Satellite monitoring of Greenland wintertime buried lake drainage حسب Jianing Wei (22400896)

"…Buried_lake_drainage_code</p><p dir="ltr">This folder contains two Python Jupyter Notebooks for detecting wintertime buried lake drainages (BLDs). …"

CpG Signature Profiling and Heatmap Visualization of SARS-CoV Genomes: Tracing the Genomic Divergence From SARS-CoV (2003) to SARS-CoV-2 (2019) حسب Tahir Bhatti (20961974)

"…</p><p dir="ltr">Heatmap Images :</p><p dir="ltr">Heatmaps for CpG counts and O/E ratios comparing Wuhan-Hu-1 with its closest and most distant relatives.</p><p dir="ltr">Python Script :</p><p dir="ltr">Full Python code used for data processing, distance calculation, and heatmap generation.…"

Monte Carlo Simulation Code for Evaluating Cognitive Biases in Penalty Shootouts Using ABAB and ABBA Formats حسب Raul MATSUSHITA (10276562)

"…<p dir="ltr">This Python code implements a Monte Carlo simulation to evaluate the impact of cognitive biases on penalty shootouts under two formats: ABAB (alternating shots) and ABBA (similar to tennis tiebreak format). …"

Table 3_Novel deep learning-based prediction of HER2 expression in breast cancer using multimodal MRI, nomogram, and decision curve analysis.docx حسب Shi Qiu (425335)

"…Statistical analyses were conducted using Python and R, with significance set at p < 0.05.</p>Results<p>In this study, we developed an integrated predictive model for HER2 status in breast cancer by combining deep learning-based MRI features and clinical data. …"

Table 2_Novel deep learning-based prediction of HER2 expression in breast cancer using multimodal MRI, nomogram, and decision curve analysis.docx حسب Shi Qiu (425335)

"…Statistical analyses were conducted using Python and R, with significance set at p < 0.05.</p>Results<p>In this study, we developed an integrated predictive model for HER2 status in breast cancer by combining deep learning-based MRI features and clinical data. …"

Table 1_Novel deep learning-based prediction of HER2 expression in breast cancer using multimodal MRI, nomogram, and decision curve analysis.docx حسب Shi Qiu (425335)

"…Statistical analyses were conducted using Python and R, with significance set at p < 0.05.</p>Results<p>In this study, we developed an integrated predictive model for HER2 status in breast cancer by combining deep learning-based MRI features and clinical data. …"

Data Sheet 1_Novel deep learning-based prediction of HER2 expression in breast cancer using multimodal MRI, nomogram, and decision curve analysis.docx حسب Shi Qiu (425335)

"…Statistical analyses were conducted using Python and R, with significance set at p < 0.05.</p>Results<p>In this study, we developed an integrated predictive model for HER2 status in breast cancer by combining deep learning-based MRI features and clinical data. …"

Thermally Activated Resonant Tunnelling in GaAs/AlGaAs Triple Barrier Heterostructures حسب Craig Allford (19079081)

"…Measurements were automated using bespoke written python code.<br><br>Results are published in the article at http://iopscience.iop.org/0268-1242/30/10/105035 <br>…"

Genomic Surveillance of Pemivibart (VYD2311) Escape-Associated Mutations in SARS-CoV-2: December 2025 BioSamples (n=2) حسب Tahir Bhatti (20961974)

"…</p><p dir="ltr"><b>Note:</b></p><p dir="ltr">Analysis was performed using a custom Python-based bioinformatics pipeline developed for <b>high-throughput surveillance of pemivibart (VYD2311) escape mutations in SARS-CoV-2</b>. …"

<b>Dataset for manuscript: </b><b>Phylogenetic and genomic insights into the evolution of terpenoid biosynthesis genes in diverse plant lineages</b> حسب Puguang Zhao (19023065)

"…</p><p dir="ltr"> 'boxplot.py': This script is executed in Visual Studio Code, using Python 3.10.4 as the runtime environment.…"

(A) Sampling locations and ranges of <i>I. feisthamelii</i> (purple) and <i>I. podalirius</i> (teal) butterflies. حسب Sam Ebdon (21072525)

