MAPPING ELIXIR COMMUNITIES: EVALUATING CONTENT-BASED AND NETWORK-BASED APPROACHES FOR SYSTEMATIC EXPERT DISCOVERY IN UK BIOMEDICAL RESEARCH

Abstract

ELIXIR, Europe’s distributed life sciences infrastructure coordinating 18 communities, currently lacks systematic methods for identifying UK researchers whose work aligns with its community goals. This dissertation develops and evaluates two computational pipelines to address this challenge: a content-based approach using semantic matching of publication content, and a network-based approach using co-authorship expansion from verified UK ELIXIR members. The content-based pipeline processed 80,849 UK-affiliated publications retrieved through structured PubMed queries derived from ELIXIR communities’ descriptions, generating BioBERT embeddings for semantic similarity search. The network-based pipeline expanded from 86 verified ELIXIR UK authors through co-authorship networks, processing 370,282 publications and assigning themes using a hybrid keyword-embedding approach. Both systems were integrated into Retrieval-Augmented Generation (RAG) architectures enabling complex expert discovery queries. Evaluation employed four complementary frameworks: coverage validation of the content-based approach achieving 100% success in identifying known UK ELIXIR authors; systematic overlap analysis revealing 24.4% author overlap but only 3.6% publication overlap between systems; literature-based expert evaluation using evidence-based bibliometrics criteria; and parameter sensitivity testing across more than 600 configurations confirming robustness of the system across different thresholds and expert scoring methods. When rigorous evaluation criteria (ten or more publications, multi-institutional collaboration, recent activity since 2020) were applied, the content-based system identified 26,111 experts with 100% confidence, while the network-based system identified 28,567 experts with 99.97% confidence. Critically, only 924 experts (5.6%) were validated by both methods using the literature-based expert evaluation, demonstrating that the approaches identify fundamentally different expert populations. Network-based discovery excelled at finding collaborative, early-career researchers (70.9% versus 49.6%) in established computational domains like Galaxy workflows. Content-based discovery excelled in finding focused specialists (99.1% single-theme) and mid-career researchers in emerging interdisciplinary areas like Rare Diseases. The 924 overlapping experts proved to be cross-domain bridges, appearing in different ELIXIR communities 93% of the time and discovered through entirely different evidence by each method. The investigation demonstrates that content-based and network-based approaches access different dimensions of expertise: intellectual contribution versus social integration, validating the need for combined deployment. Together, the systems identify over 17,333 unique UK experts across more than 54,500 researcher-community mappings, providing ELIXIR with comprehensive, quality-assessed mappings for strategic community development and engagement.