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134 results for “Matlab”
ChannelLeveeModel: Decoupled Channel-levee Evolution Model for MATLAB
<h1>ChannelLeveeModel v1.0.1</h1> <p>This is the archive of a numerical model that decoupled channel bed and levee evolution with associated simulations, dataset, and figures using MATLAB</p> <p><strong>Features</strong></p> <ul> <li>Generate random weekly hydrographs and solve the divided channel method to compute the flooded water surface elevation (Lotter, 1933)</li> <li>Identify two flood styles: Front loading and Back loading</li> <li>Run advection-settling model (Han and Kim, 2022) for Front loading events and Ponded water model (Nicholas and Walling, 1996) for Back loading events</li> <li>Visualizes results with MATLAB plotting functions</li> </ul> <p><strong>File lists</strong></p> <ul> <li><strong>Main file:</strong> <code>DecoupledCLM.m</code></li> <li><strong>Function files:</strong> <code>HydraulicGeometry.m</code>, <code>Hydrograph.m</code>, <code>OverflowLevel.m</code>, <code>LeveeBimodal.m</code>, <code>Backloading_wellmix.m</code></li> <li><strong>Plotting files:</strong> <code>plot_BE.m</code>, <code>plot_ConfinedRelease.m</code>, <code>plot_figures.m</code></li> <li><strong>Output folder:</strong> <code>simulation_code/output/</code> contains example model simulations in <code>.m</code> format. and figures in <code>.pdf</code> format.</li> <li><code>README.md</code> and <code>LICENSE</code></li> </ul>
Automated temporal front tracking toolbox in Matlab
<p>This toolbox provides the Matlab scripts that achieve temporal tracking of coherently evolving density fronts in numerical modes. It consists of three components: (1) scripts to detect density fronts based on the Canny edge detection algorithm at each time step in the model outputs; (2) scripts to automatically track coherently evolving front in time; and (3) scripts to do front pruning and remove the incoherent frontal segment that shows inconsistent frontal propagation direction. A dataset ('G_time_rho.mat') containing modeled density at successive time steps is provided for demonstration. More details of this method can be found in our work that is expected to be published soon (Wu, X., F. Feddersen and S. N. Giddings, 2021, Automated temporal tracking of coherently evolving density fronts in numerical models, Journal of Atmospheric and Oceanic Technology, in revision). Contact Xiaodong Wu (x1wu@ucsd.edu) for any questions. </p>
A river on fiber: high resolution fluvial monitoring with distributed acoustic sensing – Data, Matlab Scripts and App
<p>Matlab software and data associated with Roth et al. (submitted to Seismica, 2025).</p>
Data and Matlab functions/scripts for nuclear pair ESEEM
<p>Data set for testing prediction of electron spin echo decay by nuclear pair ESEEM simulations. Contains Hahn echo decay data and Carr-Purcell echo decay data with 2-5 <span class="math-tex">\(\pi\)</span> pulses for a spin label with deuterated methyl groups in a natural abundance 1:1 water/glycerol (v/v) glass recorder at 40 K and stimulated echo decay data as well as inversion recovery data for 3-amido-Proxyl in the same glass at 50 K. The data sets are complemented by a Cartesian coordinate file for a model box of such a water/glycerol glass at 50 K based on the OPLS-AA force field and by Matlab routines for predicting echo decay based on analytical expressions and on density operator computations.</p>
Matlab code to calibrate a structured-PDE model to data from in vitro experiments
Open the record for dataset details and reuse information.
Matlab example for Local Enrichment Analysis (LEA) analysis with real data
Open the record for dataset details and reuse information.
3DKMI: A MATLAB package to generate shape signatures from Krawtchouk moments and an application to species delimitation in planktonic foraminifera
Open the record for dataset details and reuse information.
Erora opisena optical and tomography data and associated MATLAB scripts
Open the record for dataset details and reuse information.
Micro-CT data and associated MATLAB scripts from the skeleton of the sea urchin Cidaris rugosa at four different resolutions
Open the record for dataset details and reuse information.
