The large data sets provided by in situ optical microscopes are allowing us to answer longstanding questions about the dynamics of planktonic ecosystems. To deal with the influx of information, while facilitating ecological insights, the design of these instruments increasingly must consider the data: storage standards, human annotation, and automated classification. In that context, we detail the design of the Scripps Plankton Camera (SPC) system, an in situ microscopic imaging system. Broadly speaking, the SPC consists of three units: (1) an underwater, free‐space, dark‐field imaging microscope; (2) a server‐based management system for data storage and analysis; and (3) a web‐based user interface for real‐time data browsing and annotation. Combined, these components facilitate observations and insights into the diverse planktonic ecosystem. Here, we detail the basic design of the SPC and briefly present several preliminary, machine‐learning‐enabled studies illustrating its utility and efficacy.

中文翻译：

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Scripps浮游生物相机系统：原位显微镜的框架和平台

原位光学显微镜提供的大量数据使我们能够回答有关浮游生态系统动力学的长期问题。为了处理信息的涌入，同时促进生态学见解，这些仪器的设计越来越必须考虑数据：存储标准，人工注释和自动分类。在这方面，我们详细介绍斯克里普斯浮游生物相机的设计（SPC）系统，在原位显微成像系统。从广义上讲，SPC由三个单元组成：（1）水下自由空间暗场成像显微镜；（2）基于服务器的数据存储和分析管理系统；（3）基于Web的用户界面，用于实时数据浏览和注释。这些组件结合在一起，有助于观察和洞察各种浮游生态系统。在这里，我们详细介绍了SPC的基本设计，并简要介绍了一些机器学习的初步研究，这些研究说明了其实用性和有效性。