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 90.3% 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 90.3% 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 90.3% of authors are connected to Paul Erdős and the average Erdős number among them is 4.68.
Find more on Wikipedia with an article on the"Erdős number".
In our dataset 90.3% of authors are connected to Paul Erdős and the average Erdős number among them is 4.68.
Find more on Wikipedia with an article on the"Erdős number".
Fidel Alfaro-Almagro
Orcid: 0000-0002-9133-5951
According to our database1,
Fidel Alfaro-Almagro
authored at least 17 papers
between 2017 and 2025.
Collaborative distances:
Collaborative distances:
Timeline
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
Online presence:
-
on orcid.org
On csauthors.net:
Bibliography
2025
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Dataset, September, 2025
2024
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Dataset, September, 2024
2023
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Dataset, November, 2023
ICAM-Reg: Interpretable Classification and Regression With Feature Attribution for Mapping Neurological Phenotypes in Individual Scans.
, , , , , , , , , ,
IEEE Trans. Medical Imaging, April, 2023
Automated detection of cerebral microbleeds on MR images using knowledge distillation framework.
, , , , , , , , , , , , , ,
Frontiers Neuroinformatics, March, 2023
Amplitudes of resting-state functional networks - investigation into their correlates and biophysical properties.
, , , , , , , , , ,
NeuroImage, 2023
2021
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NeuroImage, 2021
Automated Detection of Candidate Subjects With Cerebral Microbleeds Using Machine Learning.
, , , , , , , , , , , , , ,
Frontiers Neuroinformatics, 2021
ICAM-reg: Interpretable Classification and Regression with Feature Attribution for Mapping Neurological Phenotypes in Individual Scans.
, , , , , , , , ,
CoRR, 2021
2019
Modelling the distribution of white matter hyperintensities due to ageing on MRI images using Bayesian inference.
, , , , , , ,
NeuroImage, 2019
NeuroImage, 2019
Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction.
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NeuroImage, 2019
2018
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NeuroImage, 2018
Image processing and Quality Control for the first 10, 000 brain imaging datasets from UK Biobank.
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NeuroImage, 2018
2017
BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods.
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PLoS Comput. Biol., 2017
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NeuroImage, 2017
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NeuroImage, 2017