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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.

According to the NTU Research Data Policy:

  • The final research data used in establishing and validating research findings must be deposited in the NTU Data Repository or a recognized open access data repository no later than publication of the article.
  • The final research data from projects carried out at NTU shall be made available for sharing unless there are prior formal agreements with external collaborators and parties on non-disclosure or proprietary use of the data.

Deposit and publish data in DR-NTU (Data)

DR-NTU (Data) is open to NTU faculty, research staff and students. We recommend faculty to create your researcher dataverse under your school/institute/research centre dataverse, and deposit your datasets in your own dataverse. For research staff and students, you may want to deposit your datasets in an appropriate dataverse as advised by your School, Supervisors or Principal Investigators.

Prior to deposit, familiarise yourself with DR-NTU (Data) Terms of Use, Restrictions and Licenses, and FAQs. The uploaded content must not infringe upon the copyrights or other intellectual property rights, not violate any laws, not contain software viruses, and must be void of all identifiable information.

To get started, ensure that you have an account, then:

  1. Use the search box below to identify a suitable school/institute/research centre dataverse.
    If you can’t find a suitable school/institute/research centre dataverse, please contact us at Research Data Management team, NTU Library.
  2. For faculty, click 'Add Data' and select 'New Dataverse' to create your researcher/project dataverse if you don't have one yet.
    If you have an existing researcher/project dataverse that is appropriate for your use, you can start adding datasets to it, by clicking ‘Add Data’ and select ‘New Dataset’.

