Francesco V. Tenore
- Cognitive Neuroscience top 1%
- Cellular and Molecular Neuroscience top 1%
- Biomedical Engineering top 5%
- Electrical and Electronic Engineering top 10%
- Human-Computer Interaction top 5%
- Co-authors
- Nitish V. ThakorR. Jacob VogelsteinSoumyadipta AcharyaRalph Etienne-CummingsRalph Etienne‐CummingsSliman J. Bensmaı̈aAnder RamosGregg A. Tabot
- Topics
- Neuroscience and Neural Engineering (46 papers)EEG and Brain-Computer Interfaces (37 papers)Advanced Memory and Neural Computing (17 papers)
- Journals
- Proceedings of the National Academy of SciencesSHILAP Revista de lepidopterologíaScientific Reports
- Partner nations
- United StatesNetherlandsGermany
In The Last Decade
Francesco V. Tenore
49 papers receiving 1.8k citations
Peers
Comparison fields: 5 of 76
- Cognitive Neuroscience 1.4k
- Cellular and Molecular Neuroscience 1.2k
- Biomedical Engineering 886
- Electrical and Electronic Engineering 366
- Human-Computer Interaction 110
Countries citing papers authored by Francesco V. Tenore
This map shows the geographic impact of Francesco V. Tenore'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 Francesco V. Tenore with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Francesco V. Tenore more than expected).
Fields of papers citing papers by Francesco V. Tenore
This network shows the impact of papers produced by Francesco V. Tenore. 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 Francesco V. Tenore. The network helps show where Francesco V. Tenore may publish in the future.
Co-authorship network of co-authors of Francesco V. Tenore
This figure shows the co-authorship network connecting the top 25 collaborators of Francesco V. Tenore. A scholar is included among the top collaborators of Francesco V. Tenore 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 Francesco V. Tenore. Francesco V. Tenore is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 4 | |
| 3 | 29 | |
| 4 | 39 | |
| 5 | 25 | |
| 6 | 6 | |
| 7 | 1 | |
| 8 | 36 | |
| 9 | 54 | |
| 10 | 30 | |
| 11 | 47 | |
| 12 | 213 | |
| 13 | 80 | |
| 14 | 89 | |
| 15 | 28 | |
| 16 | 18 | |
| 17 | 1 | |
| 18 | 261 | |
| 19 | 116 | |
| 20 | 22 |
About Francesco V. Tenore
Francesco V. Tenore is a scholar working on Cellular and Molecular Neuroscience, Cognitive Neuroscience and Biomedical Engineering, having authored 51 papers that have together received 1.8k indexed citations. Recurring topics across this work include Neuroscience and Neural Engineering (46 papers), EEG and Brain-Computer Interfaces (37 papers) and Advanced Memory and Neural Computing (17 papers). The work is most often cited by research in Cognitive Neuroscience (1.4k citations), Cellular and Molecular Neuroscience (1.2k citations) and Biomedical Engineering (886 citations). Francesco V. Tenore has collaborated with scholars based in United States, Netherlands and Germany. Frequent co-authors include Nitish V. Thakor, R. Jacob Vogelstein, Soumyadipta Acharya, Ralph Etienne-Cummings, Ralph Etienne‐Cummings, Sliman J. Bensmaı̈a, Ander Ramos, Gregg A. Tabot, John F. Dammann and M. Anthony Lewis. Their work appears in journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and Scientific Reports.
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.