Collaborative research: CyberTraining: Implementation: Medium: Training users, developers, and instructors at the chemistry/physics/materials science interface by Francesco Paesani (5128004)

“…The primary objectives are to establish a robust community of materials modeling developers and to enhance computational training at both undergraduate and graduate levels. The project seeks to recruit and train future leaders in materials modeling, foster community engagement, and promote coding literacy across disciplines.…”

Institutions in the Complexity-Relatedness Framework by İbrahim Tuğrul Çınar (12058328)

“…<br>Variable construction and filters (5‑year rolling averages, presence matrix, complexity algorithm, relatedness density, normalisation) follow the manuscript.manuscript_numarali</li></ul><p dir="ltr">Variables (grouped overview)</p><ul><li>Identifiers: <code>reg_nuts2</code>, <code>NUTS1</code>, <code>NUTS0</code>, <code>prio_year</code></li><li>Outcomes / initial levels: <code>gdp_c_gr3</code>, <code>gva_c_gr3</code>, <code>totalemp_gr3</code> (3‑year growth rates), <code>lngdp_c</code>, <code>lngva_c</code></li><li>Controls: <code>cap_stock</code>, <code>gfcf</code>, <code>lnhwork</code> (hours worked, log), <code>capdens**</code> (capital intensity variants used in robustness), <code>population density</code> proxy (see manuscript tables)manuscript_numarali</li><li>Composite capability indices & spatial averages:</li><li><ul><li><code>tcrd_mean</code> (CD‑Mean),</li><li><code>fdenseqi_geo</code> (CDQ‑Geo),</li><li><code>tcrd_eqi</code> (CDQ),</li><li><code>_W1</code> / <code>_W0</code> suffixes = within‑NUTS‑1 / within‑country averages.…”

Robust Multi-Model Subset Selection by Anthony-Alexander Christidis (6949055)

“…The <a href="https://doi.org/10.1080/10618600.2025.2596057" target="_blank">supplementary material</a> contains all theoretical proofs, additional algorithmic and computational details, and the code and data to reproduce our numerical results.…”

Trust as moral currency: Perspectives of health researchers in sub-Saharan Africa on strategies to promote equitable data sharing by Qunita Brown (19751520)

“…High quality data from Africa will afford diversity to global data sets, reducing bias in algorithms built for artificial intelligence technologies in healthcare. …”

<b>Leveraging protected areas for dual goals of biodiversity conservation and zoonotic</b> <b>risk reduction</b> by Li Yang (13558573)

“…Supplementary Tables</p><p dir="ltr">The analysis generates several supplementary tables stored in ./3Spatial_result/5sp_tables/<b> (after you run the code)</b>:</p><p dir="ltr">Table S1. Top 30 Countries with the largest key HCN-HZR Areas, their PA Coverage on key HCN-HZR Areas, and economic income level for each country. …”

A Tutorial on the Use of Artificial Intelligence Tools for Facial Emotion Recognition in R by Austin Wyman (17559054)

“…Furthermore, we provide an introductory level explanation of the machine learning, deep learning, and computer vision algorithms that underlie most emotion detection programs in order to improve literacy of explainable artificial intelligence in the social and behavioral science literature.…”

Automatic data reduction for the typical astronomer by Bradford Holden (21789524)

“…PypeIt has been developed by a small team of astronomers with two leading philosophies: (1) build instrument-agnostic code to serve nearly any spectrograph; (2) implement algorithms that achieve Poisson-level sky-subtraction with minimal systematics to yield precisely calibrated spectra with a meaningful noise model. …”

SPIDER (v2): Synthetic Person Information Dataset for Entity Resolution by Praveen Chinnappa (20835779)

“…<p dir="ltr">SPIDER (v2) – Synthetic Person Information Dataset for Entity Resolution provides researchers with ready-to-use data for benchmarking Duplicate or Entity Resolution algorithms. The dataset focuses on person-level fields typical in customer or citizen records. …”

Collaborative Research: SCIPE: Interdisciplinary Research Support Community for Artificial Intelligence and Data Sciences by Sanmukh Kuppannagari (21798863)

“…<br><br>To achieve 10x-100x reduction in DNN energy consumption, a holistic approach is pursued, which encompasses: (1) new circuit designs that leverage emerging CMOS+X technologies; (2) a novel near-memory architecture in which processing elements are seamlessly integrated with traditional Dynamic RAM (DRAM); (3) novel 3D-matrix-based per-layer DNN computations and data-layout optimizations for kernel weights; and (4) algorithms and hardware/software co-design tailored for near-real-time DNN-based signal classification in next-generation wireless systems. …”

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

“…</p><h3><b>2. </b><code><strong>GenosophusV2.txt</strong></code></h3><h3><b>2. …”

Figure 1 from PD-1 Blockade Mitigates Surgery-Induced Immunosuppression and Increases the Efficacy of Photodynamic Therapy for Pleural Mesothelioma by Gwendolyn M. Cramer (16964052)

“…<b>D,</b> In the AB12 mouse mesothelioma TI model, IL-1Ra protein levels are higher at 4 hours after TI (<i>n</i> = 7) compared with samples of untreated TC (<i>n</i> = 6, Mann–Whitney test <i>P</i> = 0.001).…”

