Data extracted from 24 research articles for in-distribution and 4 articles for out-of-distribution evaluation. The dataset was created by Shashank Mishra using manual extraction tools like WebPlotDigitizer and was last updated on 2026-05-02. Its public availability is intended to aid in modelling and designing next-generation triboelectric nanogenerators (TENGs).
Use Cases
- Training machine learning models for TENG performance prediction based on extracted experimental data.
- Evaluating model generalization using the provided out-of-distribution (OOD) dataset.
- Benchmarking physics-informed ML frameworks for contact-separation energy harvesting systems.
Strengths
- Includes both in-distribution and out-of-distribution datasets for robust model evaluation.
- Data points were manually extracted from 28 total research articles, providing a curated foundation.
- Released under a permissive CC-BY-4.0 license for open reuse.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment for large-scale training.
- The dataset is small in scale at 192.7 KB, indicating limited scope.
Provenance
- Source
- Manual extraction from 24 research articles (in-distribution) and 4 research articles (out-of-distribution).
- Collection Method
- Data points extracted using tools like WebPlotDigitizer.
- Freshness
- Last updated 2026-05-02 22:15:25; freshness should be verified.