J. A. García
- Molecular Biology
- Endocrine and Autonomic Systems top 2%
- Computer Vision and Pattern Recognition top 5%
- Statistics, Probability and Uncertainty top 1%
- Physiology
- Co-authors
- J. Fdez‐ValdiviaRosa Rodríguez‐SánchezGermaine EscamesLuís C. LópezDarío Acuña‐CastroviejoAna LópezFrancisco OrtizXosé R. Fdez-Vidal
- Topics
- scientometrics and bibliometrics research (27 papers)Image and Signal Denoising Methods (19 papers)Advanced Data Compression Techniques (19 papers)
- Cited by
- Endocrine and Autonomic SystemsStatistics, Probability and UncertaintyBiological Psychiatry
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceHuman Molecular GeneticsMolecules
- Partner nations
- SpainUnited StatesMexico
In The Last Decade
J. A. García
112 papers receiving 1.6k citations
Peers
Comparison fields: 5 of 178
- Molecular Biology 337
- Endocrine and Autonomic Systems 331
- Computer Vision and Pattern Recognition 290
- Statistics, Probability and Uncertainty 216
- Physiology 161
Countries citing papers authored by J. A. García
This map shows the geographic impact of J. A. García'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 J. A. García with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites J. A. García more than expected).
Fields of papers citing papers by J. A. García
This network shows the impact of papers produced by J. A. García. 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 J. A. García. The network helps show where J. A. García may publish in the future.
Co-authorship network of co-authors of J. A. García
This figure shows the co-authorship network connecting the top 25 collaborators of J. A. García. A scholar is included among the top collaborators of J. A. García 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 J. A. García. J. A. García 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 | 0 | |
| 3 | 1 | |
| 4 | 17 | |
| 5 | 4 | |
| 6 | 1 | |
| 7 | 10 | |
| 8 | 2 | |
| 9 | 16 | |
| 10 | 25 | |
| 11 | 1 | |
| 12 | 85 | |
| 13 | 206 | |
| 14 | 81 | |
| 15 | 9 | |
| 16 | 2 | |
| 17 | 243 | |
| 18 | Current and Future Uses of OWL for Scientific Data Frameworks: Successes and Limitations. | 4 |
| 19 | 3 | |
| 20 | 3 |
About J. A. García
J. A. García is a scholar working on Statistics, Probability and Uncertainty, Computer Vision and Pattern Recognition and Media Technology, having authored 120 papers that have together received 1.7k indexed citations. Recurring topics across this work include scientometrics and bibliometrics research (27 papers), Image and Signal Denoising Methods (19 papers) and Advanced Data Compression Techniques (19 papers). The work is most often cited by research in Endocrine and Autonomic Systems (331 citations), Statistics, Probability and Uncertainty (216 citations) and Biological Psychiatry (73 citations). J. A. García has collaborated with scholars based in Spain, United States and Mexico. Frequent co-authors include J. Fdez‐Valdivia, Rosa Rodríguez‐Sánchez, Germaine Escames, Luís C. López, Darío Acuña‐Castroviejo, Ana López, Francisco Ortiz, Xosé R. Fdez-Vidal, Bernardino Benito and Francisco Bastida. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Human Molecular Genetics and Molecules.
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.