Matt Jones software-interoperability
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Matthew B. Jones discusses software for ecological forecasting and synthesis. He describes several existing software tools like the Ocean Health Index and Kepler that support scientific workflows for data access, analysis across packages, documentation, and provenance tracking. Scientific workflows promote interoperability and ensure executability, replicability, reproducibility, transparency, modularity, reusability, and provenance of analyses. Harnessing the "long tail" of science requires efficient data federation, interoperable software workflows, central search for discovery, and just-in-time data integration with loose coupling and schema-less storage.
Matt Jones software-interoperability
- 1. Matthew B. Jones National Center for Ecological Analysis and Synthesis (NCEAS) University of California Santa Barbara Advancing Software for Ecological Forecasting March 25, 2014 Software for Ecological Synthesis
- 2. Ocean Health Index (OHI) OceanHealthIndex Halpernetal.2012
- 3. The “long-tail” of science Heidorn, P. 2008. doi:10.1353/lib.0.0036
- 6. Data Heterogeneity Heterogeneity HighLow •Tight coupling •Simple subsetting •Explicit semantics •Loose coupling •Hard subsetting •Limited semantics Data set size LowHigh
- 7. Diverse Analysis and Modeling • Wide variety of analyses used in ecology and environmental sciences – Statistical analyses and trends – Rule-based models – Dynamic models (e.g., continuous time) – Individual-based models (agent-based) – many others • Implemented in many frameworks – R, Matlab, SAS, SPSS, Jump, C, Python, Fortran
- 8. Kepler DMP-Tool Software & Data Interoperability Plan Collect Assure Describe Preserve Discover Integrate Analyze
- 9. • Produce an open-source scientific workflow system • Design, share, and execute scientific workflows • Support scientists in a variety of disciplines • e.g., biology, ecology, oceanography, astronomy • Features • Data access • Cross analytical packages • Documentation • Provenance tracking • Model archiving and sharing
- 10. Scientific workflows promote interoperability
- 11. Why workflows? • Executability • Replicability • Reproducibility • Transparency • Modularity • Reusability • Provenance
- 12. How do we harness the long tail? • Efficient data federation • Interoperable software workflows • Central search for discovery • Just-in-time data integration – Loose coupling – Schema-less storage
Editor's Notes
- #5: Collated data is being archived publicly, online after PI approval or a 2 week waiting periodThis is the GoAmetacat URL
- #9: Other development activities during years 2-5 will focus on expanding the suite of tools that are available through the Investigator Toolkit. New tool additions will be identified and prioritized by the DataONE Users Group.