Comparative analysis of WN and PM methodologies: Figure (a) displays the bar chart comparing WN and PM across five distinct concept sets (Methods), highlighting the number of conce... by Enock Niyonkuru (21246879)

Comparison of performance between our next reaction implementation and the Python library from Ref. [3]. by Samuel Cure (22250922)

“…For each network we repeat the simulations 100 times. Dots represent average times and bars represent standard deviations.…”

Overview of one by Guillermo Pérez-Hernández (21156182)

Experimental results of data retrieval from two Atlas DataLab setups: next-to-data deployment in the CSIC-DDC Hub (bottom) and remote data access from a user’s workstation (top). by Ezequiel Cimadevilla (8412531)

Introduction to the iModulonMiner using public <i>B</i>. <i>subtilis</i> RNA-seq data as a case study. by Anand V. Sastry (6619310)

Examples of insights derived from iModulons. by Anand V. Sastry (6619310)

Proportion of poor outcomes by writing ratio group. by Yojiro Umezaki (5916593)

VPS13C is required for the regulation of SCV morphology and fission. by Anna K. Waldmann (22250729)

“…<p>Representative images are shown and the associated scale bars for fluorescence images indicate 10 μm. a, <i>VPS13C</i> KO HeLa and control cells infected with <i>S.…”

Modules organization over different course editions. by Gabriele Pozzati (21094166)

“…Individual modules have been represented as vertical bars and squares. Colors identify the same modules across all course editions. …”

Performance Benchmark: SBMLNetwork vs. SBMLDiagrams Auto-layout. by Adel Heydarabadipour (22290905)

“…Each point represents the median of 10 runs (error bars are smaller than the markers). …”

PD-associated variants of VPS13C fail to rescue the multi-bacterial SCV phenotype in <i>VPS13C</i> KO cells. by Anna K. Waldmann (22250729)

“…<p>Representative images are shown and the associated scale bars for fluorescence images indicate 10 μm. a, <i>VPS13C</i> KO HeLa cells were transfected with VPS13C^mclover, A444P-VPS13C, W395C-VPS13C or control plasmid and infected with <i>S.…”

VPS13C contributes to ER-SCV contact formation. by Anna K. Waldmann (22250729)

“…<p>a, Representative images of random 2D TEM sections of <i>VPS13C</i> KO and control HeLa cells. …”

Bacterial persistence modulates the speed, magnitude and onset of antibiotic resistance evolution by Giorgio Boccarella (22810952)

“…</p><p dir="ltr">Repository structure</p><p dir="ltr">Fig_1/</p><ul><li>Probability of emergence analysis</li><li>Fig_1.py: contour plot generation</li></ul><p dir="ltr">Fig_2/</p><ul><li>MIC evolution simulations</li><li>Fig_2_a/: R-based simulation analysis</li><li>Fig_2_b/: Python visualization</li><li>Fig_2_c/: speed of resistance evolution analysis</li><li>Fig_2_d/: time to resistance analysis</li></ul><p dir="ltr">Fig_3/</p><ul><li>Distribution analysis</li><li>Fig_3_a-b.R: density plots and bar charts (empirical and simulated)</li></ul><p dir="ltr">Fig_4/</p><ul><li>Mutation analysis</li><li>Fig_4_a-b/: mutation counting analysis</li><li><ul><li>Fig_4_a/: simulation data (sim)</li><li>Fig_4_b/: empirical data (emp)</li></ul></li><li>Fig_4_c/: gene ontology and functional analysis</li></ul><p dir="ltr">Fig_5/</p><ul><li>Large-scale evolutionary simulations</li><li>Fig_5_a-b/: heatmap visualizations</li><li>Fig_5_c/: MIC and extinction analysis (empirical)</li></ul><p dir="ltr">Fig_6/</p><ul><li>Population size effects</li><li>Fig_6.py: population size analysis simulations</li></ul><p dir="ltr">S1_figure/</p><ul><li>Supplementary experimental data</li></ul><p dir="ltr">S2_figure/</p><ul><li>Supplementary frequency analysis</li></ul><p dir="ltr">S3_figure/</p><ul><li>Supplementary probability analysis</li></ul><p dir="ltr">scripts_simulations_cluster/</p><ul><li>Large-scale, cluster-optimized simulations</li></ul><p dir="ltr">complete_data/</p><ul><li>Reference to the full data sheet (full data set deposited elsewhere)</li></ul><p dir="ltr">Script types and languages</p><p dir="ltr">Python scripts (.py)</p><ul><li>Mathematical modeling: survival functions, probability calculations</li><li>Stochastic simulations: tau-leaping population dynamics</li><li>Data processing: mutation analysis, frequency calculations</li><li>Visualization: plotting with matplotlib and seaborn</li><li>Typical dependencies: numpy, pandas, matplotlib, seaborn, scipy</li></ul><p dir="ltr">R scripts (.R)</p><ul><li>Statistical analysis: distribution fitting, density plots</li><li>Advanced visualization: publication-quality figures (ggplot2)</li><li>Data manipulation: dplyr / tidyr workflows</li><li>Typical dependencies: dplyr, tidyr, ggplot2, readxl, cowplot</li></ul><p dir="ltr">Data requirements</p><p dir="ltr">The scripts are designed to run using the complete_data.xlsx file and, where relevant, the raw simulation outputs and empirical data sets as described above. …”

Tracking when the number of individuals in the video frame changes. by Hirotsugu Azechi (20700528)

“…The diagram illustrates changes across different experimental conditions, with plots indicating the frequency for each keypoint and bars representing each ID pair. The “+” below the diagram indicates the presence of mice in the arena, while “−” indicates their absence. …”

<b>Altered cognitive processes shape tactile perception in autism.</b> (data) by Ourania Semelidou (19178362)

“…The perceptual decision-making setup was controlled by Bpod (Sanworks) through scripts in Python (PyBpod, https://pybpod.readthedocs.io/en/latest/). …”

High-throughput chemical genetics screen and titration of hit compounds vinblastine and vincristine. by Emiri Nakamura (21601773)

“…Scale bar = 100μm. (F) Fiji and Python scripts were used to process, analyze, and plot the high-content high-throughput confocal imaging data. …”