Figshare
open access
CC BY 4.0
Data Sheet 1_Key biomechanical features of jump-landing under cognitive dual-task conditions: an XGBoost-SHAP-based explainable machine learning analysis.csv
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Supplementary file 1_Feasibility of explainable machine learning for analyzing drop landing strategies: effects of landing height and fatigue.zip#635011
- Using Stimulus Preference Assessments to Identify Preferred Break Environments#411517
- Toward Postural State Classification in Immersive VR with Multimodal Data and Explainability Analysis#793082
- Toward Postural State Classification in Immersive VR with Multimodal Data and Explainability Analysis#789078
- Transcranial Direct Current Stimulation (tDCS) and Motor Training for Traumatic Brain Injury (TBI) Survivors#892578
Record · ID 669778
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive