Daniel C. Maddison

28 total papers · 795 total citations
16 papers, 576 citations indexed

About

Daniel C. Maddison is a scholar working on Molecular Biology, Cell Biology and Epidemiology. According to data from OpenAlex, Daniel C. Maddison has authored 16 papers receiving a total of 576 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 6 papers in Cell Biology and 5 papers in Epidemiology. Recurrent topics in Daniel C. Maddison's work include Mitochondrial Function and Pathology (5 papers), Autophagy in Disease and Therapy (5 papers) and Ubiquitin and proteasome pathways (3 papers). Daniel C. Maddison is often cited by papers focused on Mitochondrial Function and Pathology (5 papers), Autophagy in Disease and Therapy (5 papers) and Ubiquitin and proteasome pathways (3 papers). Daniel C. Maddison collaborates with scholars based in United Kingdom, United States and Germany. Daniel C. Maddison's co-authors include Flaviano Giorgini, Gaynor A. Smith, Bilal R. Malik, Owen M. Peters, Paul J. Muchowski, Mariaelena Repici, Jinny S. Wong, Kwadwo Opoku-Nsiah, Pasquale Pellegrini and Ana Osório Oliveira and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and The Journal of Physiology.

In The Last Decade

Daniel C. Maddison

15 papers receiving 572 citations

Author Peers

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

Author Last Decade Papers Cites
Daniel C. Maddison 244 216 99 97 88 16 576
Ameneh Zare-Shahabadi 151 0.6× 171 0.8× 121 1.2× 63 0.6× 82 0.9× 20 559
André Miguel Miranda 314 1.3× 109 0.5× 152 1.5× 91 0.9× 57 0.6× 14 564
Krzysztof Kolmus 214 0.9× 99 0.5× 94 0.9× 71 0.7× 44 0.5× 11 663
Fumika Sakaue 254 1.0× 220 1.0× 57 0.6× 131 1.4× 24 0.3× 17 621
April L. Lussier 208 0.9× 143 0.7× 147 1.5× 201 2.1× 32 0.4× 15 660
Jorge S. Valadas 171 0.7× 77 0.4× 75 0.8× 78 0.8× 47 0.5× 12 523
Hadile Ounallah-Saad 184 0.8× 187 0.9× 98 1.0× 127 1.3× 39 0.4× 8 609
Kathrin Hafner 232 1.0× 106 0.5× 72 0.7× 112 1.2× 165 1.9× 13 678
Xiaohan Yang 193 0.8× 208 1.0× 80 0.8× 127 1.3× 45 0.5× 20 623
Svenja V. Trossbach 382 1.6× 80 0.4× 86 0.9× 30 0.3× 44 0.5× 27 683

Countries citing papers authored by Daniel C. Maddison

Since Specialization
Citations

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

Fields of papers citing papers by Daniel C. Maddison

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel C. Maddison

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel C. Maddison. A scholar is included among the top collaborators of Daniel C. Maddison 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 C. Maddison. Daniel C. Maddison 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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