This map shows the geographic impact of Jan Kalina'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 Jan Kalina with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jan Kalina more than expected).
This network shows the impact of papers produced by Jan Kalina. 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 Jan Kalina. The network helps show where Jan Kalina may publish in the future.
Co-authorship network of co-authors of Jan Kalina
This figure shows the co-authorship network connecting the top 25 collaborators of Jan Kalina.
A scholar is included among the top collaborators of Jan Kalina 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 Jan Kalina. Jan Kalina is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kalina, Jan, et al.. (2021). A Comparison of Regularization Techniques for Shallow Neural NetworksTrained on Small Datasets.. 94–103.1 indexed citations
Kalina, Jan. (2010). Robust Image Analysis in the Evaluation of Gene Expression Studies.. ASEP. 2010. 52.3 indexed citations
19.
Saleh, A. K. Md. Ehsanes, Jan Picek, & Jan Kalina. (2009). Nonparametric estimation of regression parameters in measurement error models. METRON. 177–200.2 indexed citations
20.
Kalina, Jan. (2009). LEAST WEIGHTED SQUARES IN ECONOMETRIC APPLICATIONS.
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.