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DR-NTU (Data) is for research data deposit. For research paper deposits, please use DR-NTU.

Mission: DR-NTU (Data) curates, stores, preserves, makes available and enables the download of digital data generated by the NTU research community. The repository develops and provides guidance for managing, sharing, and reusing research data to promote responsible data sharing in support of open science and research integrity.

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41 to 50 of 93,541 Results
Oct 25, 2026 - ZOU Xiangyu
Zou, Xiangyu, 2026, "Replication Data for: Decentralized Reinforcement Learning-Based MAC Optimization for Dense LoRa Networks", https://doi.org/10.21979/N9/PVYJLB, DR-NTU (Data), V1
This dataset contains simulation code, experiment configurations, recorded results and analysis scripts supporting the MSc dissertation “Decentralized Reinforcement Learning-Based MAC Optimization for Dense LoRa Networks” by Xiangyu Zou. The experiments compare tabular Q-learning...
Oct 23, 2026 - TAN Zhi Wei
Tan, Zhi-Wei; Reju, V. G.; Tewari, Ritesh Chandra; Ding, Ruotong; Naylor, Patrick A.; Khong, Andy W. H., 2026, "ICASSP 2026 Radar Acoustic Speech Enhancement (RASE) Challenge: Train and Evaluation Dataset", https://doi.org/10.21979/N9/YIU09T, DR-NTU (Data), V1
Recommended Citation Researchers using this dataset in their publications are requested to cite the following paper: Z.-W. Tan, V. G. Reju, R. C. Tewari, R. Ding, P. A. Naylor, and A. W. H. Khong, "ICASSP 2026 Radar Acoustic Speech Enhancement (RASE) Challenge," in Proceedings of...
ZIP Archive - 1.8 GB - MD5: a10dcf2e0e23c3bb61569525e2f70787
Oct 23, 2026 - Ignatius Ryan PRANANTYO
Pranantyo, Ignatius Ryan; Petrillo, Giuseppe; Rino, Salman; Dal Zilio, Luca, 2026, "Replication Data for: A probabilistic tsunami hazard assessment for the southern coast of Java, Indonesia: Building an inundation database through a two-stage framework", https://doi.org/10.21979/N9/GOVTFU, DR-NTU (Data), V2
A set of input and output files for Stage 1 simulation using JAGURS and Stage 2 simulations using SFINCS.
ZIP Archive - 13.1 MB - MD5: 327d8559ce000f5bf77961a903ab6709
Results: Flood depth evaluation using large events (Supplementary Information - Appendix C.3)
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