Be FAIR to Pedestrian Dynamics Data

Maik Boltes, Alica Kandler
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引用次数: 0

Abstract

For improving the safety of people in large crowds, it is of great importance to understand the basic mechanisms of pedestrian dynamics, e.g. with help of experiments. The number of openly shared datasets of these experiments has increased in the last years also due to stricter requirements from journals and funders. We share our own experimental data by an open access data archive which data is widely used in the community. However, our data and also data of other researchers in the field of pedestrian dynamics is not annotated in a systematic or semantically harmonized way, which impairs FAIRness in general and interoperability specifically. In this paper, we propose a standardized extensible metadata schema and key data structures for trajectories and geometry. The proposed metadata schema and data structures hopefully support the interoperability within the community and will assist to make data reutilization more efficient. Our own legacy datasets are continuously annotated with essential information using this metadata schema steadily. This metadata is provided beside the converted data on our data archive and thus enhance its findability and reusability.
公平对待行人动态数据
为了提高大型人群中的人员安全,了解行人动力学的基本机制非常重要,例如借助实验。近年来,由于期刊和资助方提出了更严格的要求,公开共享这些实验数据集的数量有所增加。我们通过开放数据档案共享自己的实验数据,这些数据在社区中被广泛使用。然而,我们的数据以及行人动力学领域其他研究人员的数据并没有以系统或语义统一的方式进行注释,这在总体上影响了公平性,特别是互操作性。在本文中,我们提出了一个标准化的可扩展元数据模式以及轨迹和几何的关键数据结构。建议的元数据模式和数据结构有望支持社区内的互操作性,并有助于提高数据再利用的效率。我们自己的传统数据集不断使用这种元数据模式注释基本信息。在我们的数据档案中,这些元数据与转换后的数据一起提供,从而提高了数据的可查找性和可重用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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