UMR DIADE

DIversity - Adaptation - plant DEvelopment

The DIADE Research Unit aims to understand the diversification of tropical plants, one of the main original reservoirs of biodiversity, and for which conservation, management and exploitation are an important issue for Sustainable Development.

The nine teams forming the Unit belong to IRD, University of Montpellier, CIRAD, and CNRS.

CERES Team

DYNADIV Team

PANEEC Team

EVOGEC Team

ADvENS Team

AgroLD

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121 to 130 of 1,097 Results

AzucenaRS1_filter90.gff Oct 29, 2025 - Output from the analysis described in GrAnnoT paper Unknown - 113.7 MB - MD5: 849a84800d8a3cea65e146e8ecf4eda5 Annotation transfer on AzucenaRS1 with coverage and sequence identity filters at 90%
AzucenaRS1_filter95.gff Oct 29, 2025 - Output from the analysis described in GrAnnoT paper Unknown - 111.8 MB - MD5: 7224ffc601ff9b6dfd015b5bd77eb1e9 Annotation transfer on AzucenaRS1 with coverage and sequence identity filters at 95%
climateMetrics_EWEMBI_liebmann_west_africa_1979-2013.nc Sep 17, 2025 - Testing the impact of environmental variables on the predictive performance of genomic offset statistics: crop-specific metrics computed from the EWEMBI v.1 climate dataset. Network Common Data Form - 123.6 MB - MD5: 69a3987dbf4cc68d86230068ffeed8cc Données A NetCDF file with climate crop-specific metrics stored for each pixel (resolution 0.5°) located within a defined area covering West Africa.
climatemetrics_mil173Landraces_EWEMBI_Liebmann_1979-1990.tab Sep 17, 2025 - Testing the impact of environmental variables on the predictive performance of genomic offset statistics: crop-specific metrics computed from the EWEMBI v.1 climate dataset. Tabular Data - 508.5 KB - 185 Variables, 173 Observations - UNF:6:EdX+/x3auhCXDY0Q7OgZ4g== Données Precipatation, Temperature and radiation crop-specific climate variables computed and extracted from the EWEMBI (V1) dataset, for 173 populations of pearl millet.
Dictionary_cropmetrics.pdf Sep 17, 2025 - Testing the impact of environmental variables on the predictive performance of genomic offset statistics: crop-specific metrics computed from the EWEMBI v.1 climate dataset. Adobe PDF - 25.0 KB - MD5: baaa5328ffa5ac93176b65b0d2789aa8 Documentation Dictionary of crop climate metrics
extract_pixels_from_netcdf.py Sep 17, 2025 - Testing the impact of environmental variables on the predictive performance of genomic offset statistics: crop-specific metrics computed from the EWEMBI v.1 climate dataset. Python Source Code - 6.7 KB - MD5: bebbb223232f0f478feb8cdcd2685b46 Code A python script that extracts crop-specific metrics from the netCDF file, with a passport file containing geographical coordinates.
ReadMe_M9YGDC.txt Sep 17, 2025 - Testing the impact of environmental variables on the predictive performance of genomic offset statistics: crop-specific metrics computed from the EWEMBI v.1 climate dataset. Plain Text - 1.8 KB - MD5: b4beefe745809f4a1f5ca15b1dba090f Documentation Contains citation, description and conditions of use of dataset data
ReadMe_6FCSLF.txt Aug 27, 2025 - Development of new microsatellites markers for Cola acuminata (Malvaceae), a socio-economically important fruit tree species in Central Africa Plain Text - 1.3 KB - MD5: 74d25aa64ab14373c48e4b570ee3bd57 Documentation Contains citation, description and conditions of use of dataset data
data_dictionnary.tab Aug 27, 2025 - Development of new microsatellites markers for Cola acuminata (Malvaceae), a socio-economically important fruit tree species in Central Africa Tabular Data - 390 B - 2 Variables, 5 Observations - UNF:6:JkE4eofLBJ/AAoTufZI0Nw== Documentation Data dictionnary
Data_file_SSR_ColaAcuminata_Cameroun_Spagedi.tab Aug 27, 2025 - Development of new microsatellites markers for Cola acuminata (Malvaceae), a socio-economically important fruit tree species in Central Africa Tabular Data - 22.0 KB - 19 Variables, 189 Observations - UNF:6:bRR1Skfg9oIKXzD54ax9Gw== Données Data file

AzucenaRS1_filter90.gff

Oct 29, 2025 - Output from the analysis described in GrAnnoT paper

Unknown - 113.7 MB -

Annotation transfer on AzucenaRS1 with coverage and sequence identity filters at 90%

AzucenaRS1_filter95.gff

Oct 29, 2025 - Output from the analysis described in GrAnnoT paper

Unknown - 111.8 MB -

Annotation transfer on AzucenaRS1 with coverage and sequence identity filters at 95%

climateMetrics_EWEMBI_liebmann_west_africa_1979-2013.nc

Sep 17, 2025 - Testing the impact of environmental variables on the predictive performance of genomic offset statistics: crop-specific metrics computed from the EWEMBI v.1 climate dataset.

Network Common Data Form - 123.6 MB -

Données

A NetCDF file with climate crop-specific metrics stored for each pixel (resolution 0.5°) located within a defined area covering West Africa.

climatemetrics_mil173Landraces_EWEMBI_Liebmann_1979-1990.tab

Sep 17, 2025 - Testing the impact of environmental variables on the predictive performance of genomic offset statistics: crop-specific metrics computed from the EWEMBI v.1 climate dataset.

Tabular Data - 508.5 KB - 185 Variables, 173 Observations -

Données

Precipatation, Temperature and radiation crop-specific climate variables computed and extracted from the EWEMBI (V1) dataset, for 173 populations of pearl millet.

Dictionary_cropmetrics.pdf

Sep 17, 2025 - Testing the impact of environmental variables on the predictive performance of genomic offset statistics: crop-specific metrics computed from the EWEMBI v.1 climate dataset.

Adobe PDF - 25.0 KB -

Documentation

Dictionary of crop climate metrics

extract_pixels_from_netcdf.py

Sep 17, 2025 - Testing the impact of environmental variables on the predictive performance of genomic offset statistics: crop-specific metrics computed from the EWEMBI v.1 climate dataset.

Python Source Code - 6.7 KB -

Code

A python script that extracts crop-specific metrics from the netCDF file, with a passport file containing geographical coordinates.

ReadMe_M9YGDC.txt

Sep 17, 2025 - Testing the impact of environmental variables on the predictive performance of genomic offset statistics: crop-specific metrics computed from the EWEMBI v.1 climate dataset.