J. Maat

2.3k citations
32 papers · 1.9k · h-index 24

Impact in

    • Enzyme Production and Characterization
  • Genetics top 5%
    • Virus-based gene therapy research

Papers in

    • RNA Interference and Gene Delivery 5
    • Viral Infectious Diseases and Gene Expression in Insects 5
    • Fungal and yeast genetics research 4
    • Virus-based gene therapy research 11
    • Bacterial Genetics and Biotechnology 3

J. Maat

32 papers receiving 1.7k citations

Peers

J. Maat
Comparison fields: 5 of 88
  • Biotechnology 327
  • Genetics 709
  • Molecular Biology 1.4k
  • Nutrition and Dietetics 181
  • Plant Science 388
Replace Daniel Perlman with:
Daniel Perlman United States
Janice Pero United States
Akira Taketo Japan
Alan Sloma United States
David R. Higgins United States
Joan Tilburn United Kingdom
Constantin E. Vorgias Germany
Shigeyuki Ichihara Japan
Jesús de la Cruz Spain
Joel Jessee United States
J. Maat relative to Daniel Perlman United States Daniel Perlman's profile →
Citations per field
00.5×1.5×2.0×
Daniel Perlman · 1×
Citations per year

Countries citing papers authored by J. Maat

Since Specialization
Citations

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

Fields of papers citing papers by J. Maat

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 32 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1982180
2 1985162
3 1999159
4 1978139
5 198097
6 198592
7 198091
8 197790
9 197890
10 199982
11 199680
12 198470
13 198067
14 197956
15 199154
16 198253
17 199449
18 199343
19 199841
20 198033

About J. Maat

J. Maat is a scholar working on Molecular Biology, Genetics, Infectious Diseases, Biotechnology and Plant Science, having authored 32 papers that have together received 1.9k indexed citations. Recurring topics across this work include Virus-based gene therapy research (11 papers), Viral gastroenteritis research and epidemiology (6 papers), Enzyme Production and Characterization (5 papers), RNA Interference and Gene Delivery (5 papers), Viral Infectious Diseases and Gene Expression in Insects (5 papers), Fungal and yeast genetics research (4 papers), Bacterial Genetics and Biotechnology (3 papers) and Polysaccharides and Plant Cell Walls (3 papers). The work is most often cited by research in Biotechnology (327 citations), Genetics (709 citations), Molecular Biology (1.4k citations), Nutrition and Dietetics (181 citations) and Plant Science (388 citations). J. Maat has collaborated with scholars based in Netherlands, United States and Germany. Frequent co-authors include H. van Ormondt, Chris J. Visser, A. de Waard, Luppo Edens, R. Dijkema, Andrew J.H. Smith, A.J. van der Eb, María‐Teresa García‐Conesa, Gary Williamson and W.Russell McLauchlan. Their work appears in journals such as Gene, Nucleic Acids Research, Biochemical Journal, Carbohydrate Polymers 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.

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