Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
921
datasets available to search
ShareScore release 0.7.1
Dataset results
921 results for “neural networks”
Mediator complex interaction partners organize the transcriptional network that defines neural stem cells
GEO Series GSE109043. Mus musculus. 4 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Pax3 and Zic1 trigger the early neural crest gene regulatory network by the direct activation of multiple key neural crest specifiers [Xenopus_laevis]
GEO Series GSE53678. Xenopus laevis. 11 samples. Type: Expression profiling by array.
REST and Neural Gene Network Dysregulation in iPS Cell Models of Alzheimer’s Disease (Affymetrix iPSC data set)
GEO Series GSE117584. Homo sapiens. 10 samples. Type: Expression profiling by array.
Self-Organizing Neural Networks in Organoids Reveal Principles of Forebrain Circuit Assembly
GEO Series GSE312396. Mus musculus. 7 samples. Type: Expression profiling by high throughput sequencing.
Pax3 and Zic1 trigger the early neural crest gene regulatory network by the direct activation of multiple key neural crest specifiers [X_laevis_2]
GEO Series GSE53677. Xenopus laevis. 16 samples. Type: Expression profiling by array.
REST and Neural Gene Network Dysregulation in iPS Cell Models of Alzheimer’s Disease (Affymetrix neurons data set)
GEO Series GSE117585. Homo sapiens. 11 samples. Type: Expression profiling by array.
REST and Neural Gene Network Dysregulation in iPS Cell Models of Alzheimer’s Disease
GEO Series GSE117589. Homo sapiens. 37 samples. Type: Expression profiling by array; Expression profiling by high throughput sequencing.
Integrated Omic Analyses Identify Pathways and Regulators Associated with Chemical Alterations of in vitro Neural Network Formation
GEO Series GSE174722. Rattus norvegicus. 71 samples. Type: Expression profiling by high throughput sequencing.
Identification of a molecular network of DNA damage-induced neural cell death
GEO Series GSE31666. Mus musculus. 2 samples. Type: Expression profiling by array.
OTX2 regulatory network and maturation-associated gene programs are inherent barriers to RPE neural competency
GEO Series GSE197938. Gallus gallus. 26 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Aberrant transcriptional networks in neurogenesis of Paroxysmal Kinesigenic Dyskinesia-induced pluripotent stem cell lines though neural induction method of dual inhibition of SMAD signaling
GEO Series GSE83256. Homo sapiens. 23 samples. Type: Expression profiling by high throughput sequencing.
Pax3 and Zic1 trigger the early neural crest gene regulatory network by the direct activation of multiple key neural crest specifiers
GEO Series GSE53679. Xenopus laevis. 27 samples. Type: Expression profiling by array.
LSTM neural network for textual ngrams
<p>Cognitive neuroscience is the study of how the human brain functions on tasks like decision making, language, perception and reasoning. Deep learning is a class of machine learning algorithms that use neural networks. They are designed to model the responses of neurons in the human brain. Learning can be supervised or unsupervised. Ngram token models are used extensively in language prediction. Ngrams are probabilistic models that are used in predicting the next word or token. They are a statistical model of word sequences or tokens and are called Language Models or Lms. Ngrams are essential in creating language prediction models. We are exploring a broader sandbox ecosystems enabling for AI. Specifically, around Deep learning applications on unstructured content form on the web.<br> </p>
LyNoS: Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical prior guiding
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MetDIT: Transforming and Analyzing Clinical Metabolomics Data with Convolutional Neural Networks
<h1>MetDIT: Transforming and Analyzing Clinical Metabolomics Data with Convolutional Neural Networks</h1>
Assessing the Generalizability of Deep Neural Networks-Based Models for Black Skin Lesions
<p>Melanoma is the most severe type of skin cancer due to its ability to cause metastasis. It is more common in black people, often affecting acral regions: palms, soles, and nails. Deep neural networks have shown tremendous potential for improving clinical care and skin cancer diagnosis. Nevertheless, prevailing studies predominantly rely on datasets of white skin tones, neglecting to report diagnostic outcomes for diverse patient skin tones. In this work, we evaluate supervised and self-supervised models in skin lesion images extracted from acral regions commonly observed in black individuals. Also, we carefully curate<strong> a dataset containing skin lesions in acral regions and assess the datasets concerning the Fitzpatrick scale to verify performance on black skin.</strong> Our results expose the poor generalizability of these models, revealing their favorable performance for lesions on white skin. Neglecting to create diverse datasets, which necessitates the development of specialized models, is unacceptable. Deep neural networks have great potential to improve diagnosis, particularly for populations with limited access to dermatology. However, including black skin lesions is necessary to ensure these populations can access the benefits of inclusive technology.</p>
Graph Neural Network vs. Large Language Model: A Comparative Analysis for Bug Report Priority and Severity Prediction
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Towards Fast Region Adaptive Ultrasound Beamformer For Plane Wave Imaging Using Convolutional Neural Networks
<p>This dataset is supplementary to the <a href="https://ieeexplore.ieee.org/document/9630930">IEEE EMBC 2021 paper titled "Towards Fast Region Adaptive Ultrasound Beamformer for Plane Wave Imaging Using Convolutional Neural Networks"</a></p> <p>The dataset is in .mat format and has two variables as below:</p> <p>tofc: Time of flight corrected (delay compensated) input data</p> <p>beamformedData: The delay and sum beamformed (pre-envelope) data</p> <p>The data is in int16 format and may need to be converted to double/float for improved results.</p> <p><strong>Dataset Access: </strong>You need to download the agreement in the <a href="https://drive.google.com/file/d/1tei07_xzcOLTdHpEXUtFcgNCwjxoAynW/view?usp=sharing" target="_blank" rel="noopener">link </a>and submit the form along with the agreement in the <a href="https://forms.gle/RaXtPR12wfrhbotf9" target="_blank" rel="noopener">link</a></p>
Neural networks that locate and identify birds through their songs
<p>Zonotrichia capensis executing multiple themes</p>
Windy events detection in big bioacoustics datasets using a pre-trained Convolutional Neural Network
<p>This repository icludes the code and all relevant files used throughout our study. These encompass everything from the initial sheets of the whole acoustic dataset utilised for selecting the annotated dataset to the notebook (.ipynb) and the recordings employed in training the model.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.