Stuttgart Center for Simulation Science (SC SimTech)

EXC 2075 Project Networks

SIGDIUS

Projects without PN Affiliation

Featured Dataverses

In order to use this feature you must have at least one published or linked dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

291 to 300 of 21,003 Results

Expts_data.mat Feb 23, 2026 - Replication Data for: A framework for objectively comparing competing invasion percolation models based on highly-resolved image data MATLAB Data - 1.1 KB - MD5: 728a39b012b28db6ecb72e3e78cf9422 This file contains values from experimental data, needed to do the time-matching
foldimage.m Feb 23, 2026 - Replication Data for: A framework for objectively comparing competing invasion percolation models based on highly-resolved image data Plain Text - 1.8 KB - MD5: bf0c740023354b539403ec6af05dbc9a code to fold the images after diffusion
ForPercRankplot.m Feb 23, 2026 - Replication Data for: A framework for objectively comparing competing invasion percolation models based on highly-resolved image data Plain Text - 2.9 KB - MD5: 32ee8baf4c2d561efa3d181092308e3b code to collect the values for producing the percentage plot (figure 7, 8)
letsfindmax.m Feb 23, 2026 - Replication Data for: A framework for objectively comparing competing invasion percolation models based on highly-resolved image data Plain Text - 633 B - MD5: 81687ba5a09bcaa7154804845beeddd7 code to find the maximum values from the metric values obtained from "modelselruns.m"
MIPparameters.m Feb 23, 2026 - Replication Data for: A framework for objectively comparing competing invasion percolation models based on highly-resolved image data MATLAB Source Code - 4.6 KB - MD5: 653a1b141ecdb72388e7bacabb09d816 Contains the input parameters for the models, including the threshold filed generation.
@MIP.rar Feb 23, 2026 - Replication Data for: A framework for objectively comparing competing invasion percolation models based on highly-resolved image data RAR Archive - 6.6 KB - MD5: fd7f62671a369c054ee6841439883317 Methods for the macroscopic IP models
modelselruns.m Feb 23, 2026 - Replication Data for: A framework for objectively comparing competing invasion percolation models based on highly-resolved image data Plain Text - 5.1 KB - MD5: 55ba5e2e4ff17720a48096592791b537 This code calls the model and runs the post-processing steps to obtain the metric value.
mygaussfilt.m Feb 23, 2026 - Replication Data for: A framework for objectively comparing competing invasion percolation models based on highly-resolved image data Plain Text - 497 B - MD5: cf38db012533faddf3c33e38a13426e6 Gaussian blur filter
myintpCmap.m Feb 23, 2026 - Replication Data for: A framework for objectively comparing competing invasion percolation models based on highly-resolved image data Plain Text - 132 B - MD5: 9bec1c28645586b2f05bd1d42d761a37 interpolated colourmap
PercRankBar.m Feb 23, 2026 - Replication Data for: A framework for objectively comparing competing invasion percolation models based on highly-resolved image data Plain Text - 5.0 KB - MD5: be6b30efc8f03c167bed3a3392fa4532 code to produce Figures 7 and 8

Expts_data.mat

Feb 23, 2026 - Replication Data for: A framework for objectively comparing competing invasion percolation models based on highly-resolved image data

MATLAB Data - 1.1 KB -

This file contains values from experimental data, needed to do the time-matching

foldimage.m

Feb 23, 2026 - Replication Data for: A framework for objectively comparing competing invasion percolation models based on highly-resolved image data

Plain Text - 1.8 KB -

code to fold the images after diffusion

ForPercRankplot.m

Feb 23, 2026 - Replication Data for: A framework for objectively comparing competing invasion percolation models based on highly-resolved image data

Plain Text - 2.9 KB -

code to collect the values for producing the percentage plot (figure 7, 8)