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431
datasets available to search
ShareScore release 0.9.0
Dataset results
431 results for “DECODER”
Decoding Plasma Cell Maturation Dynamics with BCMA
GEO Series GSE276846. Mus musculus. 16 samples. Type: Expression profiling by high throughput sequencing.
Decoding the heterogeneity of human undifferentiated spermatogonia reveals RAS-dependent regulation of stem cell fate
GEO Series GSE297876. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
Enhancement of Hippocampal Spatial Decoding with a Q-Learning Method Utilizing Theta Rhythm Phase Precession as the Relative Reward Approach
<p> Winners of the 2014 Nobel Prize in Physiology or Medicine, Professors John O’Keefe, May‐Britt Moser and Edvard I. Moser found that the internal global positioning system (GPS) in the brain allows us to be able to flexibly navigate the world they live in – exploring new areas, returning quickly to remembered places, and taking shortcuts and confirmed that place cells in hippocampus and grid cells in entorhinal cortex (EC) are responsible for higher-order cognitive map of the environment. Indeed, these abilities feel so easy and natural that it is not immediately obvious how complex the underlying processes really are. In contrast, spatial navigation remains a substantial challenge for artificial agents whose abilities are far outstripped by those of mammals. </p> <p> Hippocampal place cells and interneurons in mammals have proved that they own stable place fields and theta phase precession profiles to encode the spatial information from the environment. The hippocampal CA1 neurons can be represented as the location of the animal and the prospective information of goal location. Reinforcement learning algorithm, e.g., Q-learning, has been adopted to build a navigation model of place cells for the purpose of addressing goal direction navigation problems.<br> In this study, we propose dynamical Q-learning (dQ-learning), because of its adaptive reward function based on theta phase precession, which has recently been associated with a rat’s experiences at destinations, and use of information from both place cells and interneurons as inputs to predict the animal’s trajectory. We evaluated the convergence rates and learning performances of tQ-learning and dQ-learning with different cell types. The results demonstrate that dQ-learning improves learning performance and convergence rate and place cells and interneurons with phase precession may provide valuable information to improve the prediction of trajectory. To investigate whether the enhancement of hippocampal spatial decoding with the dQ-learning method was effective in goal-direction navigation, experimental data were recorded from rats implanted with microelectrodes and trained in a water reward task. During the task electrophysiological recordings of spikes, LFPs, and movement trajectories were acquired. The proposed dQ-learning algorithm achieved better learning performance with good prediction accuracy and a high convergence rate. The adaptive reward function and cell types were found to be critical factors for hippocampal spatial decoding using the dQ-learning method. </p>
Full sequencing dataset of payload segments for "A Base Multilayer DNA Storage Architecture Enables Real-Time Decoding to Overcome Latency" (Part 2, FASTA file)
<p><span lang="EN-US">This is the full sequencing dataset of payload segments for the manuscript "A Base Multilayer DNA Storage Architecture Enables Real-Time Decoding to Overcome Latency", which is submitted for review.</span></p> <p><span lang="EN-US">We successfully encoded the first color film "Becky Sharp" (54.79 MB) into ~10.08 million DNA strands, achieving a sustained readout throughput of 459 kbit/s.</span></p> <p><span lang="EN-US">This full sequencing dataset contains all base-called data (in BIN and FASTA) of the payload segments. It can be used for recovery of the 28-layer color film data with the <br>DNA-LC software. </span></p> <p><span lang="EN-US">Due to size limitations, the dataset is divided into two parts. Part 1 contains all the files in BIN format, and Part 2 contains the corresponding files in FASTA format. This link is for Part 2.</span></p>
Full sequencing dataset of payload segments for "A Base Multilayer DNA Storage Architecture Enables Real-Time Decoding to Overcome Latency" (Part 1, BIN file)
