1. GEOSHARE-CIMSANS - Overview and Lessons from the Pilot Project Paul Hendley (Phasera Ltd. for the ILSI Research Foundation) and Tom Hertel/Nelson Villoria (Purdue).

2. Center for Integrated Modeling of Sustainable Agriculture & Nutrition Security (CIMSANS) International Life Sciences Institute (ILSI) Research Foundation initiative Collaborating and building tri-partite relationships – Between industries, academics and government – Use scientific data and approaches to address emerging challenges and help inform policy through productive dialogue Especially via improved Open Source Ag Data & improved modeling approaches Goal - conduct Sustainable Nutrition Security (SNS) impact assessment – Requires Scoping of SNS Landscape & metrics– Conceptual Model & manuscript – Requires some new assumptions, new or improved models & support tools – Requires unique combination of data – private industry may be only global source Data Translation and Reprocessing tools – to assist data sharing and recovery Model Friendly, Multi-scale Global Data Portal – Funding GEOSHARE

3. CIMSANS – GEOSHARE Pilot Project PURPOSE “…..to generate necessary infra-structure and levels of user participation and interest for collecting and curating public and private data while ensuring data quality and enhancing inter-operability. “ via examples – Cloud based Spatial Production Allocation Model (SPAM-IFPRI) Ghana and India – scalability & data density/availability/richness – HUBZero suitability for CIMSANS-relevant modeling frameworks Effective curation mechanism providing lasting added local/global value Could GEOSHARE provide framework for unified access to baseline and added value data for economic/physical modeling to support global efforts to assure secure/sustainable nutrition for all – Complementary with well established initiatives to avoid duplication

4. CIMSANS – GEOSHARE Pilot – Lessons so far Not stamp collecting – no incentives to contribute/maintain data Adding lasting value to data contributors/users via WORKFLOWS essential – New or better workflows with automated metadata, replicability advantages key Or enhanced accessibility (format, visualization, avoid pre-processing) – especially cross-discipline or cross- expertise level HUBZero offers tools that can make workflows readily publically accessible Certain key replicable “best-available” fused data not generally available – Slavish “copying” of existing tools into new cyber-infrastructure will not meet tomorrow’s needs Conceptual models defining scope of new tools need to be (RE)designed to fit anticipated needs/data – Communities of practice to input to design/endorse tools -“FIT FOR PARTICULAR PURPOSES” – Spatial allocation approaches a good example Making endorsed “Best Available” added-value data readily available to all is an important goal for improving overall quality/acceptance of food and nutrition security modeling

5. Where does GEOSHARE-CIMSANS go next?? What we are looking for from the next two days is stakeholder input through the breakouts – Examples and Q&A should give everyone a good view on scope/capabilities of HUBZero and GEOSHARE concept – Questions targeted to explore how you see GEOSHARE & HUBZero per se Necessary next steps in – Data Fusion – Aggregation of intermediate data at disparate scales/sophistication levels – Cross-sector unification/endorsement of best-available Climate/Environ/Economic data » CREDIBILITY by CONSENSUS where assumptions are needed – Assistance for making important global data available in usable formats » Improved inter-operability between data and model I/O modules

6. On to the workshop!………………

7. HUBZero and GEOSHARE Breakout 1430-1530 Q1: What specific needs should cyber-infrastructure for the geospatial community address? Do these needs vary across data/model users and data/model providers? Q2: What are the work flows implied by these needs? How much of the workflow should be on-line? Is the hub a place to ensure reproducible research? Q3: What incentives can be put in place to ensure these work flows are utilized? Q4: In what ways will the use of HUBZero contribute to transparency, credibility, and acceptance of the underlying data and work flows available within GEOSHARE? Group 1: Carol Song*, Paul Hendley**, Group 2: Tom Hertel*, Jingyu Song** Group 3: Nelson Villoria*, Ulrike Wood-Sichra**, Group 4: Paul Preckel*, Dave Gustafson**

8. Institutional Design Breakout – 1420 - 1710 Q1: How can a public-goods project like GEOSHARE be sustained over the long run? Q2: What are the appropriate roles for the Advisory Board? Q3: What are the incentives for participation in GEOSHARE, as viewed from the perspective of: node leaders, government data generators, private industry, academics, other data consortia and user communities? Q4: How are data and work flow priorities defined? Group 1: Tom Hertel*, James Jones**, Group 2: Dave Gustafson*, Hermann Lotze-Campen** Group 3: Nelson Villoria*, Kate Schneider**, Group 4: Paul Hendley*, Ron Sands**

9. Data Validation/Endorsement Breakout – 1000-1100 Q1: What is the value of GEOSHARE-endorsed geospatial data products for agriculture, food security and the environment? Who will benefit from such endorsement? Are there downsides from such endorsement? Q2: Different data sets and workflows may be suited for different purposes: Which of these is most pressing for GEOSHARE to achieve, and how might this be accomplished? Q3: Mechanics of endorsement: Who does the endorsement? How frequently, and in what form? Q4: Is broad stakeholder input needed? If so, how will this collected and integrated as part of the endorsement process? Group 1: Niven Winchester*, Paul Hendley**, Group 2: Petr Havlik*, Mark Imhoff** Group 3: Hermann Lotze-Campen*, Jawoo Koo**

10. Data Fusion/Reconciliation Breakout – 1000-1100 Q1: There is a trade-off between transparency (simplicity) and sophistication (complexity) in data fusion. Where should GEOSHARE aim on this spectrum? Q2: What is the role for prior information in this process? Q3: How can GEOSHARE harness private- and public-sector knowledge and expertise in the process of data fusion? Q4: What is the role of ground-truthing and crowdsourcing in complementing data fusion based on more census and remote-sensing sources? Group 1: Navin Ramankutty*, Sherman Robinson**, Group 2: Paul Preckel*, Stefan Siebert**, Group 3: Andrew Nelson*, Paul Hendley**

11. What does this Holy Grail look like anyway? …generating unified high quality spatial datasets that are a readily accessible to and accepted as best-available science by all potential stakeholders involved in the production and forecasting of a secure and nutritious global food supply….. “UNIFIED home” for curating open source data from all sources – open source – Mechanism for alerting potential users to new and existing data..and associated strengths and weaknesses via solid metadata Scientific recognition for data generators – “data journal” citable with DOI Cross-sector unification/endorsement of best-available Climate/Environ/Economic data – Collaboration between intermediate data “aggregation” processes – CREDIBILITY by CONSENSUS where assumptions have to be made Assistance for making important global data available in usable formats – Improved inter-operability between data and model I/O modules