CsAuthors.net database
Most of the data is coming from the
DBLP Computer Science Bibliography
and the rest is coming from CsAuthors.net own database.
We are working hard to keep everything up-to-date. However, we know that there are many papers not yet included in our dataset.
If something is wrong or missing, feel free to write me at
We are working hard to keep everything up-to-date. However, we know that there are many papers not yet included in our dataset.
If something is wrong or missing, feel free to write me at
my email address
.
The "Dijkstra number"
The Dijkstra number describes the collaborative distance between an author and
Edsger W. Dijkstra.
In our dataset 90.2% of authors are connected to Edsger W. Dijkstra and the average Dijkstra number among them is 5.08.
These kind of number/metrics are quite famous and already well defined in other fields.
In our dataset 90.2% of authors are connected to Edsger W. Dijkstra and the average Dijkstra number among them is 5.08.
These kind of number/metrics are quite famous and already well defined in other fields.
- The "Erdős number" expresses the collaborative distance with Paul Erdős, the famous Hungarian mathematician.
- The "Bacon number" expresses the co-acting distance with Kevin Bacon.
The "Erdős number"
The Erdős number describes the collaborative distance between an author and
Paul Erdős.
In our dataset 90.2% of authors are connected to Paul Erdős and the average Erdős number among them is 4.67.
Find more on Wikipedia with an article on the"Erdős number".
In our dataset 90.2% of authors are connected to Paul Erdős and the average Erdős number among them is 4.67.
Find more on Wikipedia with an article on the"Erdős number".
Tibor Auer
Orcid: 0000-0001-5153-1424
According to our database1,
Tibor Auer
authored at least 10 papers
between 2014 and 2025.
Collaborative distances:
Collaborative distances:
Timeline
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
On csauthors.net:
Bibliography
2025
A large-scale multi-centre study characterising atrophy heterogeneity in Alzheimer's disease.
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NeuroImage, 2025
2024
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Dataset, September, 2024
2023
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Dataset, November, 2023
2022
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Neuroinformatics, 2022
2021
Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis.
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NeuroImage, 2021
2020
Predicting Body Mass Index From Structural MRI Brain Images Using a Deep Convolutional Neural Network.
Frontiers Neuroinformatics, 2020
2017
BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods.
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PLoS Comput. Biol., 2017
The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample.
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NeuroImage, 2017
Predicting Cognitive Recovery of Stroke Patients from the Structural MRI Connectome Using a Naïve Bayesian Tree Classifier.
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Proceedings of the 30th IEEE International Symposium on Computer-Based Medical Systems, 2017
2014
Automatic analysis (aa): efficient neuroimaging workflows and parallel processing using Matlab and XML.
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Frontiers Neuroinformatics, 2014