DR-NTU (Data)

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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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NIE Data Repository (Harvested)

Asian School of the Environment (ASE)

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2,411 to 2,420 of 3,297 Results

Scott Rice(Nanyang Technological University) Aug 18, 2020Singapore Centre for Environmental Life Sciences Engineering (SCELSE) Research topics: • Biofilm development processes • Mixed species biofilm communities
Fully automatic CityGML-compliant city model derivation on demand(Nanyang Technological University) Aug 13, 2020Marius Erdt This dataverse hosts the data for the NRF funded project Fully automatic CityGML-compliant city model derivation on demand.
Zhao-Xun LIANG(Nanyang Technological University) Aug 12, 2020School of Biological Sciences (SBS) Research topics: • Molecular mechanism underlying bacterial pathogenesis and antibiotic resistance • Genomics-guided discovery of microbial natural products
Source code to generate LoD1 CityGML models from city area maps Aug 12, 2020 - Fully automatic CityGML-compliant city model derivation on demand Erdt, Marius; Zhang, Xingzi; Johan, Henry, 2020, "Source code to generate LoD1 CityGML models from city area maps", https://doi.org/10.21979/N9/FBWTIC, DR-NTU (Data), V2, UNF:6:zN4RTInDz93o+Dgg2Xk9oA== [fileUNF] Application source code to generate level of detail 1 models in CityGML format from an input image. In particular, the image has to be a city area map showing ground plans of buildings.
Source code and sample input CityGML models for automatic derivation of lower levels of detail Aug 11, 2020 - Fully automatic CityGML-compliant city model derivation on demand Erdt, Marius; Zhang, Xingzi; Johan, Henry, 2020, "Source code and sample input CityGML models for automatic derivation of lower levels of detail", https://doi.org/10.21979/N9/9IIGWX, DR-NTU (Data), V1 This dataset contains the source code for automatically creating lower levels of detail from a given CityGML building model. E.g. a level of detail 3 model can be reduced to level of detail 0, 1, or 2. Exemplary sample models are included as well.
Source code to generate a training dataset for text recognition of Singapore's city area names and block numbers Aug 11, 2020 - Fully automatic CityGML-compliant city model derivation on demand Erdt, Marius; Zhang, Xingzi; Johan, Henry, 2020, "Source code to generate a training dataset for text recognition of Singapore's city area names and block numbers", https://doi.org/10.21979/N9/5LJ3YC, DR-NTU (Data), V1 Source code for training a deep learning model to recognise hand written text in an image. In particular, the model recognises Singapore's city area names as well as HDB block numbers.
Replication Data for: Forming-Less Compliance-Free Multi-State Memristors as Synaptic Connections for Brain-Inspired Computing Aug 6, 2020 - Ng Si En, Timothy Ng, Timothy Si En, 2020, "Replication Data for: Forming-Less Compliance-Free Multi-State Memristors as Synaptic Connections for Brain-Inspired Computing", https://doi.org/10.21979/N9/YWTJBM, DR-NTU (Data), V3, UNF:6:RrJmBWPTzVxOHhkjk7DChA== [fileUNF] This dataset contains electrical and materials measurements for the analysis of the paper "Forming-Less Compliance-Free Multi-State Memristors as Synaptic Connections for Brain-Inspired Computing"
Related Data for: Machine Learning Estimation of users' Implicit and Explicit Aesthetic Judgments of Web-Pages Aug 6, 2020 - Social and Affective Neuroscience Esposito, Gianluca; Gabrieli, Giulio, 2019, "Related Data for: Machine Learning Estimation of users' Implicit and Explicit Aesthetic Judgments of Web-Pages", https://doi.org/10.21979/N9/YCDXNE, DR-NTU (Data), V2, UNF:6:OXjgBp2o1P5HHQM/zPdxYA== [fileUNF] The aesthetic appearance of websites can influence the perception of their usability, reliability, and trustworthiness. Several studies investigated the relationship between single aesthetic features and explicit aesthetic judgments, demonstrating the existence of attribution bia...
Subramanian Periyal Srilakshmi(Nanyang Technological University) Aug 6, 2020School of Materials Science and Engineering (MSE)
Hadke Shreyash Sudhakar(Nanyang Technological University) Aug 4, 2020School of Materials Science and Engineering (MSE)

Scott Rice(Nanyang Technological University)

Aug 18, 2020Singapore Centre for Environmental Life Sciences Engineering (SCELSE)

Research topics: • Biofilm development processes • Mixed species biofilm communities

Fully automatic CityGML-compliant city model derivation on demand(Nanyang Technological University)

Aug 13, 2020Marius Erdt

This dataverse hosts the data for the NRF funded project Fully automatic CityGML-compliant city model derivation on demand.

