Daniël Charlier
Impact in
- Biochemistry top 1%
- Amino Acid Enzymes and Metabolism
- Genetics top 2%
- Bacterial Genetics and Biotechnology
Papers in
- Genetics 60
- Bacterial Genetics and Biotechnology 60
- Biochemistry 20
- Amino Acid Enzymes and Metabolism 20
- Co-authors
- Nicolas Glansdorff (46 shared papers)Indra Bervoets (11 shared papers)Eveline Peeters (22 shared papers)Raymond Cunin (15 shared papers)André Pierard (16 shared papers)Daniel Gigot (13 shared papers)Phu Nguyen Le Minh (11 shared papers)Martine Roovers (12 shared papers)
- Journals
- Journal of Molecular Biology (13 papers)Nucleic Acids Research (12 papers)Journal of Bacteriology (9 papers)Gene (6 papers)Molecular Microbiology (6 papers)
- Partner nations
- BelgiumFranceUnited States
In The Last Decade
Daniël Charlier
117 papers receiving 3.5k citations
Peers
Comparison fields: 5 of 129
- Biochemistry 386
- Genetics 1.4k
- Endocrinology 260
- Molecular Medicine 245
- Molecular Biology 2.6k
Countries citing papers authored by Daniël Charlier
This map shows the geographic impact of Daniël Charlier'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 Daniël Charlier with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniël Charlier more than expected).
Fields of papers citing papers by Daniël Charlier
This network shows the impact of papers produced by Daniël Charlier. 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 Daniël Charlier. The network helps show where Daniël Charlier may publish in the future.
Co-authors
The 25 scholars most cited alongside Daniël Charlier, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 118 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2010 | 221 | |
| 2 | 2012 | 180 | |
| 3 | 1984 | 141 | |
| 4 | 2009 | 135 | |
| 5 | 2019 | 117 | |
| 6 | 2011 | 96 | |
| 7 | 1992 | 88 | |
| 8 | 2019 | 87 | |
| 9 | 2018 | 79 | |
| 10 | 1982 | 72 | |
| 11 | 2010 | 69 | |
| 12 | 2006 | 68 | |
| 13 | 2002 | 67 | |
| 14 | 2002 | 66 | |
| 15 | 1995 | 66 | |
| 16 | 1999 | 64 | |
| 17 | 2004 | 63 | |
| 18 | 1999 | 55 | |
| 19 | 2004 | 55 | |
| 20 | 2018 | 55 |
About Daniël Charlier
Daniël Charlier is a scholar working on Genetics, Biochemistry, Molecular Biology, Ecology and Materials Chemistry, having authored 118 papers that have together received 3.6k indexed citations. Recurring topics across this work include Bacterial Genetics and Biotechnology (60 papers), Enzyme Structure and Function (36 papers), RNA and protein synthesis mechanisms (35 papers), Bacteriophages and microbial interactions (25 papers), Amino Acid Enzymes and Metabolism (20 papers), Polyamine Metabolism and Applications (10 papers), Biochemical and Molecular Research (8 papers) and Microbial Metabolic Engineering and Bioproduction (7 papers). The work is most often cited by research in Biochemistry (386 citations), Genetics (1.4k citations), Endocrinology (260 citations), Molecular Medicine (245 citations) and Molecular Biology (2.6k citations). Daniël Charlier has collaborated with scholars based in Belgium, France and United States. Frequent co-authors include Nicolas Glansdorff, Indra Bervoets, Eveline Peeters, Raymond Cunin, André Pierard, Daniel Gigot, Phu Nguyen Le Minh, Martine Roovers, Remy Loris and Dominique Maes. Their work appears in journals such as Journal of Molecular Biology, Nucleic Acids Research, Journal of Bacteriology, Gene and Molecular Microbiology.
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.