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Abstract Technological advances in three imaging techniques have opened the door to advanced morphological analyses and habitat mapping for biologists and ecologists.At the same time, the challenge of translating complex 3D data into meaningful metrics that can be used in conjunction with biological data currently hinders progress and accessibility.We introducehabtools, an R package that provides R functions to efficiently calculate complexity and shape metrics from DEMs, 3D meshes and 2D shapes as well as some helper functions to facilitate workflow.We expect the functionality ofhabtoolsto continue to expand as new metrics and faster methods become available, and we welcome new contributions and ideas.
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A FAIR and modular image‐based workflow for knowledge discovery in the emerging field of imageomics
Abstract Image‐based machine learning tools are an ascendant ‘big data’ research avenue. Citizen science platforms, like iNaturalist, and museum‐led initiatives provide researchers with an abundance of data and knowledge to extract. These include extraction of metadata, species identification, and phenomic data. Ecological and evolutionary biologists are increasingly using complex, multi‐step processes on data. These processes often include machine learning techniques, often built by others, that are difficult to reuse by other members in a collaboration.We present a conceptual workflow model for machine learning applications using image data to extract biological knowledge in the emerging field of imageomics. We derive an implementation of this conceptual workflow for a specific imageomics application that adheres to FAIR principles as a formal workflow definition that allows fully automated and reproducible execution, and consists of reusable workflow components.We outline technologies and best practices for creating an automated, reusable and modular workflow, and we show how they promote the reuse of machine learning models and their adaptation for new research questions. This conceptual workflow can be adapted: it can be semi‐automated, contain different components than those presented here, or have parallel components for comparative studies.We encourage researchers—both computer scientists and biologists—to build upon this conceptual workflow that combines machine learning tools on image data to answer novel scientific questions in their respective fields.
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- PAR ID:
- 10502003
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 15
- Issue:
- 6
- ISSN:
- 2041-210X
- Format(s):
- Medium: X Size: p. 1129-1145
- Size(s):
- p. 1129-1145
- Sponsoring Org:
- National Science Foundation
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