Description:

Artificial intelligence is already harvesting data from agricultural genomics, genetics and breeding databases, but often in inefficient or fragmented ways that impact the resource’s service. Current data structures are designed primarily for human experts or conventional bioinformatics workflows, not for machine learning or large-scale AI models. As a result, valuable data is underutilized or inaccessible to AI systems.

The AgBioData AI-Readiness Working Group (AIR WG) will explore how agricultural data can be organized, formatted, annotated, and packaged to maximize utility for training, validation, and application of AI models. 

 

Main goals:
 

  1. Landscape the AI consumer community (academic, industry, and technology developers) to identify priority data needs and use cases.
  2. Define and promote best practices for AI-ready data formats, metadata, and data packets.
  3. Engage with members of the consumer community to explore service-friendly ways for AI to access GGB data.
  4. Assess AgBioData databases for AI accessibility and recommend improvements.

 

Time a member dedicates to this working group on average (including meetings and assigned tasks):
 

  • 30-minute meetings every 2 weeks;

  • Flexible time to contribute to writing and lesson planning (max. 2 hours per week). 

 

Desired expertise/skills/experience (one or more of the following):

  • Knowledge of AgBioData resource(s) GGB data, metadata and infrastructure;
  • Knowledge of Machine learning/AI methods applied to biological and agricultural data;
  • Experience with stakeholder engagement and landscaping analysis.

 

Benefits of joining this working group:

  • Paper authorship; 

  • Networking; 

  • Professional development.

     

Chair: Sarah Dyer

 

 

Meeting Schedule: TBD

 

 

For more information, please email agbiodata@gmail.com.

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