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Brain imaging (fMRI, EEG), neural recordings, connectome, cognitive experiments, psychology
1,787 datasets
A dataset created using LeRobot, containing 15 episodes and 6,619 frames of telemetry and video data for a single task. It was authored by Beegbrain and last updated on June 6, 2025. The data is structured for training machine learning models in robotics.
ARC Mega is a large-scale mixture of Abstraction and Reasoning Corpus (ARC) style prompts and non-ARC instruction/answer pairs. The dataset, created by mindware, originated from the jack-arc training runs and was last updated in November 2025. It is split into ARC-only and non-ARC partitions while preserving the 'MindsAI' prompt/response CSV format.
psytechlab published a Russian-translated version of the Cognitive Distortions detection dataset on 2025-08-29. The dataset was originally created by Sagarika Shreevastava and Peter W. Foltz for detecting cognitive distortions from patient-therapist interactions. The Russian version was generated using a pipeline that relies on modern large language models.
Permanent Onderzoek van de Leefsituatie - POLS rechtshulp instanties 1997-2004 is a survey dataset collected by the Centraal Bureau voor de Statistiek (CBS). It contains information on problems with potential legal implications and the use of legal assistance agencies among the Dutch population aged 18 and older. The dataset is a secured microfile, meaning it has been processed to prevent identification of individuals or households.
Diffusion tensor MRI-derived anisotropy index values from over 20,000 voxels registered into a 3D segmented rat atlas covering 150 brain areas. It was generated from a study using a fluid percussion model to concuss the right caudal or rostral cortices in Sprague Dawley rats, with data acquired five days post-injury. The research was conducted by Praveen Kulkarni and published in 2020.
A collection of real-time functional magnetic resonance imaging (rtfMRI) data used to validate an online spatial normalization method. The method combines a novel affine registration (PA-GN(β) AFR) and nonlinear registration (DCT-based NLR) for fast processing within a single repetition time (TR). The data was used to demonstrate brain activation in rtfMRI and confirm the accuracy of the proposed normalization technique.
Featuring resting-state functional connectivity MRI data from 18 healthy human subjects and 18 anesthetized rats. It includes correlation analyses with and without global signal correction and spatial smoothing, focusing on the properties of negative correlations. The data was generated to investigate the physiological basis of negative correlations in brain connectivity.
Electrophysiological recordings from mouse LGN neurons under visual stimulation paradigms. The data includes properties such as centre-surround organization, receptive field size, spontaneous firing rate, and linearity of spatial summation. It also captures temporal frequency tuning features like 'high-pass' and 'low-pass' cells, contrast gain, and a small proportion of direction/orientation selective cells.
Featuring magnetoencephalography (MEG) and electroencephalography (EEG) data from a study investigating cross-frequency phase synchrony (CFS) during visual working memory maintenance. The data was collected by Felix Siebenhühner and published in 2020 to explore load-dependent synchronization between theta, alpha, beta, and gamma oscillations across visual, fronto-parietal, and dorsal attention brain systems.
Dryad hosts a neuroimaging dataset from a study of 41 participants, including 20 Parkinson's disease patients and 21 healthy controls. The data comprises high-resolution T1-weighted and 60-direction diffusion-weighted 3T MRI images. It includes derived volumetric and diffusion property measures for cortical, deep grey matter, and white matter regions.
Multi-muscle electromyographic recordings analyzed to map motoneuron activity and extract locomotor modules during human locomotion. The data reconstructs motoneuron activation patterns for three distinct speed conditions: slow walking, fast walking, and running.
Comprising magnetoencephalography (MEG) data from 27 adult participants (13 with ASD, 14 controls) performing a motion direction discrimination task. It examines the relationship between gamma-band neural activity and varying levels of visual motion coherence.
Encompassing electrophysiological recordings and computational modeling data from gerbil medial superior olive (MSO) neurons, investigating subthreshold and spike resonance. The study characterizes resonance using impedance curves from sinusoidal stimuli and explores nonlinear voltage-dependent regimes. The data underpins findings on the role of the IKLT potassium current in neuronal resonance.
Neuroscience data from human magnetoencephalography and mouse local field potentials examines transient beta frequency events (15-29Hz) in primary somatosensory neocortex. The dataset supports analysis of how event rate and timing relate to perception and attention across species. It was authored by Hyeyoung Shin and published in 2020.
Research data from a 2020 study by Jolanda Jetten investigates the relationship between membership in multiple important social groups and personal self-esteem across diverse populations. The dataset supports analyses of longitudinal effects and mediation by collective self-esteem. Specific row counts, column details, and sample data are not provided in the input.
Jacques Jonas and colleagues recorded local neurophysiological activity from 1,678 contact electrodes implanted in the ventral occipito-temporal cortex of 28 epileptic patients. The dataset provides direct neural measures of face-selective responses, offering evidence for regional specialization previously observed in neuroimaging.
A 2020 dataset from Tobias Kühn investigates the locking of correlated neural spiking to beta-oscillations in cortical networks. It contains analytical results from mean-field and linear response theory applied to binary recurrent random networks. The data models how pairwise zero-time-lag covariances and mean activities are modulated by a periodic driving stimulus.
This dataset accompanies a statistical framework for neuroimaging data analysis based on mutual information estimated via a Gaussian copula. The framework, developed by Robin A. A. Ince, provides a method for analyzing multivariate data from modalities like MEG and EEG, validated on discrete stimulus classes and continuous stimulus features. The associated open-source code was last updated in 2020.
A collection of neuroimaging data from 43 healthy subjects with a mean age of 26.3 years. It includes diffusion tensor imaging and arterial spin labeling measurements to analyze cerebral blood flow and its relation to white matter microstructure. The analysis was performed using tract-based spatial statistics across subjects and white matter tracts.
Aggregating fMRI data from a double-blind sham-controlled pilot trial with 11 patients (6 women, 5 men) with idiopathic Parkinson Disease. It captures resting-state functional connectivity (RSFC) before and after effective or sham Automatic Mechanical Peripheral Stimulation (AMPS), focusing on seed ROIs in basal ganglia, sensory-motor cortices, supplementary motor areas, and cerebellum.