F. Hakl

976 citations
3 papers · 94 indexed · h-index 3

Impact in

    • Astrophysics and Cosmic Phenomena
    • Dark Matter and Cosmic Phenomena
    • Particle physics theoretical and experimental studies
    • Particle Detector Development and Performance
    • Machine Learning and Data Classification
    • Neural Networks and Applications
    • Imbalanced Data Classification Techniques

Papers in

F. Hakl

3 papers receiving 90 citations

Peers

F. Hakl
Comparison fields: 5 of 35
  • Nuclear and High Energy Physics 34
  • Artificial Intelligence 47
  • Statistics and Probability 11
  • Statistics, Probability and Uncertainty 5
  • Computer Vision and Pattern Recognition 14
Replace A. Vaiciulis with:
A. Vaiciulis United States
C. Li China
Javier Orduz United States
Jerzy Browkin Poland
Greg Daues United States
Sylvain Arlot France
Ewgenij Gawrilow Germany
Noam D. Elkies United States
T. Hauth Germany
T. Kuhr Germany
F. Hakl relative to A. Vaiciulis United States A. Vaiciulis's profile →
Citations per field
00.5×1.5×
A. Vaiciulis · 1×
Citations per year

Countries citing papers authored by F. Hakl

Since Specialization
Citations

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

Fields of papers citing papers by F. Hakl

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 11 scholars most cited alongside F. Hakl, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with F. Hakl Line = papers co-authored together F. Hakl links everyone, so they are left out of the graph.

All Works

3 of 3 papers shown
#Work
1 200385
2 20036
3 20023

About F. Hakl

F. Hakl is a scholar working on Computer Networks and Communications, Radiological and Ultrasound Technology, Radiation, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging, having authored 3 papers that have together received 94 indexed citations. Recurring topics across this work include Particle Detector Development and Performance (1 paper), Medical Imaging Techniques and Applications (1 paper), Radiation Detection and Scintillator Technologies (1 paper), Advanced Measurement and Metrology Techniques (1 paper), Sensor Technology and Measurement Systems (1 paper), Scientific Measurement and Uncertainty Evaluation (1 paper), Computational Physics and Python Applications (1 paper) and Radioactivity and Radon Measurements (1 paper). The work is most often cited by research in Nuclear and High Energy Physics (34 citations), Artificial Intelligence (47 citations), Statistics and Probability (11 citations), Statistics, Probability and Uncertainty (5 citations) and Computer Vision and Pattern Recognition (14 citations). F. Hakl has collaborated with scholars based in Czechia, United States and Spain. Frequent co-authors include Marcel Jiřina, R. K. Böck, A. Chilingarian, W. Wittek, M. Gaug, Petr Savický, A. Vaiciulis, Jan Klaschka, T. Hengstebeck and S. Towers. Their work appears in journals such as Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment.

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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