# Toward a Flexible Metadata Pipeline for Fish Specimen Images

arXiv: https://arxiv.org/abs/2211.15472 · HTML: https://mlsanity.com/paper/2211.15472/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 59 references that are in the map.
- First posted on arXiv 18 Nov 2022.

## Builds on (most cited first, 1 of 1)

- [The role of metadata in reproducible computational research](https://mlsanity.com/paper/2006.08589/) (2020)

## Abstract (arXiv)

Flexible metadata pipelines are crucial for supporting the FAIR data principles. Despite this need, researchers seldom report their approaches for identifying metadata standards and protocols that support optimal flexibility. This paper reports on an initiative targeting the development of a flexible metadata pipeline for a collection containing over 300,000 digital fish specimen images, harvested from multiple data repositories and fish collections. The images and their associated metadata are being used for AI-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. Assessment of the Problem, 2. Investigation of Solutions, 3. Implementation, and 4. Refinement. The work is part of the NSF Harnessing the Data Revolution, Biology Guided Neural Networks (NSF/HDR-BGNN) project and the HDR Imageomics Institute. An RDF graph prototype pipeline is presented, followed by a discussion of research implications and conclusion summarizing the results.