A Framework for a Standard-Enabled FAIR Data Management Workflow for Synthetic Biology.

Area of Science:

• Synthetic Biology
• Bioinformatics
• Data Management

Background:

• Synthetic biology research generates diverse data (sequences, models, images) often scattered across disparate systems.
• Inconsistent data formats and metadata impede data provenance, reuse, security, compliance, and scalability.
• A critical gap exists in coherently linking data, metadata, and code to ensure findability, accessibility, interoperability, and reusability (FAIR).

Purpose of the Study:

• To address the challenge of fragmented data management in synthetic biology laboratories.
• To propose a framework for an integrated data management workflow that adheres to FAIR principles.
• To provide practical guidance and software solutions for standardizing data generation and improving data traceability.

Main Methods:

• Conducted semi-structured interviews with synthetic biology researchers across the United States.
• Analyzed current data management practices and identified key challenges.
• Developed a framework mapping common data types to community standards and suggested software solutions.

Main Results:

• Identified scattered data and inconsistent metadata as major hurdles in synthetic biology research.
• Proposed a framework for integrated data management, linking data, metadata, and code.
• Offered a catalog of software solutions and adoption guidelines to facilitate FAIR data practices.

Conclusions:

• Implementing the proposed framework can standardize data generation and elevate it to FAIR status.
• Improved data management strengthens traceability for regulatory and defense applications.
• A standardized data foundation supports the development and training of machine learning models in synthetic biology.