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Deposit, archive and share your final research data in DR-NTU (Data)

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.

Who can deposit? NTU faculty, research staff and students.

What can be deposited? Final, non-sensitive research data from projects carried out at NTU. The uploaded content must not infringe upon the copyrights or other intellectual property rights, and must be void of all identifiable information.

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211 to 220 of 3,297 Results
Domenico CAMPOLO(Nanyang Technological University)
Domenico CAMPOLO logo
Jun 2, 2025School of Mechanical and Aerospace Engineering (MAE)
Research topics: The intersection between Robotics and Neuroscience, with special focus on the sensorimotor coupling in humans and for robots.
May 28, 2025 - Binu KUNDUKAD
Kundukad, Binu, 2025, "Replication Data for: Alginate exopolymer significantly modulates the viscoelastic properties and resilience of bacterial biofilms", https://doi.org/10.21979/N9/WBIFH6, DR-NTU (Data), V1, UNF:6:MJioZwH7bYme6r9P07lTpw== [fileUNF]
The data set contains the raw data of the microscopy image files of biofilms and the recolonizing bacteria, the mat files used to generate the mean square displacements in particle tracking experiments (before during and after treatment with NAC), excel files with compiled data u...
May 24, 2025 - NIE Data Repository (Harvested)
Yee, Jia'en; Lim, Fei Victor; Ng, Betsy Ling Ling; Pan, Qianqian; Koh, Elizabeth; Sindoni, Maria Grazia, 2025, "Related Data for: The student digital literacies profiling tool: Instrument validation", https://doi.org/10.25340/R4/ZVCK4I
In an era of rapid technological change, digital literacies now extend beyond basic technical skills to include the ability to combine digital tools, features, and semiotic resources to convey meaning across diverse contexts. This paper presents a framework and validated survey t...
This Dataset is harvested from our partners. Clicking the link will take you directly to the archival source of the data.
May 23, 2025 - David WILKOWSKI
Wilkowski, David, 2025, "Related Data for: Topological optical skyrmion transfer to matter", https://doi.org/10.21979/N9/EAVRTG, DR-NTU (Data), V2, UNF:6:lwjP4tuiAt8pFa4bWpuLuQ== [fileUNF]
Source file for Topological optical skyrmion transfer to matter
May 23, 2025 - HE Huajun
He, Huajun; Wang, Bo; Shen, Xuhai; Feng, Minjun; Rao, Haixia; Ye, Senyun; Nguyen, Linh Lan; Duchamp, Martial; Li, Shuzhou; Tian, He; Sum, Tze Chien, 2025, "Replication Data for: Aqueous Colloidal Perovskite Quantum Emitters", https://doi.org/10.21979/N9/8YTAAI, DR-NTU (Data), V1
Aqueous solutions of nanoparticles are the cornerstones for applications in diagnostics, catalysis and more, where control over the nanoparticle's dispersion is pivotal to tailoring the final product properties. Of late, halide perovskite nanocrystals (HPNCs) with outstanding opt...
May 22, 2025 - Chin Siang NG
Ng, Chin Siang, 2025, "Effects of Zinc Oxide Nanoparticles on Vat Photopolymerziation", https://doi.org/10.21979/N9/36TOLK, DR-NTU (Data), V1
Benchmarking images to observe overcuring, tensile tests and DMA results..
Chin Siang NG (Nanyang Technological University)
May 22, 2025School of Mechanical and Aerospace Engineering (MAE)
May 22, 2025 - S-Lab for Advanced Intelligence
Xu, Yuanmu; Hou, Guanli; Hu, Jiangbei; Ren, Tenglong; Wang, Xiaokun; Zhang, Yalan; Ban, Xiaojuan; Qian, Chen; Hou, Fei; He, Ying, 2025, "NeuS: Physics and Geometry-Augmented Neural Implicit Surfaces for Rigid Bodies", https://doi.org/10.21979/N9/LTXKFL, DR-NTU (Data), V1
This paper tackles the challenges of physics-based simulation of rigid bodies in neural rendering, focusing on 3D model representation and collision handling. A synthetic and real-world dataset is also included in the paper.
May 22, 2025 - S-Lab for Advanced Intelligence
Xu, Qianxiong; Zhu, Lanyun; Liu, Xuanyi; Lin, Guosheng; Long, Cheng; Li, Ziyue; Zhao, Rui, 2025, "Unlocking the Power of SAM 2 for Few-Shot Segmentation", https://doi.org/10.21979/N9/XIDXVT, DR-NTU (Data), V1
Few-Shot Segmentation (FSS) aims to learn class-agnostic segmentation on few classes to segment arbitrary classes, but at the risk of overfitting. To address this, some methods use the well-learned knowledge of foundation models (e.g., SAM) to simplify the learning process. Recen...
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