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></li></ul></li><li><b>Imputation_Air_Pollutants_NABEL.py</b> (Python Script)</li><li><ul><li>The Python code used to perform all data cleaning, imputation, plausibility checks, and calculations, generating the Imputed_AP_Data_Zurich_2010-25 sheet and associated stats/legend info from the raw data. …"

PYSEQM 2.0: Accelerated Semiempirical Excited-State Calculations on Graphical Processing Units حسب Vishikh Athavale (12623809)

"…PYSEQM is a Python-based package designed for efficient and scalable quantum chemical simulations. …"

Data Sheet 1_COCαDA - a fast and scalable algorithm for interatomic contact detection in proteins using Cα distance matrices.pdf حسب Rafael Pereira Lemos (9104911)

"…COCαDA demonstrated superior performance compared to the other methods, achieving on average 6x faster computation times than advanced data structures like k-d trees from NS, in addition to being simpler to implement and fully customizable. …"

Microscopic Detection and Quantification of Microplastic Particles in Environmental Water Samples حسب Derek Lam (11944213)

"…Image processing algorithms, implemented in Python using adaptive thresholding techniques, were applied to segment particles from the background. …"

<b>Challenges and Strategies for the Management of Quality-Oriented Education Bases in Universities under Informatization Background</b> حسب Yang Yanxi (21571568)

"…Final codes, together with basic demographic attributes supplied by the institutions’ HR offices, were exported to Excel and cleaned in Python 3.10 using pandas 2.2.1 and numpy 1.26. …"

Fast, FAIR, and Scalable: Managing Big Data in HPC with Zarr حسب Alfonso Ladino (21447002)

"…</p><p dir="ltr">In this work, we apply the scientific datacube model to the transformation of large-scale radar datasets from Colombia and the U.S. (NEXRAD), using open-source tools from the Python ecosystem such as Xarray, Xradar, and Dask to enable efficient parallel processing and scalable analysis. …"

Copy number contingency table. حسب Yang Wu (66682)

"…Our methods are implemented in Python and are freely available from GitHub (<a href="https://github.com/queryang/PASO" target="_blank">https://github.com/queryang/PASO</a>).…"

Gene mutation contingency table. حسب Yang Wu (66682)

"…Our methods are implemented in Python and are freely available from GitHub (<a href="https://github.com/queryang/PASO" target="_blank">https://github.com/queryang/PASO</a>).…"

Resistant & sensitive cell line Info on AZD5991. حسب Yang Wu (66682)

"…Our methods are implemented in Python and are freely available from GitHub (<a href="https://github.com/queryang/PASO" target="_blank">https://github.com/queryang/PASO</a>).…"

Resistant & sensitive drug info on COLO800. حسب Yang Wu (66682)

"…Our methods are implemented in Python and are freely available from GitHub (<a href="https://github.com/queryang/PASO" target="_blank">https://github.com/queryang/PASO</a>).…"

The codes and data for "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation" حسب FirstName LastName (20554465)

"…The model results are saved in <code>1point2dem/SampleGeneration/result</code>, and the results for <b>Table 3</b> in the paper are derived from this output.</p><p dir="ltr"><i>cd 1point2dem/CIPrediction</i></p><p dir="ltr"><i>python -u point_prediction.py --model [GCN|ChebNet|GATNet]</i></p><h3>step 4: Parallel computation</h3><p dir="ltr">This step uses the trained models to optimize parallel computation. …"

The codes and data for "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation" حسب FirstName LastName (20554465)

"…The model results are saved in <code>1point2dem/SampleGeneration/result</code>, and the results for <b>Table 3</b> in the paper are derived from this output.</p><p dir="ltr"><i>cd 1point2dem/CIPrediction</i></p><p dir="ltr"><i>python -u point_prediction.py --model [GCN|ChebNet|GATNet]</i></p><h3>step 4: Parallel computation</h3><p dir="ltr">This step uses the trained models to optimize parallel computation. …"

<b>Engineered Muscle-Derived Extracellular Vesicles Boost Insulin Sensitivity and Glucose Regulation</b> حسب Hagit Shoyhet (21090650)

"…</p><p dir="ltr"><b>miR_path_target_enrichment.csv</b></p><p dir="ltr"><b>Description:</b> KEGG pathway enrichment analysis results of shared mRNA targets of miRNAs miR-16-5p, miR-122-5p and miR-486-5p ranked by their interaction score defined in our paper. this includes the pathway name, the enrichment p-value, number of genes found in the term and number of miRNAs targeting these genes</p><p dir="ltr"><b>Code/software</b></p><p dir="ltr">Data were analyzed using R-V4.0.4, Python-V3.9.2 and GraphPad software. miRNA analyses were run in R-V4.0.4 Differential expression analysis was conducted using the “DEseq2” package and corrected for multiple hypotheses by FDR. …"

Research Database حسب Ana Paula Fermiano (22439479)

"…</p><p dir="ltr">A dataset of <b>1,157 georeferenced residential properties</b> was compiled from online real estate platforms and municipal GIS records. …"

Image 1_Differential diagnosis of pneumoconiosis mass shadows and peripheral lung cancer using CT radiomics and the AdaBoost machine learning model.tif حسب Xiaobing Li (291454)

"…LR, SVM, and AdaBoost algorithms were implemented using Python to build the models. In the training set, the accuracies of the LR, SVM, and AdaBoost models were 79.4, 84.0, and 80.9%, respectively; the sensitivities were 74.1, 74.1, and 81.0%, respectively; the specificities were 83.6, 91.8, and 80.8%, respectively; and the AUC values were 0.837, 0.886, and 0.900, respectively. …"

Image 2_Differential diagnosis of pneumoconiosis mass shadows and peripheral lung cancer using CT radiomics and the AdaBoost machine learning model.tif حسب Xiaobing Li (291454)

"…LR, SVM, and AdaBoost algorithms were implemented using Python to build the models. In the training set, the accuracies of the LR, SVM, and AdaBoost models were 79.4, 84.0, and 80.9%, respectively; the sensitivities were 74.1, 74.1, and 81.0%, respectively; the specificities were 83.6, 91.8, and 80.8%, respectively; and the AUC values were 0.837, 0.886, and 0.900, respectively. …"

Code and data for reproducing the results in the original paper of DML-Geo حسب Pengfei CHEN (8059976)

"…</p><p dir="ltr"><b>rslt.pkl</b>: A pickled Python object that stores the explainer based on geoshapley for dataset 1.…"

Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things حسب Ashok Kumar K (21441108)

"…The proposed DIDS-BRBPNN-BBWOA-IoT method is implemented using Python. The performance of the DIDS-BRBPNN-BBWOA-IoT approach is examined using performance metrics like accuracy, precision, recall, f1-score, specificity, error rate; computation time, and ROC. …"

SpatialKNifeY analysis landscape. حسب Shunsuke A. Sakai (13789939)

"…(B) Implementation of SpatialKNifeY (SKNY). A Python library of SKNY depends on stlearn [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012854#pcbi.1012854.ref023" target="_blank">23</a>] and scanpy [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012854#pcbi.1012854.ref009" target="_blank">9</a>] functions (see “Methods”) and AnnData object programming [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012854#pcbi.1012854.ref010" target="_blank">10</a>]. …"

OHID-FF dataset for forest fire detection and classification حسب xin chen (20496938)

"…</p><p dir="ltr">- For binary classification experiments with the included scripts:</p><p dir="ltr">```bash</p><p dir="ltr">python "train val scripts/main.py"</p><p>```</p><p dir="ltr"><br></p><p dir="ltr">Results and logs from training runs are saved under `results/` (see the scripts folder README for details).…"