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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.

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.

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55,641 to 55,650 of 72,651 Results
TOWRE.zip
Nov 20, 2020 - Translating Educational Neuroscience to Meet Diverse Needs of Children in Schools: Feasibility and Infrastructure
ZIP Archive - 235.1 KB - MD5: e75c80a9c55f97e610a783a037123f75
DataDocumentation
TOWRE data file and readme file
Related data for: Halide Perovskite Quantum Dots Photosensitized-Amorphous Oxide Transistors for Multimodal Synapses
Related data for: Halide Perovskite Quantum Dots Photosensitized-Amorphous Oxide Transistors for Multimodal Synapses
Nov 16, 2020 - Subramanian Periyal Srilakshmi
Subramanian Periyal Srilakshmi, 2020, "Related data 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, "𝓲-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
ISATA manual_updated.pdf
ISATA manual_updated.pdf
Nov 15, 2020 - 𝓲-SATA: A MATLAB based toolbox to estimate Current Density generated by Transcranial Direct Current Stimulation in an Individual Brain
Adobe PDF - 1.1 MB - MD5: 133601aa2adccc299304061686baaafc
Step by step procedure to rn i-SATA
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...
db.tab
Nov 14, 2020 - Related Data for: A Machine Learning Approach For The Automatic Estimation Of Fixation-Time Data Signals’ Quality
Tabular Data - 28.3 KB - 38 Variables, 112 Observations - UNF:6:Ilgsr+/po2mt0okcjiWeRQ==
Data
Database used for this study.
kNN.joblib
Nov 14, 2020 - Related Data for: A Machine Learning Approach For The Automatic Estimation Of Fixation-Time Data Signals’ Quality
Unknown - 603.8 KB - MD5: a24fc06c9c4a792d684b960a490d3615
Models
Dump of the trained classifier
LinearSVC.joblib
Nov 14, 2020 - Related Data for: A Machine Learning Approach For The Automatic Estimation Of Fixation-Time Data Signals’ Quality
Unknown - 80.1 KB - MD5: 279b1cc95325a44a08d61a20f5ffd7c7
Models
Dump of the trained classifier
ml.py
Nov 14, 2020 - Related Data for: A Machine Learning Approach For The Automatic Estimation Of Fixation-Time Data Signals’ Quality
Python Source Code - 11.7 KB - MD5: 5e56391333eb91d9a1579c89fe05c523
Code
Script used to train and test the classifiers on NTU's HPC Gekko cluster
NonLinearSVC.joblib
Nov 14, 2020 - Related Data for: A Machine Learning Approach For The Automatic Estimation Of Fixation-Time Data Signals’ Quality
Unknown - 1.0 MB - MD5: 32bad55a09af3c19e3a62f1d85828ad7
Models
Dump of the trained classifier
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