DataSuds

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L’entrepôt de données DataSuds propose aux scientifiques de l’IRD et à leurs partenaires un service pour préserver, diffuser et valoriser leurs données de recherche en facilitant leur identification, leur référencement et leur citation. En tant que plateforme de publication de données, il constitue l'un des éléments du dispositif de la Science Ouverte pour le Sud que promeut l’IRD.

Comment publier un jeu de données (dataset) dans DataSuds ?
→ Support utilisateurs : demander l'accès à une collection existante (laboratoire, équipe, projet) ou la création d'une nouvelle collection. → Mode d'emploi

UMI TransVIHMI

UMR CEREGE

UMR ESPACE-DEV

UMR PHIM

UMR DIADE

Base cartographique Sphaera

Partnerships

UMR iEES-Paris

UMR AMAP

UMR HydroSciences Montpellier

UMR Entropie

UMR MIO

UMR MoISA

UMR MIVEGEC

UMR GET

UMI SOURCE

UMR EGCE

UMR Eco&Sols

UMR INTERTRYP

UMR QualiSud

UMR LEMAR

UMR LEDa

UMR PHARMADEV

UMR CBGP

UMR PALOC

UMR MERIT

UMI UMMISCO

UMR SENS

UMR Ceped

UMR IGE

UMR LMV

UMR MARBEC

UMR G-EAU

UMR Geoazur

LMI CARIBACT

Mission pour la Science Ouverte (MSO)

LMI PathoBios

Data collected as part of M-TROPICS based research projects

Rain Cell projects

AMMA-CATCH associated projects

Paddle project

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2,081 to 2,090 of 9,150 Results

High throughput genetic diversity analyses of neglected crops: training data, codes and materials (Africa) May 16, 2024 - DYNADIV Team Barnaud, Adeline; Veltman, Margaretha; Kaczmarek, Thomas; Argout, Xavier; Vigouroux, Yves; Berthouly-Salazar, Cecile; Billot, Claire, 2024, "High throughput genetic diversity analyses of neglected crops: training data, codes and materials (Africa)", https://doi.org/10.23708/ETC16I, DataSuds, V1, UNF:6:ON0wb/ZYjxl4qqjnr8NEPA== [fileUNF] Marginal land is land that is of little agricultural value because crops produced there would be worth less than any rent paid for access to the area. Helping to develop such zones in Africa is a key goal for the EU-funded EWA-BELT project. The aim is to promote the sustainable i...
0_ReadMe.txt May 16, 2024 - High throughput genetic diversity analyses of neglected crops: training data, codes and materials (Africa) Plain Text - 2.1 KB - MD5: 0ad59fd46069aaec2008347fc45ec2ce Documentation Dataset citation, description and terms of use.
chr5_5association.txt May 16, 2024 - High throughput genetic diversity analyses of neglected crops: training data, codes and materials (Africa) Plain Text - 382.2 MB - MD5: 5888c01657ff5624ad88b8200d103076 Data This file contains the data for the GWAS analysis to be used with 'S4_script_GWAS_basic_Rcode.pdf' (Varshney et al. 2017).
GBS-chr2_raw.vcf.gz May 16, 2024 - High throughput genetic diversity analyses of neglected crops: training data, codes and materials (Africa) Gzip Archive - 53.7 MB - MD5: 7ac07ce9f286e974d6c5c572c40f78d8 Data VCF file containing the raw genomic data of pearl millet. This data file is associated with the script "S2_filter_VCF.html".These data are published in Berthouly-Salazar et al. (2016).
Introduction.pdf May 16, 2024 - High throughput genetic diversity analyses of neglected crops: training data, codes and materials (Africa) Adobe PDF - 1.8 MB - MD5: fdde654296e7c56ad70fd3ee0a2ea070 Documentation This file presents the course, its objectives and the context in which it was implemented.
millet_filtered.vcf May 16, 2024 - High throughput genetic diversity analyses of neglected crops: training data, codes and materials (Africa) text/vcard - 34.5 MB - MD5: 6eab73245746d73169f90b8141511fa0 Data VCF file containing the genomic data of the pearl millet. This data file is associated with the script "S3_Genetic_diversity+structure.html".These data are published in Berthouly-Salazar et al. (2016).
millet_metadata.tab May 16, 2024 - High throughput genetic diversity analyses of neglected crops: training data, codes and materials (Africa) Tabular Data - 2.5 KB - 5 Variables, 55 Observations - UNF:6:LNg1C4IBq05VUN9bbZDOeQ== Data Table containing the passport data of the pearl millet accessions in the VCF file. This data file is associated with the script "S3_Genetic_diversity+structure.html". These data are published in Berthouly-Salazar et al. (2016).
S1_Download_R.pdf May 16, 2024 - High throughput genetic diversity analyses of neglected crops: training data, codes and materials (Africa) Adobe PDF - 288.7 KB - MD5: dbbdb9df803c84725df9ff5b4abc42f7 Documentation This file is a tutorial for installing R and RStudio.
S1_example.fastq May 16, 2024 - High throughput genetic diversity analyses of neglected crops: training data, codes and materials (Africa) Unknown - 3.6 KB - MD5: 3bc9d9f537ea4d956c4dc7f7970e7956 Data
S1_example.txt May 16, 2024 - High throughput genetic diversity analyses of neglected crops: training data, codes and materials (Africa) Plain Text - 3.6 KB - MD5: 3bc9d9f537ea4d956c4dc7f7970e7956 Documentation

