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376 results for “scalable”
Data from: Scalable, semi-automated fluorescence reduction neutralization assay for qualitative assessment of Ebola virus-neutralizing antibodies in human clinical samples
Antibody titers against a viral pathogen are typically measured using an antigen binding assay, such as an enzyme-linked immunosorbent assay (ELISA), which only measures the ability of antibodies to identify a viral antigen of interest. Neutralization assays measure the presence of virus-neutralizing antibodies in a sample. Traditional neutralization assays, such as the plaque reduction neutralization test (PRNT), are often difficult to use on a large scale due to being both labor and resource intensive. Here we describe an Ebola virus fluorescence reduction neutralization assay (FRNA), which tests for neutralizing antibodies, that requires only a small volume of sample in a 96-well format and is easy to automate. The readout of the FRNA is the percentage of Ebola virus-infected cells measured with an optical reader or overall chemiluminescence that can be generated by multiple reading platforms and the readout is compatible with lytic and non-lytic viruses. Using blinded human clinical samples (EVD survivors or contacts) obtained in Liberia during the 2013–2016 Ebola virus disease outbreak, we demonstrate that FRNA-measured antibody titers are highly correlated with those measured by the Filovirus Animal Non-clinical Group (FANG) ELISA - the current standard for anti-EBOV antibody measurement with the important distinction of providing information on the neutralizing capabilities of the antibodies.
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>
Supplementary Material for the paper entitled "Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data"
<p>This repo contain supplementary tables from the manuscript entitled: "Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data". Clustering is a common way to identify cell types in single-cell RNA-sequencing (scRNA-seq) data. Unfortunately, current methods (i) require users to make human-in-the-loop decisions, which adds significant runtime to bioinformatic analyses, and (ii) reuse the same data twice when testing for differentially expressed genes, which can lead to an increased number of false discoveries. In this work, we overcome these limitations with NCLUSION: a Bayesian nonparametric method that simultaneously clusters cells and selects marker genes. NCLUSION operates without user-defined heuristics to set model parameters and leverages variational expectation-maximization (EM) for posterior inference which allows it to scale well up to 1 million cells. By analyzing publicly available datasets, we illustrate that NCLUSION matches the state-of-the-art clustering performance of competing approaches, achieves improved computational efficiency, and directly enables identification of biologically relevant gene sets driving cluster definitions.</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>
Reproduction Artifact for TACAS 2024 paper "Scalable Tree-based Register Automata Learning"
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PhagoStat a scalable and interpretable end to end framework for efficient quantification of cell phagocytosis in neurodegenerative disease studies
<p>This study presents a comprehensive dataset that captures the phagocytosis process in wild-type and FTD microglial cells, with each acquisition comprising 20 distinct scenes and seven hours of time-lapse video microscopy. The dataset has undergone rigorous validation and data quality correction techniques, ensuring its reliability for further analysis. The dataset includes 36,496 normalized cell and aggregate images, 1,306,131 individual instance masks for cells, 1,553,036 for aggregates, and 36,496 registered aggregates, all generated using the PhagoStat algorithm. The dataset also provides extensive biological features such as area, position, and speed, presented frame-by-frame for in-depth analysis. To enhance its value, 226 manually annotated images containing 6,430 individual cell masks were incorporated, covering different conditions and scenes. In total, the dataset comprises 235,288 files (94GB), offering researchers a valuable resource for investigating various cellular and aggregate properties.</p> <p>By providing this comprehensive dataset, the study aims to facilitate the development and application of novel methodologies in bioinformatics, computational modeling, and cellular biology. This initiative promotes innovation and collaboration among researchers in these interconnected disciplines, ultimately contributing to the understanding of cellular dynamics and the development of novel therapeutic strategies for neurodegenerative diseases like FTD.</p> <p>For more infomration please check:</p> <p>GitHub: <a href="https://github.com/ounissimehdi/PhagoStat">https://github.com/ounissimehdi/PhagoStat</a></p> <p>Paper: <a href="https://www.nature.com/articles/s41598-024-56081-7">https://www.nature.com/articles/s41598-024-56081-7</a></p> <p>DOI: 10.1038/s41598-024-56081-7</p>
Datasets for ICSE'25 submission: "Scalable and Adaptive Log-based Anomaly Detection: A Synergistic Approach"
