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Scoping Research in Sustainability Information Science

Explore the unique challenges in developing sustainability information science, focusing on ontology, metrics, and connections to related concepts. Address the dynamic nature of sustainability and highlight opportunities for research advancement and community-building.

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Scoping Research in Sustainability Information Science

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  1. Scoping Research in Sustainability Information Science Steven D. Prager Department of Geography University of Wyoming David Bennett Department of Geography University of Iowa

  2. From Reconciling Imperatives to Bridging Scholarship and Policy <INSERT MODEL HERE> ?

  3. Purpose • Why a new information science? • What makes sustainability information science different from other existing information sciences? • Is the goal to push information science forward using the unique needs of sustainability science as motivation? • Is it to adapt and synthesize existing information science such that it better supports sustainability science and decision-making?

  4. Definition • Ontology of a sustainable system • Ontology of a sustainable information system • Well defined sustainability metrics or classes of metrics • Well defined connections to related concepts (e.g., resilience and adaptive capacity)

  5. Definition Challenging because sustainability is: • Not a fixed natural state that can be known solely through scientific measurement, but culturally defined, (Dahl 2012) • Resources required for environmental, economic, and social wellbeing change through time and across space • Therefore, sustainability is contextualized in time, space, and culture • Transformation and adaptability of population/resource relationships over time and space must be represented captured and modeled (Walker et al. 2004, Folke et al. 2010).


  6. Development and Research • Spatiotemporal dynamics of natural processes spatiotemporal dynamics of human processes • The effect of boundaries- • jurisdictional • ideological • cultural • technological • Future directed • informed by the past and present • Fundamentally Uncertain • Must embrace the unknown/unknowable.

  7. Equifinality vs. Multifinality Path 1 Path 2 Outcome Path 3 Outcome 1 Path Outcome 2 Outcome 3

  8. Many Elements • Hysteresis • Potential multiple stable states • Processes spanning multiple hierarchies and scale • Spatial/Ecological • Political • Individual to national-level trends • Intersecting/integrated fast and slow processes • Complex feedbacks • Adaptive cycle • Growth/exploitation (r) • conservation (K) • Collapse/release (Ω) • reorganization (α) • Adaptive and emergent behavior • Dynamic networks • Social networks • Ecological networks • Social-ecological networks • Qualitative & quantitative data Provenance of complexity  Provenance of sustainability

  9. Research Opportunities • New ST representations • Boundary dynamics, flows, human/nature interaction, feedbacks. • Citizen science, social networks, pervasive/ubiquitous data collection. • How/when/why is this useful. • Sensor networks, data discovery (post-normal science) • Role of HPC, distributed computing, etc. • Much more…

  10. Strategies for Moving Forward • Need to build critical mass on two fronts: • Underlying Fundamentals • Ontology of SIS as a first pass at articulating a collective understanding of SIS. • Contributors and Problems • A Research Coordination Network to build/escalate synergies in SS research.

  11. Ontology as Theory • Fonseca (2007) suggests that if we build theories (ontologies for science) BEFORE conceptual modeling, we build better models. • This is in the context of information science, but why not view sustainability science from this perspective? • Assertion: ontology of sustainability science will enable better ontologies for sustainability science. • Better ontologies forsustainability science will enable better science. (Fonseca, 2007)

  12. Formalizing Information Representation Guarino (1997) Foundations of Sustainability Information Representation Theory: Spatial-Temporal Dynamics of Sustainable Systems Nyerges et al. (In Review)

  13. Building Community via DIBBs • Conceptualization Award • Developing disciplinary and interdisciplinary communities' understanding of their data. • The output of a conceptualization award will be design specifications for creating a sustainable data infrastructure that will be discoverable, searchable, accessible, and usable to the entire research and education community.

  14. Building Community via RCN • Bring sustainability information scientists working on various topics together in synergistic ways • Bring sustainability information scientists and social and natural scientists together in synergistic ways to: • form common language and conceptual framework • insure help insure computation tools developed in the name of SIScience are well conceptualized and useful • Provide case studies to help develop and contextualize SIS • Provide applications to illustrate the utility of SIS

  15. Application and case study • Sustainability science is inherently interdisciplinary • Sustainability informaticists can’t do this in isolation • Interaction with interdisciplinary teams working in sustainability science is required

  16. Opportunities for Engagement • Workshop proposal in process: • GeoVoCamp-like approach. • Collaborative, participatory. • Product oriented – foundation for later DIBBs or similar proposals. • SLCN Steering Committee: • Active coordination, community building • Preparing in anticipation of RCN and related solicitations.

  17. Thanks sdprager@uwyo.edu

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