Daniela De Canditiis

72 total papers · 489 total citations
37 papers, 316 citations indexed

About

Daniela De Canditiis is a scholar working on Computer Vision and Pattern Recognition, Molecular Biology and Artificial Intelligence. According to data from OpenAlex, Daniela De Canditiis has authored 37 papers receiving a total of 316 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Computer Vision and Pattern Recognition, 7 papers in Molecular Biology and 7 papers in Artificial Intelligence. Recurrent topics in Daniela De Canditiis's work include Image and Signal Denoising Methods (12 papers), Statistical Methods and Inference (5 papers) and Sparse and Compressive Sensing Techniques (4 papers). Daniela De Canditiis is often cited by papers focused on Image and Signal Denoising Methods (12 papers), Statistical Methods and Inference (5 papers) and Sparse and Compressive Sensing Techniques (4 papers). Daniela De Canditiis collaborates with scholars based in Italy, United States and Israel. Daniela De Canditiis's co-authors include Claudia Angelini, Marianna Pensky, Umberto Amato, Margherita Mutarelli, Carmine Serio, Brani Vidaković, Italia De Feis, Luisa Cutillo, Francesca Olivero and Giovanna De Castro and has published in prestigious journals such as Geophysical Research Letters, International Journal of Molecular Sciences and Nutrients.

In The Last Decade

Daniela De Canditiis

34 papers receiving 303 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Daniela De Canditiis	105	45	39	38	35	37	316
Radu Herbei	28 0.3×	36 0.8×	35 0.9×	68 1.8×	23 0.7×	24	325
Oleg A. Markelov	58 0.6×	15 0.3×	13 0.3×	10 0.3×	15 0.4×	46	300
Imola K. Fodor	109 1.0×	13 0.3×	15 0.4×	7 0.2×	118 3.4×	22	364
Francisco J. Caro‐Lopera	19 0.2×	36 0.8×	55 1.4×	90 2.4×	4 0.1×	47	326
Ernst Stadlober	9 0.1×	32 0.7×	32 0.8×	53 1.4×	16 0.5×	34	338
Alen Alexanderian	17 0.2×	32 0.7×	21 0.5×	14 0.4×	17 0.5×	38	360
Yu Zhu	23 0.2×	88 2.0×	48 1.2×	3 0.1×	9 0.3×	18	318
Fentaw Abegaz	68 0.6×	3 0.1×	28 0.7×	63 1.7×	2 0.1×	33	344
Ethan Anderes	34 0.3×	8 0.2×	34 0.9×	37 1.0×	14 0.4×	18	343
Alison Q. O’Neil	40 0.4×	32 0.7×	29 0.7×	14 0.4×	44 1.3×	21	324

Countries citing papers authored by Daniela De Canditiis

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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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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