Daniel Münch

891 total citations
23 papers, 650 citations indexed

About

Daniel Münch is a scholar working on Genetics, Insect Science and Ecology, Evolution, Behavior and Systematics. According to data from OpenAlex, Daniel Münch has authored 23 papers receiving a total of 650 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Genetics, 13 papers in Insect Science and 9 papers in Ecology, Evolution, Behavior and Systematics. Recurrent topics in Daniel Münch's work include Insect and Arachnid Ecology and Behavior (14 papers), Insect and Pesticide Research (11 papers) and Plant and animal studies (8 papers). Daniel Münch is often cited by papers focused on Insect and Arachnid Ecology and Behavior (14 papers), Insect and Pesticide Research (11 papers) and Plant and animal studies (8 papers). Daniel Münch collaborates with scholars based in Norway, United States and Finland. Daniel Münch's co-authors include Gro V. Amdam, Florian Wolschin, Heli Salmela, Heli Havukainen, Øyvind Halskau, Anne Baumann, Michelle Krogsgaard, Zhong Shi, Ørjan G. Martinsen and Bjørg Egelandsdal and has published in prestigious journals such as Journal of Biological Chemistry, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Daniel Münch

22 papers receiving 643 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Daniel Münch Norway 13 432 424 345 100 64 23 650
Pierre Fouillet France 20 759 1.8× 233 0.5× 288 0.8× 122 1.2× 6 0.1× 25 961
N. A. Chentsova Russia 14 297 0.7× 287 0.7× 113 0.3× 416 4.2× 7 0.1× 26 598
James T. Bradley United States 12 132 0.3× 180 0.4× 65 0.2× 142 1.4× 31 0.5× 30 461
Ellen Thomsen United Kingdom 14 374 0.9× 231 0.5× 128 0.4× 490 4.9× 37 0.6× 17 751
Е. К. Карпова Russia 17 300 0.7× 300 0.7× 110 0.3× 472 4.7× 7 0.1× 64 692
Angela M. Schaner United States 13 412 1.0× 322 0.8× 340 1.0× 237 2.4× 13 0.2× 17 662
Francine Goltzené France 11 244 0.6× 263 0.6× 91 0.3× 428 4.3× 6 0.1× 13 639
Adam Bajgar Czechia 11 284 0.7× 172 0.4× 106 0.3× 318 3.2× 2 0.0× 19 716
Peter Fluri Switzerland 14 1.2k 2.7× 916 2.2× 955 2.8× 64 0.6× 9 0.1× 24 1.3k
Honoo Satake Japan 12 258 0.6× 270 0.6× 125 0.4× 544 5.4× 4 0.1× 17 672

Countries citing papers authored by Daniel Münch

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Münch

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Münch

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

All Works

20 of 20 papers shown
1.
Salmela, Heli, Gyan Harwood, Daniel Münch, et al.. (2022). Nuclear translocation of vitellogenin in the honey bee (Apis mellifera). Apidologie. 53(1). 13–13. 17 indexed citations
2.
Egelandsdal, Bjørg, et al.. (2022). Beyond standard PSE testing: An exploratory study of bioimpedance as a marker for ham defects. Meat Science. 194. 108980–108980. 6 indexed citations
3.
Martinsen, Ørjan G., et al.. (2022). Bioimpedance-based authentication of defrosted versus fresh pork at the end of refrigerated shelf life. SHILAP Revista de lepidopterología. 13(1). 125–131. 1 indexed citations
4.
5.
Amdam, Gro V., et al.. (2021). Screening bioactive food compounds in honey bees suggests curcumin blocks alcohol-induced damage to longevity and DNA methylation. Scientific Reports. 11(1). 19156–19156. 8 indexed citations
6.
Martinsen, Ørjan G., et al.. (2021). Feasibility of Using Electrical Impedance Spectroscopy for Assessing Biological Cell Damage during Freezing and Thawing. Sensors. 21(12). 4129–4129. 13 indexed citations
7.
Münch, Daniel, et al.. (2020). Monitoring electric impedance during freezing and thawing of saline and de-ionized water. SHILAP Revista de lepidopterología. 11(1). 106–111. 1 indexed citations
8.
Egelandsdal, Bjørg, Han Zhu, Frøydis Bjerke, et al.. (2019). Detectability of the degree of freeze damage in meat depends on analytic-tool selection. Meat Science. 152. 8–19. 46 indexed citations
9.
Münch, Daniel, et al.. (2018). Increased survival of honeybees in the laboratory after simultaneous exposure to low doses of pesticides and bacteria. PLoS ONE. 13(1). e0191256–e0191256. 31 indexed citations
10.
Amdam, Gro V., et al.. (2018). Metabolic enzymes in glial cells of the honeybee brain and their associations with aging, starvation and food response. PLoS ONE. 13(6). e0198322–e0198322. 12 indexed citations
11.
Salmela, Heli, et al.. (2017). Hemocyte-mediated phagocytosis differs between honey bee (Apis mellifera) worker castes. PLoS ONE. 12(9). e0184108–e0184108. 25 indexed citations
12.
Vågbø, Cathrine Broberg, Daniel Münch, Hans E. Krokan, et al.. (2016). DNA base modifications in honey bee and fruit fly genomes suggest an active demethylation machinery with species- and tissue-specific turnover rates. Biochemistry and Biophysics Reports. 6. 9–15. 14 indexed citations
13.
Speth, Martin, et al.. (2015). Aging- and task-related resilience decline is linked to food responsiveness in highly social honey bees. Experimental Gerontology. 65. 46–52. 7 indexed citations
14.
Münch, Daniel, Kate E. Ihle, Heli Salmela, & Gro V. Amdam. (2015). Vitellogenin in the honey bee brain: Atypical localization of a reproductive protein that promotes longevity. Experimental Gerontology. 71. 103–108. 37 indexed citations
15.
Havukainen, Heli, Daniel Münch, Anne Baumann, et al.. (2013). Vitellogenin Recognizes Cell Damage through Membrane Binding and Shields Living Cells from Reactive Oxygen Species. Journal of Biological Chemistry. 288(39). 28369–28381. 105 indexed citations
16.
Münch, Daniel, et al.. (2013). Obtaining Specimens with Slowed, Accelerated and Reversed Aging in the Honey Bee Model. Journal of Visualized Experiments. 13 indexed citations
17.
Münch, Daniel, et al.. (2013). Aging and its modulation in a long-lived worker caste of the honey bee. Journal of Experimental Biology. 216(9). 1638–1649. 54 indexed citations
18.
Münch, Daniel & Gro V. Amdam. (2010). The curious case of aging plasticity in honey bees. FEBS Letters. 584(12). 2496–2503. 110 indexed citations
19.
Münch, Daniel, Swidbert R. Ott, & Hans‐Joachim Pflüger. (2010). Three‐dimensional distribution of NO sources in a primary mechanosensory integration center in the locust and its implications for volume signaling. The Journal of Comparative Neurology. 518(15). 2903–2916. 5 indexed citations
20.
Münch, Daniel, Gro V. Amdam, & Florian Wolschin. (2008). Ageing in a eusocial insect: molecular and physiological characteristics of life span plasticity in the honey bee. Functional Ecology. 22(3). 407–421. 107 indexed citations

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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