DS equips CGIAR data managers on EBS through Train-the-Trainer

From September 29 to October 17, Digital Solutions delivered a comprehensive Train-the-Trainers program on the Enterprise Breeding System, equipping data managers across CGIAR centers with advanced EBS skills. Through hands-on activities and expert virtual coaching, participants gained the capability to train end-users and drive data-driven, digitally enabled breeding improvements in their programs.

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Kenya Strengthens Rice Research Capacity Through Training on Early Generation Seed Production and Digital Breeding Tools

IRRI’s participation in KALRO’s capacity-building program strengthened Kenya’s national rice research system by advancing technical and digital competencies. Through hands-on training in EBS, Bioflow, and modern breeding practices, the initiative empowered scientists, enhanced institutional collaboration, and reinforced the commitment to accelerating digitally enabled, efficient, and high-impact rice research across the country.

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CIP Potato and Sweet Potato Breeding Joins EBS: A Milestone in CGIAR’s Digital Transformation

The International Potato Center (CIP) is taking a major step in CGIAR’s digital transformation by integrating its potato and sweet potato breeding data into the Enterprise Breeding System (EBS). As the first Roots, Tubers, and Bananas (RTB) crops fully managed within EBS, this milestone advances networked, data-driven breeding under CGIAR’s Breeding for Tomorrow vision. Through this integration, CIP enhances efficiency, transparency, and data stewardship, paving the way for faster development of climate-resilient, farmer- and consumer-preferred varieties while reinforcing CGIAR’s unified, secure, and sustainable breeding data ecosystem.

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Latest EBS version features enhanced integration and flexibility

By: Stephanie Manrilla
24 July, 2025

The Enterprise Breeding System (EBS) just released its second quarter version for 2025.

Here are the highlights:

⚡Dynamic landing page of the Molecular Data Analysis tool, providing only the most relevant information based on a user’s input,

🔎QTL profiling workflow support to fast-track identification of desired alleles,

📈Store and analyze time-series data using integrated tools,

🔁Regenerate randomization to update parameter set details,

đŸŒ±Specify number of samples needed for selected plots for genotyping, and

📍Provide geospatial coordinates to accurately locate planting sites.

Read about the rest of the new features, improvements, and fixes here.