Dataset Card for BrainData

This dataset card provides detailed information about the BrainData dataset. It has been generated using this raw template.

  Dataset Details





  Dataset Description

BrainData is a comprehensive dataset designed for multiple NLP tasks including translation, summarization, and text-to-text generation. It encompasses a variety of domains such as legal, finance, and medical, with content available in both Arabic and English. This extensive… See the full description on the dataset page: https://huggingface.co/datasets/Mohamedfadil369/BrainData.

Dataset

This dataset was created by Chris Deotte

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by Pooja Prajapathi

Contents

Largest Dataset Mapping Human Brain

In collaboration with the Lichtman Laboratory at Harvard University, Google is releasing the “H01” dataset, a 1.4 petabyte rendering of a small sample of human brain tissue, along with a companion paper, “A connectomic study of a petascale fragment of human cerebral cortex.” The H01 sample was imaged at 4nm-resolution by serial section electron microscopy, reconstructed and annotated by automated computational techniques, and analyzed for preliminary insights into the structure of the human cortex. The dataset comprises imaging data that covers roughly one cubic millimeter of brain tissue, and includes tens of thousands of reconstructed neurons, millions of neuron fragments, 130 million annotated synapses, 104 proofread cells, and many additional subcellular annotations and structures — all easily accessible with the Neuroglancer browser interface.

This dataset contains videos of specific networks. It is shared for the first time on Kaggle. It is suitable for Computer Vision and DCGAN structures.

INCLUDE

• Full Connected
• Hemibrain Connection
• EM
• Central Complex Structures
• Connects Regions: ADL02od PCT
• Connects Regions: ADM10t
• Connects Regions: APL
• Connects Regions: AVL01lo PCT
• Connects Regions: ExR3
• Connects Regions: MBON01
• Connects Regions: Olfactory LN
• Connects Regions: Ovil N

https://h01-release.storage.googleapis.com/landing.html

The Brain/MINDS Marmoset MRI NA216 and eNA91 datasets currently constitutes the largest public marmoset brain MRI resource (483 individuals), and includes in vivo and ex vivo data for large variety of image modalities covering a wide age range of marmoset subjects.
* The in vivo part corresponds to a total of 455 individuals, ranging in age from 0.6 to 12.7 years (mean age: 3.86 ± 2.63), and standard brain data (NA216) from 216 of these individuals (mean age: 4.46 ± 2.62).
T1WI, T2WI, T1WI/T2WI, DTI metrics (FA, FAc, MD, RD, AD), DWI, rs-fMRI in awake and anesthetized states, NIfTI files (.nii.gz) of label data, individual brain and population average connectome matrix (structural and functional) csv files are included.
* The ex vivo part is ex vivo data, mainly from a subset of 91 individuals with a mean age of 5.27 ± 2.39 years.
It includes NIfTI files (.nii.gz) of standard brain, T2WI, DTI metrics (FA, FAc, MD, RD, AD), DWI, and label data, and csv files of individual brain and population average structural connectome matrices.

BIDS-formatted Magnetic Resonance Imaging mouse brain data example used in the SAMRI test suite.

The dataset procured by this research was used to systematically identify the relationship between implicit learning events and neurological signals’ characteristics by measuring the participant’s brain state as they performed cognitive tasks experiments. Implicit learning is the ability to learn complex information without explicit awareness, commonly seen in small children while learning to speak their native language for the first time without learning grammar. Research suggests that people skilled at implicit learning tend to learn faster. The implicit learning of the underlying rules, commonly used in learning approaches, is of interest. Simultaneous measurement of Encephalography (EEG) and Functional Near-Infrared Spectroscopy (fNIRS) signals at shared locations over the head was obtained to understand participants’ learning ability in a laboratory setting. Utilizing the data obtained from measuring both electrophysiological activity and hemodynamic responses at the same locations at the same time could bring about new insights, leading to new findings for neurovascular coupling in the brain and extending knowledge on how brains work. This dataset comprised EEG and fNIRS data from thirty healthy adults (age 21-29) while undergoing cognitive serial reaction times task experiments. The participants’ data from each data type are divided into two main groups: participants deemed to have achieved implicit learning during the experiment and those who did not. The differentiations were evaluated during the post-interviews of the experiment. This grouping of datasets could be used for classification applications. Brain data in this research can help identify prominent brain areas and features or patterns corresponding to implicit learning events. Thus, it can be used to identify and develop a learning detection model. With the detection model, a form of neurofeedback training regimen or therapy could be developed to produce a better and novel teaching approach.

