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Dataset results
431 results for “Decoding”
Decoding Emotional Dynamics in Bipolar Disorder
ClinicalTrials.gov study NCT07221864. IPD Sharing: YES. Countries: 1. Publications: 15.
DECODING Study (Dermal Electrochemical Conductance in Diabetic Neuropathy)
ClinicalTrials.gov study NCT03495089. IPD Sharing: UNDECIDED. Countries: 1. Publications: 18.
Decoding Pain Sensitivity in Migraine With Multimodal Brainstem-based Neurosignature
ClinicalTrials.gov study NCT04702971. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Task of Acoustic-phonetic Decoding on Anatomic Deficits in Paramedical Assessment of Speech Disorders for Patients Treated for Oral or Oropharyngeal Cancer
ClinicalTrials.gov study NCT04742998. IPD Sharing: NO. Countries: 1. Publications: 3.
Data from: Decoding colouration of begging traits by the experimental addition of the appetite enhancer cyproheptadine hydrochloride in magpie (Pica pica) nestlings
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Distinct type II opsins in the eye decode light properties for background adaptation and behavioural background preference
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Decoding myofibroblast origins in human kidney fibrosis
<p>Data repository for the manuscript: Kuppe, Ibrahim et al. "Decoding myofibroblast origins in human kidney fibrosis", 2020. Please also consult the supplemental data in the paper, and the data availability statement in hte manuscript for raw FASTQ files for mouse data.</p> <p>For further data requests and questions, please contact Dr. Rafael Kramann (rkramann@ukaachen.de)</p> <p> </p> <p>File Details:</p> <p>- Human in vitro PDGFRb+ RNA-seq (bulk RNA-seq data for various NKD2 knock-out and knock-in clones)<br> * invitro_bulk_rnaseq.tar.gz: Salmon output for all samples. Please see the manuscript for further information.</p> <p>- UUO Mouse FACS sorted PDGFRa+/b+ ATAC-Seq<br> * mouse_uuo_pdgfrab_atacseq.bw: BigWig Signal file for ATAC-Seq data, PDGFRa+/b+ FACS sorted cells from day 10 UUO mouse kidneys (average of two biological replicates)<br> * mouse_uuo_pdgfrab_motifs.meme: Motifs identified based on the ATAC-Seq data and further analyzed in the paper</p> <p>- UUO and Sham Mouse FACS sorted PDGFRa+/b+ scRNA-seq (10x Genomics)<br> * Mouse_PDGFRab.tar.gz: contains the count data derived by Alevin/Salmon for the cells analyzed in the paper in matrix market format (.mtx). column data include cell cluster annotations.</p> <p>- UUO and Sham Mouse FACS sorted PDGFRb+ scRNA-seq (SmartSeq2)<br> * Mouse_PDGFRa.tar.gz: contains the expression data for the cells analyzed in the paper in matrix market format (.mtx). column data include cell cluster annotations.</p> <p>- Human FACS sorted CD10+ scRNA-seq (10x Genomics)<br> * Human_CD10plus.tar.gz: contains the count data derived by Alevin/Salmon for the cells analyzed in the paper in matrix market format (.mtx). column data include cell cluster annotations.</p> <p>- Human FACS sorted CD10- scRNA-seq (10x Genomics)<br> * Human_CD10minus.tar.gz: contains the count data derived by Alevin/Salmon for the cells analyzed in the paper in matrix market format (.mtx). column data include cell cluster annotations.</p> <p>- Human FACS sorted PDGFRb+ scRNA-seq (10x Genomics)<br> * Human_PDGFRb.tar.gz: contains the count data derived by Alevin/Salmon for the cells analyzed in the paper in matrix market format (.mtx). column data include cell cluster annotations.<br> * HumanPDGFRBpositive_Nkd2_grnboost2.csv: Gene Regulatory Network obtained by GRNboost2 on genes correlated with NKD2 in Fibroblast (Mesenchymal) cells. See manuscript for details.<br> * Human_PDGFRBplus_TFanalysis.tar.gz: TF analysis based on single cell RNA-seq for promoter and distal regions. See manuscript for details.</p> <p> </p> <p> </p> <p>- github_files.tar.gz: RData Objects associated with the paper code repository (https://github.com/mahmoudibrahim/KidneyMap)</p> <p> </p>
Dataset related to Analysis of Decoding Failures of LDPC and MDPC Codes in Out-of-Place Bit Flipping Decoding
<p>This dataset contains both the parity check matrix of the two LDPC codes employed in the experimental section of the paper Analysis of Decoding Failures of LDPC and MDPC Codes in Out-of-Place Bit Flipping Decoding, and the delta connected sets found.</p>
Data from: Decoding the locational information in the orb web vibrations of Araneus diadematus and Zygiella x-notata
A spider's web is a multifunctional structure that captures prey and provides an information platform that transmits vibrational information. Many physical factors interact to influence web vibration and information content, from vibration source properties and input location, to web physical properties and geometry. The aim of the study was to test whether orb web vibration contains information about the location of the source of vibration. We used finite-element analysis model webs to control and vary major physical factors, investigating webs where spiders use a direct or remote monitoring strategy. When monitoring with eight sensors (legs) at the web centre, a comparison of longitudinal and transverse wave amplitude between the sensors gave sufficient information to determine source direction and distance, respectively. These localization cues were robust to changes in source amplitude, input angle and location, with increased accuracy at lower source amplitudes. When remotely monitoring the web using a single thread connected to the web's hub (a signal thread), we found that locational information was not available when the angle of the source input was unknown. Furthermore, a free sector and a stiff hub were physical mechanisms to aid information transfer, which provides insights for bioinspired fibre networks for sensing technologies.
