SHiNeMaS

SHiNeMaS manages and models seed lot histories, phenotyping, environmental conditions, and cultural practices to analyze population-variety relationships and support participatory breeding and research in cultivated plants including bread wheat.

Key Features:

• Centralized data management: Stores comprehensive information about seed lots and their life cycles in a centralized repository.
• Network formalism: Represents relationships between seed lots and population-varieties using network formalism to capture historical trajectories.
• Phenotyping and environmental integration: Integrates phenotypic data with environmental conditions and cultural practices linked to each seed lot.
• Multi-species adaptability: Accommodates data for various cultivated plant species, not limited to bread wheat.
• Implementation technologies: Implemented in Python using the Django framework and PostgreSQL as the DBMS.

Scientific Applications:

• Participatory breeding programs: Supports participatory plant breeding and on-farm experiments by enabling tracking and development of methods for population-varieties.
• On-farm diversity management studies: Enables study of on-farm management of crop diversity and development of population-varieties suited to organic and low-input agriculture.
• Research and development: Supports research by tracking and analyzing seed lot data over time for investigations in plant breeding and agricultural practices.

Methodology:

Uses network formalism to map relationships between seed lots and capture their historical trajectories and life cycles, and is implemented with Python, the Django framework, and PostgreSQL.