This map shows the geographic impact of Oscar Reparaz'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 Oscar Reparaz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Oscar Reparaz more than expected).
This network shows the impact of papers produced by Oscar Reparaz. 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 Oscar Reparaz. The network helps show where Oscar Reparaz may publish in the future.
Co-authorship network of co-authors of Oscar Reparaz
This figure shows the co-authorship network connecting the top 25 collaborators of Oscar Reparaz.
A scholar is included among the top collaborators of Oscar Reparaz 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 Oscar Reparaz. Oscar Reparaz is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Karmakar, Angshuman, Sujoy Sinha Roy, Oscar Reparaz, Fréderik Vercauteren, & Ingrid Verbauwhede. (2018). Constant-Time Discrete Gaussian Sampling. IEEE Transactions on Computers. 67(11). 1561–1571.27 indexed citations
4.
Reparaz, Oscar, et al.. (2018). Multiplicative Masking for AES in Hardware. IACR Transactions on Cryptographic Hardware and Embedded Systems. 431–468.19 indexed citations
Reparaz, Oscar, Benedikt Gierlichs, & Ingrid Verbauwhede. (2012). Selecting Time Samples for Multivariate DPA Attacks. Lecture notes in computer science. 7428. 155–174.2 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.