Supplementary file 1_Multimodal data-driven prognostic model for predicting long-term outcomes in older adult patients with sarcopenia: a retrospective cohort study.pdf by Mengdie Liu (21416774)

“…Feature selection was performed using Lasso Regression, XGBoost, and Random Forest machine learning algorithms, and a nomogram model was developed using univariate and multivariate Cox regression analyses, with validation of its accuracy, concordance, and clinical applicability.…”

Chemical Composition-Driven Machine Learning Models for Predicting Ionic Conductivity in Lithium-Containing Oxides (Supporting Information) by Yudai IWAMIZU (20747538)

“…Thus, new models demonstrating improved prediction ability must be developed. This study presents the development of machine learning models for the accurate prediction of ionic conductivity in lithium-containing materials based solely on their chemical composition. …”

Verification and Validation for Trustworthy Scientific Machine Learning by Lorena A. Barba (97553)

“…This work aims to catalyze community dialogue toward consensus-based standards for predictive SciML, providing researchers with practical guidance for developing models that can be trusted for high-consequence scientific and engineering applications.…”

Comprehensive Dataset for Event Classification Using Distributed Acoustic Sensing (DAS) Systems by Adrian Tomasov (19664054)

“…This dataset is ideal for researchers developing and validating machine learning algorithms for DAS-based applications, including structural health monitoring and perimeter security.…”

Figure 2 from Autofluorescence Virtual Staining System for H&E Histology and Multiplex Immunofluorescence Applied to Immuno-Oncology Biomarkers in Lung Cancer by Jessica Loo (20526803)

“…<p>pIHC and segmentation algorithms were developed for mIF model development and evaluation. …”

Tactile, Audio, and Visual Dataset During Bare Finger Interaction with Textured Surfaces by Alexis Devillard (17000451)

“…Through carefully designed data collection protocols, encompassing both controlled and free exploration scenarios, this dataset lays the groundwork for understanding human multi-sensory integration and supports the development of algorithms for texture recognition based on multi-modal inputs. …”

N0.1 ChinaCUI: Long-term Spatiotemporal Distribution Dataset of Cropland Cultivation Intensity in China (1982–Present)​ by Bingwen QIU (17449565)

“…<p dir="ltr">This dataset is developed based on a series of robust Cropland Utilization Intensity (CUI) algorithms, enabling the automatic acquisition of large-scale agricultural cropping pattern information. …”

N0.2 ChinaCUI: Long-term Spatiotemporal Distribution Dataset of Cropland Cultivation Intensity in China (1982–Present) by Bingwen QIU (17449565)

“…<p dir="ltr">This dataset is developed based on a series of robust Cropland Utilization Intensity (CUI) algorithms, enabling the automatic acquisition of large-scale agricultural cropping pattern information. …”

DATA SHEET.csv by Dola Saha (20556497)

“…Its ability to improve accuracy, efficiency, and consistency in diagnosis makes it an indispensable tool for early detection efforts.This study focuses on developing a machine learning-based predictive model for the early detection and classification of breast cancer, utilizing the Wisconsin Breast Cancer Diagnostic dataset. …”

Behavioral raw data recorded by HABITS autonomously by bowen yu (19822908)

“…Supported by the microcontroller-based general programming framework, we have not only replicated established paradigms in current neuroscience research but also developed several novel paradigms previously unexplored in mice, resulting in more than 300 mice tested in various cognition functions. …”

Data sharing by Zihan Jin (20638007)

“…In this work, we developed a monolithically integrated self-inducted microfluidic bionic electronic tongue (SMET), combining the LSCE effect with deep learning algorithms to achieve highly reliable and intelligent sample identification and concentration detection. …”

Predictors of evacuation behavior: dataset on respondents’ route choice and web interaction by Dajana Snopková (20191691)

“…Empirical data on human evacuation behavior are invaluable for adjusting and training computational algorithms that simulate evacuation processes, including agent-based modeling. …”

Image 1_Biological and prognostic insights into the prostaglandin D2 signaling axis in lung adenocarcinoma.pdf by Qiang Liu (166143)

“…Furthermore, we found that a risk model developed based on this signaling axis could predict responses to immune therapy in hot and cold tumors, suggesting potential drugs that may benefit low-risk patients. …”

Table 1_Biological and prognostic insights into the prostaglandin D2 signaling axis in lung adenocarcinoma.xlsx by Qiang Liu (166143)

“…Furthermore, we found that a risk model developed based on this signaling axis could predict responses to immune therapy in hot and cold tumors, suggesting potential drugs that may benefit low-risk patients. …”

Data Sheet 2_Neutrophil-related IL1R2 gene predicts the occurrence and early progression of myocardial infarction.zip by Jieqiong Tang (10362206)

“…</p>Methods<p>Key genes were screened using three machine learning algorithms to establish a diagnostic model. A gene associated with the early progression of MI was identified based on single cell RNA sequencing data. …”

Data Sheet 3_Neutrophil-related IL1R2 gene predicts the occurrence and early progression of myocardial infarction.zip by Jieqiong Tang (10362206)

“…</p>Methods<p>Key genes were screened using three machine learning algorithms to establish a diagnostic model. A gene associated with the early progression of MI was identified based on single cell RNA sequencing data. …”

Table 5_Neutrophil-related IL1R2 gene predicts the occurrence and early progression of myocardial infarction.xls by Jieqiong Tang (10362206)

“…</p>Methods<p>Key genes were screened using three machine learning algorithms to establish a diagnostic model. A gene associated with the early progression of MI was identified based on single cell RNA sequencing data. …”

Table 1_Neutrophil-related IL1R2 gene predicts the occurrence and early progression of myocardial infarction.xls by Jieqiong Tang (10362206)

“…</p>Methods<p>Key genes were screened using three machine learning algorithms to establish a diagnostic model. A gene associated with the early progression of MI was identified based on single cell RNA sequencing data. …”

Table 4_Neutrophil-related IL1R2 gene predicts the occurrence and early progression of myocardial infarction.xls by Jieqiong Tang (10362206)

“…</p>Methods<p>Key genes were screened using three machine learning algorithms to establish a diagnostic model. A gene associated with the early progression of MI was identified based on single cell RNA sequencing data. …”

Data Sheet 4_Neutrophil-related IL1R2 gene predicts the occurrence and early progression of myocardial infarction.zip by Jieqiong Tang (10362206)

“…</p>Methods<p>Key genes were screened using three machine learning algorithms to establish a diagnostic model. A gene associated with the early progression of MI was identified based on single cell RNA sequencing data. …”