Model-data symbiosis is the view that there is an interdependent and mutually beneficial relationship between data and models, whereby models are data-laden and data are model-laden. In this article I elaborate and defend the second, more controversial, component of the symbiosis view and construct a taxonomy of the different ways in which theoretical and simulation models are used in the production of data sets. Each is defined and briefly illustrated with an example from the geosciences. I argue that model-filtered data are typically more accurate and reliable than so-called raw data and, hence, beneficially serve the epistemic aims of science.

中文翻译：

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迈向模型负载数据分类法

模型-数据共生是一种观点，即数据和模型之间存在相互依赖和互利的关系，即模型承载数据，数据承载模型。在本文中，我详细阐述并捍卫了共生观点的第二个更具争议性的组成部分，并构建了一个分类法，将理论和模拟模型用于生成数据集的不同方式进行分类。每一个都用地球科学中的一个例子来定义和简要说明。我认为模型过滤的数据通常比所谓的原始数据更准确和可靠，因此有益于科学的认知目标。