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431 results for “DECODER”
Data from: Consistent coordination patterns provide near perfect behavior decoding in a comprehensive motor program for insect flight
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Data from: Decoding the dynamics of dental distributions: insights from shark demography and dispersal
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Decoding the Interactions Between Hydrological Processes and Nitrogen Cycling in Bohemian Mountainous Watersheds (Slavkov Forest)
<h1><strong>DATASET <br>Decoding the Interactions Between Hydrological Processes and Nitrogen Cycling in Bohemian Mountainous Watersheds (Slavkov Forest) </strong></h1> <p>F. Buzek<sup>1</sup>, B. Cejkova<sup>1</sup>, I. Jackova<sup>1</sup>, P. Kram<sup>1</sup>, F. Oulehle<sup>1</sup>, O. Myska<sup>1</sup>, F. Veselovsky<sup>1</sup>, J. Curik<sup>1</sup> and D.A. Petrash<sup>1</sup></p> <p>This dataset contains modeling parameters and model results on the influences of hydrological factors on nitrogen (N) mineralization and denitrification processes in three adjacent mountain catchments located in the Slavkov Forest, western Czech Republic, from 2016 to 2020</p>
Decoding Pain: Uncovering the Factors that Affect Performance of Neuroimaging-Based Pain Models
<p>This repository contains the codes and data which are used in the following paper.</p> <blockquote> <p>Dong Hee Lee, Sungwoo Lee, Choong-Wan Woo, Decoding Pain: Uncovering the Factors that Affect Performance of Neuroimaging-Based Pain Models, 2023, bioRxiv (<a href="https://doi.org/10.1101/2023.12.22.573021" target="_blank" rel="noopener">link</a>)</p> </blockquote> <h2>Dependencies</h2> <ol> <li>CanlabCore toolbox (<a href="https://github.com/canlab/CanlabCore" target="_blank" rel="noopener">link</a>)</li> <li>Mediation toolbox (<a href="https://github.com/canlab/MediationToolbox" target="_blank" rel="noopener">link</a>)</li> <li>SPM12 (<a href="https://www.fil.ion.ucl.ac.uk/spm/software/spm12/" target="_blank" rel="noopener">link</a>)</li> <li>MATLAB Statistics and Machine Learning Toolbox (<a href="https://www.mathworks.com/products/statistics.html" target="_blank" rel="noopener">link</a>)</li> </ol> <h2><strong>Usage</strong></h2> <p>Below are the codes that allow you to generate the main figures in the manuscript.</p> <ul> <li><code>fig_3_litearture_survey.m</code></li> <li><code>fig_4_literature_survey.R</code></li> <li><code>fig_5_litearture_survey.m</code></li> <li><code>fig_7_benchmarking_analysis.m</code></li> <li><code>fig_8_benchmarking_analysis.m</code></li> <li><code>fig_9_benchmarking_analysis.m</code></li> <li><code>fig_10_benchmarking_analysis.m</code></li> </ul> <p>To use these scripts, download all files and unzip the zipfile (i.e. <code>data.zip</code>). Open the script that you want to run in Matlab or R and set the current folder to the working directory. Add the paths for the dependencies. The other functions (e.g., <code>lpp_plot_boxplot.m</code>) are necessary to create the main figures. Make sure that the functions have to be saved in the same directory with the main scripts.</p>
Decoding the Brain Mosaic: Region and Cell Type-Specific Dysregulation Patterns in Stress and Psychiatric Disorders
<p>Supplementary Online Tables for the doctoral thesis "Decoding the Brain Mosaic: Region and Cell Type-Specific Dysregulation Patterns in Stress and Psychiatric Disorders" by Nathalie Gerstner. </p> <p>This repository includes complete lists of differentially expressed and differential hub genes, alongside the results of enrichment analysis for 8 brain regions examined in a mouse model for transcriptomic alterations following glucocorticoid stimulation (Online Tables 1-30). Additionally, the repository encompasses the complete results of differential expression and chromatin accessibility analyses across various cell types examined in postmortem brain tissue of the orbitofrontal cortex (Online Tables 31-37). These findings delineate the transcriptomic and epigenomic changes between psychiatric cases and controls, as well as between donors with high and low genetic risk for psychiatric disorders.</p> <p>The lists of all genes tested in the respective studies, not just the significant ones, can be found in the "GRactivation_MouseBrain_tables.zip" and the "SingleNuc_PostmortemBrain_Tables.zip" archives.</p> <p> </p>
Decoding Knowledge Claims: the Evaluation of Scientific Publication Contributions through Semantic Analysis
