Jennifer A. Dunne Santa Fe Institute Pacific Ecoinformatics & Computational Ecology Lab
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Transcript of Jennifer A. Dunne Santa Fe Institute Pacific Ecoinformatics & Computational Ecology Lab
Jennifer A. Dunne
Santa Fe InstitutePacific Ecoinformatics & Computational
Ecology Lab
Rich William, Neo Martinez, et al.
www.foodwebs.org
Challenges & Opportunitiesfor Ecological Informatics
• 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
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Challenge #1: Basic Data
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
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Ecoinformatics: technologies and practices for gath-ering, analyzing, visualizing, storing, retrieving and other-wise managing ecological knowledge and information
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Ecoinformatic fantasy
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
From Data to Data Bases
In Final Development: Webs on the Web (WoW)
1) 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
2) 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
3) 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)
From Data Bases to Knowledge Bases
Challenges: usability, quality control, security, accessibility, storage, maintenance
Linking KBs through Semantic Webs Tools
““The Semantic WebThe Semantic Web is an extension of the is an extension of the current web in which information is given well-current web in which information is given well-defined meaning, better enabling computers and defined meaning, better enabling computers and people to work in cooperation.”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
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
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 languageRDF: resource description frameworkSWRL*: semantic web rules languageSPARQL: SPARQL protocol and RDF query language
Projects funded by the U.S. National Science Foundation:Projects funded by the U.S. National Science Foundation:
Webs on the Web: Webs on the Web: Internet Database, Analysis & Visualization of Ecological NetworksInternet Database, Analysis & Visualization of Ecological Networks
Science on the Semantic Web: Science on the Semantic Web: Prototypes in BioinformaticsPrototypes in Bioinformatics
Science Environment for Ecological Knowledge (NCEAS)Science Environment for Ecological Knowledge (NCEAS)