Metrics
1,006,217 Downloads
Contact Share

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.

How to deposit?

Useful links: FAQs | Collection Guidelines | NTU Research Data Management | General terms of use and Privacy policy

Featured Dataverses

In order to use this feature you must have at least one published dataverse.

Publish Dataverse

Are you sure you want to publish your dataverse? Once you do so it must remain published.

Publish Dataverse

This dataverse cannot be published because the dataverse it is in has not been published.

Delete Dataverse

Are you sure you want to delete your dataverse? You cannot undelete this dataverse.

Advanced Search

1 to 10 of 3,266 Results
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...
Xiaogang LIU(Nanyang Technological University)
Xiaogang LIU logo
Dec 19, 2025School of Chemistry, Chemical Engineering and Biotechnology (CCEB)
Research topics: My research explores the intricate structure-property relationships of organic dyes to enhance their performance in diverse applications such as bioimaging, biosensing, therapeutic interventions, and photocatalysis. At the core of our work is computational modeli...
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"
MARTINELLI Filippo(Nanyang Technological University)
Dec 17, 2025Centre for Disruptive Photonic Technologies (CDPT)
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.
Add Data

Log in to create a dataverse or add a dataset.

Log In
Share Dataverse

Share this dataverse on your favorite social media networks.

Link Dataverse
Reset Modifications

Are you sure you want to reset the selected metadata fields? If you do this, any customizations (hidden, required, optional) you have done will no longer appear.