<p>This data were used to compute the RWMD distance as described in the study submitted for the STI 2024 conference, Berlin.</p>
Optocoder: computational decoding of spatially indexed bead arrays
<p>Spatial transcriptomics technologies that can quantify gene expression in space are transforming contemporary biology research. Some of such methods use spatially barcoded bead arrays that are optically sequenced by a microscopy setup to detect bead barcodes in space which can be consecutively matched to cell barcodes from the respective single cell sequencing experiment. To have good quality barcodes and a high number of barcode matches in space, robust and efficient computational pipelines are needed to process raw microscopy images and call the bases of bead barcodes accurately. Here, we present Optocoder, a computational pipeline that takes raw optical sequencing microscopy images as input and outputs bead barcodes in space. Optocoder efficiently aligns images, detects beads, and corrects for confounding factors of the fluorescence signal such as crosstalk and phasing before base calling. Furthermore, we implement a machine learning pipeline that is trained using the signal from the beads that match to illumina barcodes in order to predict non-matching bead barcodes which can boost up the number of barcode matches. We benchmark Optocoder using data from an in-house spatial transcriptomics platform as well as data from the Slide-seq method and we show that it can efficiently process both datasets with minimal modification.</p> <p>Here, the datasets deposited include the following:</p> <p><strong>optocoder_data:</strong> the imaging and illumina data that are used for Optocoder runs. Folder structure is as following:</p> <ol> <li><strong>imaging:</strong> the images acquired via a two laser microscopy setup during the optical sequencing process . There are four pucks (P1, P2, P3, P4) and for every puck there are 12 images where every image corresponds to one cycle of optical sequencing. Every image is a 6-channel TIFF image.</li> <li><strong>illumina: </strong>cell barcodes from library sequencing that are used for the matching</li> <li><strong>external: </strong>bead optical barcodes for the Slide-Seq and Slide-SeqV2</li> </ol> <p><strong>optocoder_v0.1.1_output: </strong>these are the output files from Optocoder runs for both in-house and Slide-Seq samples , and are used to generate the figures in the publication. Scripts to generate the figures are deposited here: https://github.com/rajewsky-lab/optocoder_scripts</p> <p><strong>run1_optocoder_v0.1.1_config_files: </strong>example config files for the optocoder run files.</p> <p> </p>
Spatiotemporal dynamics of odor representations in the human brain revealed by EEG decoding
<p>This is the data and script that reproduces the results of the paper:<br> Kato et al., PNAS 2022, Spatiotemporal dynamics of odor representations in the human brain revealed by EEG decoding.<br> As the results of each analysis that are needed for other analysis are also contained in this folder, <br> each sub-section of the script can be run independently (e.g., you can plot OERP waveforms or GFPs without executing preprocessing). <br> Refer to README.txt for detailed discription.</p>
Morse Decoder
<p>This repo provides the python code (jupyter notebook) for the Morse Decoder, which is used to convert the data to Morse code and then letters. </p>
Decoding the Pair Distribution Function of Uranium in Molten Fluoride Salts from X-ray Absorption Spectroscopy Data by Machine Learning
<p>The repo contains all codes and data related to the JPCC publication entitled "Decoding the Pair Distribution Function of Uranium in Molten Fluoride Salts from X-ray Absorption Spectroscopy Data by Machine Learning"</p>
Dynamical decoding of the competition between charge density waves in a kagome superconductor
<p>P1, P2, P3 denote the peaks at (0 -1.5 2.5), (-0.5 -1 2), (0 -1.5, 3), respectively.</p>
Data for "Efficient neural decoding of self-location with a deep recurrent network"
<p>Data for reproducing results with Bayesian decoders (MLE and Bayesian with memory) reported in the article</p> <p>"Efficient neural decoding of self-location with a deep recurrent network".</p> <p> </p> <p>This data should be used with the code found in https://github.com/NeuroCSUT/RatGPS and should be placed in the Bayesian/Data folder of the codebase.</p> <p> </p>
Decoding the pedogenic weathering signals from the red clay sequence on the Chinese Loess Plateau
Open the record for dataset details and reuse information.
