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Jennifer A. Dunne Santa Fe Institute Pacific Ecoinformatics & Computational Ecology Lab

Challenges & Opportunities for Ecological Informatics. Jennifer A. Dunne Santa Fe Institute Pacific Ecoinformatics & Computational Ecology Lab Rich William, Neo Martinez, et al. www.foodwebs.org. Challenge #1: Basic Data.

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Jennifer A. Dunne Santa Fe Institute Pacific Ecoinformatics & Computational Ecology Lab

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  1. Challenges & Opportunitiesfor Ecological Informatics Jennifer A. Dunne Santa Fe Institute Pacific Ecoinformatics & Computational Ecology Lab Rich William, Neo Martinez, et al. www.foodwebs.org

  2. Challenge #1: Basic Data • Ecologists make observations and save them as spreadsheet or text files • Raw data rarely published or made available online • Summaries provide minimal ad-hoc descriptions of data context • Context (why, when, where, how) lost as time proceeds Value of observation Time since observation

  3. Challenge #2: Finding & Integrating Data • Data relevant for ecological questions is DIVERSE & DISPERSED • Available databases often primitive (little or no metadata) • Data gathering, integration, and synthesis are done by hand Value of observation Time since observation

  4. Ecoinformatics:technologies and practices for gath-ering, analyzing, visualizing, storing, retrieving and other-wise managing ecological knowledge and information Ecoinformatic fantasy Value of observation Time since observation

  5. From Data to Data Bases Early food-web researchers (primarily Joel Cohen) introduced sharable catalogs of ecological datasets: 1978: First published “catalog” included 30 food webs 1986: Catalog expanded to 113 food webs 1989: EcOWEB, the first “machine-readable data base of food webs” (now >200 webs) Interest in comparative studies led to a culture of data sharing and an early “first-generation” data base. BUT, current access to food web data is still primitive:  Requesting EcOWEB floppy disc from Cohen/Rockefeller  Hand-mining individual datasets from literature  Contacting researchers individually  Emailing me for second generation data

  6. From Data Bases to Knowledge Bases • In Final Development: Webs on the Web (WoW) • Knowledge Base • -100s to 1000s of food webs and other ecological networks, flexible data format • -10Ks to 100Ks instances of feeding interactions • -Species info (taxonomy, phylogeny, biomass, body size, metabolic rates, etc.) • -Quantitative link info (frequency, flow, preference, etc.) • -Additional info (geographic, provenance, versions, citations, geographic, etc.) • -Downloading, uploading, annotation capabilities • Analysis Tools • -Calculation of dozens of network structure properties • -Modeling (network structure, nonlinear bioenergetic dynamics) • -In silico experiments (biodiversity loss, invasions, etc.) • -Link to other software (Pajek, Mage, EcoPath/EcoSim, etc.) • -Trophic inference (phylogenetic, morphological) • -Pipeline architecture allows users to plug in their own algorithms • Visualization Tools • -Highly interactive and customizable 3D visualizations of ecological networks • -Animations of dynamics, and graphical output of simulations • -Images & movies of species & interactions (www.arkive.com - Images of Life on Earth) Challenges: usability, quality control, security, accessibility, storage, maintenance

  7. Linking KBs through Semantic Webs Tools “The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation.” -Tim Berners-Lee et al. (2001) The Semantic Web. Scientific American Semantic Web technologies are being “designed to improve communications between people using differing terminologies to extend the interoperability of databases.” -Jim Hendler (2003) Science and the Semantic Web. Science

  8. How to integrate dispersed, heterogeneous ecological information resources on the WWW? • SPiRE:Semantic Prototypes in Research Ecoinformatics • SWISST:Semantic Web Informatics for Species in Space & Time • Creating a first generation of user-friendly and highly extensible open-source software that stores, retrieves, analyzes, visualizes, and integrates distributed information relating to variation within and among species. • Ontologies: mapping between current terminologies and cleaned-up, structured categories  Integrative web services: queries & information mashups •  Integrative reasoning tools • Easy-to-use user interfaces

  9. Three Central Tasks for SPiRE & SWISST: 1) Implement new information architectures to increase functionality of ecoinformatic software 2) Implement new client tools with user-friendly GUIs (for concept refining; data entry, discovery, analysis; data visualization) 3) DBs into KBs: extend ontologies & increase metadata New Semantic Web Standards (W3C): OWL: ontology web language RDF: resource description framework SWRL*: semantic web rules language SPARQL: SPARQL protocol and RDF query language

  10. Projects funded by the U.S. National Science Foundation: Webs on the Web: Internet Database, Analysis & Visualization of Ecological Networks Science on the Semantic Web: Prototypes in Bioinformatics Science Environment for Ecological Knowledge (NCEAS)

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