Characterizing the Vector Data Ecosystem

Area of Science:

• Vector-borne disease research
• Data science and informatics
• Ecological and environmental science

Background:

• Increasing research on vector-borne diseases necessitates better data management for risk prediction, surveillance, and control.
• Vector data is diverse, spanning collections, molecular, geospatial, and experimental information, often siloed across disciplines.
• The "Vector Data Ecosystem" faces challenges with scope clarity, data obsolescence, and interoperability gaps.

Purpose of the Study:

• To characterize the current landscape of vector data efforts.
• To assess the accessibility and interoperability of existing vector data resources.
• To provide a foundational understanding for improving FAIR (Findable, Accessible, Interoperable, Reusable) data practices in vector science.

Main Methods:

• A comprehensive review of current vector data aggregation and collection initiatives.
• Analysis of data formats, types, and storage approaches within the Vector Data Ecosystem.
• Illustrative tracking of a specimen through the data ecosystem to identify access and interoperability points.

Main Results:

• A snapshot of the current state of vector data efforts is presented.
• Levels of data accessibility are reported.
• Interoperability challenges and opportunities within the ecosystem are commented upon.

Conclusions:

• Characterizing the Vector Data Ecosystem is essential for improving data flow and access.
• Enhanced understanding of data accessibility and interoperability is key to advancing vector-borne disease research.
• This study serves as a baseline for future efforts to create a more integrated and FAIR vector data landscape.