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Research Ideas and Outcomes 6: e57602
https://doi.org/10.3897/rio.6.e57602 (14 Aug 2020)
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  • 1. Introduction
    • 1.1 Scope
      • 1.1.1 Machine Learning and Training Data Sets
      • 1.1.2 Prior Research on Automation
      • 1.1.3 Crowdsourcing and Human-in-the-Loop
    • 1.2 Project Context
  • 2. Methodology
  • 3. Gap Analysis
    • 3.1 Image segmentation
    • 3.2 Feature analysis, colour analysis and image recognition (object detection)
    • 3.3 Condition checking, image trait extraction and species identification
    • 3.4 Optical character recognition of handwritten and printed/typed text
    • 3.5 Atomization, validation and classification
    • 3.6 Geographic resolution, person resolution and taxonomic resolution
    • 3.7 Label (Biological) Trait Extraction
  • 4. Building a Workflow
    • 4.1 Selecting a Human-in-the-Loop Workflow Management Systems
    • 4.2 Implementing a standardised workflow language for interoperability
    • 4.3 Incorporating prior information and the statistical framework
    • 4.4 Assembling the workflow
    • 4.5 The Specimen Data Refinery techology stack
  • 5. Conclusion
  • Glossary
  • Acknowledgements
  • References
  • Supplementary files
  • Endnotes
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