NFDI4Immuno and AIRR Knowledge Commons: Building Complementary FAIR Infrastructures for Immunological Data

The field of immunology generates diverse and complex datasets that require interoperable and well-structured research data infrastructures. NFDI4Immuno and the AIRR Knowledge Commons (AKC) address this challenge from complementary perspectives, each focusing on standardization and adherence to the FAIR principles. Despite their overlapping interests, these two consortia currently operate with different metadata models. Here, we present our approach for developing alignment strategies between these infrastructures to improve interoperability. NFDI4Immuno, established as part of the German National Research Data Infrastructure (NFDI), is developing a federated repository ecosystem tailored to the broader immunological data landscape. The consortium builds robust metadata models, automated data pipelines, and modular software stacks to support a variety of data types with a substantial initial focus on Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) data and integration into the AIRR Data Commons (ADC). The AIRR Knowledge Commons (AKC), funded by the U.S. National Institute of Allergy and Infectious Diseases (NIAID), focuses specifically on immunolglobulin (Ig) and T cell receptors (TCR) and integrates data from globally recognized, community-driven repositories such as those in the ADC, the Immune Epitope Database (IEDB), the Immune Receptor Antigen Database (IRAD), and VDJbase. AKC brings together information Ig/TCR sequences, gene polymorphisms, and antigen specificities. We will present an analysis of the current metadata models used by both infrastructures, highlighting areas of overlap and divergence. Our work examines potential points of integration between AKC's immune receptor-specific model and NFDI4Immuno's broader immunological data framework. We will propose strategies for metadata mapping that respect the different scopes while identifying opportunities for enhanced interoperability. By exploring alignment possibilities between these complementary infrastructures across institutional and national boundaries, we aim to identify pathways toward reducing barriers between and within research communities and to facilitate data reuse across scientific domains.