Cost functions implemented in Neuroptimus. by Máté Mohácsi (20469514)

“…<div><p>Finding optimal parameters for detailed neuronal models is a ubiquitous challenge in neuroscientific research. …”

<i>Spherical Textures</i> and machine-learning classifications of <i>C. elegans</i> germline nuclei. by Oane Gros (20636735)

Automatic classification of germline nuclei provides quantifications of meiotic progression. by Oane Gros (20636735)

Scope of our collection of pathogen models of metabolism. by Emma M. Glass (13102872)

“…The number in parentheses after the phylum name represents how many models are in that respective phylum. …”

A list of all facial muscle movements mentioned in the chimpFACS manual [5] and considered in our present study. by Aisha Mahmoud (20623732)

A list of all facial muscle movements mentioned in the catFACS manual [14] and considered in our present study. by Aisha Mahmoud (20623732)

[ relative elevation model data visualization for Data Bloom 2024 ] by Rusmiya Aqid (22189850)

“…<p dir="ltr">This visualization takes the digital elevation model (DEM) of a portion of the Colorado River and transforms it into a relative elevation model (REM); unlike DEM, REM shows the relative height above the river and can more clearly highlight river features. …”

Smart Reaction Templating: A Graph-Based Method for Automated Molecular Dynamics Input Generation by Julian Konrad (9609845)

“…Accurately modeling chemical reactions in molecular dynamics simulations requires detailed pre- and postreaction templates, often created through labor-intensive manual workflows. …”

Model fitting to neighborhood-level COVID-19 case data. by Renquan Zhang (13167533)

“…<p>(A), Simulations using model parameters estimated for the period from March 1, 2020 to December 13, 2020. …”

Dataset for the Modeling and Bibliometric Analysis of E-business in Entrepreneurship (1997–2024) by Aggie Moses Zeuse (21460466)

“…These include a summary of Main Information (PNG), a graph of the Annual Scientific Production (PNG), a Thematic Map (PNG) illustrating core research themes, and an analysis of Trend Topics (PNG). For the modeling component, a predictive analysis was conducted using Python to forecast future publication volumes. …”

Dataset for CNN-based Bayesian Calibration of TELEMAC-2D Hydraulic Model by Jose Zevallos (21379988)

“…</li><li>Files starting with <code>y_part</code> are flattened output arrays representing corresponding water depth values.</li></ul></li></ul><p dir="ltr">The <code>.npy</code> files were loaded and processed using the following approach in Python:</p><p dir="ltr"># Load the input and output numpy arrays</p><p dir="ltr">input_path = "..…”

LGF v7HELLAS: A Dynamical Model of Ethical Convergence and Lambda-Zero Transition by heojeongbeom heo (22544705)

“…<h2><b>Description</b></h2><p dir="ltr"><b>LGF v7.Hellas</b> represents the final, validated form of the <i>Language Gravitational Field (LGF)</i> model, completing the transition from early unstable versions (v5.2) to the fully stable, reproducible, scientifically grounded system (v5.3 → v7.0 → v7.Hellas).…”

Environmental Census: Modeling Synthetic Biology Ecological Risk with Metagenomic Enzymatic Data and High-Performance Computing by John Docter (22772100)

Exploring post-wildfire hydrologic response in central Colorado using field observations and the Landlab modeling framework by Jordan Adams (595056)

“…Landlab, an open-source, componentized model written in Python, can be used to explore landscape evolution across both short and long time scales. …”

Supplementary file 1_ParaDeep: sequence-based deep learning for residue-level paratope prediction using chain-aware BiLSTM-CNN models.docx by Piyachat Udomwong (22563212)

“…The implementation is freely available at https://github.com/PiyachatU/ParaDeep, with Python (PyTorch) code and a Google Colab interface for ease of use.…”

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)

“…</p><p dir="ltr"><br></p><p dir="ltr">01_lstm_model_making.py</p><p dir="ltr">This file is a Python script for reading battery charge-discharge data and training an LSTM model. …”

PepENS by Abel Chandra (16854753)

“…It represents a pioneering, consensus-based method by combining embeddings from ProtT5-XL-UniRef50 with Position Specific Scoring Matrices and Half-Sphere Exposure features to train an ensemble model consisting of EfficientNetB0 via image output from DeepInsight technology, CatBoost, and Logistic Regression. …”

MCCN Case Study 3 - Select optimal survey locality by Donald Hobern (21435904)

“…</p><p dir="ltr">This is a simple implementation that uses four environmental attributes imported for all Australia (or a subset like NSW) at a moderate grid scale:</p><ol><li>Digital soil maps for key soil properties over New South Wales, version 2.0 - SEED - see <a href="https://esoil.io/TERNLandscapes/Public/Pages/SLGA/ProductDetails-SoilAttributes.html" target="_blank">https://esoil.io/TERNLandscapes/Public/Pages/SLGA/ProductDetails-SoilAttributes.html</a></li><li>ANUCLIM Annual Mean Rainfall raster layer - SEED - see <a href="https://datasets.seed.nsw.gov.au/dataset/anuclim-annual-mean-rainfall-raster-layer" target="_blank">https://datasets.seed.nsw.gov.au/dataset/anuclim-annual-mean-rainfall-raster-layer</a></li><li>ANUCLIM Annual Mean Temperature raster layer - SEED - see <a href="https://datasets.seed.nsw.gov.au/dataset/anuclim-annual-mean-temperature-raster-layer" target="_blank">https://datasets.seed.nsw.gov.au/dataset/anuclim-annual-mean-temperature-raster-layer</a></li></ol><h4><b>Dependencies</b></h4><ul><li>This notebook requires Python 3.10 or higher</li><li>Install relevant Python libraries with: <b>pip install mccn-engine rocrate</b></li><li>Installing mccn-engine will install other dependencies</li></ul><h4><b>Overview</b></h4><ol><li>Generate STAC metadata for layers from predefined configuratiion</li><li>Load data cube and exclude nodata values</li><li>Scale all variables to a 0.0-1.0 range</li><li>Select four layers for comparison (soil organic carbon 0-30 cm, soil pH 0-30 cm, mean annual rainfall, mean annual temperature)</li><li>Select 10 random points within NSW</li><li>Generate 10 new layers representing standardised environmental distance between one of the selected points and all other points in NSW</li><li>For every point in NSW, find the lowest environmental distance to any of the selected points</li><li>Select the point in NSW that has the highest value for the lowest environmental distance to any selected point - this is the most different point</li><li>Clean up and save results to RO-Crate</li></ol><p><br></p>…”

<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/). …”

Global Aridity Index and Potential Evapotranspiration (ET0) Database: Version 3.1 by Robert Zomer (12796235)

“…</p><p dir="ltr">The Python programming source code used to run the calculation of ET0 and AI is provided and available online on Figshare at:</p><p dir="ltr">https://figshare.com/articles/software/Global_Aridity_Index_and_Potential_Evapotranspiration_Climate_Database_v3_-_Algorithm_Code_Python_/20005589</p><p dir="ltr">Peer-Review Reference and Proper Citation:</p><p dir="ltr">Zomer, R.J.; Xu, J.; Trabuco, A. 2022. …”