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Using Specimen Data in Scientific Workflow Environments to Connect to Metadata Archive and Discovery Services in Environmental Biology. CJ Grady, J.H. Beach, A. Stewart, J. Cavner University of Kansas Biodiversity Institute. Geospatial Metadata. Describes What it is What it looks like

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slide1

Using Specimen Data in Scientific Workflow Environments to Connect to Metadata Archive and Discovery Services in Environmental Biology

CJ Grady,

J.H. Beach, A. Stewart, J. Cavner

University of Kansas Biodiversity Institute

geospatial metadata
Geospatial Metadata
  • Describes
    • What it is
    • What it looks like
    • Who assembled it
    • When it was collected
    • Etc

1960 - 1990

slide3
EML
  • Ecological Metadata Language
    • XML Schema
    • Open Source
    • Community Driven
    • Describes ecological data
      • Occurrence Data
      • Climate Layers
      • Species Ranges
narratives
Narratives
  • Transformation of metadata into a story that is appropriate for the intended audience
  • Same metadata can be used to create narrative for:
    • Scientists
    • Undergrads
    • K-12 students
slide6

DataONE

    • Distributed system for:
      • Queries
      • Data replication
    • Initially supports EML
study of experiment reproducibility
Study of Experiment Reproducibility

Ellison, Aaron. 2010. Repeatability and transparency in ecological research. Ecology 90.

process metadata
Process Metadata
  • Data about the process used
  • Descriptive and prescriptive
  • Documents process used to generate data / metadata
    • Quality control
    • SDM experiments
    • RAD experiments
capabilities
Capabilities
  • Reproducibility
    • Actions are documented
  • Transparency
    • Experiments can be evaluated and validated
  • Publishing
    • Metadata can be published along with results
what we have done
What we have done
  • EML for all of our Species Distribution Modeling services
  • Simple process metadata
    • Documents how an experiment is ran through our cluster including what versions of software
    • Also describes what web services would be called to execute the experiment again
what we have done1
What we have done
  • Clients
    • Python library
    • VisTrails
    • QGIS
what we are doing
What we are doing
  • Publishing EML to a repository
  • Client extensions
  • Extending process metadata
    • HTTP message
    • XPath
process metadata extensions
Process Metadata Extensions
  • HTTP Message
    • Documents any web resource call over HTTP
  • XPath processing
what we will do next
What we will do next
  • Use standard APIs to communicate with DataONE
  • Continue to search for standard process metadata and include it whenever possible
  • Contribute process metadata extensions back to the community
  • Add additional conditional analysis elements to the schema (JSON, etc)
reproducibility
Reproducibility
  • Simple process metadata
  • EML process metadata extensions
  • Lifemapper client EML reader
transparency
Transparency
  • EML for all service objects
  • Descriptive process metadata
publishing aid
Publishing Aid
  • Client access to public data / metadata catalogs
  • Publish buttons
lessons learned
Lessons Learned
  • Had success starting with narrow, specific, process steps and generalizing them
    • Calls to our web services expanding to any HTTP call
  • Easy to get carried away with all of the possibilities
lifemapper funded by
Lifemapper funded by:

U.S. National Science Foundation NSF EPSCoR 0553722

NSF EPSCoR 0919443

EHR/DRL 0918590

BIO/DBI 0851290

OCI/CI-TEAM 0753336

http://www.lifemapper.org

[email protected]

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