Katiane Roversi

50 total papers · 886 total citations
38 papers, 479 citations indexed

About

Katiane Roversi is a scholar working on Cellular and Molecular Neuroscience, Nutrition and Dietetics and Physiology. According to data from OpenAlex, Katiane Roversi has authored 38 papers receiving a total of 479 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Cellular and Molecular Neuroscience, 11 papers in Nutrition and Dietetics and 10 papers in Physiology. Recurrent topics in Katiane Roversi's work include Fatty Acid Research and Health (7 papers), Adipose Tissue and Metabolism (5 papers) and Biochemical Analysis and Sensing Techniques (5 papers). Katiane Roversi is often cited by papers focused on Fatty Acid Research and Health (7 papers), Adipose Tissue and Metabolism (5 papers) and Biochemical Analysis and Sensing Techniques (5 papers). Katiane Roversi collaborates with scholars based in Brazil, Canada and United States. Katiane Roversi's co-authors include Marilise Escobar Bürger, Camila Simonetti Pase, Karine Roversi, Verônica Tironi Dias, Dalila Moter Benvegnú, Caren Tatiane de David Antoniazzi, Nardeli Boufleur, Tatiana Emanuelli, Raquel Cristine Silva Barcelos and Sébastien Talbot and has published in prestigious journals such as Brain Research, Journal of Allergy and Clinical Immunology and Small.

In The Last Decade

Katiane Roversi

36 papers receiving 477 citations

Author Peers

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

Author						Papers	Cites
Katiane Roversi	120	98	82	76	51	38	479
Azadeh Mesripour	66 0.6×	33 0.3×	66 0.8×	105 1.4×	75 1.5×	53	519
Sachie Sasaki‐Hamada	195 1.6×	28 0.3×	94 1.1×	116 1.5×	51 1.0×	33	484
Aleta L. Svitak	110 0.9×	53 0.5×	125 1.5×	167 2.2×	12 0.2×	20	523
Sarawut Lapmanee	58 0.5×	29 0.3×	78 1.0×	60 0.8×	77 1.5×	32	472
Clarissa Vasconcelos de Oliveira	150 1.3×	29 0.3×	49 0.6×	160 2.1×	10 0.2×	22	471
Amjad Abuirmeileh	149 1.2×	23 0.2×	70 0.9×	86 1.1×	38 0.7×	24	435
Amani Alghamdi	77 0.6×	52 0.5×	134 1.6×	182 2.4×	12 0.2×	40	510
Chaichan Sangdee	83 0.7×	41 0.4×	80 1.0×	99 1.3×	15 0.3×	28	480
Katarzyna Świąder	89 0.7×	23 0.2×	23 0.3×	85 1.1×	32 0.6×	42	426
Jie Dong	97 0.8×	33 0.3×	27 0.3×	136 1.8×	20 0.4×	47	524

Countries citing papers authored by Katiane Roversi

Since Specialization

Citations

This map shows the geographic impact of Katiane Roversi'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 Katiane Roversi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Katiane Roversi more than expected).

Fields of papers citing papers by Katiane Roversi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Katiane Roversi. 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 Katiane Roversi. The network helps show where Katiane Roversi may publish in the future.

Co-authorship network of co-authors of Katiane Roversi

This figure shows the co-authorship network connecting the top 25 collaborators of Katiane Roversi. A scholar is included among the top collaborators of Katiane Roversi 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 Katiane Roversi. Katiane Roversi 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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