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3 results for “Sparse Code”
Data for threshold plot in "Finite-rate sparse quantum codes aplenty"
<p>This dataset contains the quantum error correcting codes that were sampled to render the threshold plot under the erasure noise channel in the article. They are sorted by batch corresponding to different jobs on the computing cluster. Each batch contains many codes sorted by length. There are also three extra metadata files. The inputs that were given to sample codes (inputs.json), the CSP solver output (output.csv) and the result of the decoding simulation (decoding.json).</p> <p>Each file named code.json contains the sparse list of X and Z stabilizers. That is, each stabilizer is given by a list of integers corresponding to the index of the qubits supporting the stabilizer.</p>
Node-Pore Coded Coincidence Correcting Microfluidic Channel Framework: Code Design and Sparse Deconvolution
<p>This is the dataset for the work titled and authored by:</p> <p><strong>Node-Pore Coded Coincidence Correcting Microfluidic Channel Framework: Code Design and Sparse Deconvolution</strong></p> <p>Michael Kellman, Francois Rivest, Alina Pechacek, Lydia Sohn, Michael Lustig</p> <p>We present a novel method to perform individual particle (e.g. cells or viruses) coincidence correction through joint channel design and algorithmic methods. Inspired by multiple-user communication theory, we modulate the channel response, with Node-Pore Sensing, to give each particle a binary Barker code signature. When processed with our modified successive interference cancellation method, this signature enables both the separation of coincidence particles and a high sensitivity to small particles. We identify several sources of modeling error and mitigate most effects using a data-driven self-calibration step and robust regression. Additionally, we provide simulation analysis to highlight our robustness, as well as our limitations, to these sources of stochastic system model error. Finally, we conduct experimental validation of our techniques using several encoded devices to screen a heterogeneous sample of several size particles.</p> <p>Software can be found under this DOI:</p> <p>10.5281/zenodo.846448</p>
Code and Data from: An Imputation-Based Approach for Augmenting Sparse Epidemiological Signals
<p>This directory contains R code and required data to run the full data augmentation described in, "An Imputation-Based Approach for Augmenting Sparse Epidemiological Signals." This is the updated code corresponding to the updated medRxiv manuscript. It now includes ILINet data as a predictor in the imputation.</p> <p> </p> <p>"aug_pipeline.R" runs through all component steps and calls individual functions and data files within the directory. "plots_for_pipeline.R" uses data created during the aug_pipeline script to visualize individual steps in the augmentation process.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.