Stephanie Dauth

21 papers receiving 1.3k citations

Stephanie Dauth's Hit Papers

Engineered In Vitro Disease Models 2015 · 414 citations
4140+3+7Years since publication100200300400

Peers

Stephanie Dauth
Comparison fields: 5 of 117
  • Neurology 151
  • Cellular and Molecular Neuroscience 280
  • Biomedical Engineering 652
  • Developmental Neuroscience 47
  • Biomaterials 127
Replace Thomas Grevesse with:
Thomas Grevesse Belgium
Linlin Wang China
Tatsuya Osaki Japan
Xiaoping Bao United States
Roberto Rizzi Italy
Karolina Chwalek Germany
Thomas I. Zarembinski United States
Karlijn J. Wilschut Netherlands
Yongchao Mou United States
Chelsey S. Simmons United States
Stephanie Dauth relative to Thomas Grevesse Belgium Thomas Grevesse's profile →
Citations per field
00.5×1.5×
Thomas Grevesse · 1×
Citations per year

Countries citing papers authored by Stephanie Dauth

Since Specialization
Citations

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

Fields of papers citing papers by Stephanie Dauth

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Engineered In Vitro Disease Models
Hit paper breakdown →
2015414
2 2018329
3 2015130
4 2016106
5 201883
6 201681
7 201143
8 201341
9 201033
10 201115
11 201112
12 202011
13 201210
14 20199
15 20149
16 20166
17 20165
18 20241
19 20241
20 20231

About Stephanie Dauth

Stephanie Dauth is a scholar working on Molecular Biology, Immunology, Cellular and Molecular Neuroscience, Epidemiology and Neurology, having authored 21 papers that have together received 1.3k indexed citations. Recurring topics across this work include Bone Metabolism and Diseases (4 papers), S100 Proteins and Annexins (3 papers), 3D Printing in Biomedical Research (3 papers), Protease and Inhibitor Mechanisms (3 papers), Thyroid Disorders and Treatments (2 papers), Connective tissue disorders research (2 papers), Barrier Structure and Function Studies (2 papers) and Psoriasis: Treatment and Pathogenesis (2 papers). The work is most often cited by research in Neurology (151 citations), Cellular and Molecular Neuroscience (280 citations), Biomedical Engineering (652 citations), Developmental Neuroscience (47 citations) and Biomaterials (127 citations). Stephanie Dauth has collaborated with scholars based in Germany, United States and Switzerland. Frequent co-authors include Kevin Kit Parker, Ben M. Maoz, Donald E. Ingber, Anna Herland, Matthew A. Hemphill, Thomas Grevesse, Sean P. Sheehy, Bogdan Budnik, Luke A. MacQueen and Borna E. Dabiri. Their work appears in journals such as Biological Chemistry, The Journal of Comparative Neurology, Cardiovascular Pathology, Cellular and Molecular Neurobiology and Journal of Neurophysiology.

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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