INQUIRE-Search: A Framework for Interactive Discovery in Large-Scale Biodiversity Databases

• URL: http://arxiv.org/abs/2511.15656v1
• Date: Wed, 19 Nov 2025 17:42:35 GMT
• Title: INQUIRE-Search: A Framework for Interactive Discovery in Large-Scale Biodiversity Databases
• Authors: Edward Vendrow, Julia Chae, Rupa Kurinchi-Vendhan, Isaac Eckert, Jazlynn Hall, Marta Jarzyna, Reymond Miyajima, Ruth Oliver, Laura Pollock, Lauren Schrack, Scott Yanco, Oisin Mac Aodha, Sara Beery,
• Abstract summary: INQUIRE-Search is an open-source system that enables scientists to rapidly and interactively search within an ecological image database.<n>We show the diversity of scientific applications that a tool like INQUIRE-Search can support, from seasonal variation across species to forest regrowth after wildfires.
• Score: 19.569666968746166
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large community science platforms such as iNaturalist contain hundreds of millions of biodiversity images that often capture ecological context on behaviors, interactions, phenology, and habitat. Yet most ecological workflows rely on metadata filtering or manual inspection, leaving this secondary information inaccessible at scale. We introduce INQUIRE-Search, an open-source system that enables scientists to rapidly and interactively search within an ecological image database for specific concepts using natural language, verify and export relevant observations, and utilize this discovered data for novel scientific analysis. Compared to traditional methods, INQUIRE-Search takes a fraction of the time, opening up new possibilities for scientific questions that can be explored. Through five case studies, we show the diversity of scientific applications that a tool like INQUIRE-Search can support, from seasonal variation in behavior across species to forest regrowth after wildfires. These examples demonstrate a new paradigm for interactive, efficient, and scalable scientific discovery that can begin to unlock previously inaccessible scientific value in large-scale biodiversity datasets. Finally, we emphasize using such AI-enabled discovery tools for science call for experts to reframe the priorities of the scientific process and develop novel methods for experiment design, data collection, survey effort, and uncertainty analysis.

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