The MATLAB code for "A kinematic model for understanding rain formation efficiency of a convective cell"
<p>Please check the code for the 1D model and the plotting commands. Please start from main.m </p>
iTFM Matlab Code for Improved formulation of travelling fires
<p>This is the iTFM code for calculations in Matlab of gas temperature in improved formulation of travelling fires. It is written by Egle Rackauskaite and Guillermo Rein, Imperial College London, UK, and it is based on the journal paper (doi:10.1016/j.istruc.2015.06.001):</p> <p>E Rackauskaite, C Hamel, A Law, G Rein, <em>Improved formulation of travelling fires and application to concrete and steel structures</em>, <strong>Structures</strong>, 2015. http://dx.doi.org/10.1016/j.istruc.2015.06.001</p> <p>Contact authors at g.rein@imperial.ac.uk and reingu@gmail.com<br /> Work funded by Engineering and Physical Sciences Research Council and Arup<br /> File published under a Creative Commons license CC BY 4.0</p>
Matlab codes implementing the XDROM+ data-driven ENSO forecast model and some analysis of it
<p>This is the BEST forecast model of large scale features of ENSO as of today, beating (Zhao et al. Nature 2024).</p> <p>This archive is supplementary to a comment article concerning (Zhao et al. Nature 2024) intended as a "Matters Arising" piece to be submitted to Nature (https://www.researchsquare.com/article/rs-5336072/v1). Given that i criticise also the handling editor and 3 reviewers of (Zhao et al. Nature 2024) calling their incompetence out, do not be surprised if you have to look for the paper in some other journal instead. Oh well, integrity is above all else, no?! On that note, may I interest you in a bit of sci-fi? https://www.linkedin.com/pulse/crime-punishment-bit-differently-tamas-bodai-g4cvf/?trackingId=WdQkSNjgSuyxlyWVLRonrw%3D%3D</p>
Raw data and Matlab code for: Convergence in carnivorous pitcher plants reveals a mechanism for composite trait evolution
<p>Composite traits involve multiple components that, only when combined, gain a new synergistic function. Thus, how they evolve remains a puzzle. We combined field experiments, microscopy, chemical analyses and laser Doppler vibrometry with comparative phylogenetic analyses to show that two carnivorous <em>Nepenthes</em> pitcher plant species convergently evolved identical adaptations in three distinct traits to acquire a new, composite trapping mechanism. Comparative analyses suggest that this new trait arose convergently via 'spontaneous coincidence' of the required trait combination, rather than directional selection in the component traits. Our results indicate a plausible mechanism for composite trait evolution and highlight the importance of stochastic phenotypic variation as a facilitator of evolutionary novelty.</p>
MATLAB scripts and raw experimental data for the paper "Optimizing measurements of linear changes of NMR signal parameters" by Javier Agustin Romero, Krzysztof Kazimierczuk and Paweł Kasprzak
<p>Classical_fit.m - Comparison of simulation results and theoretical predictions for the linear fit of the resonance frequencies. </p> <p>Radon_transform - the same, but using Radon transform to determine linear coefficients.</p> <p>Amplitude.m - Comparison of errors of the linear coefficients for varying amplitude fit in simulations and theory.</p> <p>process_measurements.m - script to process experimental data (caffeine peak at 7.90 ppm). The data are stored in real.mat and imag.mat</p> <p>For the details of theoretical formulas, see the paper "Optimizing measurements of linear changes of NMR signal parameters" by Javier Agustin Romero, Krzysztof Kazimierczuk, and Paweł Kasprzak. The scripts were used to generate Figures in the paper.</p>
Data and code for "Quantum chaos on edge" (matfiles, a matlab script, and figures)
<p>The data files and matlab script to generate Figure 5,7,8 of the manuscript "Quantum chaos on edge" are uploaded.</p> <ul> <li>In 'script' folder, there is a single matlab file to generate Fig 5,7,8. </li> <li>In 'raw_data' folder, there are mat-files to be processed by the script file.</li> <li>In 'figures' folder, the generated figures are included.</li> </ul>
Guia Integral para la Representación Efectiva de Resultados Experimentales Extensivos mediante Matlab: Visualizaciones, Exportación y Presentación de Gráficos
<p>En esta presentacion se incluyen metodos para el procesamiento de un gran numero de reportes experimentales, esto con el fin de presentar y organizar de manera sistematica los documentos de resultados. Asi, las graficas de resutlados generadas seran consistentes en tamaño, formato y exposicion de resultados. Los metodos usados se basan en scripts en Matlab y los datos se obtienen de archivos en excel.</p>
Raw experimental data and matlab codes for the paper "State-dependent driving : A route to non-equilibrium stationary states"
<p>Raw experimental data and matlab codes used for numerical simulation in the paper - "State-dependent driving : A route to non-equilibrium stationary states". The data and codes to generate figures 2, 3 and 5 are available in the folders named "Fig 2", "Fig 3" and "Fig 5" respectively.</p>
Dataset (MATLAB format) from Gao et al (2018) A cortico-cerebellar loop for motor planning. Nature, Nov;563(7729):113-116.
