Gabriel David

41 papers receiving 703 citations

Peers

Gabriel David
Comparison fields: 5 of 127
  • Structural Biology 11
  • Communication 38
  • Molecular Biology 374
  • Computer Science Applications 28
  • Software 15
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Andrew Morin United States
Jérôme Waldispühl Canada
Dana Movshovitz‐Attias United States
Kristian Rother Poland
Kazi Zakia Sultana United States
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Citations per field
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Citations per year

Countries citing papers authored by Gabriel David

Since Specialization
Citations

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

Fields of papers citing papers by Gabriel David

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Gabriel David, 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 Gabriel David Line = papers co-authored together Gabriel David links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 46 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2009304
2 2020111
3 200537
4 201035
5 201124
6 200823
7 201019
8 200719
9 202015
10 202314
11 200510
12 200510
13 201810
14 20118
15
A metadata model for multimedia databases
20018
16 20178
17
XSDoc: an extensible Wiki-based infrastructure for Framework documentation
20037
18
FEUP at TREC 2008 Blog Track : using temporal evidence for ranking and feed distillation
20086
19
Patterns for Documenting Frameworks - Part II.
20065
20 20055

About Gabriel David

Gabriel David is a scholar working on Information Systems, Artificial Intelligence, Molecular Biology, Computer Vision and Pattern Recognition and Computer Networks and Communications, having authored 46 papers that have together received 720 indexed citations. Recurring topics across this work include Video Analysis and Summarization (5 papers), Data Quality and Management (5 papers), Advanced Image and Video Retrieval Techniques (5 papers), Web Data Mining and Analysis (5 papers), Semantic Web and Ontologies (4 papers), Image Retrieval and Classification Techniques (4 papers), Data Management and Algorithms (3 papers) and Protein Structure and Dynamics (3 papers). The work is most often cited by research in Structural Biology (11 citations), Communication (38 citations), Molecular Biology (374 citations), Computer Science Applications (28 citations) and Software (15 citations). Gabriel David has collaborated with scholars based in Portugal, France and United States. Frequent co-authors include Javier Pérez, Cristina Ribeiro, Ademar Aguiar, Sérgio Nunes, Andrea Parmeggiani, Antoine Le Gall, Jean‐Charles Walter, John Palmeri, Marcelo Nöllmann and Jean‐Yves Bouet. Their work appears in journals such as Journal of Structural Biology, Current Zoology, Journal of Hepatology, Science Advances and Molecular Cell.

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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