Daniel Cárdenas-Morales
- Statistics and Probability top 1%
- Numerical Analysis top 5%
- Applied Mathematics top 5%
- Computational Mechanics
- Computational Theory and Mathematics
- Co-authors
- Pedro GarranchoA. FraguelaIoan RaşaAli̇ AralTuncer AcarVijay GuptaJosé A. AdellA. Lopez-Moreno
- Topics
- Approximation Theory and Sequence Spaces (28 papers)Mathematical Approximation and Integration (10 papers)Advanced Numerical Analysis Techniques (9 papers)
In The Last Decade
Daniel Cárdenas-Morales
26 papers receiving 492 citations
Peers
Comparison fields: 5 of 89
- Statistics and Probability 298
- Numerical Analysis 232
- Applied Mathematics 162
- Computational Mechanics 46
- Computational Theory and Mathematics 33
Countries citing papers authored by Daniel Cárdenas-Morales
This map shows the geographic impact of Daniel Cárdenas-Morales'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 Cárdenas-Morales with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Cárdenas-Morales more than expected).
Fields of papers citing papers by Daniel Cárdenas-Morales
This network shows the impact of papers produced by Daniel Cárdenas-Morales. 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 Cárdenas-Morales. The network helps show where Daniel Cárdenas-Morales may publish in the future.
Co-authorship network of co-authors of Daniel Cárdenas-Morales
This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Cárdenas-Morales. A scholar is included among the top collaborators of Daniel Cárdenas-Morales 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 Daniel Cárdenas-Morales. Daniel Cárdenas-Morales is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 6 | |
| 3 | 27 | |
| 4 | 38 | |
| 5 | 2 | |
| 6 | 18 | |
| 7 | 12 | |
| 8 | 0 | |
| 9 | 27 | |
| 10 | Mathematics and Computers in Simulation | 249 |
| 11 | 76 | |
| 12 | 1 | |
| 13 | 1 | |
| 14 | 1 | |
| 15 | 4 | |
| 16 | 1 | |
| 17 | 4 | |
| 18 | 1 | |
| 19 | 7 | |
| 20 | 11 |
About Daniel Cárdenas-Morales
Daniel Cárdenas-Morales is a scholar working on Statistics and Probability, Numerical Analysis and Applied Mathematics, having authored 29 papers that have together received 533 indexed citations. Recurring topics across this work include Approximation Theory and Sequence Spaces (28 papers), Mathematical Approximation and Integration (10 papers) and Advanced Numerical Analysis Techniques (9 papers). The work is most often cited by research in Numerical Analysis (232 citations), Statistics and Probability (298 citations) and Applied Mathematics (162 citations). Daniel Cárdenas-Morales has collaborated with scholars based in Spain, Romania and Mexico. Frequent co-authors include Pedro Garrancho, A. Fraguela, Ioan Raşa, Ali̇ Aral, Tuncer Acar, Vijay Gupta, José A. Adell and A. Lopez-Moreno. Their work appears in journals such as Applied Mathematics and Computation, Computers & Mathematics with Applications and Applied Mathematics Letters.
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.