"…(B) Sampling locations of butterflies from the <i>Iphiclides</i> HZ. The dashed line represents the approximate HZ center, based on samples collected by Lafranchis et al. …"

Core data حسب Baoqiang Chen (21099509)

"…We divided the dataset into training and test sets, using 70% of the genes for training and 30% for testing. 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). …"

Datasets from the Programmatic Analysis of Fuel Treatments: from the landscape to the national level Joint Fire Science Project (14-5-01-1) حسب Douglas B. Rideout (19657906)

"…Included for each study site are individual rasters representing the fire affected resources for that study site. …"

End-to-end example-based sim-to-real RL policy transfer based on neural stylisation with application to robotic cutting حسب Jamie Hathaway (10285367)

"…</p><h3>policy/</h3><p dir="ltr">This folder contains pickled trajectories, in the form of a Python list.</p><p dir="ltr">The list's elements are TrajWithRew dataclass objects from the Imitation Python library (https://imitation.readthedocs.io/en/latest/)</p><p dir="ltr">TrajWithRew contains 4 main fields</p><ul><li> obs - the (unnormalised) observations, in the form of a [WINDOW_LENGTH * NUM_CHANNELS] array</li><li> acts - the actions in the form of a [WINDOW_LENGTH - 1 * NUM_ACTS] array</li><li> infos - the info values at each timestep, as a [WINDOW_LENGTH - 1] array of dicts</li><li> terminals - boolean indicating if that trajectory segment is a terminal segment</li><li> rews - the rewards as a [WINDOW_LENGTH - 1] array</li></ul><p dir="ltr">Each TrajWithRew represents not a full episodic trajectory, as is usually the case with Imitiation - rather they represent segments of a full episodic trajectory, of length WINDOW_LENGTH. …"

Hierarchical Deep Learning Framework for Automated Marine Vegetation and Fauna Analysis Using ROV Video Data حسب Bjørn Christian Weinbach (16918707)

"…</p><ol><li><b>MaskRCNN-Segmented Objects</b>:</li></ol><p dir="ltr"> - `.jpg` files representing segmented objects detected by the MaskRCNN model.…"

Supplementary Material for: The prediction of hematoma growth in acute intracerebral hemorrhage: from 2-dimensional shape to 3-dimensional morphology حسب figshare admin karger (2628495)

"…We subsequently constructed the 3-dimensional morphology models, including the probability of hematoma morphology (PHM) and the probability of comprehensive model (PCM), to predict HG. …"

Dataset for: Phylotranscriptomics reveals the phylogeny of Asparagales and the evolution of allium flavor biosynthesis, Nature Communications，DOI：10.1038/s41467-024-53943-6 حسب Xiao-Xiao Wang (2447920)

"…Extract the TPM.</p><p dir="ltr">After running Salmon, each species has three quant.sf files, renamed as quant1.sf, quant2.sf, quant3.sf.…"

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

"…Analysis of the confusion matrix revealed a critical limitation: although the model correctly identified 785 poisonous mushrooms, it misclassified 313 as edible (false negatives), which represents an unacceptable risk in a practical application.…"

Data Sheet 1_Nationwide epidemiological study of subarachnoid hemorrhage: trends in admissions, mortality, seasonality, costs, clipping, embolization, and the impact of COVID-19.pd... حسب Thiago Oscar Goulart (22485604)

"…</p>Methods<p>This retrospective study analyzed secondary data from the Brazilian public health system (DataSUS) using ICD-10 code I60 for aSAH. Key metrics included the evaluation of admissions with time-series in Python, and mortality rates, procedures, and costs.…"

Ambient Air Pollutant Dynamics (2010–2025) and the Exceptional Winter 2016–17 Pollution Episode: Implications for a Uranium/Arsenic Exposure Event حسب Thomas Clemens Carmine (19756929)

"…<br><br><b>Missing-Data Handling & Imputation:</b></p><p dir="ltr">The following sequential steps were applied to create a complete and consistent daily time series suitable for analysis (presented in the Imputed_AP_Data_Zurich_2010-25 sheet), particularly addressing the absence of routine PM₂.₅ measurements prior to January 2016. The full implementation is detailed in the accompanying Python script (Imputation_Air_Pollutants_NABEL.py). …"