Community needs for FAIR pathogen data

Area of Science:

• Public Health
• Infectious Disease Research
• Data Science

Background:

• Infectious disease datasets are crucial for public health but face data sharing and integration barriers.
• Achieving Findable, Accessible, Interoperable, and Reusable (FAIR) data is vital for scientific discovery and pandemic response.
• The specific needs of the global pathogen data community require systematic characterization.

Purpose of the Study:

• To identify infrastructural and educational priorities among infectious disease data stakeholders.
• To guide community-responsive support for data sharing and interoperability initiatives.
• To address the unmet needs of the global pathogen data community.

Main Methods:

• A cross-sectional survey was distributed to experts within the Pathogen Data Network (PDN) and open professional channels.
• 136 responses from researchers, healthcare professionals, bioinformaticians, and educators were analyzed.
• Descriptive analysis identified prioritized barriers, training needs, and preferred support mechanisms.

Main Results:

• Limited funding (74%), data aggregation challenges (68%), and personnel shortages (52%) were primary structural impediments.
• Bioinformatics for infectious disease research (68%) and using the Pathogens Portal (51%) were top training priorities.
• Virtual short courses (68%) and webinars (66%) were preferred training formats.

Conclusions:

• Barriers to FAIR pathogen data are predominantly systemic, not just technological.
• The survey framework and dataset can serve as a reusable template for needs assessment in other regions.
• Aligning support with identified priorities can enhance data interoperability and reuse in infectious disease research.