We report on a software prototype developed for the Marine Biodiversity Virtual Laboratory (MBVL), a project funded by NSF CyberSEES led by P. Fox at RPI and in collaboration with D. Mark Welch at MBL. We focused on a reproducible workflow for biodiversity indicators based on data from an underwater microscope, the Imaging FlowCytobot (IFCB). We targeted a multi-year, high temporal resolution (~20 minute) time series of IFCB data from the Martha's Vineyard Coastal Observatory. This workflow combines IFCB classification results with technical information about instrument operation to produce time series of per-class (e.g., per-species) abundance. This prototype heavily leveraged IFCB dashboard web interfaces for data access and used multiprocessing in a Jupyter notebook to achieve reasonable performance given the high volume of data being summarized. This prototype is available from our GitHub repository: https://github.com/hsosik/MBVL. We are presently developing workflows that integrate IFCB data and high-throughput sequencing data from the Visualize and Analyze data for Microbial Population Structures (VAMPS) database. Our workflows fit between "raw" and "cleaned" data in the processing pipeline for the Marine Biodiversity Observation Network (MBON). This poster and the accompanying live demo are relevant to the 2017 ESIP Winter Meeting session: "Reeling in the biologists: connecting the dots between observers, integrators, and decision makers."
Toward a marine biodiversity virtual laboratory to interact with data from an underwater microscope
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