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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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Useful links: FAQs | Collection Guidelines | NTU Research Data Management | General terms of use and Privacy policy

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72,421 to 72,430 of 73,601 Results
glmm-analysis.txt
Nov 10, 2018 - R code for data analysis
Plain Text - 11.4 KB - MD5: c8be94a5d30e3410d38b642447a5e2be
CSV files for GLMM and BRT analysis
Nov 10, 2018 - Drivers of Fire Activity Sumatra
Lee, Ser Huay Janice Teresa, 2018, "CSV files for GLMM and BRT analysis", https://doi.org/10.21979/N9/O48JED, DR-NTU (Data), V1, UNF:6:WPoLaRMWotlbETEKmNo/cg== [fileUNF]
CSV files for conducting GLMM analysis using regencies as units and BRT analysis using pixels (1km) as units.
PixelDF.tab
Nov 10, 2018 - CSV files for GLMM and BRT analysis
Tabular Data - 17.8 MB - 19 Variables, 225710 Observations - UNF:6:RLvhNW+GJpaAtza0dw/PrA==
RegencyDF.tab
Nov 10, 2018 - CSV files for GLMM and BRT analysis
Tabular Data - 8.5 KB - 22 Variables, 40 Observations - UNF:6:3CSK2fZ51Iy0Wezd7b+0Qg==
Spatial layers for pixel analysis
Spatial layers for pixel analysis
Nov 10, 2018 - Drivers of Fire Activity Sumatra
Lee, Ser Huay Janice Teresa, 2018, "Spatial layers for pixel analysis", https://doi.org/10.21979/N9/FLZGE5, DR-NTU (Data), V1
Spatial layers in the form of tif files for pixel analysis. Each file represents a layer for each predictor variable. Details on how each layer was derived can be found in the Supporting Information of the article Sze et al. (accepted).
Brawl.tif
Brawl.tif
Nov 10, 2018 - Spatial layers for pixel analysis
TIFF Image - 287.2 KB - MD5: 27626e699d27d5da7b88d7fdfc0d3116
BurnTrash.tif
BurnTrash.tif
Nov 10, 2018 - Spatial layers for pixel analysis
TIFF Image - 376.5 KB - MD5: 90ebba3e1f27d93bf03a998bb2a2e70d
DegFor.tif
DegFor.tif
Nov 10, 2018 - Spatial layers for pixel analysis
TIFF Image - 318.6 KB - MD5: 38f3f8d05d06ffeffce9330fedd666e1
FirePresence.tif
FirePresence.tif
Nov 10, 2018 - Spatial layers for pixel analysis
TIFF Image - 306.4 KB - MD5: 4d28e3330c6bfffbee09791abecfbcb5
LandConflict.tif
LandConflict.tif
Nov 10, 2018 - Spatial layers for pixel analysis
TIFF Image - 311.4 KB - MD5: b49b80b977acf6033ed6d844e5dd7322
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