David S. Long

807 total citations
26 papers, 594 citations indexed

About

David S. Long is a scholar working on Pulmonary and Respiratory Medicine, Molecular Biology and Critical Care and Intensive Care Medicine. According to data from OpenAlex, David S. Long has authored 26 papers receiving a total of 594 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Pulmonary and Respiratory Medicine, 5 papers in Molecular Biology and 4 papers in Critical Care and Intensive Care Medicine. Recurrent topics in David S. Long's work include Blood properties and coagulation (6 papers), Trauma, Hemostasis, Coagulopathy, Resuscitation (4 papers) and Cellular Mechanics and Interactions (3 papers). David S. Long is often cited by papers focused on Blood properties and coagulation (6 papers), Trauma, Hemostasis, Coagulopathy, Resuscitation (4 papers) and Cellular Mechanics and Interactions (3 papers). David S. Long collaborates with scholars based in United States, New Zealand and Germany. David S. Long's co-authors include Edward R. Damiano, Michael L. Smith, Klaus Ley, Axel R. Pries, Michael T. Cooling, Thomas M. Stace, Richard Clarke, Gideon Koren, Andrew James and Sue R. McGlashan and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Fluid Mechanics and Biophysical Journal.

In The Last Decade

David S. Long

25 papers receiving 573 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
David S. Long United States 10 217 130 109 93 91 26 594
Oleg V. Kim United States 11 289 1.3× 28 0.2× 114 1.0× 38 0.4× 74 0.8× 26 678
Michael Chew United Kingdom 10 123 0.6× 93 0.7× 45 0.4× 82 0.9× 146 1.6× 16 514
P. A. M. M. Aarts Netherlands 7 286 1.3× 27 0.2× 68 0.6× 46 0.5× 48 0.5× 7 682
Alfred L. Copley United States 19 664 3.1× 162 1.2× 171 1.6× 243 2.6× 63 0.7× 121 1.1k
J P DiOrio United States 14 476 2.2× 44 0.3× 87 0.8× 81 0.9× 47 0.5× 21 764
Carrie E. Perlman United States 12 329 1.5× 25 0.2× 148 1.4× 26 0.3× 31 0.3× 27 460
Jia Fu China 4 274 1.3× 9 0.1× 113 1.0× 35 0.4× 58 0.6× 11 698
Céline Renoux France 17 278 1.3× 6 0.0× 114 1.0× 334 3.6× 98 1.1× 54 1.0k
Malebogo Ngoepe South Africa 10 99 0.5× 20 0.2× 31 0.3× 18 0.2× 28 0.3× 23 368
S. Gambihler Germany 12 129 0.6× 11 0.1× 220 2.0× 24 0.3× 98 1.1× 14 505

Countries citing papers authored by David S. Long

Since Specialization
Citations

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

Fields of papers citing papers by David S. Long

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David S. Long. 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 S. Long. The network helps show where David S. Long may publish in the future.

Co-authorship network of co-authors of David S. Long

This figure shows the co-authorship network connecting the top 25 collaborators of David S. Long. A scholar is included among the top collaborators of David S. Long 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 S. Long. David S. Long is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Long, David S., et al.. (2022). Shallow Deep Learning using Space-filling Curves for Malware Classification. 17(1). 145–154.
2.
Schmitt, Sarah, et al.. (2022). Human dermal microvascular endothelial cell morphological response to fluid shear stress. Microvascular Research. 143. 104377–104377. 3 indexed citations
3.
Long, David S., et al.. (2019). In situ fixation and subsequent collection of cultured endothelial cells in a shear flow. MethodsX. 6. 1164–1173. 1 indexed citations
4.
Jacobson, Elsie, Jo K. Perry, David S. Long, et al.. (2018). Migration through a small pore disrupts inactive chromatin organization in neutrophil-like cells. BMC Biology. 16(1). 142–142. 42 indexed citations
5.
Kuhl, Detlef, et al.. (2017). Tensegrity structures - Computational and experimental tensegrity mechanics. AIP conference proceedings. 1863. 410005–410005. 2 indexed citations
6.
Jacobson, Elsie, Jo K. Perry, David S. Long, Mark H. Vickers, & Justin M. O’Sullivan. (2016). A potential role for genome structure in the translation of mechanical force during immune cell development. Nucleus. 7(5). 462–475. 1 indexed citations
7.
8.
Long, David S., et al.. (2016). Effect of endothelial glycocalyx layer redistribution upon microvessel poroelastohydrodynamics. Journal of Fluid Mechanics. 798. 812–852. 11 indexed citations
9.
McGlashan, Sue R., et al.. (2015). Culture and detection of primary cilia in endothelial cell models. PubMed. 4(1). 11–11. 27 indexed citations
10.
Cater, John, et al.. (2015). A boundary-integral representation for biphasic mixture theory, with application to the post-capillary glycocalyx. Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences. 471(2179). 20140955–20140955. 7 indexed citations
11.
Koschwanez, Heidi E., John Weinman, John F. Tarlton, et al.. (2015). Stress-related changes to immune cells in the skin prior to wounding may impair subsequent healing. Brain Behavior and Immunity. 50. 47–51. 8 indexed citations
12.
Cooling, Michael T., et al.. (2014). Computational models of the primary cilium and endothelial mechanotransmission. Biomechanics and Modeling in Mechanobiology. 14(3). 665–678. 9 indexed citations
13.
Long, David S. & Thomas G. McKay. (2014). A design project based approach to teaching undergraduate instrumentation. 21. 41–44. 1 indexed citations
14.
Long, David S., et al.. (2009). Finding Opportunities for Commonality in Complex Systems. DSpace@MIT (Massachusetts Institute of Technology). 83–94. 2 indexed citations
15.
Zhu, Hui, Jessica G. Shih, David S. Long, et al.. (2006). Characterizing 3-D Geometry of Mouse Aortic Arch Using Light Stereo-Microscopic Imaging. PubMed. 2006. 385–388. 2 indexed citations
16.
Long, David S., Michael L. Smith, Axel R. Pries, Klaus Ley, & Edward R. Damiano. (2004). Microviscometry reveals reduced blood viscosity and altered shear rate and shear stress profiles in microvessels after hemodilution. Proceedings of the National Academy of Sciences. 101(27). 10060–10065. 159 indexed citations
17.
Smith, Michael L., David S. Long, Edward R. Damiano, & Klaus Ley. (2003). Near-Wall μ-PIV Reveals a Hydrodynamically Relevant Endothelial Surface Layer in Venules In Vivo. Biophysical Journal. 85(1). 637–645. 167 indexed citations
18.
Long, David S., Mark A. Shannon, & N. R. Aluru. (2000). A novel approach for determining pull-in voltages in micro-electro- mechanical systems (MEMS). 481–484. 5 indexed citations
19.
Long, David S., Gideon Koren, & Andrew James. (1987). Ethics of drug studies in infants: How many samples are required for accurate estimation of pharmacokinetic parameters in neonates?. The Journal of Pediatrics. 111(6). 918–921. 21 indexed citations
20.
Long, David S., et al.. (1978). Signs of impending parturition in the laboratory bitch.. PubMed. 28(2). 178–81. 14 indexed citations

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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