This dataset comprises experimental measurements and model predictions for analyzing the Mode I interlaminar fracture toughness (GIC) of additively manufactured continuous glass fiber-reinforced polyetheretherketone (CGF/PEEK) composites. The study employed Response Surface Methodology (Box–Behnken design) to systematically investigate the effects of three key processing parameters—nozzle temperature (400–440°C), printing speed (1–5 mm/s), and layer thickness (0.16–0.24 mm)—on GIC. Core data include load-displacement curves and calculated GIC values from Double Cantilever Beam (DCB) tests, crystallinity measurements obtained via Differential Scanning Calorimetry (DSC), and microstructural images and surface roughness data acquired through optical and laser confocal microscopy. Additionally, the dataset provides input parameters and predictive results from an interlayer fusion model based on thermal history, molecular diffusion, and interfacial contact mechanisms, which demonstrated high consistency with experimental results (coefficient of determination R² ≈ 0.972).

This dataset comprises experimental measurements and model predictions for analyzing the Mode I interlaminar fracture toughness (GIC) of additively manufactured continuous glass fiber-reinforced polyetheretherketone (CGF/PEEK) composites. The study employed Response Surface Methodology (Box–Behnken design) to systematically investigate the effects of three key processing parameters—nozzle temperature (400–440°C), printing speed (1–5 mm/s), and layer thickness (0.16–0.24 mm)—on GIC. Core data include load-displacement curves and calculated GIC values from Double Cantilever Beam (DCB) tests, crystallinity measurements obtained via Differential Scanning Calorimetry (DSC), and microstructural images and surface roughness data acquired through optical and laser confocal microscopy. Additionally, the dataset provides input parameters and predictive results from an interlayer fusion model based on thermal history, molecular diffusion, and interfacial contact mechanisms, which demonstrated high consistency with experimental results (coefficient of determination R² ≈ 0.972).

This dataset consists of near-infrared (NIR) spectral data of 48 kinds of medicinal and edible homologous herbs.

This dataset consists of near-infrared (NIR) spectral data of 48 kinds of medicinal and edible homologous herbs.

This repository contains the raw data and processing codes within the paper " Enhanced superconductivity in PbTe-In hybrids"

This repository contains the raw data and processing codes within the paper " Enhanced superconductivity in PbTe-In hybrids"

Breast cancer (BC) is a prevalent global malignancy with a high recurrence rate. The effectiveness of predictive, preventive, and personalized treatment strategies is limited by a lack of reliable prognostic biomarkers. Radiotherapy significantly reduces breast cancer recurrence risk and prolongs patients’ lives. However, the role of radiation-related genes in breast cancer remains unclear.Differentially expressed radiation-related genes were identified through analysis of the BRCA gene expression matrix between radiation and non-radiation groups. Multi-omics investigation, including bulk and single-cell RNA sequencing, was conducted to explore these genes in breast cancer. A risk model was developed using random forest, stepAIC, and LASSO Cox regression analyses to predict prognosis, immune cell infiltration, immunotherapy response, and targeted drug sensitivity based on radiation-related gene expression profiles. Functional differences were assessed via Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses. We identified 133 radiation-related differentially expressed genes (DEGs), with 26 hub genes selected via LASSO and random forest models. Single-cell analysis revealed enrichment of radiation-related scores primarily in malignant cells. The radiation-related risk model, validated in the METABRIC dataset and an independent prognostic indicator in the TCGA-BRCA cohort, showed that low-risk patients had higher overall survival rates than high-risk patients. Risk scores correlated with immune infiltration, and low-risk patients exhibited greater immunotherapy response based on immune checkpoint gene expression. Drug sensitivity to gemcitabine, lapatinib, methotrexate, and doxorubicin varied across risk groups. To put it briefly, a strong efficient risk model was created to forecast prognosis, TME features, reactions to immunotherapy targeted medications in BRCA. This might lead to new understandings of individualized accurate treatment approaches. To facilitate clinical application, we have developed an R package and Excel-based calculator tool that enables clinicians to easily calculate patient risk scores using the 8-gene signature. These tools, along with detailed usage instructions, are freely available in the supplementary materials and GitHub repository.

Breast cancer (BC) is a prevalent global malignancy with a high recurrence rate. The effectiveness of predictive, preventive, and personalized treatment strategies is limited by a lack of reliable prognostic biomarkers. Radiotherapy significantly reduces breast cancer recurrence risk and prolongs patients’ lives. However, the role of radiation-related genes in breast cancer remains unclear.Differentially expressed radiation-related genes were identified through analysis of the BRCA gene expression matrix between radiation and non-radiation groups. Multi-omics investigation, including bulk and single-cell RNA sequencing, was conducted to explore these genes in breast cancer. A risk model was developed using random forest, stepAIC, and LASSO Cox regression analyses to predict prognosis, immune cell infiltration, immunotherapy response, and targeted drug sensitivity based on radiation-related gene expression profiles. Functional differences were assessed via Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses. We identified 133 radiation-related differentially expressed genes (DEGs), with 26 hub genes selected via LASSO and random forest models. Single-cell analysis revealed enrichment of radiation-related scores primarily in malignant cells. The radiation-related risk model, validated in the METABRIC dataset and an independent prognostic indicator in the TCGA-BRCA cohort, showed that low-risk patients had higher overall survival rates than high-risk patients. Risk scores correlated with immune infiltration, and low-risk patients exhibited greater immunotherapy response based on immune checkpoint gene expression. Drug sensitivity to gemcitabine, lapatinib, methotrexate, and doxorubicin varied across risk groups. To put it briefly, a strong efficient risk model was created to forecast prognosis, TME features, reactions to immunotherapy targeted medications in BRCA. This might lead to new understandings of individualized accurate treatment approaches. To facilitate clinical application, we have developed an R package and Excel-based calculator tool that enables clinicians to easily calculate patient risk scores using the 8-gene signature. These tools, along with detailed usage instructions, are freely available in the supplementary materials and GitHub repository.

This is the original data of the manuscript.

This is the original data of the manuscript.