Metrics
1,348,022 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 or linked 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

41 to 50 of 94,606 Results
VR_Build_THKphotorealistic.zip
Oct 27, 2026 - Construction of Interactive 3D Models based on Singapore Ancient Chinese Temple Architectural Heritage
ZIP Archive - 516.2 MB - MD5: 25e4c7a60fa010d9e2dae7f858cf654e
VR Build Files for Photorealistic Effect
VR_Build_THKwatercolour.zip
Oct 27, 2026 - Construction of Interactive 3D Models based on Singapore Ancient Chinese Temple Architectural Heritage
ZIP Archive - 500.7 MB - MD5: 5f9b9b7029ad28c891d87bf95115c34a
VR Building Files for Watercolour Effect
Preregistration for: Are We Scrolling into Distress? A Systematic Review of Evidence and Interpretive Bias in Research on Doomscrolling and Mental Health Among AdolescentsEmbargoed
Oct 27, 2026 - Are we scrolling into distress?
Koh, Koon Wee; Goh, Jia Yi; Styles, Suzy J, 2027, "Preregistration for: Are We Scrolling into Distress? A Systematic Review of Evidence and Interpretive Bias in Research on Doomscrolling and Mental Health Among Adolescents", https://doi.org/10.21979/N9/MVEAUB, DR-NTU (Data), V1
In social media, features such as infinite scrolling and autoplay can encourage users to stay on the platforms for longer than intended, contributing to excessive social media scrolling behaviour and interference with other aspects of daily life. Algorithmically curated feeds may...
KohGohStyles_2026_Doomscrolling_DataExtractionSheet.xlsxEmbargoed
Oct 27, 2026 - Preregistration for: Are We Scrolling into Distress? A Systematic Review of Evidence and Interpretive Bias in Research on Doomscrolling and Mental Health Among Adolescents
MS Excel Spreadsheet - 354.1 KB - MD5: 71203d82459d0be4651c868a8351be36
KohGohStyles_2026_Doomscrolling_Prereg.pdfEmbargoed
KohGohStyles_2026_Doomscrolling_Prereg.pdf
Oct 27, 2026 - Preregistration for: Are We Scrolling into Distress? A Systematic Review of Evidence and Interpretive Bias in Research on Doomscrolling and Mental Health Among Adolescents
Adobe PDF - 180.0 KB - MD5: b66d7ddccb7d0a0247947e953205b279
Are we scrolling into distress?(Nanyang Technological University)
Oct 27, 2026Digital World Psychology
This project was initiated as part of an undergraduate FYP at NTU in 2026-7
Digital World Psychology(Nanyang Technological University)
Oct 27, 2026Suzy J. STYLES
This folder houses BLIP Lab projects pertaining to the psychology of the digital world
Replication Data for: Decentralized Reinforcement Learning-Based MAC Optimization for Dense LoRa Networks
Oct 25, 2026 - ZOU Xiangyu
Zou, Xiangyu, 2026, "Replication Data for: Decentralized Reinforcement Learning-Based MAC Optimization for Dense LoRa Networks", https://doi.org/10.21979/N9/PVYJLB, DR-NTU (Data), V1
This dataset contains simulation code, experiment configurations, recorded results and analysis scripts supporting the MSc dissertation “Decentralized Reinforcement Learning-Based MAC Optimization for Dense LoRa Networks” by Xiangyu Zou. The experiments compare tabular Q-learning...
Dissertation_Materials.zip
Oct 25, 2026 - Replication Data for: Decentralized Reinforcement Learning-Based MAC Optimization for Dense LoRa Networks
ZIP Archive - 487.8 MB - MD5: 8236605a9c137a83293500aeb8176276
ZOU Xiangyu(Nanyang Technological University)
Oct 25, 2026School of Electrical and Electronic Engineering (EEE)
Code and simulation data supporting my MSc dissertation on decentralized reinforcement learning-based MAC optimization for dense LoRa networks.
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.