<p><strong>Summary</strong></p> <p>These experiments measure neuronal responses from anterior lateral motor cortex (ALM) and deep cerebellar nucleus (CN) of adult mice performing pole location discrimination with a short-term memory. In some cases, we manipulate activity of one brain region while recording from the other region.</p> <p> </p> <p>Dataset:</p> <p><em>Li N (2018). Extracellular recordings from anterior lateral motor cortex (ALM) and cerebellar nucleus neurons of adult mice performing a tactile decision behavior. </em></p> <p> </p> <p>Data included in this release:</p> <p>34 sessions (9 mice), ALM recording during fastigial or dentate photoactivation</p> <p>20 sessions (4 mice), ALM recording during DCN photoinhibition</p> <p><em>(Data already available at: </em><a href="http://dx.doi.org/10.6080/K0NS0S26"><em>http://dx.doi.org/10.6080/K0NS0S26</em></a><em>)</em></p> <p> </p> <p>185 sessions (18 mice), CN recording. In some sessions, ALM photoinhibition was tested</p> <p><em>(Data included in this release. Data is in MATLAB format).</em></p> <p> </p> <p>Data from the follow publication:</p> <p><em>Gao Z, Davis C, Thomas AM, Economo MN, Abrego AM, Svoboda K, De Zeeuw CI, Li N (2018). A cortico-cerebellar loop for motor planning. Nature, Nov;563(7729):113-116. doi: 10.1038/s41586-018-0633-x. Epub 2018 Oct 17.</em></p> <p> </p> <p> </p> <p><strong>Animals</strong></p> <p>This dataset contains data from 31 mice (age > P60, both male and female mice, Supplemental Table 1). 9 C57B1/6 mice were used for ALM recordings during photo-activation of the CN. 4 L7-cre (<a href="https://zenodo.org/record/6647629#_ENREF_3">Lewis et al., 2004</a>) crossed to Ai32 (Rosa26-LSL-ChR2-EYFP, JAX Stock#012569) (<a href="https://zenodo.org/record/6647629#_ENREF_5">Madisen et al., 2012</a>) mice were used for ALM recordings during CN photo-inhibition.</p> <p>10 C57B1/6 mice were used for CN recording experiments. 8 VGAT-ChR2-EYFP mice (Jackson laboratory, JAX Stock#014548) (<a href="https://zenodo.org/record/6647629#_ENREF_7">Zhao et al., 2011</a>) were used for CN recordings during ALM photo-inhibition.</p> <p> </p> <p> </p> <p> </p> <p><strong>Experimental methods</strong></p> <p>Detailed experimental methods are described in the manuscript (Gao et al 2018).</p> <p> </p> <p><strong><em>Behavior</em></strong></p> <p>Mice measured the location of an object using their whiskers during a sample epoch (1.3 s) (<a href="https://zenodo.org/record/6647629#_ENREF_6">O'Connor et al., 2010</a>). After the sample epoch they must hold their decision about object location in memory for a delay period (1.3 s) (<a href="https://zenodo.org/record/6647629#_ENREF_2">Guo et al., 2014</a>). At the end of the delay period, an auditory cue (0.1) instructed the mice to report their decision with directional licking (“lick left”/”lick right”). </p> <p> </p> <p><strong><em>CN ChR2 photo-activation </em></strong></p> <p>For ChR2 photo-activation of the CN, wild-type mice injected with AAV2-hSyn1-(h134R)ChR2-EYFP virus were used. Light from a 473 nm laser (Laser Quantum, Part# Gem 473) was controlled by an acousto-optical modulator (AOM; Quanta Tech) and a shutter (Vincent Associates). To prevent the mice from distinguishing photostimulation trials from control trials using visual cues, a ‘masking flash’ was delivered using 470 nm LEDs (Luxeon Star) near the eyes of the mice. The masking flash began as the pole started to move and continued through the end of the epoch in which photostimulation could occur. The photostimulus was pulses of light (5 ms pulse duration) delivered at 20 Hz and a range of peak powers (5, 10, 15mW). The power values reported in the paper indicate average powers (0.5, 1, 1.5 mW). The powers were measured at the fiber tip. The photostimulus started at the beginning of a task epoch and continued for 0.455 s (10 pulses).