INQUIRE-Search: Interactive Discovery in Large-Scale Biodiversity Databases
Edward Vendrow, Julia Chae, Rupa Kurinchi-Vendhan, Isaac Eckert, Jazlynn Hall, Marta Jarzyna, Reymond Miyajima, Ruth Oliver, Laura Pollock, Lauren Shrack, Scott Yanco, Oisin Mac Aodha, Sara Beery

TL;DR
INQUIRE-Search is an open-source system that uses natural language to enable rapid, scalable discovery of complex ecological phenomena in large biodiversity image databases, significantly improving efficiency over manual methods.
Contribution
The paper introduces INQUIRE-Search, a novel AI-enabled tool that allows interactive, natural language-based search and analysis of ecological images at scale.
Findings
Concentrates relevant observations 3-25x more efficiently than manual inspection.
Demonstrates ecological insights such as seasonal behavior variation and post-wildfire forest regrowth.
Shows potential to unlock scientific value in large biodiversity datasets.
Abstract
Many ecological questions center on complex phenomena, such as species interactions, behaviors, phenology, and responses to disturbance, that are inherently difficult to observe and sparsely documented. Community science platforms such as iNaturalist contain hundreds of millions of biodiversity images, which often contain evidence of these complex phenomena. However, current workflows that seek to discover and analyze this evidence often rely on manual inspection, leaving this information largely inaccessible at scale. We introduce INQUIRE-Search, an open-source system that uses natural language to enable scientists to rapidly search within an ecological image database like iNaturalist for specific phenomena, verify and export relevant observations, and use these outputs for downstream scientific analysis. Across five illustrative case studies, INQUIRE-Search concentrates relevant…
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Taxonomy
TopicsSpecies Distribution and Climate Change · Cell Image Analysis Techniques · Research Data Management Practices
