This resource contains the experimental data that was included in tecplot input files but in matlab files. dba1_cp has all the results is dimensioned (7,2) first dimension is 1-7 for each span station 2nd dimension is 1 for upper surface, 2 for lower surface. dba1_cp(ispan,isurf).x are the x/c locations at span station (ispan) and upper(isurf=1) or lower(isurf=2) dba1_cp(ispan,isurf).y are the eta locations at span station (ispan) and upper(isurf=1) or lower(isurf=2) dba1_cp(ispan,isurf).cp are the pressures at span station (ispan) and upper(isurf=1) or lower(isurf=2) Unsteady CP is dimensioned with 4 columns 1st column, real 2nd column, imaginary 3rd column, magnitude 4th column, phase, deg M,Re and other pertinent variables are included as variables and also included in casedata.M, etc

This is a zip file containing the example data for nSTAT matlab toolbox (http://doi.org/10.1016/j.jneumeth.2012.08.009) . The data directory should be un-zipped into the main nSTAT directory. Updated 7-2-2017

Compositional data, which is data consisting of fractions or probabilities, is common in many fields including ecology, economics, physical science and political science. If these data would otherwise be normally distributed, their spread can be conveniently represented by a multivariate normal distribution truncated to the non-negative space under a unit simplex. Here this distribution is called the simplex-truncated multivariate normal distribution. For calculations on truncated distributions, it is often useful to obtain rapid estimates of their integral, mean and covariance; these quantities characterising the truncated distribution will generally possess different values to the corresponding non-truncated distribution.

In the paper Adams, Matthew (2022) Integral, mean and covariance of the simplex-truncated multivariate normal distribution. PLoS One, 17(7), Article number: e0272014. https://eprints.qut.edu.au/233964/, three different approaches that can estimate the integral, mean and covariance of any simplex-truncated multivariate normal distribution are described and compared. These three approaches are (1) naive rejection sampling, (2) a method described by Gessner et al. that unifies subset simulation and the Holmes-Diaconis-Ross algorithm with an analytical version of elliptical slice sampling, and (3) a semi-analytical method that expresses the integral, mean and covariance in terms of integrals of hyperrectangularly-truncated multivariate normal distributions, the latter of which are readily computed in modern mathematical and statistical packages. Strong agreement is demonstrated between all three approaches, but the most computationally efficient approach depends strongly both on implementation details and the dimension of the simplex-truncated multivariate normal distribution.

This dataset consists of all code and results for the associated article.

Network datasets and meta data using in work which develops a new model of spatial network structure: http://arxiv.org/abs/1210.4246

Scripts and data acquired at the Mirror Lake Research Site, cited by the article submitted to Water Resources Research: Distributed Acoustic Sensing (DAS) as a Distributed Hydraulic Sensor in Fractured Bedrock M. W. Becker(1), T. I. Coleman(2), and C. C. Ciervo(1) 1 California State University, Long Beach, Geology Department, 1250 Bellflower Boulevard, Long Beach, California, 90840, USA. 2 Silixa LLC, 3102 W Broadway St, Suite A, Missoula MT 59808, USA. Corresponding author: Matthew W. Becker (matt.becker@csulb.edu).

This data set consists of Conductivity, Temperature, Depth (CTD) data in MATLAB Format from the 2002 Polar Star Mooring Cruise (AWS-02-I). These data are provided in a single mat-file (MATLAB) for the entire cruise.

