Maya Sieber‐Blum

4.0k citations
81 papers · 3.4k · h-index 34

Impact in

Papers in

Maya Sieber‐Blum

81 papers receiving 3.3k citations

Peers

Maya Sieber‐Blum
Comparison fields: 5 of 107
  • Developmental Neuroscience 792
  • Urology 354
  • Cellular and Molecular Neuroscience 891
  • Genetics 311
  • Molecular Biology 1.9k
Replace Jean M. Hébert with:
Jean M. Hébert United States
Élisabeth Dupin France
Henk Roelink United States
Miloš Grim Czechia
Shigemi Hayashi United States
Jean G. Toma Canada
Sacri R. Ferrón Spain
Mākoto Ishibashi Japan
Amel Gritli-Linde Sweden
Marie‐Aimée Teillet France
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Citations per field
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Citations per year

Countries citing papers authored by Maya Sieber‐Blum

Since Specialization
Citations

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

Fields of papers citing papers by Maya Sieber‐Blum

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Maya Sieber‐Blum. 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 Maya Sieber‐Blum. The network helps show where Maya Sieber‐Blum may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 81 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2004303
2 1980262
3 1991159
4 1989129
5 2004110
6 1981105
7 1993101
8 200697
9 200396
10 200690
11 199187
12 201176
13 199170
14 199368
15 199367
16 201066
17 199364
18 200862
19 197857
20
Dye-mediated photosensitization of murine neuroblastoma cells.
198655

About Maya Sieber‐Blum

Maya Sieber‐Blum is a scholar working on Molecular Biology, Developmental Neuroscience, Cellular and Molecular Neuroscience, Urology and Cell Biology, having authored 81 papers that have together received 3.4k indexed citations. Recurring topics across this work include Neurogenesis and neuroplasticity mechanisms (24 papers), Developmental Biology and Gene Regulation (18 papers), Nerve injury and regeneration (14 papers), Hair Growth and Disorders (12 papers), Congenital heart defects research (11 papers), melanin and skin pigmentation (10 papers), Neuropeptides and Animal Physiology (9 papers) and Pluripotent Stem Cells Research (8 papers). The work is most often cited by research in Developmental Neuroscience (792 citations), Urology (354 citations), Cellular and Molecular Neuroscience (891 citations), Genetics (311 citations) and Molecular Biology (1.9k citations). Maya Sieber‐Blum has collaborated with scholars based in United States, United Kingdom and Czechia. Frequent co-authors include Miloš Grim, Alan M. Cohen, Yao Hu, Viktor Szeder, Fritz Sieber, Kazuo Ito, Michael K. Richardson, Kazuo Itô, Oliver Clewes and Carol J. Langtimm. Their work appears in journals such as Developmental Biology, Developmental Dynamics, Developmental Neuroscience, Brain Research and Molecular and Cellular Neuroscience.

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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