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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.

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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31 to 40 of 2,312 Results
Related Data for: Two Years to Madness: A 30-Month Journal of Human–Machine Communication and Second-Hand Reality
Jan 9, 2026 - Andrew PRAHL
Prahl, Andrew, 2026, "Related Data for: Two Years to Madness: A 30-Month Journal of Human–Machine Communication and Second-Hand Reality", https://doi.org/10.21979/N9/4URUZD, DR-NTU (Data), V1
This data is related to the study "Two Years to Madness: A 30-Month Journal of Human–Machine Communication and Second-Hand Reality" and includes journal entries, coding steps, memos, and consolidation into thematic analysis.
Replication Data for: Investigating extreme sea levels and compound flooding in Singapore and the South China SeaEmbargoed
Jan 7, 2026 - XU Jiamin
Xu, Jiamin, 2026, "Replication Data for: Investigating extreme sea levels and compound flooding in Singapore and the South China Sea", https://doi.org/10.21979/N9/GGWL4H, DR-NTU (Data), V2, UNF:6:Ike5okl/q+GZgw/TPn9wZA== [fileUNF]
This dataset comprises a database of tide gauge data, precipitation data, and codes used in the thesis to ensure reproducibility of the research (2025)
Replication Data for: Ultrafast Vibrational Wave Packet Dynamics of the Aqueous Guanine Radical Anion Induced by Photodetachment
Jan 2, 2026 - LOH Zhi Heng
Loh, Zhi Heng; Ling, Christine Chun Hui; Chan, Wei Xin; Siow, Jing Xuan, 2026, "Replication Data for: Ultrafast Vibrational Wave Packet Dynamics of the Aqueous Guanine Radical Anion Induced by Photodetachment", https://doi.org/10.21979/N9/AO0A22, DR-NTU (Data), V1
Replication Data for: Ultrafast Vibrational Wave Packet Dynamics of the Aqueous Guanine Radical Anion Induced by Photodetachment
Replication Data for: Ultrafast Electronic Relaxation Dynamics of Atomically Thin MoS2 Is Accelerated by Wrinkling
Jan 2, 2026 - LOH Zhi Heng
Loh, Zhi Heng; Xu, Ce; Zhou, Guoqing; Alexeev, Evgeny M.; Cadore, Alisson R.; Paradisanos, Ioannis; Ott, Anna K.; Soavi, Giancarlo; Tongay, Sefaattin; Cerullo, Giulio; Ferrari, Andrea C.; Prezhdo, Oleg V., 2026, "Replication Data for: Ultrafast Electronic Relaxation Dynamics of Atomically Thin MoS2 Is Accelerated by Wrinkling", https://doi.org/10.21979/N9/8PBMPA, DR-NTU (Data), V1
Replication Data for: Ultrafast Electronic Relaxation Dynamics of Atomically Thin MoS2 Is Accelerated by Wrinkling
Replication Data for: Ultrafast Charge Transfer and Recombination Dynamics in Monolayer–Multilayer WSe2 Junctions Revealed by Time-Resolved Photoemission Electron Microscopy
Jan 2, 2026 - LOH Zhi Heng
Loh, Zhi Heng; Xu, Ce; Barden, Natalie; Alexeev, Evgeny M.; Wang, Xiaoli; Long, Run; Cadore, Alisson R.; Paradisanos, Ioannis; Ott, Anna K.; Soavi, Giancarlo; Tongay, Sefaattin; Cerullo, Giulio; Ferrari, Andrea C.; Prezhdo, Oleg V., 2026, "Replication Data for: Ultrafast Charge Transfer and Recombination Dynamics in Monolayer–Multilayer WSe2 Junctions Revealed by Time-Resolved Photoemission Electron Microscopy", https://doi.org/10.21979/N9/PDTBBV, DR-NTU (Data), V1
Replication Data for: Ultrafast Charge Transfer and Recombination Dynamics in Monolayer–Multilayer WSe2 Junctions Revealed by Time-Resolved Photoemission Electron Microscopy
Computational Data of Wash-Free Bioimaging Review
Dec 19, 2025 - Xiaogang LIU
Liu, Xiaogang, 2025, "Computational Data of Wash-Free Bioimaging Review", https://doi.org/10.21979/N9/UNEWBM, DR-NTU (Data), V1
The electronic structures and excited-state properties were derived using computational data obtained from density functional theory (DFT) and time-dependent density functional theory (TD-DFT) calculations. All computational studies were conducted using Gaussian 16 software. The...
Replication Data for: Inline Quantum Measurements with SNSPDs Coupled to Photonic Bound States
Replication Data for: Inline Quantum Measurements with SNSPDs Coupled to Photonic Bound States
Dec 17, 2025 - MARTINELLI Filippo
Martinelli, Filippo, 2025, "Replication Data for: Inline Quantum Measurements with SNSPDs Coupled to Photonic Bound States", https://doi.org/10.21979/N9/7CDIWX, DR-NTU (Data), V1, UNF:6:WcE+2Z1SMMsP5+0xE0H/UQ== [fileUNF]
Data for the paper "Inline Quantum Measurements with SNSPDs Coupled to Photonic Bound States"
Related data for: Global fall and rise of digital learning inequality
Dec 17, 2025 - NIE Data Repository (Harvested)
Ali, Farhan, 2025, "Related data for: Global fall and rise of digital learning inequality", https://doi.org/10.25340/R4/CRGJLE
Related data for: Global fall and rise of digital learning inequality
This Dataset is harvested from our partners. Clicking the link will take you directly to the archival source of the data.
Related Data for: Learning data science in elementary school mathematics: A comparative curriculum analysis
Dec 17, 2025 - NIE Data Repository (Harvested)
Ow-Yeong, Yook Kit; Yeter, Ibrahim H.; Farhan Ali, 2025, "Related Data for: Learning data science in elementary school mathematics: A comparative curriculum analysis", https://doi.org/10.25340/R4/YVQMVF
Background Data literacy is increasingly important in today’s data-driven world. Students across many educational systems first formally learn about data in elementary school not as a separate subject but via the mathematics curriculum. This experience can create tensions in the...
This Dataset is harvested from our partners. Clicking the link will take you directly to the archival source of the data.
Related Data for: Predicting how well adolescents get along with peers and teachers: A machine learning approach
Dec 17, 2025 - NIE Data Repository (Harvested)
Farhan Ali; Ang, Rebecca P., 2025, "Related Data for: Predicting how well adolescents get along with peers and teachers: A machine learning approach", https://doi.org/10.25340/R4/SA2FV8
How well adolescents get along with others such as peers and teachers is an important aspect of adolescent development. Current research on adolescent relationship with peers and teachers is limited by classical methods that lack explicit test of predictive performance and cannot...
This Dataset is harvested from our partners. Clicking the link will take you directly to the archival source of the data.
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