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Envisioning Better Integration and Synthesis: A Data Provider Perspective

World Data Center for Human Interactions in the Environment. Envisioning Better Integration and Synthesis: A Data Provider Perspective. Marc Levy CIESIN Earth Institute, Columbia University Presentation to Arctic System Synthesis Workshop: New Perspectives through Data Discovery and Modeling

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Envisioning Better Integration and Synthesis: A Data Provider Perspective

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  1. World Data Center for Human Interactions in the Environment Envisioning Better Integration and Synthesis: A Data Provider Perspective Marc Levy CIESIN Earth Institute, Columbia University Presentation to Arctic System Synthesis Workshop: New Perspectives through Data Discovery and Modeling 2–4 April 2007 Seattle, WA

  2. Researcher Researcher Research community Researcher Researcher Researcher Researcher Cyberinfrastructure Data Repository Decision-making community Public Public Evolution of the Data Provider Model

  3. Discovery Access Reliability and continuity On-line databases Catalogs Stewardship Domain Understanding (What do these data mean? Are they suitable for my use?) Data Documentation, visualization Standards; georeferencing; time-stamping Integration Linkage Understanding(How do these data relate to other data?) Interoperability

  4. Discovery Access Reliability and continuity What data communities provide Domain Understanding (What do these data mean? Are they suitable for my use?) Data Linkage Understanding(How do these data relate to other data?) Interoperability

  5. What research and policy communities need What data communities provide Data

  6. Helping Users Make Wise Choices is Hard! Traditional Documentation not enough Multi-faceted approach required Comparative Guides Visualizations Common Pitfalls Examples Citations Ph.D B.A. High School 5th Grade

  7. http://sedac.ciesin.org/citations/CitationIndex.html

  8. Thornton, P.K., Kruska, R.L., Henninger, N., Kristjanson, P.M., Reid, R.S., and Robinson, T.P. (2003) Locating Poor Livestock Keepers at the Global Level for Research and Development Targeting. Land Use Policy, 20(4): 311-322. Tobler, W., Deichmann, U., Gottsegen, J., and Maloy, K. (1997) World Population in a Grid of Spherical Quadrilaterals. International Journal of Population Geography, 3: 203-225. van Lieshout, M., Kovats, R.S., Livermore, M.T.J., and Martens, P. (2004) Climate Change and Malaria: Analysis of the SRES Climate and Socio-Economic Scenarios. Global Environmental Change, 14(1): 87-99. Vassolo, S. and Döll, P. (2005) Global-scale Gridded Estimates of Thermoelectric Power and Manufacturing Water Use. Water Resources Research, 41. Verburg, P.H., and Chen, Y. (2000) Multiscale Characterization of Land-Use Patterns in China. Ecosystems, 3(4): 369-385. Viviroli, D. and Weingartner, R. (2004) The Hydrological Significance of Mountains: from Regional to Global Scale. Hydrology and Earth System Science, 8(6): 1016-1029. Vorosmarty, C.J., Green, P., Salisbury, J., and Lammers, R.B. (2000) Global Water Resources: Vulnerability From Climate Change Acid Population Growth. Science, 289(5477): 284-288. Vorosmarty, C.J., and Sahagian, D. (2000) Anthropogenic Disturbance of the Terrestrial Water Cycle. Bioscience, 50(9): 753-765. White, M.A., Hoffman, F., Hargrove, W.W., and Nemani, R.R. (2005) A Global Framework for Monitoring Phenological Responses to Climate Change. Geophysical Research Letters, 32(L04705): 4pp. White, M.A., Nemani, R.R., Thornton, P.E., and Running, S.W. (2002) Satellite Evidence of Phenological Differences Between Urbanized and Rural Areas of the Eastern United States Deciduous Broadleaf Forest. Ecosystems, 5(3): 260-273. Wilson, S.J., Steenhuisen, F., Pacnya, J.M., and Pacnya, E.G. (2006) Mapping the Spatial Distribution of Global Anthropogenic Mercury Atmospheric Emission Inventories. Atmospheric Environment, 40(24): 4621-4623.

  9. Information on data quality is critical to judging goodness of fit

  10. It shouldn’t even be this hard

  11. Helping users make wise choices is a community-building and community- strengthening task

  12. Moore’s Law Benefits Data Collection Processes Unequally Pace of progress across data domains is very uneven. Greater the divergence, greater the Integration Frustration = /

  13. Identify and Fill Gaps! CIESIN, Gridded Population of the World, 350,000 census input units

  14. 73 M people. Largest urban extent

  15. Seismic hazards include earthquakes and volcanoes; hydrological hazards include floods, cyclones, and landslides Exposure to Multiple Natural Hazards

  16. Many more gaps to fill! • Roads • Migration • Time-series spatial data on population, urbanziation • Spatial economic data • Soil fertility • Spatial health data Prioritize Assign roles Be transparent Persevere!

  17. Clear Explanation Incomprehensible Explanation Challenge of model data Output data sets Harmonized data sets Input data sets Model Conclusions TOUGHEST NUT

  18. Interoperability Standards Develop, adopt, refine, encourage use of standards for representing and distributing data Brute Force Reprocess, reformat, recode data to be consistent with established framework data Example: Household Surveys

  19. Framework data • Data that helps structure observations, models, analysis: makes cumulative progress possible • Permanent features of the topography • Ice cover • Permafrost • Climate zones • Navigable sea lanes • Organizational devices with universal utility • Latitude, longitude grids • Location of settlements • Coastal villages Challenge How do you create frameworks when everything is in flux? (How do you build a house during an earthquake?) You can’t ignore it, but there aren’t many useful examples in interdisciplinary research

  20. Stewardship • Almost always under-provided • Everyone underestimates the speed by which data becomes invisible or unintelligible • Inter-disciplinary, problem-oriented data especially vulnerable

  21. Discovery Access Reliability and continuity On-line databases Catalogs Stewardship Domain Understanding (What do these data mean? Are they suitable for my use?) Data Documentation, visualization Standards; georeferencing; time-stamping Integration Linkage Understanding(How do these data relate to other data?) Interoperability

  22. Conclusions • We don’t know how to do everything yet, but we know a lot more now than a decade ago • The investments show positive economies of scale • each step forward getting the data questions right generates more research and policy return than previous steps • But what remains is going to require sustained, focused effort • There’s a lot of hard stuff yet to do • Historically, funders don’t like this kind of work • That seems to be changing

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