Dominika Tkaczyk

668 total citations
12 papers, 285 citations indexed

About

Dominika Tkaczyk is a scholar working on Artificial Intelligence, Information Systems and Molecular Biology. According to data from OpenAlex, Dominika Tkaczyk has authored 12 papers receiving a total of 285 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 5 papers in Information Systems and 3 papers in Molecular Biology. Recurrent topics in Dominika Tkaczyk's work include Semantic Web and Ontologies (5 papers), Handwritten Text Recognition Techniques (3 papers) and Biomedical Text Mining and Ontologies (3 papers). Dominika Tkaczyk is often cited by papers focused on Semantic Web and Ontologies (5 papers), Handwritten Text Recognition Techniques (3 papers) and Biomedical Text Mining and Ontologies (3 papers). Dominika Tkaczyk collaborates with scholars based in Poland, Ireland and United Kingdom. Dominika Tkaczyk's co-authors include Łukasz Bolikowski, Jennifer Lin, Patricia Feeney, Joeran Beel, Páraic Sheridan, К. Rusek and Joshua P. French and has published in prestigious journals such as International Journal on Document Analysis and Recognition (IJDAR), D-Lib Magazine and Quantitative Science Studies.

In The Last Decade

Dominika Tkaczyk

12 papers receiving 269 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Dominika Tkaczyk Poland	7	140	96	55	50	49	12	285
Sagnik Ray Choudhury United States	12	134 1.0×	73 0.8×	98 1.8×	16 0.3×	26 0.5×	18	251
Łukasz Bolikowski Poland	8	123 0.9×	73 0.8×	52 0.9×	14 0.3×	38 0.8×	18	218
Donna Bergmark United States	8	81 0.6×	132 1.4×	19 0.3×	55 1.1×	34 0.7×	22	277
Michael Ley Germany	4	133 0.9×	87 0.9×	25 0.5×	31 0.6×	18 0.4×	16	242
Jean-Charles Lamirel France	8	142 1.0×	57 0.6×	37 0.7×	24 0.5×	8 0.2×	43	213
Jagdev Bhogal United Kingdom	7	251 1.8×	176 1.8×	35 0.6×	6 0.1×	17 0.3×	18	376
Jan Brase Germany	10	129 0.9×	224 2.3×	20 0.4×	14 0.3×	151 3.1×	26	376
Patrick Glenisson Belgium	8	118 0.8×	76 0.8×	13 0.2×	50 1.0×	11 0.2×	10	310
Iqra Safder Pakistan	10	220 1.6×	56 0.6×	37 0.7×	42 0.8×	8 0.2×	18	312
Hidetsugu Nanba Japan	14	393 2.8×	128 1.3×	37 0.7×	32 0.6×	11 0.2×	53	509

Countries citing papers authored by Dominika Tkaczyk

Since Specialization

Citations

This map shows the geographic impact of Dominika Tkaczyk's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dominika Tkaczyk with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dominika Tkaczyk more than expected).

Fields of papers citing papers by Dominika Tkaczyk

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dominika Tkaczyk. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dominika Tkaczyk. The network helps show where Dominika Tkaczyk may publish in the future.

Co-authorship network of co-authors of Dominika Tkaczyk

This figure shows the co-authorship network connecting the top 25 collaborators of Dominika Tkaczyk. A scholar is included among the top collaborators of Dominika Tkaczyk based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Dominika Tkaczyk. Dominika Tkaczyk is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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12 of 12 papers shown

1.

Tkaczyk, Dominika, et al.. (2020). Crossref: The sustainable source of community-owned scholarly metadata. Quantitative Science Studies. 1(1). 414–427. 86 indexed citations

2.

Tkaczyk, Dominika, et al.. (2018). A Novel Approach to Recommendation Algorithm Selection using Meta-Learning.. 210–219. 3 indexed citations

3.

Tkaczyk, Dominika, et al.. (2018). Evaluation and Comparison of Open Source Bibliographic Reference Parsers: A Business Use Case.. 4 indexed citations

4.

Tkaczyk, Dominika, et al.. (2018). Machine Learning vs. Rules and Out-of-the-Box vs. Retrained. 99–108. 22 indexed citations

5.

Tkaczyk, Dominika, et al.. (2017). CeON/CERMINE: CERMINE 1.13. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations

6.

Tkaczyk, Dominika, et al.. (2015). Structured Affiliations Extraction from Scientific Literature. D-Lib Magazine. 21(11/12). 5 indexed citations

7.

Tkaczyk, Dominika, et al.. (2015). CERMINE: automatic extraction of structured metadata from scientific literature. International Journal on Document Analysis and Recognition (IJDAR). 18(4). 317–335. 111 indexed citations

8.

Tkaczyk, Dominika, et al.. (2014). GROTOAP2 The Methodology of Creating a Large Ground Truth Dataset of Scientific Articles. D-Lib Magazine. 20(11/12). 18 indexed citations

9.

Tkaczyk, Dominika, et al.. (2014). CERMINE -- Automatic Extraction of Metadata and References from Scientific Literature. 217–221. 15 indexed citations

10.

Tkaczyk, Dominika, et al.. (2012). A Modular Metadata Extraction System for Born-Digital Articles. CeON Repository (Centre for Evaluation in Education and Science). 11–16. 9 indexed citations

11.

Tkaczyk, Dominika, et al.. (2012). GROTOAP. CeON Repository (Centre for Evaluation in Education and Science). 381–382. 10 indexed citations

12.

Tkaczyk, Dominika & Łukasz Bolikowski. (2011). Workflow of Metadata Extraction from Retro-Born Digital Documents. Czech digital mathematics library. 39–44. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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