Science Environment for Ecological Knowledge
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Science Environment for Ecological Knowledge




Beam Knowledge Rep SMS Mar 05


  • Steve Cox (
  • David Chalcraft (
  • Shawn Bowers (
  • Bertram Ludaescher (
  • Deana Pennington (
  • Mark Schildhauer (
  • Katy Suding (
  • Evan Weiher (
  • Dan Higgins (
  • Manu Jayal (
  • Aimee Stewart (
  • Nico Franz (
  • Else Cleland (


  1. High-level ecological ontologies: develop detailed ontologies for Biodiversity & Productivity-- in terms of relevant concepts and relationships from the very general theoretical level, and drilling all the way down to the operational level (algorithms and measurements used in quantifying biodiversity and productivity).
  2. Ontologies of data sets and analyses: develop detailed ontologies of the data sets and analyses needed for some "past" Biodiversity/Productivity research, including clarifying the semantics and data transformations carried out to merge/integrate/summarize data, as well as "describing" the analytical components in ways that sufficiently expose their Inputs/Outputs and "functions" in ways that will facilitate discover and re-use of these components in alternative scientific workflows.
  3. Ontologies of ecological methodologies: develop detailed ontologies that capture the essential features and differentiating nuances of the field and other methodologies employed in capturing the data to be dealt with in these investigations (overlaps with task 2 above). (We must not lose sight of the need for "spatiotemporal ontologies", and the specific capabilities these will provide us with regards to 1-3 here)
  4. Scientific workflows: develop detailed scientific workflows for some "past" Biodiversity/Productivity research, to formally capture at a fine grain the steps and operations, with sufficient semantic annotation to facilitate their transparency and reusability by other researchers.
  5. Taxonomic capabilities: identify the specific types of services that "ecologists" might need on the select use case to improve the ability to deal with taxonomic names, especially in the context of historical, long-term and globally distributed biodiversity information
  6. Ecological research challenge: conceive of a "Challenging" Biodiversity/Productivity analyses, which could be enabled via a scientific workflow managment (Kepler), and that demonstrates the power of accessing distributed data and computing resources, and would otherwise be highly inefficient or intractable to a "typical" individual researcher.


Monday, March 7th

  • 8:30-9:30 Introductions, Overview of Goals, Status Update (Shawn, Deana, Mark)
  • 9:30-10:00 Linkages with Taxon (Aimee, Deana, Mark)
  • 10:00-10:30 Update from Manu about Spatiotemporal ontology work
  • 10:30-10:45 Break
  • 10:45-12:30 Work on Domain Ontologies (evaluate Rich's ontologies, GrOWL, SPARROW)
  • 12:30-1:30 Lunch
  • 1:30-3:30 Cont. Domain Ontologies
  • 3:30-4:00 Break
  • 4:30-5:30 Discussion and Preview for tomorrow (past analyses and data)

Tuesday, March 8th

  • 8:30-10:30 Develop Scientific Workflows
  • 10:30-10:45 Break
  • 10:45-12:30 Cont. with Scientific Workflows
  • 12:30-1:30 Lunch
  • 1:30-3:30 Develop Ontologies of Scientific Methods
  • 3:30-4:00 Break
  • 4:00-5:30 Discussion and Preview for tomorrow (new scientific challenge; new analysis and data needs)

Wednesday, March 9th

  • 8:30-10:30 Cont. with Ontologies for Scientific Methods (including presentation by NicoFranz about Use Case involving Taxonomic Name Resolution)
  • 10:30-10:45 Break
  • 10:45-12:30 Data Integration Ontologies
  • 12:30-1:30 Lunch
  • 1:30-3:30 Conceive of new BiodivProd challenge addressable through Kepler/KR/SMS
  • 3:30-4:00 Break
  • 4:00-5:30 Further define next step challenge

Thursday, March 10th

  • 8:30-10:30 Discussion (ontologies, workflows, next steps, assignments)
  • 10:30-10:45 Break
  • 10:45-12:00 Continue wrapup, next meeting?, and adjourn


March 7th

  • Introduction, etc., Mark
    • Went over the SEEK acronym soup
    • Goals of the meeting / Agenda

  • Aimee on Taxon
    • Now trying to get data into the system
    • Presentation from TDWG (Taxonomic Database Working Group)
      • solution
        • connect data and ideas in dbs
        • allow searches and retrievals
        • be customizable by the user
      • architecture
      • allow alternative algorithms to weight matches
      • allow taxonomists to: author new ideas, make new connections
        • along with a pub., publish the new connections, etc.
      • german moss data most comprehensive so far; 24000 concepts
      • ITIS is shallow/incomplete concepts
      • How to fit in with functional traits?
        • Some dbs in Europe; not in US
        • Can't derive functional traits to taxon; but a lot of constraints are taxonomic
        • Characteristic data ... hard to obtain
        • Ancillary db mined from literature on characteristics of species, mining data from these Floras
        • Building an analysis that works off of gene sequencing ... e.g., by incorporating GenBank

  • Manu
    • Reconciling datasets
      • Why don't datasets match, how to bring them "to the same level"
    • Observations
      • entities, context, methodology, unit, ..., representation
      • e.g., observations of overhunting
      • certain "functions" are called: e.g., rescaling, reprojecting, etc.
      • how to represent these rules? ...
        • A lot of discussion on transformation
        • Mark wants to know how to do unit conversion

  • Shawn
    • Onto stuff

  • Some datasets
    • arc: lgcover2001
    • gce: PLT-GCED-0409
    • Niwot and Konza sites have data, e.g., that require multiple datasets to create an "integratable" source

  • Use case: Reconstructing an Experiment
    • Data Request Form:
      1. Selected Sites with plants with only little or none secondary growth (shrubs) ... Herbacious dominated
      2. Replicated Plots (to compute means)
      3. Experimental manipulation of resources
      4. Species specific abundance in some way
      5. At first, wanted time series, but then couldn't do it -- chose the most recent year

  • Data integration
    • Do a column-by-column merge
      • for example r1(A, B) and r2(C, D) creates: r(A, B, C, D)
      • or, if you know, e.g., that A and C are similar (e.g., Area), then: r(AC, S, B, D), where S is a lineage column

Tuesday 8 March 2005

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This page last changed on 11-Mar-2005 08:03:30 PST by LTER.dpennington.