David W. Speicher
- Molecular Biology top 0.2%
- Cell Biology top 0.1%
- Physiology top 0.5%
- Oncology top 1%
- Immunology top 1%
- Co-authors
- Hsin‐Yao TangRobert E. HandschumacherMatthew W. HardingDennis E. DischerSandra L. HarperJeffrey S. RiceRhett J DruggeVincent T. Marchesi
- Topics
- Erythrocyte Function and Pathophysiology (62 papers)Advanced Proteomics Techniques and Applications (56 papers)Mass Spectrometry Techniques and Applications (39 papers)
- Partner nations
- United StatesGermanyRussia
In The Last Decade
David W. Speicher
275 papers receiving 18.9k citations
Hit Papers
Peers
Comparison fields: 5 of 163
- Molecular Biology 12.6k
- Cell Biology 3.6k
- Physiology 3.4k
- Oncology 2.3k
- Immunology 2.0k
Countries citing papers authored by David W. Speicher
This map shows the geographic impact of David W. Speicher'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 David W. Speicher with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David W. Speicher more than expected).
Fields of papers citing papers by David W. Speicher
This network shows the impact of papers produced by David W. Speicher. 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 David W. Speicher. The network helps show where David W. Speicher may publish in the future.
Co-authorship network of co-authors of David W. Speicher
This figure shows the co-authorship network connecting the top 25 collaborators of David W. Speicher. A scholar is included among the top collaborators of David W. Speicher based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David W. Speicher. David W. Speicher is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 17 | |
| 2 | 1 | |
| 3 | 3 | |
| 4 | 18 | |
| 5 | 28 | |
| 6 | 57 | |
| 7 | 51 | |
| 8 | 24 | |
| 9 | 25 | |
| 10 | 42 | |
| 11 | 33 | |
| 12 | 116 | |
| 13 | 142 | |
| 14 | 15 | |
| 15 | Nuclear Lamin-A Scales with Tissue Stiffness and Enhances Matrix-Directed Differentiationbreakdown → | 1498 |
| 16 | SP11 Reducing Sample Complexity for Proteomics | 1 |
| 17 | Forced unfolding modulated by disulfide bonds in the immunoglobulin domains of a cell adhesion molecule | 1 |
| 18 | 5 | |
| 19 | 44 | |
| 20 | 58 |
About David W. Speicher
David W. Speicher is a scholar working on Spectroscopy, Molecular Biology and Physiology, having authored 278 papers that have together received 19.3k indexed citations. Recurring topics across this work include Erythrocyte Function and Pathophysiology (62 papers), Advanced Proteomics Techniques and Applications (56 papers) and Mass Spectrometry Techniques and Applications (39 papers). The work is most often cited by research in Cell Biology (3.6k citations), Molecular Biology (12.6k citations) and Physiology (3.4k citations). David W. Speicher has collaborated with scholars based in United States, Germany and Russia. Frequent co-authors include Hsin‐Yao Tang, Robert E. Handschumacher, Matthew W. Harding, Dennis E. Discher, Sandra L. Harper, Jeffrey S. Rice, Rhett J Drugge, Vincent T. Marchesi, V. Marchesi and Frank J. Rauscher. Their work appears in journals such as Nature, Science and Cell.
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.