Linking Global Science Funding to Research Publications

2026-03-25

AI Summary

This work addresses the challenge of accurately linking global research funding agencies to their supported scholarly publications to enable systematic analysis of research funding flows. To this end, we propose a multi-stage hybrid disambiguation framework that integrates lexical normalization, similarity-based clustering, rule-based matching, named entity recognition, and manual validation to map ambiguous funding statements to unique organizational identifiers. Our approach successfully standardizes 1.9 million distinct funding strings, achieving high recall and precision as validated through document-level comparison and expert review. By publicly releasing annotations of match types alongside unresolved cases, our method significantly enhances transparency, reproducibility, and the capacity for large-scale analysis of the global research funding landscape.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

Funding acknowledgments in scholarly publications provide large-scale trace data on organizations that support scientific research. We present a dataset for linking global science funding organizations to research publications by systematically disambiguating unique funding acknowledgment strings extracted from publication metadata. Funder names are matched to standardized organizational identifiers using a multi-stage pipeline that combines lexical normalization, similarity-based clustering, rule-based matching, named entity recognition assistance, and manual validation. The resulting dataset links 1.9 million unique funder strings to canonical organization identifiers and records match types and unresolved cases to support transparency. Technical validation includes paper-level comparisons across bibliometric sources and manual verification against full-text acknowledgment sections, with reported recall and precision metrics. This dataset supports analyses of funding flows, institutional funding portfolios, regional representation, and concentration patterns in the global research system.

Problem

• funding acknowledgment
• research funding
• organization disambiguation
• bibliometric linkage
• scientific publication

Innovation

• funding acknowledgment disambiguation
• organization identifier mapping
• multi-stage matching pipeline
• bibliometric validation
• research funding dataset