A growing body of information on vector-borne diseases has arisen as increasing research focus has been directed towards the need for anticipating risk, optimizing surveillance, and understanding the fundamental biology of vector-borne diseases to direct efforts to control and mitigation. The scope and scale of this information, in the form of data, comprising database efforts, data storage, and serving approaches, mean that it is distributed across many formats, and data types. Data ranges from collections records to molecular characterization, geospatial data to interactions of vectors and traits, infection experiments to field trials. New initiatives arise, often spanning the effort traditionally siloed in specific research disciplines, and other efforts wane, perhaps in response to funding declines, different research directions, or lack of sustained interest. Thusly, the world of vector data - the Vector Data Ecosystem - can become unclear in scope, and the flows of data through these various efforts can become stymied by obsolescence, or simply by gaps in access and interoperability. As increasing attention is paid to creating FAIR (Findable Accessible Interoperable, and Reusable) data, simply characterizing what is ‘out there’, and how these existing data aggregation and collection efforts interact, or interoperate with each other, is a useful exercise. This study presents a snapshot of current vector data efforts, reporting on level of accessibility, and commenting on interoperability using an illustration to track a specimen through the data ecosystem to understand where it occurs for the database efforts anticipated to describe it (or parts of its extended specimen data).
Entomology, Life Sciences, Research Methods in Life Sciences
data accessibility, databases, ecoinformatics, interoperability, Metadata, vector-borne diseases
Published: 2022-08-05 22:30
CC-BY Attribution-NonCommercial 4.0 International
Conflict of interest statement:
The authors of this paper declare they are funded by the VectorByte Project, and have received previous funding from the VectorBite project. Rund has received funding, receives funding, or is affiliated with VectorMap, VectorBase, VEuPathDB, ClinEpiDB, and the TPT. These projects are described in this manuscript.