BiOS: An open-source framework for the integration of heterogeneous biodiversity data

Resumen

The era of Big Data has reshaped biodiversity and ecological research. Nevertheless, much of the potential held within these data is still constrained by their heterogeneity, database-incompatible schemas, and fragmentation of resources. Whilst standards such as Darwin Core have provided guidelines for standardizing biodiversity data, significant barriers persist in harmonizing heterogeneous biological datasets, including taxonomic mismatches (e.g., discordances in accepted taxa classifications and nomenclature), variability in genetic marker nomenclature (e.g., lack of standardized genetic marker names), inconsistencies in ecological or biological trait definitions (e.g., variation in trait terminology and measurement units), and the absence of a unified coordinate reference system for species distribution information (e.g., use of both WGS84 and UTM). Collectively, these challenges hinder straightforward research workflows, forcing researchers to make compromises that potentially reduce operational efficiency and may introduce biases or distortions in the results. Here, we present the Biodiversity Observatory System (BiOS), a comprehensive, open-source software designed to address these impediments through a modular and community-driven architecture. BiOS departs from monolithic database designs by decoupling the back-end data management from the front-end presentation layer. This separation supports a dual-access model tailored to diverse stakeholder needs. For researchers and developers, the system offers a comprehensive Application Programming Interface that exposes all back-end functionalities, enabling seamless programmatic access, automated data retrieval, and integration with external analytical workflows. At the same time, this software offers a user-friendly web interface designed to support non-technical users in accessing and exploring the data. Beyond access and usability, BiOS follows strict validation rules that ensure consistent data storage (e.g., taxonomic tree construction) and structures inputs using shared schemas and data formats that improve interoperability across biodiversity datasets. In addition, the system includes a synonymy management framework for both species and molecular marker names, helping to resolve nomenclatural redundancies and improve data consistency across sources. In this context, BiOS acts as a relational engine capable of integrating heterogeneous data streams while maintaining compliance with FAIR-oriented design principles. By providing a flexible, interoperable core that supports the ‘seven shortfalls’ framework of biodiversity knowledge, BiOS offers a turnkey solution to overcome data fragmentation and enhance collaborative conservation efforts.