Distinct type II opsins in the eye decode light properties for background adaptation and behavioural background preference
<p>Crypsis increases survival by reducing predator detection. <i>Xenopus laevis</i> tadpoles decode light properties from the substrate to induce two responses: A cryptic coloration response where dorsal skin pigmentation is adjusted to the colour of the substrate (background adaptation) and a behavioural crypsis where organisms move to align with a specific colour surface (background preference). Both processes require organisms to detect reflected light from the substrate. We explored the relationship between background adaptation and preference and the light properties able to trigger both responses. We also analysed which retinal photosensor (type II opsin) is involved. Our results showed that these two processes are segregated mechanistically, as there is no correlation between the preference for a specific background with the level of skin pigmentation, and different dorsal retina-localized type II opsins appear to underlie the two crypsis modes. Indeed, inhibition of melanopsin affects background adaptation but not background preference. Instead, we propose pinopsin is the photosensor involved in background preference. <i>pinopsin</i> mRNA is co-expressed with mRNA for the <i>sws1</i> cone photopigment in dorsally-located photoreceptors. Importantly, the developmental onset of pinopsin expression aligns with the emergence of the preference for a white background, but after the background adaptation phenotype appears. Furthermore, white background preference of tadpoles is associated with increased <i>pinopsin</i> expression, a feature that is lost in pre-metamorphic froglets along with a preference for a white background. Thus, our data show a mechanistic dissociation between background adaptation and background preference, and we suggest melanopsin and pinopsin, respectively, initiate the two responses.</p>
Characterization of deep neural network features by decodability from human brain activity
<p>We present a dataset derived through the DNN feature decoding analyses (<a href="https://www.nature.com/articles/ncomms15037">Horikawa and Kamitani, 2017</a>), including true and decoded feature values of DNNs (AlexNet and VGG19) and decoding accuracies of individual DNN features with their rankings. The decoding accuracies of individual DNN features were highly correlated across subjects, suggesting the systematic differences between the brain and DNNs. The unpreprocessed fMRI data is available from the OpenNeuro (<a href="https://openneuro.org/datasets/ds001246">https://openneuro.org/datasets/ds001246</a>). We hope the present dataset will contribute to reveal the gap between the brain and DNNs and provide an opportunity to make use of the decoded features for further applications.</p>
Decoded Artemis I Orion S-band telemetry frames recieved with the Allen Telescope Array on 2022-11-16
<p>This datasets contains the AOS Space Data Link frames obtained by decoding the Artemis I Orion Multipurpose Crewed Vehicle S-band telemetry signal at 2261.5 MHz using some IQ recordings done at the Allen Telescope Array on 2022-11-16, about 7 hours after launch.</p> <p>The 128-byte AOS frames are stored in raw binary files, which contain all the frames concatenated, with no headers or delimiters.</p> <p>The IQ recordings from which these frames have been decoded can be found in the datasets Recording of Artemis I Orion with the <a href="https://zenodo.org/record/7395644">Allen Telescope Array on 2022-11-16 (part 1/2)</a> and <a href="https://zenodo.org/record/7396606">Recording of Artemis I Orion with the Allen Telescope Array on 2022-11-16 (part 2/2)</a>.</p>
On the benefits of self-taught learning for brain decoding - Data
<p><strong>DERIVED DATA FROM PAPER "<em>On the benefits of self-taught learning for brain decoding</em>"</strong></p> <p>Here are stored the data necessary to reproduce the full analysis of the paper "<em>On the benefits of self-taught learning for brain decoding</em>". </p> <p>We study the benefits of using a large public neuroimaging database composed of fMRI statistic maps, in a self-taught learning framework, for improving brain decoding on new tasks. First, we leverage the NeuroVault database to train, on a selection of relevant statistic maps, a convolutional autoencoder to reconstruct these maps. Then, we use this trained encoder to initialize a supervised convolutional neural network to classify tasks or cognitive processes of unseen statistic maps from large collections of the NeuroVault database. We show that such a self-taught learning process always improves the performance of the classifiers but the magnitude of the benefits strongly depends on the number of data available both for pre-training and finetuning the models and on the complexity of the targeted downstream task.