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1 to 10 of 995 Results
Correlation between GNSS-TEC and eruption magnitude supports the use of ionospheric sensing to complement volcanic hazard assessment
Nov 26, 2020 - Manta Fabio
Manta, Fabio; Occhipinti, Giovanni; Hill, Emma; Perttu, Anna; Assink, Jelle; Taisne, Benoit, 2020, "Correlation between GNSS-TEC and eruption magnitude supports the use of ionospheric sensing to complement volcanic hazard assessment", https://doi.org/10.21979/N9/GIP5TJ, DR-NTU (Data), V1
This dataset contains all the outputs obtained during the GNSS data processing aiming to explore the relationship between the power of volcanic explosion and ionospheric TEC perturbation. This dataset also contain the codes used to process the data
Related data for: Rapid identification of tsunamigenic earthquakes using GNSS ionospheric sounding
Nov 26, 2020 - Manta Fabio
Manta, Fabio; Emma Hill; Lujia Feng; Giovanni Occhipinti, 2020, "Related data for: Rapid identification of tsunamigenic earthquakes using GNSS ionospheric sounding", https://doi.org/10.21979/N9/3KUEM5, DR-NTU (Data), V2, UNF:6:jPra5s5ZponYgqUGFALotw== [fileUNF]
This dataset contains all the outputs obtained during the GNSS data processing aiming to explore the signature of tsunamigenic earthquake in the ionospheric TEC. This dataset also contain the codes used to process the data
Replication Data for: Fixed-end slab parametric study and sandwich and steel panels quasi static and impact tests (D7)
Nov 25, 2020 - (Research Project) Numerical Studies to Model Combined Blast and Fragments Loading on Protective Structures
Del Linz, Paolo; Fung, Tat Ching; Lee, Chi King; Yu, Qingjun, 2020, "Replication Data for: Fixed-end slab parametric study and sandwich and steel panels quasi static and impact tests (D7)", https://doi.org/10.21979/N9/BN2RQ1, DR-NTU (Data), V1
LS-DYNA input files for blast FEA comp-2-2mm-1-0-1 (typical case of sandwich steel-concrete panel loaded by cased charge- SPH method as an option)
Replication Data for: Blast tests and FEA calibration (D6)
Nov 25, 2020 - (Research Project) Numerical Studies to Model Combined Blast and Fragments Loading on Protective Structures
Del Linz, Paolo; Fung, Tat Ching; Lee, Chi King, 2020, "Replication Data for: Blast tests and FEA calibration (D6)", https://doi.org/10.21979/N9/HMX8ZP, DR-NTU (Data), V1
LS-DYNA input files for blast FEA (Chapter 3) 8-0-1-bare LS-DYNA input files for blast FEA (Chapter 3) steel2-5-2100
Replication Data for: Impact tests and pre blast tests predictions (D5)
Nov 25, 2020 - (Research Project) Numerical Studies to Model Combined Blast and Fragments Loading on Protective Structures
Del Linz, Paolo; Fung, Tat Ching; Lee, Chi King, 2020, "Replication Data for: Impact tests and pre blast tests predictions (D5)", https://doi.org/10.21979/N9/UEZLQ5, DR-NTU (Data), V1
LS-DYNA input files for impact FEA (Chapter 3) conc-75-5-2000 (4 cases) LS-DYNA input files for impact FEA (Chapter 3) st-str-8-5-5-2000 (5 cases)
Investigating the Structure-Function Relationship in Triple Cation Perovskite Nanocrystals for Light-Emitting Diode Applications
Nov 21, 2020 - Parth Vashishtha
Vashishtha, Parth; Sjoerd A. Veldhuis; Sai S. H. Dintakurti; Nicole L. Kelly; Benjamin E. Griffith; Alasdair A. M. Brown; Mohammed Suhail Ansari; Annalisa Bruno; Nripan Mathews; Yanan Fang; Tim White; Subodh G. Mhaisalkar; John V. Hanna, 2020, "Investigating the Structure-Function Relationship in Triple Cation Perovskite Nanocrystals for Light-Emitting Diode Applications", https://doi.org/10.21979/N9/TUYTCY, DR-NTU (Data), V1, UNF:6:zMIfnfm4By1qd5A5P0BxcA== [fileUNF]
Raw data for "Investigating the Structure-Function Relationship in Triple Cation Perovskite Nanocrystals for Light-Emitting Diode Applications"
Translating Educational Neuroscience to Meet Diverse Needs of Children in Schools: Feasibility and Infrastructure
Nov 20, 2020 - Brain Literacy
Chen, S. H. Annabel; Ng, Betsy; Tan, Seng Chee; Poon, Kin Loong Kenneth; Yeo, Lay See; Aljunied, Mariam; Hale, James Bradford, 2019, "Translating Educational Neuroscience to Meet Diverse Needs of Children in Schools: Feasibility and Infrastructure", https://doi.org/10.21979/N9/SWPBE9, DR-NTU (Data), V2
This project seeks to develop the infrastructure to begin the translation of educational neuroscience into classroom practice. As part of the project, we established the norms of Test of Word Reading Efficiency – Second Edition (TOWRE-2) and Singapore Reading Maze Test (SRMT) as...
Related files for: Halide Perovskite Quantum Dots Photosensitized-Amorphous Oxide Transistors for Multimodal Synapses
Related files for: Halide Perovskite Quantum Dots Photosensitized-Amorphous Oxide Transistors for Multimodal Synapses
Nov 16, 2020 - Subramanian Periyal Srilakshmi
Subramanian Periyal Srilakshmi, 2020, "Related files for: Halide Perovskite Quantum Dots Photosensitized-Amorphous Oxide Transistors for Multimodal Synapses", https://doi.org/10.21979/N9/JFNION, DR-NTU (Data), V4
This dataset contains electrical and materials measurements for the analysis of the journal article titled "Halide Perovskite Quantum Dots Photosensitized-Amorphous Oxide Transistors for Multimodal Synapses"
𝓲-SATA: A MATLAB based toolbox to estimate Current Density generated by Transcranial Direct Current Stimulation in an Individual Brain
𝓲-SATA: A MATLAB based toolbox to estimate Current Density generated by Transcranial Direct Current Stimulation in an Individual Brain
Nov 15, 2020 - CRADLE - Centre for Research and Development in Learning
Kashyap, Rajan; Bhattacharjee, Sagarika; Ramaswamy, Arumugam; Oishi, Kenichi; Desmond, John E; Chen, S. H. Annabel, 2020, "��-SATA: A MATLAB based toolbox to estimate Current Density generated by Transcranial Direct Current Stimulation in an Individual Brain", https://doi.org/10.21979/N9/5W3RIM, DR-NTU (Data), V2
𝓲-SATA: A MATLAB based toolbox to estimate Current Density generated by Transcranial Direct Current Stimulation in an Individual Brain
Related Data for: "A Machine Learning Approach For The Automatic Estimation Of Fixation-Time Data Signals’ Quality"
Nov 14, 2020 - Early Cognition Lab
Gabrieli, Giulio; Zhang, Lijun; Setoh, Peipei, 2020, "Related Data for: "A Machine Learning Approach For The Automatic Estimation Of Fixation-Time Data Signals’ Quality"", https://doi.org/10.21979/N9/0FU9ZG, DR-NTU (Data), V2, UNF:6:kPNKAuB3E6Sk9elIDziVzA== [fileUNF]
Fixation time measures have been widely adopted in infants' and young children's studies, because they can successfully tap on infants' meaningful nonverbal behaviors. While recording preverbal children's behavior is relatively simple, the analysis of collected signals requires e...
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