</p> <p> </p> <p><strong><em>CN photo-inhibition</em></strong></p> <p>In L7-cre × Ai32 mice, ChR2 was expressed in cerebellar Purkinje cells. We photostimulated Purkinje cells to inhibit neurons in the CN. The photostimulus was a 40 Hz sinusoid (average power, 4.5 mW) lasting for 1.3 sec, including a 100-200ms linear ramp during the laser offset to reduce rebound neuronal activity.</p> <p> </p> <p><strong><em>ALM photo-inhibition</em></strong></p> <p>ALM is centered on bregma anterior 2.5 mm, lateral 1.5 mm (<a href="https://zenodo.org/record/6647629#_ENREF_1">Chen et al., 2017</a>; <a href="https://zenodo.org/record/6647629#_ENREF_2">Guo et al., 2014</a>; <a href="https://zenodo.org/record/6647629#_ENREF_4">Li et al., 2016</a>). For photo-inhibition of ALM, we photostimulated cortical GABAergic neurons in VGAT-ChR2-EYFP mice (8 mice). Photostimulation was performed through the clear-skull cap implant by directing the blue laser over the skull (beam diameter: 400 µm at 4σ, bregma anterior 2.5 mm, lateral 1.5 mm). The light transmission through the intact skull was 50% (<a href="https://zenodo.org/record/6647629#_ENREF_2">Guo et al., 2014</a>). We photo-inhibited ALM for 1.3 s at the beginning of the delay epoch, including a 100 ms linear ramp at the laser offset to minimize rebound excitation. This photostimulus was empirically determined to produce robust photo-inhibition in ALM (<a href="https://zenodo.org/record/6647629#_ENREF_2">Guo et al., 2014</a>; <a href="https://zenodo.org/record/6647629#_ENREF_4">Li et al., 2016</a>). The photo-inhibition silenced 90% of spikes in a cortical area of 1mm radius (at half-max) through all cortical layers. For unilateral ALM photo-inhibition, we used a 40 Hz sinusoidal photostimulus (1.5mW average power at the skull surface) at 2.5 mm anterior and 1.5 mm lateral from bregma. For bilateral ALM photo-inhibition, we used a constant photostimulus and a scanning galvo (GVSM002, Thorlabs), which stepped the laser beam sequentially through the photo-inhibition sites at the rate of 1 step per 5 ms (step time: 0.2 ms; dwell time: 4.8 ms; measured using a photodiode). 8 photo-inhibition sites were spaced in 1 mm at anterior 2-3 mm and lateral 1-2 mm from bregma, covering ALM. Peak power was adjusted based on the number of photo-inhibition sites to achieve 1.5 mW average power per site.</p> <p> </p> <p> </p> <p><strong><em>Electrophysiology</em></strong></p> <p>Extracellular spikes were recorded using 32-channel NeuroNexus silicon probes (Part# A4x8-5mm-100-200-177) or 64-channel Cambridge NeuroTech silicon probes (H2 acute probe, 25 µm spacing, 2 shanks). The 32-channel voltage signals were multiplexed, digitized by a PCI6133 board at 400 kHz (National Instruments) at 14 bit, demultiplexed (sampling at 25,000 Hz) and stored for offline analysis. The 64-channel voltage signals were amplified and digitized on an Intan RHD2164 64-Channel Amplifier Board (Intan Technology) at 16 bit, recorded on an Intan RHD2000-Series Amplifier Evaluation System (sampling at 20,000 Hz) using Open-Source RHD2000 Interface Software from Intan Technology (version 1.5.2), and stored for offline analysis.</p> <p> </p> <p>The extracellular recording traces were band-pass filtered (300-6 kHz). Events that exceeded an amplitude threshold (4 standard deviations of the background) were subjected to manual spike sorting to extract single-units (<a href="https://zenodo.org/record/6647629#_ENREF_2">Guo et al., 2014</a>).