Data from: Detecting rare asymmetrically methylated cytosines and decoding methylation patterns in the honeybee genome
Context-dependent gene expression in eukaryotes is controlled by several mechanisms including cytosine methylation that primarily occurs in the CG dinucleotides (CpGs). However, less frequent non-CpG asymmetric methylation has been found in various cell types, such as mammalian neurons, and recent results suggest that these sites can repress transcription independently of CpG contexts. In addition, an emerging view is that CpG hemimethylation may arise not only from deregulation of cellular processes but also be a standard feature of the methylome. Here, we have applied a novel approach to examine whether asymmetric CpG methylation is present in a sparsely methylated genome of the honeybee, a social insect with a high level of epigenetically driven phenotypic plasticity. By combining strand-specific ultra-deep amplicon sequencing of illustrator genes with whole-genome methylomics and bioinformatics, we show that rare asymmetrically methylated CpGs can be unambiguously detected in the honeybee genome. Additionally, we confirm differential methylation between two phenotypically and reproductively distinct castes, queens and workers, and offer new insight into the heterogeneity of brain methylation patterns. In particular, we challenge the assumption that symmetrical methylation levels reflect symmetry in the underlying methylation patterns and conclude that hemimethylation may occur more frequently than indicated by methylation levels. Finally, we question the validity of a prior study in which most of cytosine methylation in this species was reported to be asymmetric.
Data from: Decoding of baby calls: can adult humans identify the eliciting situation from emotional vocalizations of preverbal infants?
Preverbal infants often vocalize in emotionally loaded situations, yet the communicative potential of these vocalizations is not well understood. The aim of our study was to assess how accurately adult listeners extract information about the eliciting situation from infant preverbal vocalizations. Vocalizations of 19 infants aged 5-10 months were recorded in 3 negative (Pain, Isolation, Demand for Food) and 3 positive (Play, Reunion, After Feeding) situations. The recordings were later rated by 333 adult listeners on the scales of emotional valence and intensity. Subsequently, the listeners assigned the eliciting situations in a forced choice task. Listeners were almost perfectly able to discriminate whether a recording came from a negative or a positive situation. Their discrimination may have been based on perceived valence as they consistently assigned higher valence when listening to positive, and lower valence when listening to negative, recordings. Ability to identify the particular situation within the negative or positive realm was substantially weaker, with only three of the six situations being discriminated above chance. The best discriminated situation, Play, was associated with high perceived intensity. The weak qualitative discrimination of negative situations seemed to be based on graded perception of negative recordings, from the most intense and unpleasant (assigned to Pain) to the least intense and least unpleasant (assigned to Demand for Food). Parenthood and younger age, but not gender of listeners, had weak positive effects on the accuracy of judgments. Our results indicate that adults almost flawlessly distinguish positive and negative infant sounds, but are rather inaccurate regarding identification of the specific needs of the infant and may normally employ other sensory channels to gain this information.