Zhao-Xun LIANG(Nanyang Technological University)

Aug 12, 2020School of Biological Sciences (SBS)

Research topics: • Molecular mechanism underlying bacterial pathogenesis and antibiotic resistance • Genomics-guided discovery of microbial natural products

Source code to generate LoD1 CityGML models from city area maps

Aug 12, 2020 - Fully automatic CityGML-compliant city model derivation on demand

Erdt, Marius; Zhang, Xingzi; Johan, Henry, 2020, "Source code to generate LoD1 CityGML models from city area maps", https://doi.org/10.21979/N9/FBWTIC, DR-NTU (Data), V2, UNF:6:zN4RTInDz93o+Dgg2Xk9oA== [fileUNF]

Application source code to generate level of detail 1 models in CityGML format from an input image. In particular, the image has to be a city area map showing ground plans of buildings.

Source code and sample input CityGML models for automatic derivation of lower levels of detail

Aug 11, 2020 - Fully automatic CityGML-compliant city model derivation on demand

Erdt, Marius; Zhang, Xingzi; Johan, Henry, 2020, "Source code and sample input CityGML models for automatic derivation of lower levels of detail", https://doi.org/10.21979/N9/9IIGWX, DR-NTU (Data), V1

This dataset contains the source code for automatically creating lower levels of detail from a given CityGML building model. E.g. a level of detail 3 model can be reduced to level of detail 0, 1, or 2. Exemplary sample models are included as well.

Source code to generate a training dataset for text recognition of Singapore's city area names and block numbers

Aug 11, 2020 - Fully automatic CityGML-compliant city model derivation on demand

Erdt, Marius; Zhang, Xingzi; Johan, Henry, 2020, "Source code to generate a training dataset for text recognition of Singapore's city area names and block numbers", https://doi.org/10.21979/N9/5LJ3YC, DR-NTU (Data), V1

Source code for training a deep learning model to recognise hand written text in an image. In particular, the model recognises Singapore's city area names as well as HDB block numbers.

Replication Data for: Forming-Less Compliance-Free Multi-State Memristors as Synaptic Connections for Brain-Inspired Computing

Aug 6, 2020 - Ng Si En, Timothy

Ng, Timothy Si En, 2020, "Replication Data for: Forming-Less Compliance-Free Multi-State Memristors as Synaptic Connections for Brain-Inspired Computing", https://doi.org/10.21979/N9/YWTJBM, DR-NTU (Data), V3, UNF:6:RrJmBWPTzVxOHhkjk7DChA== [fileUNF]

This dataset contains electrical and materials measurements for the analysis of the paper "Forming-Less Compliance-Free Multi-State Memristors as Synaptic Connections for Brain-Inspired Computing"

Related Data for: Machine Learning Estimation of users' Implicit and Explicit Aesthetic Judgments of Web-Pages

Aug 6, 2020 - Social and Affective Neuroscience

Esposito, Gianluca; Gabrieli, Giulio, 2019, "Related Data for: Machine Learning Estimation of users' Implicit and Explicit Aesthetic Judgments of Web-Pages", https://doi.org/10.21979/N9/YCDXNE, DR-NTU (Data), V2, UNF:6:OXjgBp2o1P5HHQM/zPdxYA== [fileUNF]

The aesthetic appearance of websites can influence the perception of their usability, reliability, and trustworthiness. Several studies investigated the relationship between single aesthetic features and explicit aesthetic judgments, demonstrating the existence of attribution bia...

Subramanian Periyal Srilakshmi(Nanyang Technological University)

Aug 6, 2020School of Materials Science and Engineering (MSE)

Hadke Shreyash Sudhakar(Nanyang Technological University)

Aug 4, 2020School of Materials Science and Engineering (MSE)

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