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IOOC Observational Network (ION) Project

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The IOOC Organizational Network (ION) is a project aimed at organizing the relationships between ocean observing institutions and groups. The initial ION data and visualizations were completed using Gephi Software, which is an open graph and visualization platform.  The links below reflect outcomes from this initial analysis.

Network analysis is valuable in the context of large organizational problems and can provide a tool to identify information needs within the network of organizations, as well as a clearer understanding of the impacts of current network structures on operations beyond organizational boundaries.  By coding for number of relationships or types of relationships, network analysis can indicate centrality of certain players, providing a powerful tool for determining those pathways that will most effectively disseminate information, messaging, or action items.

International Database

National Database

In order to build the current database of information on ocean observing networks at the national and international level, the analyst conducted online legal research, and visited each organization/agency website to find legal instruments and documents that reflected relationships between agencies and between global organizations, as well as policy documents and mission statements that contained information on the players basic interest or involvement in the 26 Core IOOS variables.  Based on this research, the data was binned and coded in multiple ways.

Partnership strengths were binned according to a Collaboration, Coordination, Cooperation scale found in use by U.S. agencies and in literature on categorizing organizational relationship strengths (Mandell, 2007).  At the national scale, relationships were binned according to the kind of legal instrument utilized, and these instruments were further binned on the three C’s scale outlined above:



Legal Instruments



Program, Work Plan, Working Group



MOU, MOA, Charter, Policy



Law, Agency, Executive Order

At the international and national levels, relationships were also binned by four categories reflecting a broad understanding of the nature of the relationship (Wagner, 2005):

Relationship Type



Relationships categorized under “Data” are those in which the primary purpose is to share data, or provide complementary data analysis.  The products of Data relationships could be co-managed databases, international co-authorships, and contribution to ongoing data sets.


Relationships categorized under “Infrastructure” are those in which the primary purpose of the partnership is to maintain high cost equipment (e.g. research vessels, buoys) or maintain long-term research projects that depend on access to high cost equipment and therefore depend on partnerships that can pool funding resources and and create greater stability.

Human Resources

Partnerships categorized under “Human Resources” involve relationships whose primary purpose is to share and gain access to scarce or unique resources, which could include funding, access to unique expertise or skills, and access to administrative support (Bammer, 2008).


Relationships categorized under “Communications” imply a focus on enhancing the exposure of research, ocean observations, and ocean policy among organizations and out to a public audience.  Activities include increasing messaging capacity and developing guidance materials for inter-organizational operations.



  1. Steven G. Ackleson, et al. Integrated Ocean Observing Decadal Challenges, IOOS Summit Whitepaper (2012), available at

  2. Anderson, D.M. et al., Harmful Algal Bloom (HAB) Sensors in Ocean Observing Systems, IOOS Summit Whitepaper (2012), available at

  3. Block, B.A. et al., Toward a U.S. Animal Telemetry Observing Network (US ATN) for our Oceans, Coasts, and Great Lakes, IOOS Summit Whitepaper (2012), available at

  4. Alexander, C., Priorities for IOOS Data Management and Communications (DMAC)

  5. Anderson, D.M. et al., Harmful Algal Bloom (HAB) Sensors in Ocean Observing Systems, IOOS Summit Whitepaper (2012), available at

  6. Bailey, B.H. et al., The Need for Improved Met-Ocean Data to Facilitate Offshore Renewable energy Development, IOOS Summit Whitepaper (2012), available at

  7. Bayler, E. et al., IOOS Data Assimilation: Connecting Regional Associations and the National Backbone, IOOS Summit Whitepaper (2012), available at

  8. Birkemeier, W. et al., Revising the IOOS National Wave Observation Plan, IOOS Summit Whitepaper (2012), available at

  9. Blumberg, A.F., Multi-Scale, Multi-Model Data-Based Prediction Systems Supporting Navigation and Maritime Safety, IOOS Summit Whitepaper (2012), available at

