Jung Heejung, Amini Maryam, Hunt Bethany J, Murphy Eilis I, Sadil Patrick, Halchenko Yaroslav O, Petre Bogdan, Miao Zizhuang, Kragel Philip A, Han Xiaochun, Heilicher Mickela O, Sun Michael, Collins Owen G, Lindquist Martin A, Wager Tor D
Dartmouth College, Hanover, NH, USA.
Johns Hopkins University, Baltimore, MD, USA.
Sci Data. 2025 Aug 22;12(1):1465. doi: 10.1038/s41597-025-05154-x.
Cognitive neuroscience has advanced significantly due to the availability of openly shared datasets. Large sample sizes, large amounts of data per person, and diversity in tasks and data types are all desirable, but are difficult to achieve in a single dataset. Here, we present an open dataset with N = 101 participants and 6 hours of scanning per participant, including 6 multifaceted functional tasks, 2 hours of naturalistic movie viewing, structural T1 images and multi-shell diffusion imaging as well as autonomic physiological data. This dataset's combination of sample size, extensive data per participant (>600 iso-hours of data), and a wide range of experimental conditions - including cognitive, affective, social, and somatic/interoceptive tasks - positions it uniquely for probing important questions in cognitive neuroscience.
由于公开共享数据集的可用性，认知神经科学取得了显著进展。大样本量、每人大量的数据以及任务和数据类型的多样性都是理想的，但在单个数据集中很难实现。在这里，我们展示了一个开放数据集，有N = 101名参与者，每位参与者扫描6小时，包括6个多方面的功能任务、2小时的自然电影观看、结构T1图像和多壳扩散成像以及自主生理数据。该数据集在样本量、每位参与者的大量数据（超过600个等效小时的数据）以及广泛的实验条件（包括认知、情感、社会和躯体/内感受任务）方面的结合，使其在探究认知神经科学中的重要问题方面具有独特的地位。
