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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341 to 350 of 1,369 Results

grannot_analysis_command_lines.txt Oct 29, 2025 - Output from the analysis described in GrAnnoT paper Plain Text - 8.1 KB - MD5: 6f5be639cf4f13c66772a12e41b711f2 Documentation Contains all the command lines used to produce the files in this dataset. The input data is available at : https://doi.org/10.23708/DO1RTF
liftoff_o157h7_to_s88.gff Oct 29, 2025 - Output from the analysis described in GrAnnoT paper Unknown - 2.9 MB - MD5: 16c7a8c67ec05111e9d9c98e4e4b906a annotation transferred by Liftoff
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. 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 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

grannot_analysis_command_lines.txt

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

Plain Text - 8.1 KB -

Documentation

Contains all the command lines used to produce the files in this dataset. The input data is available at : https://doi.org/10.23708/DO1RTF

liftoff_o157h7_to_s88.gff

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

Unknown - 2.9 MB -

annotation transferred by Liftoff

readme_outputs.txt

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

Plain Text - 4.9 KB -

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 -

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.

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 -

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.