Vera Vasas

1.1k total citations
21 papers, 701 citations indexed

About

Vera Vasas is a scholar working on Ecology, Evolution, Behavior and Systematics, Cellular and Molecular Neuroscience and Cognitive Neuroscience. According to data from OpenAlex, Vera Vasas has authored 21 papers receiving a total of 701 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Ecology, Evolution, Behavior and Systematics, 6 papers in Cellular and Molecular Neuroscience and 6 papers in Cognitive Neuroscience. Recurrent topics in Vera Vasas's work include Plant and animal studies (7 papers), Insect and Arachnid Ecology and Behavior (4 papers) and Visual perception and processing mechanisms (3 papers). Vera Vasas is often cited by papers focused on Plant and animal studies (7 papers), Insect and Arachnid Ecology and Behavior (4 papers) and Visual perception and processing mechanisms (3 papers). Vera Vasas collaborates with scholars based in United Kingdom, Hungary and Spain. Vera Vasas's co-authors include Eörs Szathmáry, Mauro Santos, Chrisantha Fernando, Stuart Kauffman, Ferenc Jordán, Lars Chıttka, Tibor Magura, Béla Tóthmérész, Phil Husbands and Simon McGregor and has published in prestigious journals such as Proceedings of the National Academy of Sciences, PLoS ONE and Scientific Reports.

In The Last Decade

Vera Vasas

20 papers receiving 685 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Vera Vasas United Kingdom 13 291 287 154 120 115 21 701
András Szilágyi Hungary 14 312 1.1× 277 1.0× 109 0.7× 86 0.7× 131 1.1× 40 712
Jay E. Mittenthal United States 19 923 3.2× 104 0.4× 271 1.8× 88 0.7× 291 2.5× 46 1.4k
Derek Caetano-Anollés United States 20 921 3.2× 129 0.4× 48 0.3× 87 0.7× 160 1.4× 26 1.2k
Juan‐Carlos Letelier Chile 12 206 0.7× 176 0.6× 75 0.5× 48 0.4× 31 0.3× 20 470
Arnaud Pocheville France 11 170 0.6× 67 0.2× 37 0.2× 128 1.1× 34 0.3× 24 565
Eörs Szathmáry Hungary 7 549 1.9× 449 1.6× 78 0.5× 188 1.6× 76 0.7× 8 1.2k
Péter Szabó Hungary 12 115 0.4× 113 0.4× 16 0.1× 86 0.7× 93 0.8× 27 594
M. W. Ho United Kingdom 16 234 0.8× 77 0.3× 54 0.4× 61 0.5× 22 0.2× 27 864
Martin Mahner United States 10 147 0.5× 48 0.2× 14 0.1× 120 1.0× 56 0.5× 16 711
Olaf Breidbach Germany 18 125 0.4× 27 0.1× 447 2.9× 173 1.4× 97 0.8× 102 864

Countries citing papers authored by Vera Vasas

Since Specialization
Citations

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

Fields of papers citing papers by Vera Vasas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Vera Vasas

This figure shows the co-authorship network connecting the top 25 collaborators of Vera Vasas. A scholar is included among the top collaborators of Vera Vasas 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 Vera Vasas. Vera Vasas 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.
2.
Vasas, Vera, et al.. (2024). Recording animal-view videos of the natural world using a novel camera system and software package. PLoS Biology. 22(1). e3002444–e3002444. 4 indexed citations
3.
Vasas, Vera, et al.. (2024). Spontaneous biases enhance generalization in the neonate brain. iScience. 27(7). 110195–110195. 3 indexed citations
4.
Hanley, Daniel, et al.. (2024). Through an animal’s eye: the implications of diverse sensory systems in scientific experimentation. Proceedings of the Royal Society B Biological Sciences. 291(2027). 20240022–20240022. 8 indexed citations
5.
Vasas, Vera, et al.. (2023). A spontaneous gravity prior: newborn chicks prefer stimuli that move against gravity. Biology Letters. 19(2). 20220502–20220502. 12 indexed citations
6.
Vasas, Vera, Fei Peng, HaDi MaBouDi, & Lars Chıttka. (2019). Randomly weighted receptor inputs can explain the large diversity of colour-coding neurons in the bee visual system. Scientific Reports. 9(1). 8330–8330. 8 indexed citations
7.
Vasas, Vera & Lars Chıttka. (2018). Insect-Inspired Sequential Inspection Strategy Enables an Artificial Network of Four Neurons to Estimate Numerosity. iScience. 11. 85–92. 30 indexed citations
8.
Vasas, Vera, et al.. (2017). Sheep in wolf's clothing: multicomponent traits enhance the success of mimicry in spider-mimicking moths. Animal Behaviour. 127. 219–224. 11 indexed citations
9.
Vasas, Vera, Daniel Hanley, Peter G. Kevan, & Lars Chıttka. (2017). Multispectral images of flowers reveal the adaptive significance of using long-wavelength-sensitive receptors for edge detection in bees. Journal of Comparative Physiology A. 203(4). 301–311. 14 indexed citations
10.
Baracchi, David, et al.. (2017). Foraging bumblebees use social cues more when the task is difficult. Behavioral Ecology. 29(1). 186–192. 13 indexed citations
11.
Vasas, Vera, et al.. (2017). Color discrimination is not just limited by photoreceptor noise: a comment on Olsson et al.. Behavioral Ecology. 29(2). 285–286. 5 indexed citations
12.
Vasas, Vera, Chrisantha Fernando, András Szilágyi, et al.. (2015). Primordial evolvability: Impasses and challenges. Journal of Theoretical Biology. 381. 29–38. 16 indexed citations
13.
McGregor, Simon, Vera Vasas, Phil Husbands, & Chrisantha Fernando. (2012). Evolution of Associative Learning in Chemical Networks. PLoS Computational Biology. 8(11). e1002739–e1002739. 42 indexed citations
14.
Vasas, Vera, Chrisantha Fernando, Mauro Santos, Stuart Kauffman, & Eörs Szathmáry. (2012). Evolution before genes. Biology Direct. 7(1). 1; discussion 1–1; discussion 1. 253 indexed citations
15.
Fernando, Chrisantha, Vera Vasas, Eörs Szathmáry, & Phil Husbands. (2011). Evolvable Neuronal Paths: A Novel Basis for Information and Search in the Brain. PLoS ONE. 6(8). e23534–e23534. 12 indexed citations
16.
Vasas, Vera, Eörs Szathmáry, & Mauro Santos. (2010). Lack of evolvability in self-sustaining autocatalytic networks constraints metabolism-first scenarios for the origin of life. Proceedings of the National Academy of Sciences. 107(4). 1470–1475. 115 indexed citations
17.
Fedor, Anna & Vera Vasas. (2009). The robustness of keystone indices in food webs. Journal of Theoretical Biology. 260(3). 372–378. 20 indexed citations
18.
Vasas, Vera, Tibor Magura, Ferenc Jordán, & Béla Tóthmérész. (2009). Graph theory in action: evaluating planned highway tracks based on connectivity measures. Landscape Ecology. 24(5). 581–586. 43 indexed citations
19.
Vasas, Vera & Ferenc Jordán. (2006). Topological keystone species in ecological interaction networks: Considering link quality and non-trophic effects. Ecological Modelling. 196(3-4). 365–378. 46 indexed citations
20.
Jordán, Ferenc, István Scheuring, Vera Vasas, & János Podani. (2006). Architectural classes of aquatic food webs based on link distribution. Community Ecology. 7(1). 81–90. 8 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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