Metrics
990,723 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

52,431 to 52,440 of 73,632 Results
Figures-07.pdf
Figures-07.pdf
Apr 3, 2021 - Replication Data for: A flat carborane with multiple aromaticity beyond Wade–Mingos’ rules
Adobe PDF - 68.4 KB - MD5: c1ad64128db1e0cc5bf6ffc5fc4806a9
1H NMR spectrum of 3
Figures-08.pdf
Figures-08.pdf
Apr 3, 2021 - Replication Data for: A flat carborane with multiple aromaticity beyond Wade–Mingos’ rules
Adobe PDF - 63.5 KB - MD5: 6ad04e4ff6ba00e87d49a09d6deaec91
11B NMR spectrum of 3
Figures-09.pdf
Figures-09.pdf
Apr 3, 2021 - Replication Data for: A flat carborane with multiple aromaticity beyond Wade–Mingos’ rules
Adobe PDF - 80.3 KB - MD5: 4d18ce876af82d60b1a788445f27a787
13C NMR spectrum of 3
Figures-10.pdf
Figures-10.pdf
Apr 3, 2021 - Replication Data for: A flat carborane with multiple aromaticity beyond Wade–Mingos’ rules
Adobe PDF - 67.1 KB - MD5: 666194a8e9a660ce110f34e876817b7f
13C NMR DEPT135 spectrum of 3
Figures-11.pdf
Figures-11.pdf
Apr 3, 2021 - Replication Data for: A flat carborane with multiple aromaticity beyond Wade–Mingos’ rules
Adobe PDF - 56.1 KB - MD5: 6c2208c52046f29f25d326299ecdc526
FT-IR of 3
Figures-12.pdf
Figures-12.pdf
Apr 3, 2021 - Replication Data for: A flat carborane with multiple aromaticity beyond Wade–Mingos’ rules
Adobe PDF - 21.6 KB - MD5: 2410e2772f46960baac3a2049d4fd206
CV of 3
Replication Data for: Ollivier Persistent Ricci Curvature-Based Machine Learning for the Protein–Ligand Binding Affinity Prediction
Replication Data for: Ollivier Persistent Ricci Curvature-Based Machine Learning for the Protein–Ligand Binding Affinity Prediction
Apr 2, 2021 - Xia Kelin
Xia, Kelin, 2021, "Replication Data for: Ollivier Persistent Ricci Curvature-Based Machine Learning for the Protein–Ligand Binding Affinity Prediction", https://doi.org/10.21979/N9/JHXLSG, DR-NTU (Data), V1
Efficient molecular featurization is one of the major issues for machine learning models in drug design. Here, we propose a persistent Ricci curvature (PRC), in particular, Ollivier PRC (OPRC), for the molecular featurization and feature engineering, for the first time. The filtr...
build_lig_2007.py
Apr 2, 2021 - Replication Data for: Ollivier Persistent Ricci Curvature-Based Machine Learning for the Protein–Ligand Binding Affinity Prediction
Python Source Code - 4.9 KB - MD5: a7fc5e0fe3e67110c6ca3b80a0a667b4
build_lig_2013.py
Apr 2, 2021 - Replication Data for: Ollivier Persistent Ricci Curvature-Based Machine Learning for the Protein–Ligand Binding Affinity Prediction
Python Source Code - 5.0 KB - MD5: d29767025776d11799e1537143538959
build_lig_2016.py
Apr 2, 2021 - Replication Data for: Ollivier Persistent Ricci Curvature-Based Machine Learning for the Protein–Ligand Binding Affinity Prediction
Python Source Code - 5.7 KB - MD5: 072bdd541bb2abe98dcd7aa52258cb51
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.