</p> <p> </p> <p> </p> <p><strong>Data analysis</strong></p> <p>For ALM recordings, units are classified based on spike shape. Spike widths were computed as the trough-to-peak interval in the mean spike waveform. Units with spike width < 0.35 ms were defined as fast-spiking neurons (82/1309) and units with spike widths > 0.45 ms as putative pyramidal neurons (1194/1309). Units with intermediate values (0.35 - 0.45 ms, 33/1309) were excluded from analyses. This classification was previously verified by optogenetic tagging of GABAergic neurons (<a href="https://zenodo.org/record/6647629#_ENREF_2">Guo et al., 2014</a>).</p> <p> </p> <p>For CN recordings, units are classified based on recording location. We estimated unit locations based on recording track labeling, recording depth, and the lamination of activity patterns across the recording shanks. In <em>post-hoc</em> histology, CN boundaries were visible in DAPI staining.</p> <p> </p> <p> </p> <p><strong>References</strong></p> <p>Chen, T.W., Li, N., Daie, K., and Svoboda, K. (2017). A Map of Anticipatory Activity in Mouse Motor Cortex. Neuron<em> 94</em>, 866-879 e864.</p> <p>Guo, Z.V., Li, N., Huber, D., Ophir, E., Gutnisky , D.A., Ting, J.T., Feng, G., and Svoboda, K. (2014). Flow of cortical activity underlying a tactile decision in mice. Neuron<em> 81</em>, 179-194.</p> <p>Lewis, P.M., Gritli-Linde, A., Smeyne, R., Kottmann, A., and McMahon, A.P. (2004). Sonic hedgehog signaling is required for expansion of granule neuron precursors and patterning of the mouse cerebellum. Dev Biol<em> 270</em>, 393-410.</p> <p>Li, N., Daie, K., Svoboda, K., and Druckmann, S. (2016). Robust neuronal dynamics in premotor cortex during motor planning. Nature.</p> <p>Madisen, L., Mao, T., Koch, H., Zhuo, J.M., Berenyi, A., Fujisawa, S., Hsu, Y.W., Garcia, A.J., 3rd, Gu, X., Zanella, S.<em>, et al.</em> (2012). A toolbox of Cre-dependent optogenetic transgenic mice for light-induced activation and silencing. Nature neuroscience<em> 15</em>, 793-802.</p> <p>O'Connor, D.H., Clack, N.G., Huber, D., Komiyama, T., Myers, E.W., and Svoboda, K. (2010). Vibrissa-based object localization in head-fixed mice. The Journal of neuroscience : the official journal of the Society for Neuroscience<em> 30</em>, 1947-1967.</p> <p>Zhao, S., Ting, J.T., Atallah, H.E., Qiu, L., Tan, J., Gloss, B., Augustine, G.J., Deisseroth, K., Luo, M., Graybiel, A.M.<em>, et al.</em> (2011). Cell type-specific channelrhodopsin-2 transgenic mice for optogenetic dissection of neural circuitry function. Nature methods<em> 8</em>, 745-752.</p>
Additonal material for the dissertation "An Accelerated Solution Method for Two-Stage Stochastic Models in Disaster Management": Data, MATLAB codes and results
<p>File "DataImport" contains a "ReadMe" file, raw data for all case studies in Excel and the MATLAB code "ImportData.m" importing Excel data into MATLAB</p> <p>File "LShaped" contains a "ReadMe" file, all data in the form of matrices and the MATLAB code "LShaped_MultiCut.m" solving all case studies via the standard or accelerated L-shaped method using a multi-cut approach</p> <p>File "Results" contains a "ReadMe" file, results of all case studies and computation time required by Gurobi, der standard L-shaped method and accelerated L-shaped method</p>
Matlab Files of Lab Spectra + PDS Dataset Names
<p>Endmember spectra for sulfuric acid octahydrate ice (pure), water ice (pure), SAO ice - water ice mixtures, and bischofite. All files are taken of samples 90 to 106 microns in diameter except bischofite, which was 75 to 90 microns in diameter.</p> <p>Rows in Key correspond to rows in data</p> <p>Wavelength in microns, spectra are in reflectance</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.