Matlab has a reputation for running slowly. Here are some pointers on how to speed computations, to an often unexpected degree. Subjects currently covered: Matrix Coding Implicit Multithreading on a Multicore Machine Sparse Matrices Sub-Block Computation to Avoid Memory Overflow Matrix Coding - 1 Matlab documentation notes that efficient computation depends on using the matrix facilities, and that mathematically identical algorithms can have very different runtimes, but they are a bit coy about just what these differences are. A simple but telling example: The following is the core of the GD-CLS algorithm of Berry et.al., copied from fig. 1 of Shahnaz et.al, 2006, "Document clustering using nonnegative matrix factorization': for jj = 1:maxiter A = W'*W + lambda*eye(k); for ii = 1:n b = W'*V(:,ii); H(:,ii) = A \ b; end H = H .* (H>0); W = W .* (V*H') ./ (W*(H*H') + 1e-9); end Replacing the columwise update of H with a matrix update gives: for jj = 1:maxiter A = W'*W + lambda*eye(k); B = W'*V; H = A \ B; H = H .* (H>0); W = W .* (V*H') ./ (W*(H*H') + 1e-9); end These were tested on an 8049 x 8660 sparse matrix bag of words V (.0083 non-zeros), with W of size 8049 x 50, H 50 x 8660, maxiter = 50, lambda = 0.1, and identical initial W. They were run consecutivly, multithreaded on an 8-processor Sun server, starting at ~7:30PM. Tic-toc timing was recorded. Runtimes were respectivly 6586.2 and 70.5 seconds, a 93:1 difference. The maximum absolute pairwise difference between W matrix values was 6.6e-14. Similar speedups have been consistantly observed in other cases. In one algorithm, combining matrix operations with efficient use of the sparse matrix facilities gave a 3600:1 speedup. For speed alone, C-style iterative programming should be avoided wherever possible. In addition, when a couple lines of matrix code can substitute for an entire C-style function, program clarity is much improved. Matrix Coding - 2 Applied to integration, the speed gains are not so great, largely due to the time taken to set up the and deal with the boundaries. The anyomous function setup time is neglegable. I demonstrate on a simple uniform step linearly interpolated 1-D integration of cos() from 0 to pi, which should yield zero: tic; step = .00001; fun = @cos; start = 0; endit = pi; enda = floor((endit - start)/step)step + start; delta = (endit - enda)/step; intF = fun(start)/2; intF = intF + fun(endit)delta/2; intF = intF + fun(enda)(delta+1)/2; for ii = start+step:step:enda-step intF = intF + fun(ii); end intF = intFstep toc; intF = -2.910164109692914e-14 Elapsed time is 4.091038 seconds. Replacing the inner summation loop with the matrix equivalent speeds things up a bit: tic; step = .00001; fun = @cos; start = 0; endit = pi; enda = floor((endit - start)/step)*step + start; delta = (endit - enda)/step; intF = fun(start)/2; intF = intF + fun(endit)*delta/2; intF = intF + fun(enda)*(delta+1)/2; intF = intF + sum(fun(start+step:step:enda-step)); intF = intF*step toc; intF = -2.868419946011613e-14 Elapsed time is 0.141564 seconds. The core computation take

This zip file contains the data and the Matlab scripts for data analysis and generation of figures.

MATLAB led the global advanced analytics and data science software industry in 2025 with a market share of ***** percent. First launched in 1984, MATLAB is developed by the U.S. firm MathWorks.

Matlab functions for maximum likelihood estimation of a variety of probabilistic discounting models from choice experiments. Data should take the form of binary choices between immediate and delayed rewards. The available discount functions are: 1) exponential 2) hyperbolic (Mazur's one-parameter hyperbolic) 3) generalized hyperbolic 4) Laibson's beta-delta

Matlab code to simulate equilibrium geometry of selected cross-sections on the Lower American and Sacramento Rivers in California.

Two folders are provided. The MATLAB folder contains MATLAB codes for running Kernel PLS cross-validation, calibration and prediction of properties in various data sets: corn, yerba, wheat and meat. The R folder contains the same data with R codes for the same activities.

This is sample Matlab script for postprocessing of DHSVM bias and low flow corrected data using Integrated Scenarios Project CMIP5 climate forcing data to model future projected streamflow in the Skagit River Basin. Testing HydroShare Collections...testing, testing.

