Overview of the implemented simulation tool and training framework in Diffrax. by Paul van Lent (16876977)

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Representative MSD plots, Fig. 6c,d by Hirotake Udono (21008660)

Fitting a feast/famine cycle to a glycolysis model. by Paul van Lent (16876977)

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Jaxkineticmodel allows for hybridizing kinetic models with neural networks. by Paul van Lent (16876977)

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Analysis of convergence properties for a collection of SBML models. by Paul van Lent (16876977)

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Overview of the SBML models used in this study. The collection of models was retrieved from Biomodels [43] and a previously reported collection of SBML benchmark models [11]. The n... by Paul van Lent (16876977)

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(a) System dynamics: schematic representation of all the reactions in the Tree model, which are aging, reproduction, color cycle, and competition. by Fabricio Cravo (21387854)

Simulated dynamics of the Mutual Annihilation protocol [26]. by Fabricio Cravo (21387854)

BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories by Elizaveta Mukhaleva (20602550)

“…We describe here the software’s uses, the methods associated with it, and a comprehensive Python interface to the underlying generalist BNM code. …”

BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories by Elizaveta Mukhaleva (20602550)

“…We describe here the software’s uses, the methods associated with it, and a comprehensive Python interface to the underlying generalist BNM code. …”

BaNDyT: Bayesian Network Modeling of Molecular Dynamics Trajectories by Elizaveta Mukhaleva (20602550)

“…We describe here the software’s uses, the methods associated with it, and a comprehensive Python interface to the underlying generalist BNM code. …”

A comparison between the static Python-based visualizations of the p65 activity in activated fibroblasts and the dynamic, HTML-based visualizations that use these same reduction me... by Hector Torres (11708207)

“…<p><b>(a)</b> UMAP, t-SNE, PCA, and Diffmap were first generated using the Python libraries Scikit-learn, UMAP, and PyDiffmap within Jupyter to generate static graphs as a starting point. …”

PTPC-UHT bounce by David Parry (22169299)

“…</p><p dir="ltr">Included files:</p><ul><li>Python source code (<code>PTPC_UHT_bounce.py</code>)</li><li>Parameter sets for representative runs (α,β,Tcrit,V(T)\alpha, \beta, T_{\mathrm{crit}}, V(T)α,β,Tcrit​,V(T))</li><li>Output arrays of trajectories across multiple bounces</li><li>Multi-panel plots (PNG/PDF) for a(t)a(t)a(t), H(t)H(t)H(t), T(t)T(t)T(t), S(t)S(t)S(t), R(t)R(t)R(t), and ρT\rho_TρT​</li></ul><p dir="ltr">This dataset ensures full reproducibility of the PTPC simulations and provides a starting point for independent verification, parameter exploration, and future model extensions.…”

Comparative analysis of WordNet replacement impact on data distribution. by Enock Niyonkuru (21246879)

Data for "A hollow fiber membrane permeance evaluation device demonstrating outside-in and inside-out performance differences" by Timothy Warner (20222838)

“…</li><li>Flux decline data generated by the analysis software.</li><li>Plot data derived from the above data sources.</li><li>Python code to generate figures from the plot data.…”

Segmentation module training details. by Khelina Fedorchuk (19130089)

Fig 4. - mdciao: Accessible Analysis and Visualization of Molecular Dynamics Simulation Data by Guillermo Pérez-Hernández (21156182)

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

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

Overview of the <i>B</i>. <i>subtilis</i> iModulon structure. by Anand V. Sastry (6619310)