Daniel Langr

47 papers receiving 530 citations

Peers

Daniel Langr
Comparison fields: 5 of 45
  • Computational Mathematics 19
  • Hardware and Architecture 163
  • Nuclear and High Energy Physics 291
  • Spectroscopy 123
  • Computer Networks and Communications 134
Replace A. G. Sibiryakov with:
A. G. Sibiryakov Germany
Hai Ah Nam United States
M. E. Sevior Australia
Chi‐Chung Lam United States
Bálint Joó United States
Ronald Babich United States
N. Eicker Germany
S. J. Goldsack United Kingdom
R. van Dantzig Netherlands
Bradley Mitchell United States
Daniel Langr relative to A. G. Sibiryakov Germany A. G. Sibiryakov's profile →
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Citations per year

Countries citing papers authored by Daniel Langr

Since Specialization
Citations

This map shows the geographic impact of Daniel Langr'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 Daniel Langr with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Langr more than expected).

Fields of papers citing papers by Daniel Langr

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Langr. 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 Daniel Langr. The network helps show where Daniel Langr may publish in the future.

Co-authors

The 25 scholars most cited alongside Daniel Langr, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel Langr Line = papers co-authored together Daniel Langr links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 51 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2015101
2 201399
3 202068
4 201635
5 201218
6 201517
7 202115
8
Adaptive-blocking hierarchical storage format for sparse matrices
201214
9 202214
10 201914
11 201913
12 201212
13 201212
14 200511
15 20228
16
Storing sparse matrices to files in the adaptive-blocking hierarchical storage format
20137
17 20147
18 20136
19 20205
20 20125

About Daniel Langr

Daniel Langr is a scholar working on Hardware and Architecture, Nuclear and High Energy Physics, Computer Networks and Communications, Spectroscopy and Atomic and Molecular Physics, and Optics, having authored 51 papers that have together received 540 indexed citations. Recurring topics across this work include Parallel Computing and Optimization Techniques (24 papers), Nuclear physics research studies (19 papers), Quantum Chromodynamics and Particle Interactions (17 papers), Advanced Data Storage Technologies (15 papers), Distributed and Parallel Computing Systems (14 papers), Advanced NMR Techniques and Applications (12 papers), Matrix Theory and Algorithms (6 papers) and Algorithms and Data Compression (4 papers). The work is most often cited by research in Computational Mathematics (19 citations), Hardware and Architecture (163 citations), Nuclear and High Energy Physics (291 citations), Spectroscopy (123 citations) and Computer Networks and Communications (134 citations). Daniel Langr has collaborated with scholars based in Czechia, United States and Canada. Frequent co-authors include Pavel Tvrdı́k, T. Dytrych, J. P. Draayer, Kristina D. Launey, Pieter Maris, James P. Vary, Masha Sosonkina, Érik Saule, M. A. Caprio and Ümit V. Çatalyürek. Their work appears in journals such as Computer Physics Communications, Physical Review Letters, The International Journal of High Performance Computing Applications, IEEE Transactions on Parallel and Distributed Systems and Journal of Applied Crystallography.

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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