This repository contains the Matlab code and generated data for the manuscript "Data-driven geometric parameter optimization for PD-GMRES" which uses a quadtree approach to optimize parameters for the iterative solver PD-GMRES. It includes hardware specific data to allow for reproducibity of our results. Our calculations were performed using MATLAB R2019a and should be reproducible up to and including version R2022a. A change in version R2022b leads to different numerical behavior. However, the code does run on newer Matlab versions. Further information is contained in the README.

Routine to reproduce Figs 1D–1F, 2B, 2C, 3, 4 and 5 and S1–S6, panel B in S7, and S8 Figs. (ZIP)

This is the data and Matlab code associated with "Predicting Monoclonal Antibody Binding Sequences from a Sparse Sampling of All Possible Sequences ". Note that there are Arizona State University Patents associated with the algorithms involved in this work.

This dataset contains relevant MATLAB/SIMULINK(R) code and supporting data in relation to CHAPTER 2 of the dissertation 'Advances in Dynamic Inversion-based Flight Control Law Design: Multivariable Analysis and Synthesis of Robust and Multi-Objective Design Solutions' by T.S.C. Pollack (2024) [DOI: 10.4233/uuid:28617ba0-461d-48ef-8437-de2aa41034ea]. It concerns the impact of inversion strategy on the robustness of Incremental Dynamic Inversion (IDI) based control laws against mixed uncertainty. The MATLAB(R) Robust Control Toolbox serves as the main instrument for data generation. For more information, we refer to the respective thesis chapter / publication.

The following datasets are related:

1) MATLAB/SIMULINK(R) Code and Supporting Data for Multi-loop Robust Design of INDI-based Flight Control Laws (DOI: https://doi.org/10.4121/4a2afa5b-cc72-4ebe-adca-970e2fc0d988)

2) MATLAB/SIMULINK(R) Code and Supporting Data for Assessment of Commonalities between Hybrid INDI-based and PID-based Flight Control Design (DOI: https://doi.org/ 10.4121/1c425f5d-943c-4e9c-8c6b-4e026dba20ca)

3) MATLAB/SIMULINK(R) Code and Supporting Data for Design of Multi-objective Incremental Control Allocation-based Flight Control Laws (DOI: https://doi.org/10.4121/b265ae09-64ef-4faf-bd77-d18712c11239)

File List pacSalmonIterator.m extendVectorToLength.m get_ts_stats.m salmon_ts_SR_comparison.m short_forcing_scenarios.m pacSalmonIterator_single_figures.m read_agedistribution_data_Bristol.m read_agedistribution_data_Fraser.m salmon_ts_diagnostics.m

    BristolBay_Agedist_Jan2010.mat
    Fraser_AgeDist_May2010.mat

    ageStructuredSRSim.m
    ageStructuredSREq.m
    checkPersist.m
    BevertonHolt.m
    pacSalmonF.m
    pacSalmonF_fixed.m
    semelparousFluctuatingLogSurvival.m
   semelparousFluctuatingLogSurvival_fixed.m
    Ricker.m
    wavelet.m
    chisquare_inv.m
    chisquare_solve.m
    coif.m
    contwt.m
    invcwt.m
    wave_bases.m
    wave_signif.m

xcorr_simple.m Description

   The Matlab script pacSalmonIterator.m is a wrapper that defines parameters and runs the simulations.
    All of the simulation functions are in the directory ageStructuredSimulator, which must be a sub-directory to the working directory where pacSalmonIterator.m resides.

The range of forcing values and all other parameters used in the paper are given in the manuscript. The mean age at spawning was 4 years, corresponding to Fraser R. stocks.

Other files used in simulations, extracting data, etc. The Matlab script extendVectorToLength.m is a helper file when assembling the age-structured population. The Matlab script get_ts_stats.m calculates time series metrics. Given a time series and wavelet, this will calculate:

CV_PowerDom_Scale - A measure of cyclic consistency, called 'C' in the ms 
Mean_maxmin_ratio - A measure of cyclic dominance. Called 'D' in the ms 

    The Matlab script salmon_ts_SR_comparison.m compares time series statistics to position on stock-recruit curve for actual sockeye data (Fig. 6 in the ms). 

