of EarthCube presented here is an open access archive of all publicly available geosciences data in the United States and eventually the world, capable of supporting the technical needs to store and offer easy access to petabyte‐size datasets in the next decade, offering long‐term format translation, rich provenance and metadata, advanced cross‐disciplinary portals that can be tailored for use from the citizen scientist to graduate students to expert users, offers community features to facilitate and promote collaboration, and has strong governance and sustainability models to ensure long‐term success. WP26

13. ….the EarthCube vision to promote an integrated system for discovery and access to geoscience information can be realized with an approach emphasizing the use of open-source standards, connecting data provider and consumer software components using standardized interfaces and interchange formats, and committing resources to support community engagement and education. Community-based operating standards and proto-cols developed with a requirement-driven design methodology make incorporation of additional data and information…. WP68 ….provides a cyberinfrastructure in which everybody can propose arguments for positions, recommendations, or theses, or can contribute to debates with further arguments or counterarguments. The results are graphically organized argument maps in which knowledge is presented in a network of justifications. This way, knowledge becomes visible in the graphical structure of arguments and more complex argumentations that together support one main claim. The reasons of these argumentations can then be linked with existing infrastructure and data bases. WP60 Cyberarchitecture represents a paradigm shift in scientific research that facilitates collaboration across disciplines and across great distances, and enables breakthroughs to be reached more quickly and efficiently. WP11

14. Implement organizational and technological systems that enable the scientific community to publish their research in online documents that enable the reader to access the data and analysis and display processes that are described in the publication. UR10 Our vision is an integrated computing environment that provides a seamless flow of data between sensors, repositories and models in a highly interactive and socially networked setting, accessible from a broad range of computing devices (mobile, pad, laptop, desktop, HDTV). Data analysis, visualization, modeling and synthesis merge to create a system for knowledge exploration and management. WP56 A goal for Earth cube should be to develop, demonstrate and deploy enabling technological frameworks that can accelerate cross Earth systemscience discipline discovery and knowledge generation in both theoretical and applied domains. WP68

15. Provide capabilities for scientists, forecasters, and the public to understand environmental conditions near the earth's surface that affect public safety and economic well being of the country UR33 Seamless integration of data sources, products, intellectual output. A system where scientists spend time doing scientific research and not hunting and gathering for data. UR16 Integrated, flexible scenario-testing models of these systems, linked to global forecasting systems, to support decision-making. UR18 I want to be able to easily access and analyze our rapidly growing body of knowledge on the properties of Earth in a temporally and spatially explicit fashion UR37 Synthesize these insights at the regional, national, and inter-national scales UR15 Linking high-resolution atmospheric databases with impacts and effects information in order to create automated decision-assistance tools for decision-makers in different professions (e.g., aviation, emergency management, policy planning).UR21 Science at the speed of thought, with increased integration of atmospheric and geophysical data. UR48 Through CI, internet and remote sensing, providing a series of solutions for the emergency respond and advanced decision-support for real time issues UR55

16. In my vision of the future I will be able to have my students conduct a massive simulation on the supercomputer facility. They will open the model results in the visualization package and it will determine the time of the event, send queries for the all available data products from ground and space based observations and conduct baseline comparisons. This data will be run through a standard set of metric computations and compared with previous model performance. They will also be able to access a library of advance analysis packages that have been developed by previous students. Once they have had a chance to digest with this information they will develop new tools to address their particular question which will be come a part of the library of tools available to the community.UR98 Combining laboratory data, field data, and numerical simulations to understand mechanics of processes at the earth's surface. UR63 I would like to see completion of observational systems to the degree required to continually map and understand global processes in Earth’s near-space environment.UR86 Better quantification and modeling of climate change impacts; better cross-disciplinary collaboration in research to solve problems related to climate change adaptation UR94

17. A major wish would be ability to carry out truly global-scale analyses of species distributions for the late Quaternary, with the capacity to generate e.g. global-scale reconstructions of forest cover and maps of species diversity. And the capacity to link these maps to a fully integrated network of paleoclimatic proxy data and paleoclimatic model simulations that would provide the best-available information about past climates. UR102 I would like to see our modeling capability advance significantly - so that setting up, running, and validating a given simulation is automated and made much faster. Then new students and postdocs could focus more on using these powerful tools to solve science questions, instead of struggling for a year or two with the programming. UR117 Bring higher power and performance modeling hardware and software that is tightly coupled to hard data. UR124 To build systems capable of federating disparate and eclectic sources of scientific data through machine learning and semantic web technologies. UR107

18. EarthCube Design