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Dominika Tkaczyk

Dominika Tkaczyk

Dominika joined Crossref in 2018 as a Principal R&D Developer, where she focused on metadata matching research aimed at enriching the scholarly record through the discovery of new relationships. In 2024, she became Crossref’s Director of Data Science and established the Data Science team, with a mission to explore innovative ways of using data to support the scholarly community, enrich the Research Nexus with more metadata and relationships, and develop collaborations with like-minded community initiatives. Since 2025, Dominika has served as Director of Technology, leading a unified technology team that integrates infrastructure, software development, and data science functions. Dominika holds a PhD in Computer Science from the Polish Academy of Sciences. Prior to joining Crossref, she she was a researcher and a data scientist at the University of Warsaw, Poland, and a postdoctoral researcher at Trinity College Dublin, Ireland.

Read more about Dominika Tkaczyk on their team page.

Reference matching: for real this time

In my previous blog post, Matchmaker, matchmaker, make me a match, I compared four approaches for reference matching. The comparison was done using a dataset composed of automatically-generated reference strings. Now it’s time for the matching algorithms to face the real enemy: the unstructured reference strings deposited with Crossref by some members. Are the matching algorithms ready for this challenge? Which algorithm will prove worthy of becoming the guardian of the mighty citation network? Buckle up and enjoy our second matching battle!

Matchmaker, matchmaker, make me a match

Matching (or resolving) bibliographic references to target records in the collection is a crucial algorithm in the Crossref ecosystem. Automatic reference matching lets us discover citation relations in large document collections, calculate citation counts, H-indexes, impact factors, etc. At Crossref, we currently use a matching approach based on reference string parsing. Some time ago we realized there is a much simpler approach. And now it is finally battle time: which of the two approaches is better?

What does the sample say?

At Crossref Labs, we often come across interesting research questions and try to answer them by analyzing our data. Depending on the nature of the experiment, processing over 100M records might be time-consuming or even impossible. In those dark moments we turn to sampling and statistical tools. But what can we infer from only a sample of the data?