Daniela De Canditiis
- Molecular Biology
- Atmospheric Science
- Global and Planetary Change
- Statistics and Probability top 10%
- Computer Vision and Pattern Recognition
- Co-authors
- Claudia AngeliniMarianna PenskyUmberto AmatoMargherita MutarelliCarmine SerioBrani VidakovićItalia De FeisLuisa Cutillo
- Topics
- Image and Signal Denoising Methods (12 papers)Statistical Methods and Inference (5 papers)Sparse and Compressive Sensing Techniques (4 papers)
- Partner nations
- ItalyUnited StatesAustralia
In The Last Decade
Daniela De Canditiis
34 papers receiving 303 citations
Peers
Comparison fields: 5 of 95
- Molecular Biology 106
- Atmospheric Science 45
- Global and Planetary Change 39
- Statistics and Probability 38
- Computer Vision and Pattern Recognition 35
Countries citing papers authored by Daniela De Canditiis
This map shows the geographic impact of Daniela De Canditiis'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 Daniela De Canditiis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniela De Canditiis more than expected).
Fields of papers citing papers by Daniela De Canditiis
This network shows the impact of papers produced by Daniela De Canditiis. 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 Daniela De Canditiis. The network helps show where Daniela De Canditiis may publish in the future.
Co-authorship network of co-authors of Daniela De Canditiis
This figure shows the co-authorship network connecting the top 25 collaborators of Daniela De Canditiis. A scholar is included among the top collaborators of Daniela De Canditiis 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 Daniela De Canditiis. Daniela De Canditiis 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 | 1 | |
| 3 | 1 | |
| 4 | 8 | |
| 5 | 2 | |
| 6 | 7 | |
| 7 | 1 | |
| 8 | 2 | |
| 9 | 38 | |
| 10 | 2 | |
| 11 | 2 | |
| 12 | 22 | |
| 13 | 6 | |
| 14 | 42 | |
| 15 | 28 | |
| 16 | 3 | |
| 17 | 21 | |
| 18 | 27 | |
| 19 | 3 | |
| 20 | 3 |
About Daniela De Canditiis
Daniela De Canditiis is a scholar working on Immunology and Allergy, Statistics and Probability and Computer Vision and Pattern Recognition, having authored 38 papers that have together received 317 indexed citations. Recurring topics across this work include Image and Signal Denoising Methods (12 papers), Statistical Methods and Inference (5 papers) and Sparse and Compressive Sensing Techniques (4 papers). The work is most often cited by research in Immunology and Allergy (30 citations), Statistics and Probability (38 citations) and Atmospheric Science (45 citations). Daniela De Canditiis has collaborated with scholars based in Italy, United States and Australia. Frequent co-authors include Claudia Angelini, Marianna Pensky, Umberto Amato, Margherita Mutarelli, Carmine Serio, Brani Vidaković, Italia De Feis, Luisa Cutillo, Giovanna De Castro and Giulia Brindisi. Their work appears in journals such as Geophysical Research Letters, International Journal of Molecular Sciences and Nutrients.
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.