Supplementary file 3_Optimising the selection of welfare indicators in farm animals.docx by Jon Day (19128586)

“…The enhanced algorithm is data-agnostic and enables users to optimise indicator selection with diverse datasets spanning research, industry, and policy contexts. …”

Supplementary file 2_Optimising the selection of welfare indicators in farm animals.docx by Jon Day (19128586)

“…The enhanced algorithm is data-agnostic and enables users to optimise indicator selection with diverse datasets spanning research, industry, and policy contexts. …”

Supplementary file 6_Optimising the selection of welfare indicators in farm animals.docx by Jon Day (19128586)

“…The enhanced algorithm is data-agnostic and enables users to optimise indicator selection with diverse datasets spanning research, industry, and policy contexts. …”

Supplementary file 1_Optimising the selection of welfare indicators in farm animals.docx by Jon Day (19128586)

“…The enhanced algorithm is data-agnostic and enables users to optimise indicator selection with diverse datasets spanning research, industry, and policy contexts. …”

Supplementary file 4_Optimising the selection of welfare indicators in farm animals.docx by Jon Day (19128586)

“…The enhanced algorithm is data-agnostic and enables users to optimise indicator selection with diverse datasets spanning research, industry, and policy contexts. …”

Supplementary file 5_Optimising the selection of welfare indicators in farm animals.docx by Jon Day (19128586)

“…The enhanced algorithm is data-agnostic and enables users to optimise indicator selection with diverse datasets spanning research, industry, and policy contexts. …”

The Comparison of MOTA for Different Thresholds. by Yibin Zhang (1426579)

“…Tracking is the most crucial data processing step to generate accurate and reliable trajectories of road users from raw point clouds collected from LiDAR sensors. …”

Demonstration of the Result of Clustering. by Yibin Zhang (1426579)

“…Tracking is the most crucial data processing step to generate accurate and reliable trajectories of road users from raw point clouds collected from LiDAR sensors. …”

Summary of Dataset Scenario and Performance. by Yibin Zhang (1426579)

“…Tracking is the most crucial data processing step to generate accurate and reliable trajectories of road users from raw point clouds collected from LiDAR sensors. …”

Image 3_Toward precision oncology in LUAD: a prognostic model using single-cell sequencing and WGCNA based on a disulfidptosis relative gene signature.tif by Panpan Li (484033)

“…Gene expression in single cell RNA sequencing (scRNA-seq) data was assessed using the AUcell algorithm. In the TCGA [LUAD] dataset, disulfidptosis-related enrichment scores were calculated using ssGSEA, and core gene sets were identified through the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm. …”

Image 5_Toward precision oncology in LUAD: a prognostic model using single-cell sequencing and WGCNA based on a disulfidptosis relative gene signature.tif by Panpan Li (484033)

“…Gene expression in single cell RNA sequencing (scRNA-seq) data was assessed using the AUcell algorithm. In the TCGA [LUAD] dataset, disulfidptosis-related enrichment scores were calculated using ssGSEA, and core gene sets were identified through the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm. …”

Image 2_Toward precision oncology in LUAD: a prognostic model using single-cell sequencing and WGCNA based on a disulfidptosis relative gene signature.tif by Panpan Li (484033)

“…Gene expression in single cell RNA sequencing (scRNA-seq) data was assessed using the AUcell algorithm. In the TCGA [LUAD] dataset, disulfidptosis-related enrichment scores were calculated using ssGSEA, and core gene sets were identified through the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm. …”

Image 1_Toward precision oncology in LUAD: a prognostic model using single-cell sequencing and WGCNA based on a disulfidptosis relative gene signature.tif by Panpan Li (484033)

“…Gene expression in single cell RNA sequencing (scRNA-seq) data was assessed using the AUcell algorithm. In the TCGA [LUAD] dataset, disulfidptosis-related enrichment scores were calculated using ssGSEA, and core gene sets were identified through the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm. …”

Table 1_Toward precision oncology in LUAD: a prognostic model using single-cell sequencing and WGCNA based on a disulfidptosis relative gene signature.docx by Panpan Li (484033)

“…Gene expression in single cell RNA sequencing (scRNA-seq) data was assessed using the AUcell algorithm. In the TCGA [LUAD] dataset, disulfidptosis-related enrichment scores were calculated using ssGSEA, and core gene sets were identified through the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm. …”

Image 4_Toward precision oncology in LUAD: a prognostic model using single-cell sequencing and WGCNA based on a disulfidptosis relative gene signature.tif by Panpan Li (484033)

“…Gene expression in single cell RNA sequencing (scRNA-seq) data was assessed using the AUcell algorithm. In the TCGA [LUAD] dataset, disulfidptosis-related enrichment scores were calculated using ssGSEA, and core gene sets were identified through the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm. …”

Image 6_Toward precision oncology in LUAD: a prognostic model using single-cell sequencing and WGCNA based on a disulfidptosis relative gene signature.tif by Panpan Li (484033)

“…Gene expression in single cell RNA sequencing (scRNA-seq) data was assessed using the AUcell algorithm. In the TCGA [LUAD] dataset, disulfidptosis-related enrichment scores were calculated using ssGSEA, and core gene sets were identified through the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm. …”

Dataset for Partial Parallelism Plot Analysis in Neurodegeneration Biomarker Assays (2010–2024) by Axel Petzold (7076261)

“…<br></p><p dir="ltr">Each dataset entry is annotated with:</p><ul><li>Sample type (serum, plasma, cerebrospinal fluid)</li><li>Assay platform and dilution steps</li><li>Classification of outcome (partial parallelism achieved or not)</li></ul><p dir="ltr"><b>Use cases:</b><br>This dataset is designed to help researchers, assay developers, and meta-analysts to:</p><ul><li>Reproduce figures and analyses from the published review</li><li>Benchmark or validate new assay performance pipelines</li><li>Train algorithms for automated detection of dilutional non-parallelism</li></ul><p dir="ltr"><b>Files included:</b></p><ul><li><code>.csv</code> files containing dilution–response data</li><li>Metadata spreadsheets with assay and sample annotations</li></ul><p></p>…”

<b>Research on Semantic Segmentation of PCB Point Clouds Based on Adaptive Dynamic Graph Convolution</b> by zedong huang (22221292)

“…In recent years, graph convolution networks (GCNs) have garnered increasing attention, particularly in the realm of Non-Euclidean data processing. Against this backdrop, our research proposes a point cloud segmentation method for electronic components based on Adaptive Dynamic Graph Convolution. …”

Early Parkinson’s disease identification via hybrid feature selection from multi-feature subsets and optimized CatBoost with SMOTE by Subhashree Mohapatra (17387852)

“…The analysis was conducted on a PD dataset derived from speech recording signals. To address the data imbalance, the synthetic minority oversampling technique (SMOTE) is applied as a pre-processing step to improve the robustness and reliability of the model. …”

Additional file 1 of The two ends of the spectrum: comparing chronic schizophrenia and premorbid latent schizotypy by actigraphy by Szandra László (21420583)

“…There is provided further information about data collection and processing, machine learning algorithms, and other program codes, and more details about the findings…”