A data article is being submitted for publication in a journal. If the manuscript is accepted, we will provide a link to the article for more information about the dataset.

brain data

The Blue Brain Open Data represents an extensive neuroscience dataset encompassing a diverse range of data types, including experimental, model, and simulation data, along with images and videos depicting reconstructed neurons and brain regions.

28_pbo24_100kV9WLE2Obj0p4xBin2t1sP801_recon.zip: X-ray tomography data of a lizard head (Podarcis bocagei) using Zeiss Xradia Versa 520 at Stockholm University Brain Imaging Center (SUBIC).SegmFromDLalgorithm.zip: X-ray microCT (microcomputed tomography) data of lizard heads, cropped down to brain part, and the corresponding labels of brain regions segmented by deep learning algorithm.SegmForDLmanual.zip: X-ray microCT data of lizard heads, cropped down to brain part, and the corresponding labels of brain regions segmented semi-manually used as training dataset for deep learning.06_recon.zip: an example of original X-ray microCT data of a lizard head before cropping.The trained networks below can be used in Biomedisa https://biomedisa.info :NetworkTrainedWith5data.tar.h5: trained network using 5 datasetsNetworkTrainedWith7data.tar.h5: trained network using 7 datasetsNetworkTrainedWith9data.tar.h5: trained network using 9 datasetsNetworkTrainedWith11data.tar.h5: trained network using 11 datasets

This dataset contains smRNA-seq data from post-mortem human brain tissue of the frontal lobe of patients with FTD and healthy controls. The smRNA-sequencing was done in two parts, this dataset depicts the data generated at the DZNE Tübingen.

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Advancements in the field of microscopy and imaging have pushed the boundaries of what was once thought possible in many fields of research. New techniques coupled with the application of new technologies allow researchers to probe further and with greater accuracy to answer increasingly complex questions. While these new techniques allow for far greater specificity of observation and increased sensitivity in regard to both resolution and frequency, the amount of data generated is increasing to a point where conventional systems are unable to manage it. At the current time, there is no practical way to analyze, mine, share or interact with large (100+TB) brain image datasets. The development of a national, scalable archival solution and gateway for such datasets is a pressing problem extremely important and central to the NIH mission as in the future there will be a continuous and sustained growth in data scale. To address this issue, we are establishing the BRAIN Imaging Archive. The Archive will encompass the deposition of datasets, the integration of datasets into a searchable web- accessible system, the redistribution of datasets, and a computational enclave to allow researchers to process datasets in-place and share restricted and pre-release datasets. The Archive will, for the first time, provide researchers with a practical way to analyze, mine, share or interact with large (100+TB) image datasets by creating a unique public resource for the BRAIN research community.

This dataset contains data used in the paper titled "Significant and pervasive effects of RNA degradation on Nanopore direct RNA sequencing. The data consists of one post mortem sample that was sequenced with direct RNA sequencing form Oxford Nanopore Technologies on a promethION flow cell.

Fully-Automated μMRI Morphometric Phenotyping of the Tc1 Mouse Model of Down Syndrome - Table 2

In this data report we make available to the community a highly variable longitudinal MRI mouse brain data set of ischemic lesion after transient middle cerebral artery occlusion (tMCAo). Together with the provided semi-automated and automated segmentations, these data can be used to further improve the method proposed by Mulder et al. (2017) and also to serve as a benchmark for comparison between different approaches to segment ischemic lesions in MRI mouse brain data. It can also be used to develop and validate algorithms that further classify the stroke area into core and penumbra.