The Matlab script short_forcing_scenarios.m creates the parameters needed to run some short, non-stochastic forcings of different types to see how/if they set up cycles (Fig. 5 in the ms). The Matlab script pacSalmonIterator_single_figures.m runs the simulations specified by short_forcing_scenarios.m and plots the results. The Matlab script read_agedistribution_data_Bristol.m reads in Bristol Bay data and formats it. The Matlab script read_agedistribution_data_Fraser.m reads in Fraser River data and formats it. The Matlab script salmon_ts_diagnostics.m calculates time series metrics for actual sockeye data by calling get_ts_stats.m. Data Files: The Matlab data file BristolBay_Agedist_Jan2010.mat contains age structure data as of Jan 2010 from Randall Peterman. The Matlab data file Fraser_AgeDist_May2010.mat containes age structure data as of May 2010 from Mike LaPointe. Files in ageStructuredSimulator: The Matlab script ageStructuredSRSim.m models the age-structured population dynamics. This is the code that was used for most of the simulations in the paper. This code requires inputting various function handles to define the fecundity and survival functions. These are explained in the text of the m-file. In addition to running the simulations, this file calls wavelet.m to perform wavelet analysis. The Matlab script ageStructuredSREq.m calculates the equilibrium of an age-structured model with density-dependent recruitment. A simplified version of ageStructuredSRSim.m The Matlab script checkPersist.m determines whether a given parameter set has a nonzero equilibrium (i.e., deterministic persistence). Functions that define the demography of the species being modeled

These functions are specified as function handles (@function) when calling ageStructuredSRSim.m in pacSalmonIterator.m

The Matlab script BevertonHolt.m calculates a stock-recruit curve, given parameters. The Matlab script pacSalmonF.m specifies a fecundity function for salmon. It returns the probability of spawning at each age, not fecundity per se. The Matlab script pacSalmonF_fixed.m specifies a fecundity function, just like pacSalmonF.m. This one allows for user-specified variation at specific times, rather than stochastic variation. The Matlab script semelparousFluctuatingLogSurvival.m calculates survival of an iteroparous species, which varies on the log scale. So S = exp(log(S)+sigma), where sigma is random normal error. The Matlab script semelparousFluctuatingLogSurvival_fixed.m is just like semelparousFluctuatingLogSurvival.m, but allows user-specified variation at specific times, rather than stochastic variation. The Matlab script Ricker.m calculates a stock-recruit curve, given parameters. The Matlab script wavelet.m calculates the wavelet spectrum. Originally written by Terrence and Compo, with some minor modifications. One key modification is to calculate the lag-1 autocorrelation directly from the data set (this is done in ageStructuredSRSim.m) rather than just use the default value, which seems to be from the NINO3 data set. The wavelet.m code uses a number of helper files:

     chisquare_inv.m 
     chisquare_solve.m 
     coif.m 
     contwt.m 
     invcwt.m 
     wave_bases.m 
     wave_signif.m 
     xcorr_simple.m (if you don't have the signal processing toolbox, this calculates the cross-correlation for you)

Comprehensive YouTube channel statistics for MATLAB TECH, featuring 210,000 subscribers and 7,962,014 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Education category and is based in US. Track 557 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

Abstract: This contribution provides MATLAB scripts to assist users in factor analysis, constrained least squares regression, and total inversion techniques. These scripts respond to the increased availability of large datasets generated by modern instrumentation, for example, the SedDB database. The download (.zip) includes one descriptive paper (.pdf) and one file of the scripts and example output (.doc). Other Description: Pisias, N. G., R. W. Murray, and R. P. Scudder (2013), Multivariate statistical analysis and partitioning of sedimentary geochemical data sets: General principles and specific MATLAB scripts, Geochem. Geophys. Geosyst., 14, 4015–4020, doi:10.1002/ggge.20247.