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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.

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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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1 to 10 of 88,614 Results

Replication Data for: Dynamic water-wave tweezers Aug 21, 2026 - Yijie SHEN Yijie Shen, 2026, "Replication Data for: Dynamic water-wave tweezers", https://doi.org/10.21979/N9/ITFQS8, DR-NTU (Data), V1 Replication Data for: Dynamic water-wave tweezers
Recovering topological information of light by topological learning Aug 21, 2026 - Replication Data for: Recovering topological information of light by topological learning Wang, Benquan, 2026, "Recovering topological information of light by topological learning", https://doi.org/10.21979/N9/4EJCDH, DR-NTU (Data), V1 Repilcation Data for paper: Recovering topological information of light by topological learning
Replication Data for: Deep reinforcement learning for coordinated air-conditioner control in groups of buildings using smart meter data Aug 20, 2026 - Sharath Ram KUMAR Ram Kumar, Sharath, 2026, "Replication Data for: Deep reinforcement learning for coordinated air-conditioner control in groups of buildings using smart meter data", https://doi.org/10.21979/N9/XQRYSK, DR-NTU (Data), V1, UNF:6:yVOI7v3nxbWQJc7oj81xpQ== [fileUNF] This file contains data required to replicate the results in the following publication: https://doi.org/10.1016/j.engappai.2025.113536 To be used alongside the code repo: https://github.com/sharathramkumar/multi-building-rl-eaai-feb-2026
approximate_cons_models_100_3.dat Aug 20, 2026 - Replication Data for: Deep reinforcement learning for coordinated air-conditioner control in groups of buildings using smart meter data Fixed Field Text Data - 123.9 MB - MD5: 7cd2b57371973cec17482efe4baa38af Approximate consumer models using simulation data
readme.md Aug 20, 2026 - Replication Data for: Deep reinforcement learning for coordinated air-conditioner control in groups of buildings using smart meter data Markdown Text - 738 B - MD5: 99dac3f6c8b4464ca698e66431bfa302 Readme
real_consumer_models_100.dat Aug 20, 2026 - Replication Data for: Deep reinforcement learning for coordinated air-conditioner control in groups of buildings using smart meter data Fixed Field Text Data - 310.9 MB - MD5: 82fd6c3d135cd8fc988d120f3d9dfa80 Actual models used for evaluation
real_consumer_models_sarran_override_model.dat Aug 20, 2026 - Replication Data for: Deep reinforcement learning for coordinated air-conditioner control in groups of buildings using smart meter data Fixed Field Text Data - 78.2 MB - MD5: 02ceb3e8dd7e1778575a6ce9f5e2f2c9 Actual models with override behaviour based on Sarran et al 2024
real_consumer_models_stochastic_override_model.dat Aug 20, 2026 - Replication Data for: Deep reinforcement learning for coordinated air-conditioner control in groups of buildings using smart meter data Fixed Field Text Data - 310.9 MB - MD5: 83fc5920cc453ffa08e19e41dc2ec15b Actual models with stochastic override behaviour
SGP_Singapore.486980_IWEC.tab Aug 20, 2026 - Replication Data for: Deep reinforcement learning for coordinated air-conditioner control in groups of buildings using smart meter data Tabular Data - 7.7 MB - 32 Variables, 8760 Observations - UNF:6:yVOI7v3nxbWQJc7oj81xpQ== Weather data for Singapore
Resolution and robustness bounds for reconstructive spectrometers Aug 17, 2026 - ZHU Changyan Zhu, Changyan, 2026, "Resolution and robustness bounds for reconstructive spectrometers", https://doi.org/10.21979/N9/V6JUBY, DR-NTU (Data), V1 Reconstructive spectrometers are a promising emerging class of devices that combine complex light scattering with inference to enable compact, high-resolution spectrometry. Thus far, the physical determinants of these devices' performance remain under-explored. We show that under...

Replication Data for: Dynamic water-wave tweezers

Aug 21, 2026 - Yijie SHEN

Yijie Shen, 2026, "Replication Data for: Dynamic water-wave tweezers", https://doi.org/10.21979/N9/ITFQS8, DR-NTU (Data), V1

Replication Data for: Dynamic water-wave tweezers

Recovering topological information of light by topological learning

Aug 21, 2026 - Replication Data for: Recovering topological information of light by topological learning

Wang, Benquan, 2026, "Recovering topological information of light by topological learning", https://doi.org/10.21979/N9/4EJCDH, DR-NTU (Data), V1

Repilcation Data for paper: Recovering topological information of light by topological learning

Replication Data for: Deep reinforcement learning for coordinated air-conditioner control in groups of buildings using smart meter data

Aug 20, 2026 - Sharath Ram KUMAR

Ram Kumar, Sharath, 2026, "Replication Data for: Deep reinforcement learning for coordinated air-conditioner control in groups of buildings using smart meter data", https://doi.org/10.21979/N9/XQRYSK, DR-NTU (Data), V1, UNF:6:yVOI7v3nxbWQJc7oj81xpQ== [fileUNF]

This file contains data required to replicate the results in the following publication: https://doi.org/10.1016/j.engappai.2025.113536 To be used alongside the code repo: https://github.com/sharathramkumar/multi-building-rl-eaai-feb-2026

approximate_cons_models_100_3.dat

Aug 20, 2026 - Replication Data for: Deep reinforcement learning for coordinated air-conditioner control in groups of buildings using smart meter data

Fixed Field Text Data - 123.9 MB -

Approximate consumer models using simulation data

readme.md

Aug 20, 2026 - Replication Data for: Deep reinforcement learning for coordinated air-conditioner control in groups of buildings using smart meter data

Markdown Text - 738 B -

Readme

real_consumer_models_100.dat

Aug 20, 2026 - Replication Data for: Deep reinforcement learning for coordinated air-conditioner control in groups of buildings using smart meter data

Fixed Field Text Data - 310.9 MB -

Actual models used for evaluation

real_consumer_models_sarran_override_model.dat

Aug 20, 2026 - Replication Data for: Deep reinforcement learning for coordinated air-conditioner control in groups of buildings using smart meter data

Fixed Field Text Data - 78.2 MB -

Actual models with override behaviour based on Sarran et al 2024

real_consumer_models_stochastic_override_model.dat

Aug 20, 2026 - Replication Data for: Deep reinforcement learning for coordinated air-conditioner control in groups of buildings using smart meter data

Fixed Field Text Data - 310.9 MB -

Actual models with stochastic override behaviour

SGP_Singapore.486980_IWEC.tab

Aug 20, 2026 - Replication Data for: Deep reinforcement learning for coordinated air-conditioner control in groups of buildings using smart meter data

Tabular Data - 7.7 MB - 32 Variables, 8760 Observations -

Weather data for Singapore

Resolution and robustness bounds for reconstructive spectrometers

Aug 17, 2026 - ZHU Changyan

Zhu, Changyan, 2026, "Resolution and robustness bounds for reconstructive spectrometers", https://doi.org/10.21979/N9/V6JUBY, DR-NTU (Data), V1

Reconstructive spectrometers are a promising emerging class of devices that combine complex light scattering with inference to enable compact, high-resolution spectrometry. Thus far, the physical determinants of these devices' performance remain under-explored. We show that under...

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