<b>Optimizing Task Scheduling and Containers in Cloud Data Centers</b> by Arash GhorbanniaDelavar (22563696)

“…Overall, TPMCD provides a scalable and cost-efficient framework that enhances both <b>performance and sustainability</b> in cloud data centers.</p><p dir="ltr"><code>Original article DOI: https://doi.org/10.1016/j.jnca.2025.104132</code></p>…”

Supplementary file 1_Predicting first-time anaphylaxis in the elderly using stacked machine learning and population registers.docx by Toni Mora (8145624)

“…Key predictors included healthcare utilisation patterns, age, socioeconomic proxy (copayment level), and specific diagnostic codes related to allergic conditions.…”

CSPP instance by peixiang wang (19499344)

“…</li><li>Containers are randomly assigned to different attribute groups (<code>attr_group_no</code>).</li></ul></li><li><b>Yard Location Assignment</b>:</li><li><ul><li>The script systematically assigns each container a specific location in the simulated yard, including <code>area_no</code>, <code>area_bay</code>, <code>area_row</code>, and <code>area_level</code>.…”

Supplementary Data: Biodiversity and Energy System Planning - Queensland 2025 by Andrew Rogers (17623239)

“…</li></ul><h3>Analysis Scripts</h3><p dir="ltr">Complete set of R scripts for reproducing all analyses:</p><ul><li><b>percent cost increase_line plot.R</b>: Creates visualizations of energy cost impacts under different conservation scenarios</li><li><b>Zonation curves.R</b>: Generates conservation performance curves and coverage statistics</li><li><b>NPV_bar_plot.R</b>: Produces economic analysis plots with Net Present Value breakdowns</li><li><b>domestic_export_map_iterations.R</b>: Creates spatial maps of renewable energy infrastructure for domestic and export scenarios</li></ul><h2>Technical Specifications</h2><h3>Data Formats</h3><ul><li><b>Spatial Data</b>: ESRI Geodatabase (.gdb), Shapefile (.shp), GeoTIFF (.tif)</li><li><b>Tabular Data</b>: CSV, Microsoft Excel (.xlsx, .xls)</li><li><b>Analysis Code</b>: R scripts (.R)</li></ul><h3>Software Requirements</h3><ul><li><b>R</b> (≥4.0.0) with packages: sf, dplyr, ggplot2, readr, readxl, tidyr, furrr, ozmaps, ggpattern</li><li><b>ESRI ArcGIS</b> or <b>QGIS</b> for geodatabase access and spatial analysis</li><li><b>Zonation</b> conservation planning software (for methodology understanding)</li></ul><h3>Hardware Recommendations</h3><ul><li><b>RAM</b>: 16GB minimum (32GB recommended for full spatial analysis)</li><li><b>Storage</b>: 15GB free space for data extraction and processing</li><li><b>CPU</b>: Multi-core processor recommended for parallel processing scripts</li></ul><h2>Detailed Description of the VRE Siting and Cost-Minimization Model</h2><p><br></p><p dir="ltr">This section provides an in-depth description of the Variable Renewable Energy (VRE) siting model, including the software, the core algorithm, and the optimisation process used to determine the least-cost, spatially constrained development trajectory for VRE infrastructure in Queensland, Australia.…”

Supplementary file 1_A real-world disproportionality analysis of FDA adverse event reporting system (FAERS) events for lecanemab.docx by Linlin Yan (4480570)

“…Using the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS) algorithms, we conducted a comprehensive analysis of lecanemab-related AEs, restricting the analysis to AEs with the role code of primary suspect (PS).…”

Data Sheet 1_Forestry climate adaptation with HarvesterSeasons service—a gradient boosting model to forecast soil water index SWI from a comprehensive set of predictors in Destinat... by Mikko Strahlendorff (20458865)

“…<p>Soil wetness forecasts on a local level are needed to ensure sustainable forestry operations during summer when the soil is neither frozen nor covered with snow. …”

16S rRNA sequencing raw data from a thermophilic trickle bed reactor for biogas upgrading by Getachew Birhanu Abera (20402390)

“…Noteworthy, for both data sets lowabundant OTUs making up < 0.01 % of the total mapped reads within each sample were disregarded as a data denoisingstep. …”

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

“…</p><p dir="ltr"><b>Reference:</b> <code>cluster_perceptions.py</code> script logic</p><p dir="ltr">Stages:</p><ul><li>(a) Data input</li><li>(b) K-nearest neighbours spatial weights matrix</li><li>(c) Initial agglomerative clustering</li><li>(d) Merging small clusters</li><li>(e-f) Iterative splitting of largest clusters</li></ul><h4>Figure 7: Predicted Urban Perception Scores</h4><p dir="ltr">ET cell-level perception scores across the study area.…”

Additional file 1 of The origin and evolution of cultivated rice and genomic signatures of heterosis for yield traits in super-hybrid rice by Yiyong Zhao (21466062)

“…This table delineates the results of gene expression clustering using the MFUZZ algorithm for three super-hybrid rice varieties, namely LYP9, Y900, and XLY900, along with their progenitor strains. …”