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7
datasets available to search
ShareScore release 0.9.0
Dataset results
7 results for “Deep Learning (DL)”
DL-RMD: A geophysically constrained electromagnetic resistivity model database for deep learning applications (Dataset)
<p>Deep learning algorithms have shown incredible potential in many applications. The success of these data-hungry methods is largely associated with the availability of large-scale data sets, as millions of observations are often required to achieve acceptable performance levels. Recently, there has been an increased interest in applying deep learning methods to geophysical applications where electromagnetic methods are used to map the subsurface geology by observing variations in the electrical resistivity of the subsurface materials. To date, there are no standardized datasets for electromagnetic methods, which hinders the progress, evaluation, benchmarking, and evolution of deep learning algorithms due to data inconsistency. Therefore, we present a large-scale electrical resistivity model database of a wide variety of geologically plausible and geophysically resolvable subsurface structures for the commonly deployed ground-based and airborne electromagnetic systems. The presented database can potentially be used to build surrogate models of well-known processes and aid in labour intensive tasks. The geophysically constrained property of this database will not only achieve enhanced performance and improved generalization but, more importantly, it will incorporate consistency and credibility in deep learning models. We urge the geophysical community interested in deep learning for electromagnetic methods to utilize the presented database.</p>
Physical and deep learning retrieved fine mode fraction (Phy-DL FMF)
<p>Aerosol fine-mode fraction (FMF) is a key parameter for discriminating anthropogenic aerosols from natural ones, but it is highly uncertainty from satellite-based retrieval, especially over land. Here we integrated physical and deep learning (Phy-DL) methods to retrieval aerosol FMF in a global land scale based on MODIS data and produced the 20-year (2001-2020) Phy-DL FMF (500 nm) at daily temporal resolution and 1° spatial resolution.</p> <p>The Phy-DL FMF is uploaded as Geotiff format, stretched from -89.5° to 89.5° latitude and from -179.5° to 179.5° longitude. In some certain days Phy-DL FMF might be inavailable, due to the missing MODIS satellite data used for calculating Phy-DL FMF. </p>
Evaluating Replacement of Standard-of-care Low Dose Computer Tomography (CT) Scans With Radiation-free Bone Imaging by Deep-learning Augmented Zero Echo Time (DL-ZTE) Magnetic Resonance Tomography (MR
ClinicalTrials.gov study NCT06579547. IPD Sharing: NO. Countries: 1. Publications: 0.
Application of machine learning (ML) / deep learning (DL) using multiple epigenetic features reveals H3K27Ac as driver of gene expression prediction across patients with glioblastoma
GEO Series GSE296948. Homo sapiens. 11 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Application of machine learning (ML) / deep learning (DL) using multiple epigenetic features reveals H3K27Ac as driver of gene expression prediction across patients with glioblastoma [ATAC-seq]
GEO Series GSE296947. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Application of machine learning (ML) / deep learning (DL) using multiple epigenetic features reveals H3K27Ac as driver of gene expression prediction across patients with glioblastoma [RNA-Seq]
GEO Series GSE296945. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
Application of machine learning (ML) / deep learning (DL) using multiple epigenetic features reveals H3K27Ac as driver of gene expression prediction across patients with glioblastoma [ChIP-Seq]
GEO Series GSE296944. Homo sapiens. 7 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
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.