DYNADIV Team

The main focus of DYNADIV Team is
tounderstand how biodiversity of tropical plants is shaped through evolutionary forces resulting from human and environmental impacts.

Its activity is essentially based on population genetics but also integrates a multidisciplinary approach including ecology, botany, molecular phylogenetics and anthropology. It is structured around three axes:

Evolutionary history & domestication;
Diversity & conservation;
Global changes, diversity & adaptation.

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1 to 10 of 137 Results

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
ReadMe_SN2T4A.txt Jun 23, 2025 - VCF files from the study of low recombining genomic regions in pearl millet Plain Text - 2.0 KB - MD5: 8f93a5fcb1e298749d0b547f19f85602 Documentation Contains citation, description and conditions of use of dataset data
Autof-Pod103sr8_scaffolds_assembly.fasta.gz Jun 23, 2025 - VCF files from the study of low recombining genomic regions in pearl millet Gzip Archive - 541.6 MB - MD5: c4ae489835bf07b1f4868ccdac0d6cb0 Données Scaffolds of Autof-Pod103sr8

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.