David W. Speicher
- Cell Biology top 0.1%
- Molecular Biology top 0.2%
- Protein Structure and Dynamics 19
- Glycosylation and Glycoproteins Research 19
- Protein purification and stability 15
- Physiology top 0.5%
- Erythrocyte Function and Pathophysiology 62
- Spectroscopy top 0.5%
- Advanced Proteomics Techniques and Applications 56
- Mass Spectrometry Techniques and Applications 39
- Cancer Research top 1%
-
- Blood properties and coagulation 26
-
- Peptidase Inhibition and Analysis 16
- Co-authors
- Hsin‐Yao TangRobert E. HandschumacherMatthew W. HardingDennis E. DischerSandra L. HarperJeffrey S. RiceRhett J DruggeVincent T. Marchesi
- 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
- Cell Biology 3.6k
- Molecular Biology 12.6k
- Physiology 3.4k
- Spectroscopy 1.7k
- Cancer Research 1.4k
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
The 25 scholars most cited alongside David W. Speicher, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 17 | |
| 2 | 2023 | 1 | |
| 3 | 2023 | 3 | |
| 4 | 2022 | 18 | |
| 5 | 2021 | 28 | |
| 6 | 2020 | 57 | |
| 7 | 2020 | 51 | |
| 8 | 2019 | 24 | |
| 9 | 2019 | 25 | |
| 10 | 2018 | 42 | |
| 11 | 2018 | 33 | |
| 12 | 2016 | 116 | |
| 13 | 2016 | 142 | |
| 14 | 2014 | 15 | |
| 15 | Nuclear Lamin-A Scales with Tissue Stiffness and Enhances Matrix-Directed Differentiationbreakdown → | 2013 | 1498 |
| 16 | SP11 Reducing Sample Complexity for Proteomics | 2007 | 1 |
| 17 | Forced unfolding modulated by disulfide bonds in the immunoglobulin domains of a cell adhesion molecule | 2001 | 1 |
| 18 | 1993 | 5 | |
| 19 | 1992 | 44 | |
| 20 | 1987 | 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), Mass Spectrometry Techniques and Applications (39 papers), Blood properties and coagulation (26 papers), Protein Structure and Dynamics (19 papers), Glycosylation and Glycoproteins Research (19 papers), Peptidase Inhibition and Analysis (16 papers) and Protein purification and stability (15 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.