Digital libraries in the scientific domain provide users access to a wide range of information to satisfy their diverse information needs. Here, ranking results play a crucial role in users' satisfaction. Exploiting bibliometric metadata, e.g., publications' citation counts or bibliometric indicators in general, for automatically identifying the most relevant results can boost retrieval performance. This work proposes bibliometric data fusion, which enriches existing systems' results by incorporating bibliometric metadata such as citations or altmetrics. Our results on three biomedical retrieval benchmarks from TREC Precision Medicine (TREC-PM) show that bibliometric data fusion is a promising approach to improve retrieval performance in terms of normalized Discounted Cumulated Gain (nDCG) and Average Precision (AP), at the cost of the Precision at 10 (P@10) rate. Patient users especially profit from this lightweight, data-sparse technique that applies to any digital library.

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