High throughput genetic diversity analyses of neglected crops: training data, codes and materials (Africa)

May 16, 2024 - DYNADIV Team

Barnaud, Adeline; Veltman, Margaretha; Kaczmarek, Thomas; Argout, Xavier; Vigouroux, Yves; Berthouly-Salazar, Cecile; Billot, Claire, 2024, "High throughput genetic diversity analyses of neglected crops: training data, codes and materials (Africa)", https://doi.org/10.23708/ETC16I, DataSuds, V1, UNF:6:ON0wb/ZYjxl4qqjnr8NEPA== [fileUNF]

Marginal land is land that is of little agricultural value because crops produced there would be worth less than any rent paid for access to the area. Helping to develop such zones in Africa is a key goal for the EU-funded EWA-BELT project. The aim is to promote the sustainable i...

0_ReadMe.txt

May 16, 2024 - High throughput genetic diversity analyses of neglected crops: training data, codes and materials (Africa)

Plain Text - 2.1 KB -

Documentation

Dataset citation, description and terms of use.

chr5_5association.txt

May 16, 2024 - High throughput genetic diversity analyses of neglected crops: training data, codes and materials (Africa)

Plain Text - 382.2 MB -

Data

This file contains the data for the GWAS analysis to be used with 'S4_script_GWAS_basic_Rcode.pdf' (Varshney et al. 2017).

GBS-chr2_raw.vcf.gz

May 16, 2024 - High throughput genetic diversity analyses of neglected crops: training data, codes and materials (Africa)

Gzip Archive - 53.7 MB -

Data

VCF file containing the raw genomic data of pearl millet. This data file is associated with the script "S2_filter_VCF.html".These data are published in Berthouly-Salazar et al. (2016).

Introduction.pdf

May 16, 2024 - High throughput genetic diversity analyses of neglected crops: training data, codes and materials (Africa)

Adobe PDF - 1.8 MB -

Documentation

This file presents the course, its objectives and the context in which it was implemented.

millet_filtered.vcf

May 16, 2024 - High throughput genetic diversity analyses of neglected crops: training data, codes and materials (Africa)

text/vcard - 34.5 MB -

Data

VCF file containing the genomic data of the pearl millet. This data file is associated with the script "S3_Genetic_diversity+structure.html".These data are published in Berthouly-Salazar et al. (2016).

millet_metadata.tab

May 16, 2024 - High throughput genetic diversity analyses of neglected crops: training data, codes and materials (Africa)

Tabular Data - 2.5 KB - 5 Variables, 55 Observations -

Data

Table containing the passport data of the pearl millet accessions in the VCF file. This data file is associated with the script "S3_Genetic_diversity+structure.html". These data are published in Berthouly-Salazar et al. (2016).

S1_Download_R.pdf

May 16, 2024 - High throughput genetic diversity analyses of neglected crops: training data, codes and materials (Africa)

Adobe PDF - 288.7 KB -

Documentation

This file is a tutorial for installing R and RStudio.

S1_example.fastq

May 16, 2024 - High throughput genetic diversity analyses of neglected crops: training data, codes and materials (Africa)

Unknown - 3.6 KB -

Data

S1_example.txt

May 16, 2024 - High throughput genetic diversity analyses of neglected crops: training data, codes and materials (Africa)

Plain Text - 3.6 KB -

Documentation

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