Dániel Mucs

25 total papers · 1.6k total citations
17 papers, 1.3k citations indexed

About

Dániel Mucs is a scholar working on Health, Toxicology and Mutagenesis, Computational Theory and Mathematics and Molecular Biology. According to data from OpenAlex, Dániel Mucs has authored 17 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Health, Toxicology and Mutagenesis, 6 papers in Computational Theory and Mathematics and 3 papers in Molecular Biology. Recurrent topics in Dániel Mucs's work include Computational Drug Discovery Methods (6 papers), Toxic Organic Pollutants Impact (5 papers) and Effects and risks of endocrine disrupting chemicals (4 papers). Dániel Mucs is often cited by papers focused on Computational Drug Discovery Methods (6 papers), Toxic Organic Pollutants Impact (5 papers) and Effects and risks of endocrine disrupting chemicals (4 papers). Dániel Mucs collaborates with scholars based in Sweden, United Kingdom and United States. Dániel Mucs's co-authors include Christian Lindh, Tony Fletcher, Pia Tallving, Kristina Jakobsson, Ying Li, Kristin Scott, Ulf Norinder, Jin Zhang, Fredrik Svensson and Richard A. Bryce and has published in prestigious journals such as PLoS ONE, Chemosphere and Environment International.

In The Last Decade

Dániel Mucs

16 papers receiving 1.3k citations

Hit Papers

Half-lives of PFOS, PFHxS... 2017 2026 2020 2023 2017 200 400 600

Author Peers

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

Author Last Decade Papers Cites
Dániel Mucs 824 695 225 191 162 17 1.3k
Katherine E. Pelch 764 0.9× 1.1k 1.5× 273 1.2× 91 0.5× 182 1.1× 33 1.8k
Steven R. Frame 1.3k 1.5× 1.1k 1.6× 221 1.0× 246 1.3× 236 1.5× 38 1.8k
Huiming Cao 544 0.7× 559 0.8× 148 0.7× 48 0.3× 229 1.4× 55 1.3k
Caroline Tebes-Stevens 1.0k 1.2× 756 1.1× 370 1.6× 44 0.2× 54 0.3× 24 1.5k
Robert D. Zehr 1.2k 1.4× 995 1.4× 107 0.5× 326 1.7× 198 1.2× 32 1.6k
Wenyue Hu 893 1.1× 876 1.3× 248 1.1× 105 0.5× 186 1.1× 21 1.3k
Diane L. Nabb 644 0.8× 815 1.2× 202 0.9× 46 0.2× 85 0.5× 25 1.1k
Miyoung Yoon 305 0.4× 792 1.1× 36 0.2× 189 1.0× 128 0.8× 69 1.5k
Wendy Heiger‐Bernays 723 0.9× 1.1k 1.6× 297 1.3× 92 0.5× 180 1.1× 53 1.8k
Raymond A. Kemper 514 0.6× 507 0.7× 129 0.6× 36 0.2× 291 1.8× 27 1.1k

Countries citing papers authored by Dániel Mucs

Since Specialization
Citations

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

Fields of papers citing papers by Dániel Mucs

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dániel Mucs

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