The "I" in FAIR: Translating from Interoperability in Principle to Interoperation in Practice

TL;DR

This paper introduces Babel and ORION, two tools that address practical challenges in data interoperability by standardizing identifiers and data models, thereby enabling seamless integration of FAIR-compliant resources.

Contribution

The paper presents novel tools Babel and ORION that bridge the gap between FAIR principles and real-world data interoperation.

Findings

Babel creates curated identifier mappings for interoperability.

ORION transforms knowledge bases into a common data model.

A library of interoperable knowledge bases is available for download.

Abstract

The FAIR (Findable, Accessible, Interoperable, and Reusable) data principles [1] promote the interoperability of scientific data by encouraging the use of persistent identifiers, standardized vocabularies, and formal metadata structures. Many resources are created using vocabularies that are FAIR-compliant and well-annotated, yet the collective ecosystem of these resources often fails to interoperate effectively in practice. This continued challenge is mainly due to variation in identifier schemas and data models used in these resources. We have created two tools to bridge the chasm between interoperability in principle and interoperation in practice. Babel solves the problem of multiple identifier schemes by producing a curated set of identifier mappings to create cliques of equivalent identifiers that are exposed through high-performance APIs. ORION solves the problems of multiple data models by ingesting knowledge bases and transforming them into a common, community-managed data model. Here, we describe Babel and ORION and demonstrate their ability to support data interoperation. A library of fully interoperable knowledge bases created through the application of Babel and ORION is available for download and use at https://robokop.renci.org.