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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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581 to 590 of 2,486 Results
Sep 24, 2025 - Project: Analysing Present and Future Compound Hydroclimate Extremes in Southeast Asia
Jian, Wei; Valentina, I Gusti Ayu Diah; Lo, Edmond Yat-Man, 2025, "Related Data for: Spatiotemporal Trends of Compound Weather and Climate Extremes and Their Impacts in Southeast Asia from 1983 to 2022", https://doi.org/10.21979/N9/UJ3UCE, DR-NTU (Data), V1
This dataset contains the identified compound extreme days across Southeast Asia (10S-30N, 90-130E) for the period 1983-2022. It includes concurrent precipitation and wind extremes (CPWEs), concurrent dry spell and heat extremes (DSHEs), and humid heatwave extremes (HHWEs). These...
Sep 19, 2025 - Navin Kumar VERMA
Verma, Navin Kumar, 2025, "The novel quinoline derivative SKA-346 as a KCa3.1 channel selective activator", https://doi.org/10.21979/N9/WD4RDP, DR-NTU (Data), V2
Main and supplementary figures
Sep 18, 2025 - KUMAR Rahul
Kumar, Rahul, 2025, "Related Data for: Late-Holocene sea-level markers preserved in a beach ridge system on Phra Thong Island, Thailand", https://doi.org/10.21979/N9/6OM7B4, DR-NTU (Data), V1
100 MHz ground penetrating radar (GPR) data were collected from Phra Thong Island, Thailand.
Sep 17, 2025 - S-Lab for Advanced Intelligence
Li, Ruibo; Shi, Hanyu; Wang, Zhe; Lin, Guosheng, 2025, "Weakly and Self-Supervised Class-Agnostic Motion Prediction for Autonomous Driving", https://doi.org/10.21979/N9/PE8MLE, DR-NTU (Data), V1
Understanding motion in dynamic environments is critical for autonomous driving, thereby motivating research on class-agnostic motion prediction. In this work, we investigate weakly and self-supervised class-agnostic motion prediction from LiDAR point clouds. Outdoor scenes typic...
Sep 17, 2025 - Ahmad IBRAHIM
Ibrahim, Ahmad, 2025, "Replication Data for: The role of alkali doping in enhancing bulk and interface properties of Cu2CdSnS4 solar cells", https://doi.org/10.21979/N9/A5FZZW, DR-NTU (Data), V1
Replication Data for: The role of alkali doping in enhancing bulk and interface properties of Cu2CdSnS4 solar cells
Sep 17, 2025 - Sreenivasulu Reddy MOGALI
Mogali, Sreenivasulu Reddy, 2025, "Replication Data for: Evaluation by medical students of the educational value of multi-material and multi-colored three-dimensional printed models of the upper limb for anatomical education", https://doi.org/10.21979/N9/HVNKUE, DR-NTU (Data), V1
This dataset comprises participants' perceptions—collected through surveys and focus group discussions—regarding their learning experience and the perceived educational value of a 3D-printed anatomical model of the human upper limb.
Sep 12, 2025 - CHENG Zheyu
Cheng, Zheyu, 2025, "Replication Data for: Observation of returning Thouless pumping", https://doi.org/10.21979/N9/9JT69V, DR-NTU (Data), V1
This repository contains the data and code used for the analyses presented in the manuscript "Cheng, Z., Yue, S., Long, Y. et al. Observation of returning Thouless pumping. Nat Commun 16, 9669 (2025). https://doi.org/10.1038/s41467-025-64671-w" It is also related to preprint arXi...
Sep 11, 2025 - S-Lab for Advanced Intelligence
Dai, Yuekun; Li, Haitian; Zhou, Shangchen; Loy, Chen Change, 2025, "Trans-Adapter: A Plug-and-Play Framework for Transparent Image Inpainting", https://doi.org/10.21979/N9/4NI0GT, DR-NTU (Data), V1
RGBA images, with the additional alpha channel, are crucial for any application that needs blending, masking, or transparency effects, making them more versatile than standard RGB images. Nevertheless, existing image inpainting methods are designed exclusively for RGB images. Con...
Sep 10, 2025 - S-Lab for Advanced Intelligence
Xie, Haozhe; Chen, Zhaoxi; Hong, Fangzhou; Liu, Ziwei, 2025, "Compositional Generative Model of Unbounded 4D Cities", https://doi.org/10.21979/N9/CHQPCL, DR-NTU (Data), V1
3D scene generation has garnered growing attention in recent years and has made significant progress. Generating 4D cities is more challenging than 3D scenes due to the presence of structurally complex, visually diverse objects like buildings and vehicles, and heightened human se...
Sep 9, 2025 - Computational Geophysics Lab
Tang, Rongjiang; Dal Zilio, Luca, 2025, "Replication Data for: PyQuake3D: A Python Tool for 3-D Earthquake Sequence Simulations of Seismic and Aseismic Slip", https://doi.org/10.21979/N9/VUVEUX, DR-NTU (Data), V3
This repository contains the source code and input data necessary to reproduce the numerical experiments presented in the accompanying methodological paper. It includes the full PyQuake3D simulation framework (version 1.0.0), along with all input geometry and parameter files used...
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