Curve of step response signal of 6 algorithms. by Guangwei Liu (181992)

Raw results. by Mahmood A. Jumaah (22272371)

Algorithms and evaluation metrics integrated in the COPS framework. by Teemu J. Rintala (19333003)

Dendrogram of the stock prices. by Muhammad Hilal Alkhudaydi (21560690)

“…This article uses feature-based models to investigate the primary elements that contribute to the optimal composition of a specific portfolio. …”

Descriptive statistics on stock prices. by Muhammad Hilal Alkhudaydi (21560690)

“…This article uses feature-based models to investigate the primary elements that contribute to the optimal composition of a specific portfolio. …”

Correlation heatmap of the principal components. by Muhammad Hilal Alkhudaydi (21560690)

“…This article uses feature-based models to investigate the primary elements that contribute to the optimal composition of a specific portfolio. …”

DataSheet1_An integrated framework for UAV-based precision plant protection in complex terrain: the ACHAGA solution for multi-tea fields.zip by Pengyang Zhang (11256688)

DataSheet3_An integrated framework for UAV-based precision plant protection in complex terrain: the ACHAGA solution for multi-tea fields.zip by Pengyang Zhang (11256688)

DataSheet2_An integrated framework for UAV-based precision plant protection in complex terrain: the ACHAGA solution for multi-tea fields.zip by Pengyang Zhang (11256688)

DataSheet4_An integrated framework for UAV-based precision plant protection in complex terrain: the ACHAGA solution for multi-tea fields.zip by Pengyang Zhang (11256688)

SBM 2023 Poster: Development and Validation of Multivariable Prediction Algorithms to Estimate Future Walking Behavior in Adults: Retrospective Cohort Study by Junghwan Park (15195436)

“…</p> <p><strong>Objectives: </strong>To develop and validate algorithms that predict walking (i.e., >5 minutes) within the next 3 hours, predicted from the participants’ previous five weeks’ steps per minute data.…”

Table_1_One-Time Optimization of Advanced T Cell Culture Media Using a Machine Learning Pipeline.DOCX by Paul Grzesik (11136582)

“…Here we present the implementation of a machine learning pipeline into the DoE-based design of cell culture media to optimize T cell cultures in one experimental step (one-time optimization). …”

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

“…The proposed framework leverages a strong categorical boosting (CatBoost) algorithm optimized using Grid Search Optimization (GSO). …”

Data_Sheet_1_Rapid Identification of Mycobacterium tuberculosis Complex Using Mass Spectrometry: A Proof of Concept.docx by Simon Robinne (12324872)

Table_4_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX by Hui Tang (226667)

“…<p>Quantifying or labeling the sample type with high quality is a challenging task, which is a key step for understanding complex diseases. Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. …”

Table_2_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX by Hui Tang (226667)

“…<p>Quantifying or labeling the sample type with high quality is a challenging task, which is a key step for understanding complex diseases. Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. …”

Table_1_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.docx by Hui Tang (226667)

“…<p>Quantifying or labeling the sample type with high quality is a challenging task, which is a key step for understanding complex diseases. Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. …”

Table_3_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLS by Hui Tang (226667)

“…<p>Quantifying or labeling the sample type with high quality is a challenging task, which is a key step for understanding complex diseases. Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. …”

Table_5_High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.XLSX by Hui Tang (226667)

“…<p>Quantifying or labeling the sample type with high quality is a challenging task, which is a key step for understanding complex diseases. Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. …”

An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows by Pierre-Alexis DELAROCHE (22092572)

“…Cloud Workflows}, author={AlbumForge Research Team}, year={2025}, publisher={Figshare}, doi={10.6084/m9.figshare.XXXXXXX}, url={https://figshare.com/articles/dataset/XXXXXXX} } Contributing and Data Governance Issue Reporting Technical issues, data quality concerns, or methodological questions should be reported via GitHub Issues with the following template: **Issue Type**: [Bug Report / Data Quality / Methodology Question] **Hardware Configuration**: [Specify if applicable] **Dataset Version**: [e.g., v1.0.0] **Description**: [Detailed description of the issue] **Reproducibility**: [Steps to reproduce if applicable] **Expected Behavior**: [What should happen] **Actual Behavior**: [What actually happens] Data Update Protocol Dataset versioning follows semantic versioning (SemVer) principles: Major version (X.0.0): Incompatible schema changes Minor version (0.X.0): Backward-compatible feature additions Patch version (0.0.X): Backward-compatible bug fixes Technical Support and Community For advanced technical discussions, algorithmic improvements, or collaborative research opportunities, please contact: Primary Maintainer: research@albumforge.com Technical Issues: github.com/albumforge/ecological-benchmark/issues Methodology Discussions: [Academic collaboration portal] Industry Partnerships: partnerships@albumforge.com Acknowledgments: This research was conducted using computational resources provided by AlbumForge (https://albumforge.com) under the Green Computing Initiative. …”