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.
To address the growing scale and complexity of scholarly data, we’ve launched a new data science function at Crossref. In April, we were excited to welcome our first data scientists, Jason Portenoy and Alex BĂ©dard-VallĂ©e, to the team. With their arrival, the Data Science team is now fully up and running. In this blog post, we’re sharing our vision and what’s ahead for data science at Crossref.
Marking our 25th anniversary, we launch the Crossref Metadata Awards to emphasise our community’s role in stewarding and enriching the scholarly record.
We are pleased to recognise Noyam Publishers, GigaScience Press, eLife, American Society for Microbiology, and Universidad La Salle Arequipa Perú with the Crossref Metadata Excellence Awards, and Instituto Geologico y Minero de España wins the Crossref Metadata Enrichment Award. These inaugural awards highlight the leadership of members who show dedication to the best metadata practices.
Dominika Tkaczyk, Wednesday, Jan 8, 2025
https://doi-org.turing.library.northwestern.edu/10.13003/axeer1ee
In our previous entry, we explained that thorough evaluation is key to understanding a matching strategy’s performance. While evaluation is what allows us to assess the correctness of matching, choosing the best matching strategy is, unfortunately, not as simple as selecting the one that yields the best matches. Instead, these decisions usually depend on weighing multiple factors based on your particular circumstances. This is true not only for metadata matching, but for many technical choices that require navigating trade-offs. In this blog post, the last one in the metadata matching series, we outline a subjective set of criteria we would recommend you consider when making decisions about matching.
Dominika Tkaczyk, Wednesday, Nov 6, 2024
https://doi-org.turing.library.northwestern.edu/10.13003/ief7aibi
In our previous blog post in this series, we explained why no metadata matching strategy can return perfect results. Thankfully, however, this does not mean that it’s impossible to know anything about the quality of matching. Indeed, we can (and should!) measure how close (or far) we are from achieving perfection with our matching. Read on to learn how this can be done!
How about we start with a quiz? Imagine a database of scholarly metadata that needs to be enriched with identifiers, such as ORCIDs or ROR IDs. Hopefully, by this point in our series this is recognizable as a classic matching problem. In searching for a solution, you identify an externally-developed matching tool that makes one of the below claims. Which of the following would demonstrate satisfactory performance?
Dominika Tkaczyk, Wednesday, Aug 28, 2024
https://doi-org.turing.library.northwestern.edu/10.13003/pied3tho
In our previous instalments of the blog series about matching (see part 1 and part 2), we explained what metadata matching is, why it is important and described its basic terminology. In this entry, we will discuss a few common beliefs about metadata matching that are often encountered when interacting with users, developers, integrators, and other stakeholders. Spoiler alert: we are calling them myths because these beliefs are not true! Read on to learn why.
