Mendeley Data Showcase

This dataset contains the CDDA framework code for analyzing precomputed 3-s SDV / 30-s ORP sleep-depth trajectories. It includes trajectory alignment, feature extraction, adaptive two-state GMM and HMM decoding, CAP A1–A3 validation, and figure-generation scripts. The frozen ANN-SDV front-end is an external dependency (not included). Input: aligned tables of 3-s SDV or 30-s ORP. Output: dynamics indicators, event features, diagnostics, and cohort summaries. Runtime: Python 3.12 (NumPy, SciPy, scikit-learn, hmmlearn, matplotlib).

Geometry-referenced encodings of 24 commercial ceiling nail guns (protocol coordinates, station widths, handle thickness and terminal form) with their measured mass, centre-of-gravity position and actuation push force; the three-analyst, three-round reproducibility records obtained with and without the protocol; and the participant-level questionnaire and hand-measurement records of the user validation study, in which 20 professional users rated eight of the products relative to a fixed reference. The stimulus key links the blinded stimulus codes to the anonymised product numbers. The accompanying analysis scripts read these tables and reproduce every statistic reported in the article. All records are anonymised; the study received an institutional ethics exemption and written informed consent was obtained from every participant.

reproducibility package for project "Estimated built surface increase within water related lowland screening areas in Vietnam from 2000 to 2025"

Supplemental Materials for the manuscript titled "Recycled Waste Fibre Based Aerogel Composite Material with Enhanced Mechanical and Thermal Performance"

Knapsack Problem instances for N = 500 generated with various Quality Diversity Methods. Each instance includes its target solver and the method used to generate it. The instances have been sorted by their item weight-profit tuples.

Knapsack Problem instances for N = 1000 generated with various Quality Diversity Methods. Each instance includes its target solver and the generation method. The instances have been sorted by their item weight-profit tuples.

Knapsack Problem instances for N = 100 were generated with various Quality Diversity Methods. Each instance includes its target solver and the method used to generate it. The instances have been sorted by their item weight-profit tuples.

Bin Packing problem instances for N = 120. The dataset includes the target solver and the generation method employed to produce such instance.

Data for the article : Lifetime Thermal Compensation as a Design Criterion for Renewable-Assisted Deep Borehole Heat Exchangers

Travelling Salesman Problem instances for N = 50 were generated with various Quality Diversity Methods. Each instance includes its target solver and the method used to generate it. The instances have been sorted by their cities' coordinates to facilitate the computing of unique instances..