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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.

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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 76,896 Results
Tabular Data - 15.9 KB - 8 Variables, 245 Observations - UNF:6:JGbH9j8s+4DzIbEvjtEIxw==
Data
Data containing individual participants' damage and recovery estimates.
R Syntax - 22.4 KB - MD5: f16231e8e4a1614c5a82b9f310aae1e6
Code
Used to pre-process the raw (not anonymized - unavailable on repo due to confidentiality) data to sift out bad data.
R Syntax - 25.5 KB - MD5: 29c564400bc13c2dfae77b1a2f7c7b2f
Code
Redundant.
Jun 23, 2026 - Andrea VEROLINO
Verolino, Andrea, 2026, "Replication Data for: A Semi-automated Framework for Global Detection of Previously Undocumented Submarine Calderas", https://doi.org/10.21979/N9/FLUTSL, DR-NTU (Data), V1, UNF:6:EAzbh2xcpwfeBnqGcBldVA== [fileUNF]
Here I provide the output original and filtered datasets from the Crater Detection Algorithm (CDA) we used to find submarine calderas globally. The original CDA used by Lee and Hogan (2021) only detects depressions, regardless of whether they are at the top of a mound or on a fla...
Tabular Data - 104.2 KB - 22 Variables, 514 Observations - UNF:6:b+JtDqGhWenSRPyv3RNxtQ==
Tabular Data - 8.2 MB - 15 Variables, 55818 Observations - UNF:6:ktNeLO53/Yf4eSfsKJ3BTg==
Tabular Data - 4.7 MB - 15 Variables, 31617 Observations - UNF:6:nbbUZNOyxOaTL1esMc3hnQ==
Jun 19, 2026 - QIU Lin
Chan, Sarah Hian May; Qiu, Lin; Ky, Phong Mai, 2026, "Vertical Greenery Buffers Against Stress: Evidence from Psychophysiological Responses in Virtual Reality", https://doi.org/10.21979/N9/WZNVEH, DR-NTU (Data), V1
This dataset contains all data related to the project: Vertical Greenery Buffers Against Stress: Evidence from Psychophysiological Responses in Virtual Reality. This research examines the stress-buffering effects of vertical greenery using virtual reality. 111 participants were r...
ZIP Archive - 482.2 MB - MD5: 7176ee401300ed08975855e5d6a7c3a8
ZIP Archive - 174.7 MB - MD5: 6cb12f525c33f02109c81c4893576a69
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