面向鱼类标本图像的灵活元数据管道

Dom Jebbia, Xiaojun Wang, Y. Bakiş, H. Bart, J. Greenberg
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引用次数: 1

摘要

灵活的元数据管道对于支持FAIR数据原则至关重要。尽管有这种需求,研究人员很少报告他们识别支持最佳灵活性的元数据标准和协议的方法。本文报告了一项旨在开发灵活的元数据管道的倡议,该管道包含来自多个数据存储库和鱼类收集的超过300,000个数字鱼类标本图像。这些图像及其相关元数据被用于人工智能相关的科学研究,包括自动物种识别、分割和特征提取。本文提供了上下文背景,随后提出了一个四阶段的方法,包括:1。问题的评估,2。解的调查,3。4.实施;细化。这项工作是NSF利用数据革命,生物引导神经网络(NSF/HDR- bgnn)项目和HDR图像组学研究所的一部分。提出了一个RDF图原型管道,讨论了研究意义,并对研究结果进行了总结。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Toward a Flexible Metadata Pipeline for Fish Specimen Images
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.
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