Nicolas Städler

1.3k citations
18 papers · 434 indexed · h-index 11
Topics
Bayesian Methods and Mixture Models (4 papers)Statistical Methods and Inference (4 papers)Statistical Methods and Bayesian Inference (3 papers)
Partner nations
SwitzerlandFranceGermany

In The Last Decade

Nicolas Städler

18 papers receiving 428 citations

Peers

Nicolas Städler
Comparison fields: 5 of 94
  • Oncology 151
  • Molecular Biology 135
  • Immunology 70
  • Statistics and Probability 66
  • Pulmonary and Respiratory Medicine 57
Replace Ursula Becker with:
Ursula Becker Germany
David Erichsen United States
Spiridon Tsavachidis United States
Tatsuya Ando Japan
Harris A. Jaffee United States
Dejun Tang China
Jarosław Śmieja Poland
Minki Kim South Korea
Benhuai Xie United States
S. Kataoka Japan
Nicolas Städler relative to Ursula Becker Germany Ursula Becker's profile →
Citations per field
00.5×5.3×
Ursula Becker · 1×
Citations per year

Countries citing papers authored by Nicolas Städler

Since Specialization
Citations

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

Fields of papers citing papers by Nicolas Städler

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nicolas Städler

This figure shows the co-authorship network connecting the top 25 collaborators of Nicolas Städler. A scholar is included among the top collaborators of Nicolas Städler 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 Nicolas Städler. Nicolas Städler is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
#WorkIndexed citations
1 1
2 1
3 4
4 12
5 8
6 18
7 173
8 11
9 29
10 8
11 13
12 14
13 10
14 45
15 8
16 53
17 4
18 22

About Nicolas Städler

Nicolas Städler is a scholar working on Statistics and Probability, Developmental Neuroscience and Biophysics, having authored 18 papers that have together received 434 indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (4 papers), Statistical Methods and Inference (4 papers) and Statistical Methods and Bayesian Inference (3 papers). The work is most often cited by research in Statistics and Probability (66 citations), Developmental Neuroscience (30 citations) and Oncology (151 citations). Nicolas Städler has collaborated with scholars based in Switzerland, France and Germany. Frequent co-authors include Peter Bühlmann, Zherui Wu, Patricia Forgez, E. Segal, Antonio Bobbio, Diane Damotte, Filippo Lococo, Jean Trédaniel, Marco Alifano and Philippe Icard. Their work appears in journals such as Proceedings of the National Academy of Sciences, Bioinformatics and Molecular and Cellular Biology.

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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