Omkar2118/datasets huggingface dataset:
AOMIC
Full name: Amsterdam Open MRI Collection

The Amsterdam Open MRI Collection (AOMIC) is a collection of three datasets with multimodal (3T) 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.

Provide:

Three datasets with multimodal 3T MRI data and detailed demographics and psychometric variables
BOLD5000

a large-scale, slow event-related human fMRI study incorporating 5,000 real-world images as stimuli. BOLD5000 is an order of magnitude larger than any extant slow event-related fMRI dataset, with ∼20 hours of MRI scanning per each of four participants. By scaling the size of the image dataset used in fMRI, we hope to facilitate greater integration between the fields of human and computer vision. To that end, BOLD5000 also uniquely uses images drawn from the three most commonly-used computer vision datasets: SUN, COCO, and ImageNet. Beyond standard fMRI analysis techniques, we use both representational similarity analysis14 and, uniquely, t-distributed stochastic neighbor embedding visualizations15, to validate the quality of our data. In sum, we hope that BOLD5000 engenders greater collaboration between the two fields of vision science, fulfilling Marr’s dream.
Courtois-Neuromod

The Courtois NeuroMod project aims at training artificial neural networks using extensive experimental data on individual human brain activity and behaviour. Courtois NeuroMod data are freely shared with the scientific community to advance research at the interface of neuroscience and artificial intelligence.

Provide:

* 6 subjects, 100+ hours of functional neuroimaging per subject
* A wide range of cognitive tasks featuring extensive naturalistic paradigms, with movie watching and video games
* Biosignals are collected concurrent to imaging, including cardiac, respiratory, skin conductance and high-resolution eye-tracking signals
* Functional Magnetic Resonance Imaging is acquired on a 3T Prisma Siemens, using simultaneous multislice sequences
* Magnetoencephalography is acquired on a 275-sensor system manufactured in Canada by CTF Ltd.
* Longitudinal structural imaging of the brain and spinal cord
Forrest

This is an extension to the studyforrest dataset – a versatile resource for studying the behavior of the human brain in situations of real-life complexity (http://studyforrest.org). This release adds more high-resolution, ultra high-field (7 Tesla) functional magnetic resonance imaging (fMRI) data from the same individuals. The twenty participants were repeatedly stimulated with a total of 25 music clips, with and without speech content, from five different genres using a slow event-related paradigm. The data release includes raw fMRI data, as well as precomputed structural alignments for within-subject and group analysis. In addition to fMRI, simultaneously recorded cardiac and respiratory traces, as well the complete implementation of the stimulation paradigm, including stimuli, are provided. An initial quality control analysis reveals distinguishable patterns of response to individual genres throughout a large expanse of areas known to be involved in auditory and speech processing. The present data can be used to, for example, generate encoding models for music perception that can be validated against the previously released fMRI data from stimulation with the “Forrest Gump” audio-movie and its rich musical content. In order to facilitate replicative and derived works, only free and open-source software was utilized.
HSIRS
Full name: High-quality Spectral Image Resonstruction and Segmentation Dataset

We introduce HSIRS, a large scale dataset of hyper-spectral images along with corresponding manually annotated segmentation maps for material characterization and classification based on spectral signature. Such data can be used to simulate any type of spectrometer and to train DNNs end-to-end for spectral reconstruction and image segmentation tasks. HSIRS features scenes containing real and fake (made of polyester, plastic or ceramic) food items with different backgrounds and scene layouts, some scenes contain also color checkers. Spectral bands are sequentially captured using a VariSpecTM tunable color filter and the scene is illuminated with 4 Halogen light sources.