Fast, FAIR, and Scalable: Managing Big Data in HPC with Zarr by 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. by 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. by 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. by 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. by 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" by 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" by 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. …”

Research Database by 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 by 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 by 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 by 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 by 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. by 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 by 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).…”

MSc Personalised Medicine at Ulster University by Steven Watterson (100045)

“…</p><p dir="ltr">The programme has oversight from a dedicated Employer Advisory Board, comprising over 15 industrial partners located throughout the UK, Ireland and the US.…”

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

“…Patients underwent 3T MRI scans with T1, T2, and contrast-enhanced (DCE) sequences. Imaging data from four medical centers were standardized through preprocessing steps, including intensity normalization, registration, and motion correction. …”

Data and code for: Automatic fish scale analysis by Christian Vogelmann (21646472)

“…GUI, pre/post-processing) is available upon request from the authors and is not included here.</i></li></ul></li><li><b>README.txt</b> – detailed file explanations and usage instructions</li></ul><p dir="ltr">The full statistical analysis and visualization pipeline is implemented in R and hosted on GitHub:<br>https://github.com/Birdy332/Automatic-fish-scale-analysis-r-scripts</p><p dir="ltr"><br></p><p dir="ltr">All figures shown in the manuscript can be reproduced using these scripts and the datasets provided here.…”

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

“…Patients underwent 3T MRI scans with T1, T2, and contrast-enhanced (DCE) sequences. Imaging data from four medical centers were standardized through preprocessing steps, including intensity normalization, registration, and motion correction. …”

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

“…Patients underwent 3T MRI scans with T1, T2, and contrast-enhanced (DCE) sequences. Imaging data from four medical centers were standardized through preprocessing steps, including intensity normalization, registration, and motion correction. …”

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

“…Patients underwent 3T MRI scans with T1, T2, and contrast-enhanced (DCE) sequences. Imaging data from four medical centers were standardized through preprocessing steps, including intensity normalization, registration, and motion correction. …”