设想一个全球基础设施,利用数字化收藏的潜力
Quentin Groom1, Mathias Dillen1, Wouter Addink2,3
1Meise Botanic Garden, Meise, Belgium Meise Botanic Garden Meise Belgium.
Biodiversity data journal
|December 11, 2023
概括
生物收藏可以通过集中数以百万计的数字化图像进行先进的机器学习和计算机视觉分析来解锁新的研究. 这种基础设施是可行的和有价值的.
科学领域:
- 生物多样性信息学 生物多样性信息学
- 数字标本成像成像数字标本成像
- 计算生物学是一种计算生物学.
背景情况:
- 在过去的二十年里,数以百万计的生物样本图像已被数字化并在线访问.
- 图像分析技术,特别是机器学习和计算机视觉,已经取得了显著的进步.
- 目前在生物收藏中使用图像分析是有限的,因为图像数据分散.
研究的目的:
- 倡导基础设施的发展,以支持生物收藏对象的大规模图像分析.
- 突出未开发的研究潜力,以创建一个统一的集合图像集.
- 为了证明投资于这样的基础设施的可行性和价值.
主要方法:
- 集中图像数据基础设施的概念框架.
- 审查与生物收藏相关的当前图像分析能力.
- 拟议基础设施的可行性评估.
主要成果:
- 通过集中式生物图像数据,存在着大量未开发的潜力,用于新型研究和应用.
- 支持对收藏物品图像分析的拟议基础设施在技术上是可行的.
- 投资于此类基础设施的潜在科学回报充分证明了这一点.
结论:
- 生物收集图像的集中基础设施对于推进研究至关重要.
- 机器学习和计算机视觉可以有效地应用于大型,统一的图像体.
- 开发这种基础设施对科学界来说是一个有价值的投资.
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