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CJ Grady, J.H. Beach, A. Stewart, J. Cavner University of Kansas Biodiversity Institute

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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CJ Grady, J.H. Beach, A. Stewart, J. Cavner University of Kansas Biodiversity Institute

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  1. 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

  2. Geospatial Metadata • Describes • What it is • What it looks like • Who assembled it • When it was collected • Etc 1960 - 1990

  3. EML • Ecological Metadata Language • XML Schema • Open Source • Community Driven • Describes ecological data • Occurrence Data • Climate Layers • Species Ranges

  4. 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

  5. Narrative Example

  6. DataONE • Distributed system for: • Queries • Data replication • Initially supports EML

  7. Study of Experiment Reproducibility Ellison, Aaron. 2010. Repeatability and transparency in ecological research. Ecology 90.

  8. There is a Solution!

  9. Process Metadata • Data about the process used • Descriptive and prescriptive • Documents process used to generate data / metadata • Quality control • SDM experiments • RAD experiments

  10. Capabilities • Reproducibility • Actions are documented • Transparency • Experiments can be evaluated and validated • Publishing • Metadata can be published along with results

  11. 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

  12. What we have done • Clients • Python library • VisTrails • QGIS

  13. What we are doing • Publishing EML to a repository • Client extensions • Extending process metadata • HTTP message • XPath

  14. Process Metadata Extensions • HTTP Message • Documents any web resource call over HTTP • XPath processing

  15. 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)

  16. Reproducibility • Simple process metadata • EML process metadata extensions • Lifemapper client EML reader

  17. Transparency • EML for all service objects • Descriptive process metadata

  18. Publishing Aid • Client access to public data / metadata catalogs • Publish buttons

  19. 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

  20. 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 cjgrady@ku.edu

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