<p><span lang="EN-US">This is the full sequencing dataset of payload segments for the manuscript "A Base Multilayer DNA Storage Architecture Enables Real-Time Decoding to Overcome Latency", which is submitted for review.</span></p> <p><span lang="EN-US">We successfully encoded the first color film "Becky Sharp" (54.79 MB) into ~10.08 million DNA strands, achieving a sustained readout throughput of 459 kbit/s.</span></p> <p><span lang="EN-US">This full sequencing dataset contains all base-called data (in BIN and FASTA) of the payload segments. It can be used for recovery of the 28-layer color film data with the<br>DNA-LC software. </span></p> <p><span lang="EN-US">Due to size limitations, the dataset is divided into two parts. Part 1 contains all the files in BIN format, and Part 2 contains the corresponding files in FASTA format. This link is for Part 1.</span></p>
Verification dataset of "A Base Multilayer DNA Storage Architecture Enables Real-Time Decoding to Overcome Latency"
<p>This is a dataset for the proposed base-layered DNA data storage. </p> <p>The first color film “Becky Sharp” (54.79 MB) was encoded into ~10.08 million DNA strands<span lang="EN-US">, achieving a sustained readout throughput of 459 kbit/s</span>.</p> <p>For the film, it was used to verify the performance of the proposed DNA data storage method, only for academic research, not related to the content.</p> <p>The dataset consists of three categories: the original data, the oligo sequence data, and the sequencing data (in BIN and FASTA formats).</p>
Data for "New circuits and an open source decoder for the colorcode"
<p>Code written, circuits generated, and statistics collected for the paper "New circuits and an open source decoder for the colorcode".</p>
Decoded fingerprints of hyperresponsive, expanding product space from polyether cascade cyclizations as tools to elucidate supramolecular catalysis
<p>Original data</p>
Imperial College Ear-EEG Dataset for Auditory Attention Decoding
<p>This repository contains the ear-EEG data and the speech material that were used in our publication "Decoding of Selective Attention to Speech From Ear-EEG Recordings" [1].</p> <p>The data are stored in the archive "icl_earEEG_dataset.zip", which contains:</p> <ul> <li>audio/ - this directory contains the original speech material from each EEG trial.</li> <li>eeg_data.h5: this file contains the unprocessed EEG recordings from each trial, sampled at the original rate of 256 Hz.</li> </ul> <p>The onsets of the EEG and speech material are already aligned - all you need to do is resample the audio (or the features derived therefrom) to the same sample rate as the EEG (256 Hz) in order to obtain time-aligned signals. </p> <p>If you use this dataset for your research, please cite this repository as well as the following article:</p> <p>[1] M. Thornton, D. Mandic, T. Reichenbach, "Decoding of Selective Attention to Speech From Ear-EEG Recordings". <em>submitted</em>. arXiv:2401.05187.</p> <div> <div> <div></div> </div> </div>
Dataset for "Decoding host-microbiome interactions through co-expression network analysis within the non-human primate intestine"
<p>Dataset:</p> <p>Data1. Host gene expression profile</p> <p>Data2. Microbiome gene expression profile</p>
Decoding Developmental Disorders in Humams
ClinicalTrials.gov study NCT06260319. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Decoding the Clinical Impact of Host and Microbial Intestinal Proteomic Landscape in Crohn's Disease
ClinicalTrials.gov study NCT06494826. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Decoding the Genetic Landscape of Skeletal Diseases
ClinicalTrials.gov study NCT05876416. IPD Sharing: NO. Countries: 1. Publications: 0.
Decoding the Association of Imaging and Tumor Microenvironment in Lung Cancer Using Radiogenomic Approach（Radiogenomics-Lung）
ClinicalTrials.gov study NCT06500312. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Decoding the Extracellular Vesicles-driven Communication in the Microenvironment of Hairy Cell Leukemia to Improve Patient Care Management
ClinicalTrials.gov study NCT06774677. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Decoding Personalized Nutritional, Microbiome and Host Patterns Impacting Clinical and Prognostic Features in Crohn's Disease
ClinicalTrials.gov study NCT04283864. IPD Sharing: NO. Countries: 2. Publications: 0.
Decoding Chronic Pain With fMRI
ClinicalTrials.gov study NCT01766973. IPD Sharing: Not stated. Countries: 1. Publications: 0.
StereoEEG Motor Neuronal Potentials Decoding
ClinicalTrials.gov study NCT06432907. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Neuroimaging Study for Decoding Emotional States and Identifying Neural Circuits to Disengage From Negative Thinking
ClinicalTrials.gov study NCT06254144. IPD Sharing: NO. Countries: 1. Publications: 0.
Invasive Decoding and Stimulation of Altered Reward Computations in Depression Patients
ClinicalTrials.gov study NCT05239780. IPD Sharing: YES. Countries: 1. Publications: 0.
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.