Metrics
991,317 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

411 to 420 of 3,263 Results
Related Data for: Effects of bevelled nozzles on standoff shocks in supersonic impinging jets
Oct 4, 2024 - LIM Haoxiang Desmond
Lim, Haoxiang Desmond, 2024, "Related Data for: Effects of bevelled nozzles on standoff shocks in supersonic impinging jets", https://doi.org/10.21979/N9/K9WOKD, DR-NTU (Data), V1
Schlieren images of supersonic impinging jets
Efficient Diffusion Model for Image Restoration by Residual Shifting
Oct 4, 2024 - S-Lab for Advanced Intelligence
Yue, Zongsheng; Wang, Jianyi; Loy, Chen Change, 2024, "Efficient Diffusion Model for Image Restoration by Residual Shifting", https://doi.org/10.21979/N9/VYPJ0O, DR-NTU (Data), V1
While diffusion-based image restoration (IR) methods have achieved remarkable success, they are still limited by the low inference speed attributed to the necessity of executing hundreds or even thousands of sampling steps. Existing acceleration sampling techniques, though seekin...
Related Data for: Flow characterization of supersonic jets issuing from double-beveled nozzles
Related Data for: Flow characterization of supersonic jets issuing from double-beveled nozzles
Oct 4, 2024 - LIM Haoxiang Desmond
Lim, Haoxiang Desmond, 2024, "Related Data for: Flow characterization of supersonic jets issuing from double-beveled nozzles", https://doi.org/10.21979/N9/WTNLFH, DR-NTU (Data), V1
Schlieren images of Md=1.45 supersonic jet
Related Data for: Supersonic PIV measurements of over-expanded beveled jets
Oct 4, 2024 - LIM Haoxiang Desmond
Lim, Haoxiang Desmond, 2024, "Related Data for: Supersonic PIV measurements of over-expanded beveled jets", https://doi.org/10.21979/N9/0SIHAA, DR-NTU (Data), V1
PIV data of over-expanded supersonic jet
Related Data for: Development of schlieren-image based flow diagnostics and analysis for supersonic jet flows
Related Data for: Development of schlieren-image based flow diagnostics and analysis for supersonic jet flows
Oct 4, 2024 - LIM Haoxiang Desmond
Lim, Haoxiang Desmond, 2024, "Related Data for: Development of schlieren-image based flow diagnostics and analysis for supersonic jet flows", https://doi.org/10.21979/N9/XBIH1S, DR-NTU (Data), V1
Time-resolved schlieren images of Md=1.45 supersonic jets. The perfectly expanded pressure condition is NPR=3.4.
Related Data for: Development of schlieren-image based flow diagnostics and analysis for supersonic jet flows
Related Data for: Development of schlieren-image based flow diagnostics and analysis for supersonic jet flows
Oct 4, 2024 - LIM Haoxiang Desmond
Lim, Haoxiang Desmond, 2024, "Related Data for: Development of schlieren-image based flow diagnostics and analysis for supersonic jet flows", https://doi.org/10.21979/N9/0J3RVA, DR-NTU (Data), V1
TRPIV of indeterminate-origin (IO) jets at Re=5000, performed in SJTU.
LIM Haoxiang Desmond(Nanyang Technological University)
Oct 4, 2024School of Mechanical and Aerospace Engineering (MAE)
Related data for: Can machine learning help accelerate article screening for systematic reviews? Yes, when article separability in embedding space is high
Oct 4, 2024 - NIE Data Repository (Harvested)
Ali, Farhan, 2025, "Related data for: Can machine learning help accelerate article screening for systematic reviews? Yes, when article separability in embedding space is high", https://doi.org/10.25340/R4/VB85L1
Dataset and Python code for "Can machine learning help accelerate article screening for systematic reviews? Yes, when article separability in embedding space is high"
This Dataset is harvested from our partners. Clicking the link will take you directly to the archival source of the data.
Related Data for: Development of high order WENO schemes for large-eddy simulation of compressible flows in OpenFOAM
Oct 3, 2024 - U S VEVEK
Vevek, U S, 2024, "Related Data for: Development of high order WENO schemes for large-eddy simulation of compressible flows in OpenFOAM", https://doi.org/10.21979/N9/TKYQI6, DR-NTU (Data), V1
Raw LES data for NPR=4 supersonic jet from circular nozzle.
U S VEVEK(Nanyang Technological University)
Oct 3, 2024School of Mechanical and Aerospace Engineering (MAE)
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.