</p> <p><strong>Contents overview </strong></p> <p><strong>1. data.zip</strong></p> <p><strong>1.1 original</strong></p> <p>The <strong>original</strong> directory contains 3 subdirectories:<br> - NeuroVault dataset<br> - HCP dataset<br> - BrainPedia dataset</p> <p>Each subdirectory contains:<br> - text files with NeuroVault IDs of statistic maps selected in the global datasets, in the test and validation datasets and in each fold of these datasets ; <br> - csv files corresponding to informations on each statistic map of the datasets (classification labels, subject IDs for split...) ;<br> - an `original` directory in which original statistic maps downloaded from NeuroVault will be stored when executing the `src/download_and_preprocess_data_notebook.ipynb`.</p> <p><strong>1.2 preprocessed</strong></p> <p>The <strong>preprocessed</strong> directory contains 3 subdirectories:<br> - NeuroVault dataset<br> - HCP dataset<br> - BrainPedia dataset</p> <p>Each subdirectory contains:<br> - text files with NeuroVault IDs of statistic maps selected in the global datasets, in the test and validation datasets and in each fold of these datasets ; <br> - csv files corresponding to informations on each statistic map of the datasets (classification labels, subject IDs for split...) ;<br> - several subdirectores (`resampled`, `resampled masked`...) in which preprocessed statistic maps will be stored when executing the `src/download_and_preprocess_data_notebook.ipynb`.</p> <p><strong>1.3 derived</strong></p> <p>The <strong>derived </strong>directory contains 3 subdirectories:<br> - NeuroVault dataset<br> - HCP dataset<br> - BrainPedia dataset</p> <p>Each subdirectory contains subdirectories in which the parameters of models trained on the different datasets are stored. These subdirectories are named in the following way: </p> <pre><code>{name_of_the_dataset}_maps_classification_{classification_task}_model_cnn_{model_architecture}_valid_{type_of_experiment}_retrain_{type_of_initialization}_{preprocessing_type}_epochs_{number_of_epochs}_batch_size_{batch_size}_lr_{learning_rate}</code></pre> <p>For instance, parameters for the following experiment:<br> - <strong>Dataset</strong>: HCP Dataset subset 50 subjects<br> - <strong>Classification task</strong>: contrast classification<br> - <strong>Model</strong>: 4 layers CNN<br> - <strong>Type of experiment</strong>: Performance evaluation<br> - <strong>Initialization</strong>: Default <br> - <strong>Preprocessing type</strong>: Resampled masked normalized<br> - <strong>Epochs</strong>: 500<br> - <strong>Batch</strong>: 32<br> - <strong>Learning rate</strong>: 1e-04 <br> will be contained in the directory: </p> <pre><code class="language-bash">hcp_dataset_50_maps_classification_contrast_model_cnn_4layers_valid_perf_retrain_no_resampled_masked_normalized_epochs_500_batch_size_32_lr_1e-04</code></pre> <p> </p>
Neural preprocessed data associated to "Decoding grasp and speech signals from the cortical grasp circuit in a tetraplegic human"
<p>This dataset is composed of electrophysiology data from a tetraplegic human participant implanted with three 96 channel Utah arrays (Blackrock) in the supramarginal gyrus (SMG), ventral premotor cortex (PMV) and somatosensory cortex. The dataset includes preprocessed (spike sorted) firing rate data for 96 recorded channels, as described in Wandelt et al (2022), "Decoding grasp and speech signals from the cortical grasp circuit in a tetraplegic human" published in Neuron (<a href="https://doi.org/10.1016/j.neuron.2022.03.009">10.1016/j.neuron.2022.03.009</a>).</p> <p>To run the code associated with the processed data, download it here https://zenodo.org/record/6330179.</p>
JUICE telemetry frames decoded from an Allen Telescope Array recording on 2023-04-15
<p>This dataset contains the TM Space Data Link frames from the ESA spacecraft JUICE (Jupiter Icy Moons Explorer), decoded from the IQ recording in datsets <a href="https://zenodo.org/record/7856893">Recording of JUICE with the Allen Telescope Array on 2023-04-15 (antenna 1a)</a> and <a href="https://zenodo.org/record/7856895">Recording of JUICE with the Allen Telescope Array on 2023-04-15 (antenna 5c)</a>.</p> <p>The frames are stored back-to-back with no metadata or delimiters in a binary file. Each frame is 1113 bytes long. The Frame Error Control Field (FECF, CRC-16) has been checked and removed by the decoder (only frames with valid CRC-16 are present in the file, but there are a few frames corresponding to false decodes).</p>
Stable sound decoding despite modulated sound representation in the auditory cortex
<p>Two-photon calcium imaging data (df/f) from the mouse auditory cortex during a two-alternative choice auditory decision-making task. This is the data set of the paper titled:</p> <p>"Stable sound decoding despite modulated sound representation in the auditory cortex"</p> <p>Current Biology 33, 1–14, October 23, 2023</p> <p>DOI:<a href="https://doi.org/10.1016/j.cub.2023.09.031">https://doi.org/10.1016/j.cub.2023.09.031</a></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.