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Flexible metadata pipelines are crucial for supporting the FAIR dataprinciples. Despite this need, researchers seldom report their approaches foridentifying metadata standards and protocols that support optimal flexibility.This paper reports on an initiative targeting the development of a flexiblemetadata pipeline for a collection containing over 300,000 digital fishspecimen images, harvested from multiple data repositories and fishcollections. The images and their associated metadata are being used forAI-related scientific research involving automated species identification,segmentation and trait extraction. The paper provides contextual background,followed by the presentation of a four-phased approach involving: 1. Assessmentof the Problem, 2. Investigation of Solutions, 3. Implementation, and 4.Refinement. The work is part of the NSF Harnessing the Data Revolution, BiologyGuided Neural Networks (NSF/HDR-BGNN) project and the HDR Imageomics Institute.An RDF graph prototype pipeline is presented, followed by a discussion ofresearch implications and conclusion summarizing the results.

Toward a Flexible Metadata Pipeline for Fish Specimen Images