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Scalability PICE
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DATASET - Room Temperature Pulsed Laser Deposition of Aluminum Zinc Oxide (AZO): Enabling Scalable Indium-Free Transparent Conductive Oxides
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Scalable Non-Volatile Tuning of Photonic Computational Memories by Automated Silicon Ion Implantation
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Scalable clustering with supervised linkage methods
<p>This data contains protein expression values from over 25 million cells from both Lupus patients and Healthy donors (HD). The indices correspond to single cells, and the columns include the proteins, the clinical status of each cell (0 if from HD, 1 if from a lupus patient), and the cell population (membership assigned by the HAL-x clustering algorithm). </p>
Supplementary material 1 from: Birand A, Cassey P, Ross JV, Thomas PQ, Prowse TAA (2022) Scalability of genetic biocontrols for eradicating invasive alien mammals. NeoBiota 74: 93-103. https://doi.org/10.3897/neobiota.74.82394
Tables S1, S2
Figure 1a from: Ioanas H, Saab B, Rudin M (2017) Gentoo Linux for Neuroscience - a replicable, flexible, scalable, rolling-release environment that provides direct access to development software. Research Ideas and Outcomes 3: e12095. https://doi.org/10.3897/rio.3.e12095
Figure 1a - Minimal (excluding all optional features) dependency graph of the contributed neuroscience package set.
Figure 1b from: Ioanas H, Saab B, Rudin M (2017) Gentoo Linux for Neuroscience - a replicable, flexible, scalable, rolling-release environment that provides direct access to development software. Research Ideas and Outcomes 3: e12095. https://doi.org/10.3897/rio.3.e12095
Figure 1b - Maximal (including all optional features) dependency graph of the contributed neuroscience package set.
Transposase-Assisted Tagmentation: An Economical and Scalable Strategy for Single-Worm Whole-Genome Sequencing (other species)
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Bio-Rollup: a new privacy protection solution for biometrics based on two-layer scalability-focused blockchain
<p>The eddx source file of Fig2 drawn by EdrawMax software.</p>
Replication Package for: Scalable and Reliable Multi-Dimensional Aggregation of Sensor Data Streams
<p>This repository contains a replication package and experimental results for our study on <em>Scalable and Reliable Multi-Dimensional Aggregation of Sensor Data Streams</em>.</p> <p>It features the presented implementation with Kafka Streams, tools for load generation and data collection, scripts for executing the presented evaluations as well as our raw results and script for analysis. A detailed description is given in the top-level README.md file.</p>
Raw Image Data Repository: Repurposing Large-Format Microarrays for Scalable Spatial Transcriptomics
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Figure 1 from: Mietchen D, Rasberry L, Morata T, Sadowski JP, Novakovich J, Heilman JM (2021) Developing a scalable framework for partnerships between health agencies and the Wikimedia ecosystem. Research Ideas and Outcomes 7: e68121. https://doi.org/10.3897/rio.7.e68121
Figure 1 Schematic overview of the steps involved in developing a public health agency model for Wikipedia
Data from: Scalability of asynchronous networks is limited by one-to-one mapping between effective connectivity and correlations
Network models are routinely downscaled compared to nature in terms of numbers of nodes or edges because of a lack of computational resources, often without explicit mention of the limitations this entails. While reliable methods have long existed to adjust parameters such that the first-order statistics of network dynamics are conserved, here we show that limitations already arise if also second-order statistics are to be maintained. The temporal structure of pairwise averaged correlations in the activity of recurrent networks is determined by the effective population-level connectivity. We first show that in general the converse is also true and explicitly mention degenerate cases when this one-to-one relationship does not hold. The one-to-one correspondence between effective connectivity and the temporal structure of pairwise averaged correlations implies that network scalings should preserve the effective connectivity if pairwise averaged correlations are to be held constant. Changes in effective connectivity can even push a network from a linearly stable to an unstable, oscillatory regime and vice versa. On this basis, we derive conditions for the preservation of both mean population-averaged activities and pairwise averaged correlations under a change in numbers of neurons or synapses in the asynchronous regime typical of cortical networks. We find that mean activities and correlation structure can be maintained by an appropriate scaling of the synaptic weights, but only over a range of numbers of synapses that is limited by the variance of external inputs to the network. Our results therefore show that the reducibility of asynchronous networks is fundamentally limited.
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.