Data_Sheet_1_Potential diagnostic markers and therapeutic targets for non-alcoholic fatty liver disease and ulcerative colitis based on bioinformatics analysis and machine learning... by Zheng Luo (284622)

Data_Sheet_1_Prediction of Mental Health in Medical Workers During COVID-19 Based on Machine Learning.ZIP by Xiaofeng Wang (119575)

Data_Sheet_1_Degenerate Beta autoregressive model for proportion time-series with zeros or ones: An application to antimicrobial resistance rate using R shiny app.PDF by Jevitha Lobo (14368401)

Data_Sheet_1_Degenerate Beta autoregressive model for proportion time-series with zeros or ones: An application to antimicrobial resistance rate using R shiny app.PDF by Jevitha Lobo (14368401)

A data-driven machine learning approach for discovering potent LasR inhibitors by Christabel Ming Ming Koh (16798162)

“…Moreover, with many promising therapeutics falling short of expectations in clinical trials, targeting the <i>las</i> quorum sensing (QS) system remains an attractive therapeutic strategy to combat <i>P. aeruginosa</i> infection. Thus, our primary goal was to develop a drug prediction algorithm using machine learning to identify potent LasR inhibitors. …”

DataSheet_2_Optimising Treatment Outcomes for Children and Adults Through Rapid Genome Sequencing of Sepsis Pathogens. A Study Protocol for a Prospective, Multi-Centre Trial (DIREC... by Adam D. Irwin (11011752)

“…</p>Methods<p>The DIRECT study is a pilot prospective, non-randomized multicentre trial of an integrated diagnostic and therapeutic algorithm combining rapid direct pathogen sequencing and software-guided, personalised antibiotic dosing in children and adults with sepsis on ICU.…”

DataSheet_1_Optimising Treatment Outcomes for Children and Adults Through Rapid Genome Sequencing of Sepsis Pathogens. A Study Protocol for a Prospective, Multi-Centre Trial (DIREC... by Adam D. Irwin (11011752)

“…</p>Methods<p>The DIRECT study is a pilot prospective, non-randomized multicentre trial of an integrated diagnostic and therapeutic algorithm combining rapid direct pathogen sequencing and software-guided, personalised antibiotic dosing in children and adults with sepsis on ICU.…”

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

“…When using this data in your research, please cite: @dataset{ecological_benchmark_2025, title={An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. 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. …”

Table_1_A Phenotyping of Diastolic Function by Machine Learning Improves Prediction of Clinical Outcomes in Heart Failure.DOCX by Haruka Kameshima (11870333)

“…Cluster-based classification predicted events with a hazard ratio 1.68 (p = 0.019) that was independent from and incremental to the Meta-analysis Global Group in Chronic Heart Failure (MAGGIC) risk score for HF, and from left ventricular end-diastolic volume and global longitudinal strain, whereas guidelines-based classification did not retain its independent prognostic value (hazard ratio = 1.25, p = 0.202).…”

S1 File - The critical effects of self-management strategies on predicting cancer survivors’ future quality of life and health status using machine learning techniques by Ju Youn Jung (22139209)

Supplementary Material for: The importance of early diagnosis and intervention in chronic kidney disease: Calls-to-action from nephrologists based mainly in Central/Eastern Europe by Covic A. (4148122)

“…Background Chronic kidney disease (CKD) has a global prevalence of 9.1–13.4%. Comorbidities are abundant and may cause and affect CKD. …”

Data_Sheet_1_Predicting successful trading in the West Texas Intermediate crude oil cash market with machine learning nature-inspired swarm-based approaches.docx by Ehsan Zohreh Bojnourdi (18977486)

“…In this paper, a novel decompose-ensemble prediction approach is proposed by integrating various optimization algorithms, namely biography-based optimization (BBO), backtracking search algorithm (BSA), teaching-learning-based algorithm (TLBO), cuckoo optimization algorithm (COA), multi-verse optimization (MVO), and multilayer perceptron (MLP). …”

LCLU location-allocation with spatial contiguity and compactness by Sitarani Safitri (18129556)

“…In 2022, Indonesia, in general, and Java, in particular, already experienced a biocapacity deficit based on a study by the Global Footprint Network. …”

Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method by Tao Huang (110613)

“…The XGBoost-based SHAP algorithm not only elucidated the global effects of 17 SNPs across all samples, but also described the interaction between SNPs, providing a visual representation of how SNPs impact the prediction of MetS in an individual. …”

IUTF Dataset(Enhanced): Enabling Cross-Border Resource for Analysing the Impact of Rainfall on Urban Transportation Systems by Xuhui Lin (19505503)

“…</p><h2>Data Structure</h2><p dir="ltr">The dataset is organized into four primary components:</p><ol><li><b>Road Network Data</b>: Topological representations including spatial geometry, functional classification, and connectivity information</li><li><b>Traffic Sensor Data</b>: Sensor metadata, locations, and measurements at both 5-minute and hourly resolutions</li><li><b>Precipitation Data</b>: Hourly meteorological information with spatial grid cell metadata</li><li><b>Derived Analytical Matrices</b>: Pre-computed structures for advanced spatial-temporal modelling and network analyses</li></ol><h2>File Formats</h2><ul><li><b>Tabular Data</b>: Apache Parquet format for optimal compression and fast query performance</li><li><b>Numerical Matrices</b>: NumPy NPZ format for efficient scientific computing</li><li><b>Total Size</b>: Approximately 2 GB uncompressed</li></ul><h2>Applications</h2><p dir="ltr">The IUTF dataset enables diverse analytical applications including:</p><ul><li><b>Traffic Flow Prediction</b>: Developing weather-aware traffic forecasting models</li><li><b>Infrastructure Planning</b>: Identifying vulnerable network components and prioritizing investments</li><li><b>Resilience Assessment</b>: Quantifying system recovery curves, robustness metrics, and adaptive capacity</li><li><b>Climate Adaptation</b>: Supporting evidence-based transportation planning under changing precipitation patterns</li><li><b>Emergency Management</b>: Improving response strategies for weather-related traffic disruptions</li></ul><h2>Methodology</h2><p dir="ltr">The dataset creation involved three main stages:</p><ol><li><b>Data Collection</b>: Sourcing traffic data from UTD19, road networks from OpenStreetMap, and precipitation data from ERA5 reanalysis</li><li><b>Spatio-Temporal Harmonization</b>: Comprehensive integration using novel algorithms for spatial alignment and temporal synchronization</li><li><b>Quality Assurance</b>: Rigorous validation and technical verification across all cities and data components</li></ol><h2>Code Availability</h2><p dir="ltr">Processing code is available at: https://github.com/viviRG2024/IUTDF_processing</p>…”