A collection of 11 brain maps. Each brain map is a 3D array of values representing properties of the brain at different locations.

Collection description

In computational neurolinguistics, it has been demonstrated that hierarchical models like Recurrent Neural Network Grammars (RNNGs), which jointly generate word sequences and their syntactic structures via the syntactic composition, better explained human brain data than sequential models such as Long Short-Term Memory networks (LSTMs). However, while the vanilla RNNG adopted in the previous literature has employed the top-down parsing strategy, the psycholinguistics literature have pointed out that the top-down parsing strategy is suboptimal for head-final/left-branching languages, and alternatively proposed the left-corner parsing strategy as the psychologically plausible parsing strategy. In this paper, building on this line of inquiry, we investigate not only whether hierarchical models like RNNGs better explain human brain data than sequential models like LSTMs, but also which parsing strategy is more neurobiologically plausible, by constructing a novel fMRI corpus where participants read newspaper articles naturalistically through the fMRI experiment in a head-final/left-branching language, namely Japanese.

For the whole brain analysis, the design matrices were created fro the first-level GLM. All predictors (word rate, word length, word frequency, sententence ID, sentence position, five-gram, LSTM, suprisals estimated from RNNGs, the distance computed from RNNGs) were included except for head movement parameters.One-sample t-tests were performed for the second-level analysis.

Related article: https://doi.org/10.1162/nol_a_00118

Brain_tumor_project

## Overview

Brain_tumor_project is a dataset for object detection tasks - it contains Tumor annotations for 967 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Neural networks are potentially valuable for many of the challenges associated with MRS data. The purpose of this manuscript is to describe the AGNOSTIC dataset, which contains 259,200 synthetic 1H MRS examples for training and testing neural networks. AGNOSTIC was created using 270 basis sets that were simulated across 18 field strengths and 15 echo times. The synthetic examples were produced to resemble in vivo brain data with combinations of metabolite, macromolecule, residual water signals, and noise. To demonstrate the utility, we apply AGNOSTIC to train two Convolutional Neural Networks (CNNs) to address out-of-voxel (OOV) echoes. A Detection Network was trained to identify the point-wise presence of OOV echoes, providing proof of concept for real-time detection. A Prediction Network was trained to reconstruct OOV echoes, allowing subtraction during post-processing. Complex OOV signals were mixed into 85% of synthetic examples to train two separate CNNs for the detection and predi..., AGNOSTIC was created using 270 basis sets that were simulated across 18 field strengths and 15 echo times. The synthetic examples were produced to resemble in vivo brain data with combinations of metabolite, macromolecule, and residual water signals, and noise. All of the parameters (i.e., amplitudes, relaxation decays, etc.) are included in each of the NumPy zipped archive file., NumPy archive files can be opened using Python and NumPy., # AGNOSTIC: Adaptable Generalized Neural-Network Open-source Spectroscopy Training dataset of Individual Components

Published in Imaging Neuroscience: Application of a 1H brain MRS benchmark dataset to deep learning for out-of-voxel artifacts

• Aaron T. Gudmundson, Johns Hopkins School of Medicine, Kennedy Krieger Institute, ORCID: 0000-0001-5104-0959
• Christopher W. Davies-Jenkins, Johns Hopkins School of Medicine, Kennedy Krieger Institute, ORCID: 0000-0002-6015-762X
• Ä°pek Özdemir, Johns Hopkins School of Medicine, Kennedy Krieger Institute, ORCID: 0000-0001-6807-9390
• Saipavitra Murali-Manohar, Johns Hopkins School of Medicine, Kennedy Krieger Institute, ORCID: 0000-0002-4978-0736
• Helge J. Zöllner, Johns Hopkins School of Medicine, Kenne...

This is an example analysis output data from Stereo-seq Mouse Brain Data Analysis

Explore the historical Whois records related to braindata.net (Domain). Get insights into ownership history and changes over time.