| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Electronics and Electrical Engineering | 269 | 36 | 60 | 14 |
| Mechanical and Aerospace Engineering | 269 | 13 | 17 | 10 |
Frontiers in Neurorobotics was organized to reinforce research efforts on Artificial intelligence, Robot, Human–computer interaction, Artificial neural network and Computer vision. Topics in Artificial intelligence explored in the journal were investigated in conjunction with research in Machine learning and Pattern recognition. The studies in Robot featured incorporate elements of Control theory, Simulation and Robotic arm.
The work on Simulation presented in it focuses on Exoskeleton in particular. Studies on Human–computer interaction discussed in the journal link to the field of Human–robot interaction.
The published articles primarily tackle Artificial intelligence, Robot, Human–computer interaction, Reinforcement learning and Simulation. The works on Artificial intelligence tackled in the most cited publications bring together disciplines like Machine learning and Computer vision. The most cited publications with studies in Robot featured incorporate elements of Mirror neuron, Electroencephalography and Set (psychology).
The main research concerns discussed in the journal are Artificial intelligence, Robot, Human–computer interaction, Artificial neural network and Computer vision. Frontiers in Neurorobotics addresses concerns in Artificial intelligence which are intertwined with other disciplines, such as Machine learning and Pattern recognition. Frontiers in Neurorobotics focuses on Robot but the discussions also offer insight into other areas such as Control theory, Trajectory and Set (psychology).
The journal facilitates discussions on Human–computer interaction that incorporate concepts from other fields like Perception, Human–robot interaction, Neuroergonomics, Control (management) and Haptic technology. Artificial neural network study tackled is connected to the field of Cognitive neuroscience of visual object recognition. It holds forums on Deep learning that merges themes from other disciplines such as Speech recognition and Convolutional neural network.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in Frontiers in Neurorobotics (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in Frontiers in Neurorobotics (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 10.20% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 17.42% were posted by at least one author from the top 10 institutions publishing in the journal. Another 9.09% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 20.45% of all publications and 53.03% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
Christian Di Natali;Ali Sadeghi;Alessio Mondini;Eliza Bottenberg
(2020)Hub Ali;Dawei Gong;Meng Wang;Xiaolin Dai
(2020)Bin Fang;Quan Zhou;Fuchun Sun;Jianhua Shan
(2020)Jérémie Knüsel;Jérémie Knüsel;Alessandro Crespi;Jean-Marie Cabelguen;Auke Jan Ijspeert
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