[ 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. …”

HCC Evaluation Dataset and Results by Jens-Rene Giesen (18461928)

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

“…</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 = "..…”

Genosophus: A Dynamical-Systems Diagnostic Engine for Neural Representation Analysis by Alan Glanz (22109698)

“…</b><code><strong>GenosophusV2.py</strong></code></h3><p dir="ltr">Executable Python implementation of the Genosophus Engine.</p><h3><b>2. …”

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. …”

<b>Code: Inferred movements of SARS-CoV-2 infections between the 25 administrative regions of Peru (Figure 3) + </b><b>International spread of four SARS-CoV-2 sub-lineages of Peruv... by Pablo Tsukayama (22614461)

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. …”

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)

“…(<b>DOI:</b> 10.1038/s41598-024-80371-9 )<br><br>A zip file contains following data and codes.</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. …”

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.…”

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>…”

Comparison data 7 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

“…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”

Sample data for <i>Neolamprologus multifasciatus</i>. by Nicolai Kraus (19949667)

“…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”

Sample data for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

“…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”

Comparison data 3 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

“…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”

Sample data for <i>Telmatochromis temporalis</i>. by Nicolai Kraus (19949667)

“…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”