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Publication . Article . Preprint . 2021

The Amsterdam Open MRI Collection, a set of multimodal MRI datasets for individual difference analyses

Lukas Snoek; Maite M. van der Miesen; Tinka Beemsterboer; Andries R. van der Leij; Annemarie Eigenhuis; H. Steven Scholte;
Open Access
English
Published: 19 Mar 2021 Journal: Scientific Data, volume 8 (eissn: 2052-4463, Copyright policy )
Publisher: Nature Publishing Group UK
Country: Netherlands
Abstract
We present the Amsterdam Open MRI Collection (AOMIC): three datasets with multimodal (3 T) MRI data including structural (T1-weighted), diffusion-weighted, and (resting-state and task-based) functional BOLD MRI data, as well as detailed demographics and psychometric variables from a large set of healthy participants (N = 928, N = 226, and N = 216). Notably, task-based fMRI was collected during various robust paradigms (targeting naturalistic vision, emotion perception, working memory, face perception, cognitive conflict and control, and response inhibition) for which extensively annotated event-files are available. For each dataset and data modality, we provide the data in both raw and preprocessed form (both compliant with the Brain Imaging Data Structure), which were subjected to extensive (automated and manual) quality control. All data is publicly available from the OpenNeuro data sharing platform.
Measurement(s) T2*-weighted images • T1-weighted images • diffusion-weighted images • Respiration • cardiac trace Technology Type(s) Gradient Echo MRI • Magnetization-Prepared Rapid Gradient Echo MRI • Diffusion Weighted Imaging • breathing belt • Pulse Oximetry Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13606946
Subjects by Vocabulary

Microsoft Academic Graph classification: Data sharing Set (abstract data type) Data structure Computer science Artificial intelligence business.industry business Modality (human–computer interaction) Metadata Neuroimaging Natural language processing computer.software_genre computer Emotion perception Cognition Face perception

Subjects

Data Descriptor, Cognitive neuroscience, Human behaviour, Social neuroscience, Library and Information Sciences, Statistics, Probability and Uncertainty, Computer Science Applications, Education, Information Systems, Statistics and Probability, FMRI, SIGNAL, PERSONALITY, VALIDATION, NOISE, SEGMENTATION, REGISTRATION, ACTIVATION, RESPONSES, MOVEMENT, Science, Q

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