Low-complexity Reinforcement Learning Decoders for Autonomous, Scalable, Neuromorphic intra-cortical Brain Machine Interfaces
<p><strong>General Description. </strong>TThis dataset comprises recordings from four BMI (Brain-Machine Interface) experiments conducted on two adult macaques. Three of the experiments involved joystick-controlled tasks, while the fourth was a center-out reaching task. In the center-out task, the macaque was trained to maneuver a joystick-controlled cursor from a central position on a computer screen to one of eight square-shaped target locations. The macaques were able to use a wireless integrated system to control a robotic platform (on which they were seated) enabling independent mobility driven by neuronal activity in their motor cortices. Neural activity was recorded from populations of single neurons via multiple electrode arrays implanted in the arm region of the primary motor cortex. A general overview is provided below:</p> <ol> <li>A titanium head post (Crist Instruments, MD, USA) was surgically affixed before implanting the microelectrode arrays. In NHP-A, four microelectrode arrays with 16 electrodes each were implanted, while NHP-B was implanted with one array containing 100 electrodes in the hand/arm region of the left primary motor cortex.</li> <li>Spike signals were recorded using an in-house 100-channel wireless neural recording system, sampled at 13 kHz. The wide-band signals were band-pass filtered between 300 and 3000 Hz to eliminate low-frequency components. Spike detection thresholds were determined using the formula: Thr = 5σ, where σ = median(|x| / 0.6745), <em>x</em> is the filtered signal, and <em>σ</em> estimates the standard deviation of background noise.</li> </ol> <p>In Experiments 1, 2, and 3, the behavioral task involved controlling the motion of a robotic wheelchair using a three-directional, spring-loaded joystick. These tasks included: a) turning 90° right, b) moving forward by 2 meters, c) turning 90° left, and d) remaining stationary for 5 seconds (stop task). The success rate varied across experiments. Experiment 4 also involved joystick control, but followed a classical center-out reaching paradigm. </p> <p><strong>Dataset Description. </strong>The dataset is organized into folders (labeled as experiment 1, 2, 3, and 4) containing data from both NHP-A and NHP-B. Each folder contains data from separate dates labeled as YYMMDD (at the end of the filename). For experiment 1, data from the following dates are present: 15-10-08, 15-10-12, 15-10-19, 15-10-26, 15-11-02, 15-11-16, 15-11-23, and 15-12-10. For experiment 2, following dates are: 18-02-20, 18-03-06, 18-03-08, 18-03-20, 18-03-26, 18-04-13, 18-04-16, 18-04-23. For experiment 3: 14-08-14, 14-08-18, 14-08-20, 15-10-14. For experiment 4: 18-12-03, 18-12-13, 19-01-07, 19-02-20. File naming conventions across all experiments are as follows</p> <ol> <li>targTest: This corresponds to the direction of the joystick recorded for each trial. (decoded using the decoder)</li> <li>targTrain: Ground truth label, corresponding to the actual direction of the joystick.</li> <li>testSet: Number of spike counts from each channel (used for testing corresponding to all the sessions)</li> <li>trainSet: Number of spike counts from each channel (used for calibration, mostly) </li> </ol> <p><strong>Additional Information.</strong> This dataset is a simplified and curated version designed to reproduce the results presented in the associated paper. Note that Experiments 1 and 3 have partial datasets already publicly available at: <a href="https://osf.io/dce96/" target="_new" rel="noopener">https://osf.io/dce96/</a>. However, those versions are raw and can be processed using the variable descriptions below to extract spike counts with a specified bin width. Each file includes the following fields:</p> <ol> <li> joystick_adfreq: The frequency of operation of the joystick.</li> <li>X_Voltage: The voltage reading corresponding to the x-coordinate (while joystick operation).</li> <li>Y_Voltage: The voltage reading corresponding to the y-coordinate (while joystick operation).</li> <li>Spike_data(Channel Number): The Channel Number corresponding to which the neuronal data is recorded.</li> <li>Spike_data(Cluster): Descripting the cluster on which the channels are placed.</li> <li>Spike_data(Spike Times): The timestamp corresponding to the detection of a spike.</li> <li>Spike_data(Spike Number): The total number of spikes calculated for a particular trial from a particular channel.</li> <li>Spike_data(Mean Spike Waveform): The mean neuronal data (for that trial from a particular channel) describing a spike.</li> <li>Spike_data(Spike Amplitude): The mean spike amplitude of that particular channel.</li> <li>IMETrainingData(SentSignals): The truth labels corresponding to a particular trial.