FAR-1: A Fast Integer Reduction Algorithm Compared to Collatz and Half-Collatz by Faizan Ali (21689861)

Data_Sheet_1_Decoding emotional resilience in aging: unveiling the interplay between daily functioning and emotional health.PDF by Minhua Guo (18392646)

PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

Where to Handle an Exception? Recommending Exception Handling Locations from a Global Perspective by Xiangyang Jia (10026932)

LBQANA python code + Merged Gene Expression Dataset from GSE10810, GSE17907, GSE20711, GSE42568, GSE45827, and GSE61304 for Breast Cancer Biomarker Discovery by M RN (9866504)

Hippocampal and cortical activity reflect early hyperexcitability in an Alzheimer's mouse model by Marina Diachenko (19739092)

“…-E., & Linkenkaer-Hansen, K. (2024). Functional excitation-inhibition ratio indicates near-critical oscillations across frequencies. …”

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

GridScopeRodents: High-Resolution Global Typical Rodents Distribution Projections from 2021 to 2100 under Diverse SSP-RCP Scenarios by Yang Lan (20927512)

“…These values represent the probability of species presence at each grid cell, transformed using the cloglog output function in MaxEnt, which is approximately interpretable as the probability of occurrence under typical presence-only assumptions. …”

<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) by Erola Fenollosa (20977421)

“…<p dir="ltr">This dataset contains the data used in the article <a href="https://academic.oup.com/aob/advance-article/doi/10.1093/aob/mcaf043/8074229" rel="noreferrer" target="_blank">"Machine Learning and digital Imaging for Spatiotemporal Monitoring of Stress Dynamics in the clonal plant Carpobrotus edulis: Uncovering a Functional Mosaic</a>", which includes the complete set of collected leaf images, image features (predictors) and response variables used to train machine learning regression algorithms.…”

MOMA compared to MFA-derived estimates, carbon yield efficiencies and CBA co-factor profile comparison across unconstrained, manually curated and experimentally constrained solutio... by Laura de Arroyo Garcia (9226096)

“…MOMA ranges were estimated using the wild type solution as a reference and sequentially implementing the single-gene knockouts studied by Long et al. (2019) [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1008125#pcbi.1008125.ref046" target="_blank">46</a>], with biomass formation as the objective function. MFA ranges were extracted from a pre-existing dataset (Long et al., 2019), using a Python algorithm to select the minimal and maximal flux ranges.…”

Table 1_Kefir and healthy aging: revealing thematic gaps through AI-assisted screening and semantic evidence mapping.docx by Francesco Chiani (2661328)

“…Yet, studies specifically addressing kefir in the context of aging remain limited and scattered across diverse biological fields. To overcome this fragmentation, we applied an integrative approach that combines a cutting-edge AI-assisted algorithm for evidence screening with a Python-based semantic clustering pipeline. …”

Table 2_Kefir and healthy aging: revealing thematic gaps through AI-assisted screening and semantic evidence mapping.docx by Francesco Chiani (2661328)

“…Yet, studies specifically addressing kefir in the context of aging remain limited and scattered across diverse biological fields. To overcome this fragmentation, we applied an integrative approach that combines a cutting-edge AI-assisted algorithm for evidence screening with a Python-based semantic clustering pipeline. …”

Table 3_Kefir and healthy aging: revealing thematic gaps through AI-assisted screening and semantic evidence mapping.xlsx by Francesco Chiani (2661328)

“…Yet, studies specifically addressing kefir in the context of aging remain limited and scattered across diverse biological fields. To overcome this fragmentation, we applied an integrative approach that combines a cutting-edge AI-assisted algorithm for evidence screening with a Python-based semantic clustering pipeline. …”

Table 4_Kefir and healthy aging: revealing thematic gaps through AI-assisted screening and semantic evidence mapping.docx by Francesco Chiani (2661328)

“…Yet, studies specifically addressing kefir in the context of aging remain limited and scattered across diverse biological fields. To overcome this fragmentation, we applied an integrative approach that combines a cutting-edge AI-assisted algorithm for evidence screening with a Python-based semantic clustering pipeline. …”

NanoDB: Research Activity Data Management System by Lorenci Gjurgjaj (19702207)

“…Cross-Platform Compatibility: Works on Windows, macOS, and Linux. In a Python environment or as an executable. Ease of Implementation: Using the flexibility of the Python framework all the data setup and algorithm can me modified and new functions can be easily added. …”

Predictive Analysis of Mushroom Toxicity Based Exclusively on Their Natural Habitat. by Enrico Bertozzi (22461709)

“…The analysis was conducted in a Jupyter Notebook environment, using Python and libraries such as Scikit-learn and Pandas. …”

Spatiotemporal Soil Erosion Dataset for the Yarlung Tsangpo River Basin (1990–2100) by peng xin (21382394)

“…Bias correction was conducted using a 25-year baseline (1990–2014), with adjustments made monthly to correct for seasonal biases. The corrected bias functions were then applied to adjust the years (2020–2100) of daily rainfall data using the "ibicus" package, an open-source Python tool for bias adjustment and climate model evaluation. …”

GameOfLife Prediction Dataset by David Towers (12857447)

“…Excluding 0, the lower numbers also get increasingly unlikely, though more likely than higher numbers, we wanted to prevent gaps and therefore limited to 25 contiguous classes</p><p dir="ltr">NumPy (.npy) files can be opened through the NumPy Python library, using the `numpy.load()` function by inputting the path to the file into the function as a parameter. …”

Code by Baoqiang Chen (21099509)

“…We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”

Core data by Baoqiang Chen (21099509)

“…We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”