Digital Twin for Chemical Sciences by Jin Qian (19339035)

“…Lastly, we use the Figure 4.ipynb notebook in the 3_outputs folder to plot the subfigures in d), e), f). Observing that Basin Hopping performs better than Gaussian Process, we plot the degeneracy result with 0.1 error cutoff to obtain the subfigures in g), h), i). …”

Minami_etal_2025 by Keiichi Mochida (4670800)

“…<h2>Code files related to Minami et al (2025)</h2><p dir="ltr">accession_plot.py:Python script used to generate Fig4a.</p><p dir="ltr">Bd21-3_Bd21.Rmd:R script (mrkdown) used to run rQTL and generte Supplementary Fig. 3c (Bd21-3 x Bd21)</p><p dir="ltr">.…”

Oka et al., Supplementary Data for "Development of a battery emulator using deep learning model to predict the charge–discharge voltage profile of lithium-ion batteries" by Kanato Oka (20132185)

“…For a single file, test data is read, and the prediction plot is output. To use this Python script, you need to modify the "CFG (config)" and "Convenient" sections within the script.…”

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

“…</p><p dir="ltr">complete_data.xlsx</p><p dir="ltr">A single Excel file containing 18 sheets with data from all figures:</p><p dir="ltr">Sheet names and descriptions:</p><ul><li>Fig_1: Probability of emergence contour data</li><li>Fig_2_b: MIC evolution simulation data</li><li>Fig_2_c: Speed of resistance evolution data</li><li>Fig_2_d: Time to resistance data</li><li>Fig_2_a_d_time_series_sim7: Simulation time series data (representative simulation, low persistence)</li><li>Fig_2_a_d_MIC_values_sim7: MIC values from simulation (representative simulation, low persistence)</li><li>Fig_2_a_p_time_series_sim5: Simulation time series data (representative simulation, high persistence)</li><li>Fig_2_a_p_MIC_values_sim5: MIC values from simulation (representative simulation, high persistence)</li><li>Fig_3_a-b: Distribution plot simulation data</li><li>Fig_3_a-b_empirical: Distribution plot empirical data</li><li>Fig_4_a: Mutation count simulation data</li><li>Fig_4_b: Mutation count empirical data</li><li>Fig_4_c: Mutation functional data</li><li>Fig_5_a-b: Large-scale simulation results (heatmap data)</li><li>Fig_5_c_mic: MIC heatmap empirical data</li><li>Fig_5_c_extinction: Extinction heatmap empirical data</li><li>Fig_6: Population size analysis simulation data</li><li>S1_figure: Supplementary experimental survival data</li></ul><p dir="ltr">Column naming convention</p><p dir="ltr">All sheets use consistent, tidy column names.…”

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

“…<p>Log–log plot of median wall-clock time for SBMLNetwork’s C++-based auto-layout engine (blue circles, solid fit) and SBMLDiagrams’ implementation of the pure-Python NetworkX spring_layout algorithm (red squares, dashed fit), applied to synthetic SBML models containing 20–2,000 species, with a fixed 4:1 species-to-reaction ratio. …”

Intersection and distinction of essential compounds and targets from 5 primary sources. by Gyaltsen Dakpa (21439920)

“…<b>(B)</b> UniProt and SwissTarget Prediction-predicted target proteins and 658 actives are intersected using the Jvenn Python to plot the intersection of targets in which Purple represents <i>R. officinalis</i>, Green indicates S. officinalis, Orange represents <b><i>T.…”

<b>Dataset for manuscript: </b><b>Phylogenetic and genomic insights into the evolution of terpenoid biosynthesis genes in diverse plant lineages</b> by Puguang Zhao (19023065)

“…</p><p dir="ltr"> Generates a scatter plot with a linear regression line and saves it as 'Correlation_Plot.pdf' (Figure 5D).…”

The global dryland lake storage (GDLS) dataset by Gang Zhao (18368925)

“…</b><b>ipynb</b><br>A Python example to show the location of a given lake and to plot monthly time series of area/elevation/storage for this lake.…”

Core-Based Smart Sampling Framework: A Theoretical and Experimental Study on Randomized Partitioning for SAT Problems by DURGHAM QARALLEH (21904172)

“…We provide theoretical guarantees on complexity reduction and probabilistic completeness, apply the method to SAT instances, and evaluate its performance using experimental Python implementations. The results show that smart sampling drastically reduces the effective complexity of SAT problems and offers new insights into the structure of NP-complete problems.…”

Interleg coordination in 103 adult <i>Hypsibius exemplaris</i> individuals. by Emma M. Anderson (19697573)

“…D. Probability density plot of ϕ_C vs. ϕ_I for second and third pair legs, n = 4414 strides. …”

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. …”

Sonification of Warming Stripes by Christopher Harrison (9448751)

“…A zip file containing all of the scripts (in the form of Python Jupyter notebooks) that were used to produce the sonification, and the plots for the explanation article (Sonification Warming Stripes.pdf). …”

<b>Rethinking neighbourhood boundaries for urban planning: A data-driven framework for perception-based delineation</b> by Shubham Pawar (22471285)

“…Each color represents a distinct cluster.</p><p dir="ltr"><b>Code to generate:</b></p><pre><pre>import geopandas as gpd<br>import matplotlib.pyplot as plt<br><br>gdf = gpd.read_file('.…”

Scripts for pairwise nucleotide identity graphs by NEIL YOUNG (1140230)

“…Next, a pairwise nucleotide distance matrix was built using a custom python script is available in this folder entitled 'distance'. …”

(A) Sampling locations and ranges of <i>I. feisthamelii</i> (purple) and <i>I. podalirius</i> (teal) butterflies. by Sam Ebdon (21072525)

“…(B) Sampling locations of butterflies from the <i>Iphiclides</i> HZ. The dashed line represents the approximate HZ center, based on samples collected by Lafranchis et al. …”

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. …”

<b>Engineered Muscle-Derived Extracellular Vesicles Boost Insulin Sensitivity and Glucose Regulation</b> by Hagit Shoyhet (21090650)

“…Proteomic analyses were run with Python-V3.9.2 miRNA and protein figures were plotted using R</p>…”