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.
Featured Dataverses

In order to use this feature you must have at least one published or linked 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

181 to 190 of 1,207 Results
readme_outputs.txt
Oct 29, 2025 - Output from the analysis described in GrAnnoT paper
Plain Text - 4.9 KB - MD5: 55ecaf7ec9b62c3778d5866dfde2d81b
Documentation
Contains a description of all folders in this dataset.
S88_fa4fe08.gff
Oct 29, 2025 - Output from the analysis described in GrAnnoT paper
Unknown - 1.8 MB - MD5: 7ecf8459a73585ee129b054d09b00c20
annotation transferred by GrAnnoT
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.
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.
Sep 17, 2025 - DYNADIV Team
Kaczmarek, Thomas; Faye, Adama; Ilmi Ahmed, Aicha; Veltman, Margaretha; Thuillet, Anne-Celine; Scarcelli, Nora; Sultan, Benjamin; Vigouroux, Yves; Cubry, Philippe, 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.", https://doi.org/10.23708/M9YGDC, DataSuds, V1, UNF:6:EdX+/x3auhCXDY0Q7OgZ4g== [fileUNF]
The ability of species and populations to adapt to their environment faces increasing challenges as climate change accelerates. Recent methods based on genomic offset (GO) statistics aim to quantify the risk of non-adaptation of populations to future climates. While several studi...
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
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
Development of new microsatellites markers for Cola acuminata (Malvaceae), a socio-economically important fruit tree species in Central Africa
Development of new microsatellites markers for Cola acuminata (Malvaceae), a socio-economically important fruit tree species in Central Africa
Aug 27, 2025 - DYNADIV Team
Duminil, Jerome; Dupiol, Pablo; Chakocha Ngandjui, Armel Franklin; Tientcheu, Avana Marie Louise; Mariac, Cedric; Charahabil, Mohamed Mahamoud; Barnaud, Adeline, 2025, "Development of new microsatellites markers for Cola acuminata (Malvaceae), a socio-economically important fruit tree species in Central Africa", https://doi.org/10.23708/6FCSLF, DataSuds, V1, UNF:6:JjyY2CKXXeBPZvLKRN5hnA== [fileUNF]
Data from the publication "Development of new microsatellites markers for Cola acuminata (Malvaceae), a socio-economically important fruit tree species in Central Africa". Manuscript PJS_2_147801 Plant Ecology and Evolution. We developed a new set of nuclear microsatellite marker...
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
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.