Eldesoky Ahmed Hazem, Gil Jorge, Kindvall Oskar, Stavroulaki Ioanna, Jonasson Leif, Bennett David, Yang Wenqing, Martínez Diaz Alonso Francisco, Lichter Rachel, Petrou Frixos, Pont Meta Berghauser
Spatial Morphology Group (SMoG), Department of Architecture and Civil Engineering, Chalmers University of Technology, Gothenburg, Sweden.
Calluna AB, Linköping, Sweden.
Sci Data. 2025 Jul 10;12(1):1180. doi: 10.1038/s41597-025-05481-z.
Bird species occurrence datasets are a valuable resource for understanding the effects of urbanization on various biotic conditions (e.g., species occupancy and richness). Existing datasets offer promising opportunities to explore variations among cities and along the urban-rural gradient. However, there is a lack of observation data to systematically capture intra-urban variations at a fine spatial scale, especially in dense urban areas. Here, we describe the production and validation of a machine learning-generated bird occurrence dataset based on 10,691 hours of passive audio recordings systematically collected across different types of dense urban forms in Gothenburg, Sweden. The dataset is available in a standard Darwin Core Archive (DwC-A) format, to ensure data interoperability, and includes 239,597 occurrence records of 61 species from April 21 to June 16, 2024, across 30 sites in Gothenburg. We anticipate that this dataset will be a valuable resource for researchers in urban ecology, planning, and design to better understand the relationship between the characteristics of different types of dense urban forms and various biotic conditions in cities.
鸟类物种出现数据集是了解城市化对各种生物状况(如物种占有率和丰富度)影响的宝贵资源。现有数据集为探索不同城市之间以及城乡梯度上的差异提供了契机。然而,缺乏观测数据来系统地捕捉精细空间尺度上的城市内部差异,尤其是在密集城市地区。在此,我们描述了一个基于在瑞典哥德堡不同类型密集城市形态中系统收集的10691小时被动音频记录而生成的机器学习鸟类出现数据集的制作与验证过程。该数据集采用标准达尔文核心档案(DwC - A)格式提供,以确保数据的互操作性,包含2024年4月21日至6月16日期间哥德堡30个地点61种鸟类的239597条出现记录。我们预计该数据集将成为城市生态学、规划和设计领域研究人员的宝贵资源,有助于他们更好地理解不同类型密集城市形态特征与城市中各种生物状况之间的关系。