Daniel R. Montagna

545 citations
8 papers · 443 indexed · h-index 6
Topics
Alzheimer's disease research and treatments (3 papers)RNA Research and Splicing (3 papers)Computational Drug Discovery Methods (2 papers)
Partner nations
United StatesDenmark

In The Last Decade

Daniel R. Montagna

8 papers receiving 441 citations

Peers

Daniel R. Montagna
Comparison fields: 5 of 58
  • Molecular Biology 325
  • Physiology 199
  • Cancer Research 69
  • Computational Theory and Mathematics 56
  • Pharmacology 49
Replace Bhramdeo Bassit with:
Bhramdeo Bassit United States
Charlotte Nerelius Sweden
Hélène Vignaud France
Ruth MacLeod United Kingdom
Anna M. Lilja Sweden
Chaeyoung Kim South Korea
Sarah Domnitz United States
Kai Prager Germany
David A. Hicks United Kingdom
Aurelio Vázquez de la Torre Spain
Daniel R. Montagna relative to Bhramdeo Bassit United States Bhramdeo Bassit's profile →
Citations per field
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Citations per year

Countries citing papers authored by Daniel R. Montagna

Since Specialization
Citations

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

Fields of papers citing papers by Daniel R. Montagna

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel R. Montagna. 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 Daniel R. Montagna. The network helps show where Daniel R. Montagna may publish in the future.

Co-authorship network of co-authors of Daniel R. Montagna

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

All Works

8 of 8 papers shown
#WorkIndexed citations
1 5
2 5
3 125
4 3
5 106
6 3
7 113
8 83

About Daniel R. Montagna

Daniel R. Montagna is a scholar working on Physiology, Computational Theory and Mathematics and Pharmacology, having authored 8 papers that have together received 443 indexed citations. Recurring topics across this work include Alzheimer's disease research and treatments (3 papers), RNA Research and Splicing (3 papers) and Computational Drug Discovery Methods (2 papers). The work is most often cited by research in Physiology (199 citations), Molecular Biology (325 citations) and Cancer Research (69 citations). Daniel R. Montagna has collaborated with scholars based in United States and Denmark. Frequent co-authors include Dennis J. Selkoe, Michael S. Wolfe, Michael S. Wolfe, Bente Finsen, John R. Dickson, Carla Kruse, Mihaela‐Rita Mihailescu, Yongli Gu, Jean‐François Fisette and Warren D. Hirst. Their work appears in journals such as Proceedings of the National Academy of Sciences, Scientific Reports and Journal of Neurochemistry.

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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