</li> <li>IMETrainingData(Timestamps): Time stamps corresponding to each sent signal (data).</li> <li>IMETrainingData(ReasonFail): String data; Reason if the trial failed.</li> <li>IMETrainingData(TrialOutcomes): Trial results corresponding to successful or unsuccessful!</li> <li>IMETrainingData(StartTime): corresponding to the beginning of each trial.</li> <li>IMETrainingData(EndTime): corresponding to the end of each trial.</li> </ol> <p><strong>Possible use cases. </strong>This dataset is well-suited for designing, training, and evaluating iBMI decoders. It provides a valuable resource for researchers aiming to model sensorimotor cortical spiking, benchmark iBMI decoders under consistent conditions, or explore neuromorphic and reinforcement learning-based approaches to decoder design.</p> <p><strong>Contact Information. </strong>We would be delighted to hear from you if you find this dataset useful—especially if it contributes to a publication. Contact: A. Basu <arinbasu@cityu.edu.hk>; A. Ghosh <aghosh14@illinois.edu>.</p> <p><strong>Citation. </strong>A. Ghosh, S. Shaikh, B. Zhou, P. S. V. Sun, C. Libedinsky, R. So, A. Basu, "Low-complexity Reinforcement Learning Decoders for Autonomous, Scalable, Neuromorphic intra-cortical Brain Machine Interfaces," Neuroelectronics 2025(2):0006, <a href="https://doi.org/10.55092/neuroelectronics20250006">https://doi.org/10.55092/neuroelectronics20250006</a></p>
Dataset underpinning: "Scalable Neural Decoder for Topological Surface Codes"
<p>The dataset belonging to the paper: Scalable Neural Decoder for Topological Surface Codes</p>
Inclusive Pattern Generation Protocols to Decode Thiol-Mediated Uptake: Original Data
<p>Original data underlying the publication entitled "Inclusive Pattern Generation Protocols to Decode Thiol-Mediated Uptake." Data are sorted in accordance with the supporting information.</p>
Associated data for "SOX10, MITF, and microRNAs: decoding their interplay in regulating melanoma plasticity (doi: 10.1002/ijc.35499)".
<p>This deposit contains the data and analysis to recreate the results in the paper- <span>Lai X</span>, <span>Luan C</span>, <span>Zhang Z</span>, et al. <span>SOX10, MITF, and microRNAs: Decoding their interplay in regulating melanoma plasticity</span>. <em>Int J Cancer</em>. <span>2025</span>; <span>1</span>-<span>17</span>. doi: <a title="Link to external resource: 10.1002/ijc.35499" href="https://doi.org/10.1002/ijc.35499" target="_blank" rel="noopener">10.1002/ijc.35499.</a></p> <p>If you have used the data for your research, please cite the original publication. Thank you very much.</p> <p> </p> <p> </p>
Joint identification of groundwater contamination source and heterogeneous hydrogeological parameters in LNAPL contaminated site based on deep convolutional encoder-decoder neural networks
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Supplementary Material: Decoding sequence determinants of gene expression in diverse cellular and disease states
<p>Supplementary material for the following publication:</p> <p><strong>Decoding sequence determinants of gene expression in diverse cellular and disease states</strong></p> <p>Avantika Lal*,1, Alexander Karollus*,1,2,3, Laura Gunsalus1, David Garfield4, Surag Nair1, Alex M Tseng1, M Grace Gordon5, John Blischak6, Bryce van de Geijn6, Tushar Bhangale6, Jenna L Collier1, Nathaniel Diamant1, Tommaso Biancalani1, Hector Corrada Bravo1, Gabriele Scalia1, Gokcen Eraslan1</p> <p>*Equal contributions</p> <p>1Biology Research | AI Development, gRED Computational Sciences, Genentech, South San Francisco, CA 94080, USA</p> <p>2School of Computation, Information and Technology, Technical University of Munich, Germany</p> <p>3Munich Center for Machine Learning </p> <p>4OMNI Bioinformatics and Department of Regenerative Medicine, Genentech, South San Francisco, CA 94080, USA</p> <p>5 Department of Cellular and Tissue Genomics, Genentech Research and Early Development, Genentech, South San Francisco, CA 94080, USA</p> <p>6 Department of Human Genetics, Genentech, South San Francisco, CA 94080, USA</p> <p><strong><br></strong>Correspondence: Avantika Lal (<a href="mailto:lal.avantika@gene.com">lal.avantika@gene.com</a>), Gokcen Eraslan (<a href="mailto:eraslan.gokcen@gene.com">eraslan.gokcen@gene.com</a>)</p>
Unsupervised functional alignment via data-driven ANN-based visual decoding
<p>This repository is for the under-review paper "Unsupervised functional alignment via data-driven ANN-based visual decoding". Xin-Ya Zhang, Hang Lin, Zeyu Deng, Markus Siegel, Earl K. Miller and Gang Yan.</p>
A Multi-Objective Optimization Framework for Code Generation Decoding Strategies Based on Code Token Trees
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Dataset for "Decoding Web3: In-depth Analysis of the Third-Party Package Supply Chain"
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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.