  10. Fredericks, J., et al. Outreach and Collaboration Emerging Activities, IOOS Summit Whitepaper (2012), available at

  11. DiGiacomo, P.M., Malone, T.C., Requirements for Global Implementation of the Strategic Plan for Coastal GOOS, IOOS Summit Whitepaper (2012), available at

  12. Guillermo, A. and Crowley, H., Ocean observing needs in a rapidly changing environment: The Alaska Outer Continental Shelf, IOOS Summit Whitepaper (2012), available at

  13. Crowley, M. et al., Building Coastal IOOS for the Next Decade: Following up on the Regional IOOS Build out Plans, IOOS Summit Whitepaper (2012), available at

  14. Gledhill, D.K., et al. An Integrated Coastal Ocean Acidification Observing System (ICOAOS), IOOS Summit Whitepaper (2012), available at

  15. Johnson, W.R., Payton, D., MacFadyen, A., Utilization of IOOS Observations for Planning and Emergency Response to Oil Spills, IOOS Summit Whitepaper (2012), available at

  16. Kohut, J. et al., Spatial and Temporal Monitoring of Dissolved Oxygen (DO) in New Jersey Coastal Waters Using Autonomous Gliders, IOOS Summit Whitepaper (2012), available at

  17. Kudela, R.M., et al. Leveraging Ocean Observatories to Monitor and Forecast Harmful Algal Blooms: A Case Study of the U.S. West Coast, IOOS Summit Whitepaper (2012), available at

  18. Leonard, L., et al., Identifying Stakeholder Driven User Needs: Lessons Learned in the Southeast, IOOS Summit Whitepaper (2012), available at

  19. Rosenfield, L. et al., IOOS Modeling Subsystem: Vision and Implementation Strategy, IOOS Summit Whitepaper (2012), available at

  20. Wanninkhof, R. et al., An Integrated Ocean Carbon Observing System (IOCOS), IOOS Summit Whitepaper (2012), available at

  21. Simoniello, C. et al., Creating Education and Outreach Opportunities for the U.S. IOOS, IOOS Summit Whitepaper (2012), available at

  22. Tronvig, K.A. et al., Interagency Collaboration for Operationalizing Datums Standards, IOOS Summit Whitepaper (2012), available at

  23. Emilie, M., & Hafner-Burton, M. K. (2009). Network analysis for international relations. International Organization, 63, 559-92.

  24. Ward, M. D., Stovel, K., & Sacks, A. (2011). Network analysis and political science. Annual Review of Political Science, 14, 245-264.

  25. Hall, T. E., & O’Toole, L. J. (2000). Structures for Policy Implementation An Analysis of National Legislation, 1965-1966 and 1993-1994. Administration & Society, 31(6), 667-686.

  26. Lubell, M., Scholz, J., Berardo, R., & Robins, G. (2012). Testing policy theory with statistical models of networks. Policy Studies Journal, 40(3), 351-374.

  27. Abbasi, A., & Altmann, J. (2011). On the correlation between research performance and social network analysis measures applied to research collaboration networks. In 44th Hawaii International Conference on Systems Science (HICSS-44), Jan. 4-7, Hawaii, USA..

  28. Marin, A., & Wellman, B. (2011). Social network analysis: An introduction. The Sage Handbook of Social Network Analysis, London, Sage, 11-25.

  29. Phelps, C., Heidl, R., & Wadhwa, A. (2012). Knowledge, Networks, and Knowledge Networks A Review and Research Agenda. Journal of Management, 38(4), 1115-1166.

  30. Wagner, C. S. (2005). Six case studies of international collaboration in science. Scientometrics, 62(1), 3-26.  See also, Gazni, A., Sugimoto, C. R., & Didegah, F. (2012). Mapping world scientific collaboration: authors, institutions, and countries. Journal of the American Society for Information Science and Technology, 63(2), 323-335.

  31. Bammer, G. (2008). Enhancing research collaborations: three key management challenges. Research Policy, 37(5), 875-887.

  32. Mandell, Myrna and Robyn Keast (2007). Evaluating Network Arrangements: Toward Revised Performance Measures. Public Performance & Management Review, 30(4), 574-597.