S. Picoli

705 citations
25 papers · 513 · h-index 15

Impact in

Papers in

S. Picoli

25 papers receiving 501 citations

Peers

S. Picoli
Comparison fields: 5 of 89
  • Modeling and Simulation 89
  • Statistical and Nonlinear Physics 206
  • Statistics and Probability 88
  • Economics and Econometrics 164
  • Statistics, Probability and Uncertainty 38
Replace L. S. Lucena with:
L. S. Lucena Brazil
Naoko Arimitsu Japan
U.M.S. Costa Brazil
Thierry Huillet France
Giovani L. Vasconcelos Brazil
L. V. Tanatarov Ukraine
Zhiming Li China
Sı́lvio M. Duarte Queirós Brazil
Jan Korbel Austria
S. Gluzman Russia
S. Picoli relative to L. S. Lucena Brazil L. S. Lucena's profile →
Citations per field
00.5×10.5×
L. S. Lucena · 1×
Citations per year

Countries citing papers authored by S. Picoli

Since Specialization
Citations

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

Fields of papers citing papers by S. Picoli

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 18 scholars most cited alongside S. Picoli, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with S. Picoli Line = papers co-authored together S. Picoli links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200987
2 200368
3 201551
4 201231
5 201128
6 200528
7 201422
8 200822
9 201020
10 201717
11 201716
12 201316
13 200614
14 201014
15
Dynamics of tournaments: the soccer case: A random walk approach modeling soccer leagues
201014
16 201113
17 200713
18 201411
19 200511
20 20064

About S. Picoli

S. Picoli is a scholar working on Statistical and Nonlinear Physics, Economics and Econometrics, Modeling and Simulation, Molecular Biology and Sociology and Political Science, having authored 25 papers that have together received 513 indexed citations. Recurring topics across this work include Complex Systems and Time Series Analysis (10 papers), Statistical Mechanics and Entropy (6 papers), Complex Network Analysis Techniques (6 papers), Statistical Distribution Estimation and Applications (3 papers), COVID-19 epidemiological studies (3 papers), Fractional Differential Equations Solutions (3 papers), Sports Analytics and Performance (2 papers) and Theoretical and Computational Physics (2 papers). The work is most often cited by research in Modeling and Simulation (89 citations), Statistical and Nonlinear Physics (206 citations), Statistics and Probability (88 citations), Economics and Econometrics (164 citations) and Statistics, Probability and Uncertainty (38 citations). S. Picoli has collaborated with scholars based in Brazil, Indonesia and United States. Frequent co-authors include R. S. Mendes, L. C. Malacarne, Haroldo V. Ribeiro, E. K. Lenzi, P. A. Santoro, Jorge Juarez Vieira Teixeira, Leandro Santos Costa, Luiz G. A. Alves, Marcos V. Moro and L. R. da Silva. Their work appears in journals such as Europhysics Letters (EPL), PLoS ONE, Physica A Statistical Mechanics and its Applications, The European Physical Journal B 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.

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