J. Maat

2.3k citations
35 papers · 2.1k · h-index 24

Impact in

    • Enzyme Production and Characterization
  • Genetics top 2%
    • Virus-based gene therapy research

Papers in

    • Viral Infectious Diseases and Gene Expression in Insects 6
    • RNA Interference and Gene Delivery 5
    • Fungal and yeast genetics research 4
    • Virus-based gene therapy research 12
    • Bacterial Genetics and Biotechnology 3

J. Maat

35 papers receiving 1.8k citations

Peers

J. Maat
Comparison fields: 5 of 91
  • Biotechnology 350
  • Genetics 761
  • Molecular Biology 1.5k
  • Nutrition and Dietetics 204
  • Plant Science 425
Replace Daniel Perlman with:
Daniel Perlman United States
Janice Pero United States
Akira Taketo Japan
Michel Guérineau France
Tomas Kempe United States
B. Cami France
Antonio Jiménez Spain
P. J. Piggot United States
Constantin E. Vorgias Germany
Yoshiho Nagata Japan
J. Maat relative to Daniel Perlman United States Daniel Perlman's profile →
Citations per field
00.5×1.5×2.1×
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 35 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1982204
2 1985177
3 1999168
4 1978151
5 1985105
6 1977102
7 1980101
8 198098
9 197896
10 199689
11 198486
12 199985
13 198071
14 197963
15 199159
16 198258
17 199454
18 199846
19 199345
20 198037

About J. Maat

J. Maat is a scholar working on Molecular Biology, Genetics, Infectious Diseases, Biotechnology and Plant Science, having authored 35 papers that have together received 2.1k indexed citations. Recurring topics across this work include Virus-based gene therapy research (12 papers), Viral gastroenteritis research and epidemiology (7 papers), Viral Infectious Diseases and Gene Expression in Insects (6 papers), RNA Interference and Gene Delivery (5 papers), Enzyme Production and Characterization (5 papers), Fungal and yeast genetics research (4 papers), Polysaccharides and Plant Cell Walls (3 papers) and Bacterial Genetics and Biotechnology (3 papers). The work is most often cited by research in Biotechnology (350 citations), Genetics (761 citations), Molecular Biology (1.5k citations), Nutrition and Dietetics (204 citations) and Plant Science (425 citations). J. Maat has collaborated with scholars based in Netherlands, United Kingdom and United States. 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, Peter RAVESTEIN, María‐Teresa García‐Conesa and C. Theo Verrips. Their work appears in journals such as Gene, Nucleic Acids Research, Carbohydrate Polymers, Biochemical Journal and FEBS Letters.

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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