Dae‐Shik Kim

3.6k total citations · 1 hit paper
43 papers, 2.7k citations indexed

About

Dae‐Shik Kim is a scholar working on Radiology, Nuclear Medicine and Imaging, Organic Chemistry and Biotechnology. According to data from OpenAlex, Dae‐Shik Kim has authored 43 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Radiology, Nuclear Medicine and Imaging, 17 papers in Organic Chemistry and 11 papers in Biotechnology. Recurrent topics in Dae‐Shik Kim's work include Advanced Neuroimaging Techniques and Applications (16 papers), Advanced MRI Techniques and Applications (15 papers) and Synthetic Organic Chemistry Methods (15 papers). Dae‐Shik Kim is often cited by papers focused on Advanced Neuroimaging Techniques and Applications (16 papers), Advanced MRI Techniques and Applications (15 papers) and Synthetic Organic Chemistry Methods (15 papers). Dae‐Shik Kim collaborates with scholars based in United States, South Korea and Germany. Dae‐Shik Kim's co-authors include Amos B. Smith, Alard Roebroeck, Elia Formisano, Rainer Goebel, Kâmil Uǧurbil, Mathieu Ducros, Timothy Q. Duong, Seong‐Gi Kim, Pierre‐François Van de Moortele and Cyril Poupon and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Journal of Neuroscience.

In The Last Decade

Dae‐Shik Kim

42 papers receiving 2.6k citations

Hit Papers

Investigating directed cortical interactions in time-reso... 2003 2026 2010 2018 2003 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dae‐Shik Kim United States 26 1.1k 978 654 248 242 43 2.7k
Yoshinobu Goto Japan 24 463 0.4× 861 0.9× 201 0.3× 914 3.7× 223 0.9× 145 2.7k
Paul D. Acton United States 34 572 0.5× 1.2k 1.3× 78 0.1× 731 2.9× 412 1.7× 83 3.8k
David Labaree United States 30 337 0.3× 431 0.4× 193 0.3× 730 2.9× 110 0.5× 97 2.2k
Thomas C. Britton United States 32 704 0.6× 106 0.1× 1.3k 1.9× 1.2k 4.7× 868 3.6× 49 3.9k
Jogeshwar Mukherjee United States 31 516 0.5× 876 0.9× 173 0.3× 1.3k 5.3× 455 1.9× 166 3.9k
Jiapei Dai China 31 322 0.3× 289 0.3× 123 0.2× 706 2.8× 146 0.6× 103 2.8k
Eli Livni United States 23 287 0.3× 521 0.5× 78 0.1× 356 1.4× 320 1.3× 52 1.9k
William P. Melega United States 38 380 0.3× 616 0.6× 106 0.2× 1.1k 4.3× 685 2.8× 77 4.1k
Mario Matarrese Italy 19 392 0.3× 413 0.4× 74 0.1× 298 1.2× 119 0.5× 47 1.5k
Keiichi Oda Japan 31 306 0.3× 629 0.6× 76 0.1× 918 3.7× 368 1.5× 111 2.6k

Countries citing papers authored by Dae‐Shik Kim

Since Specialization
Citations

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

Fields of papers citing papers by Dae‐Shik Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dae‐Shik Kim. 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 Dae‐Shik Kim. The network helps show where Dae‐Shik Kim may publish in the future.

Co-authorship network of co-authors of Dae‐Shik Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Dae‐Shik Kim. A scholar is included among the top collaborators of Dae‐Shik Kim 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 Dae‐Shik Kim. Dae‐Shik Kim 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
3.
Ronen, Itamar, Xiaoying Fan, Sahil Jain, et al.. (2009). Regional age-related effects in the monkey brain measured with 1H magnetic resonance spectroscopy. Neurobiology of Aging. 32(6). 1138–1148. 3 indexed citations
4.
Cho, Zang‐Hee, Jae‐Yong Han, Dae‐Shik Kim, et al.. (2009). Quantitative analysis of the hippocampus using images obtained from 7.0 T MRI. NeuroImage. 49(3). 2134–2140. 43 indexed citations
5.
Jang, Sung Ho, Dae‐Shik Kim, Su Min Son, et al.. (2009). Clinical application of diffusion tensor tractography for elucidation of the causes of motor weakness in patients with traumatic brain injury. Neurorehabilitation. 24(3). 273–278. 12 indexed citations
6.
Upadhyay, Jaymin, Tracey A. Knaus, Kristen A. Lindgren, et al.. (2008). Effective and Structural Connectivity in the Human Auditory Cortex. Journal of Neuroscience. 28(13). 3341–3349. 74 indexed citations
7.
Jang, Sung Ho, Dai-Seg Bai, Su Min Son, et al.. (2008). Motor outcome prediction using diffusion tensor tractography in pontine infarct. Annals of Neurology. 64(4). 460–465. 62 indexed citations
8.
Kim, Dae‐Shik, et al.. (2008). Demonstration of Recovery of a Severely Damaged Corticospinal Tract. Journal of Computer Assisted Tomography. 32(3). 418–420. 14 indexed citations
9.
Kim, Yun‐Hee, Dae‐Shik Kim, Ji Heon Hong, et al.. (2008). Corticospinal tract location in internal capsule of human brain: diffusion tensor tractography and functional MRI study. Neuroreport. 19(8). 817–820. 38 indexed citations
10.
Roebroeck, Alard, Ralf A. W. Galuske, Elia Formisano, et al.. (2007). High-resolution diffusion tensor imaging and tractography of the human optic chiasm at 9.4 T. NeuroImage. 39(1). 157–168. 72 indexed citations
12.
Upadhyay, Jaymin, Kevin Hallock, Mathieu Ducros, Dae‐Shik Kim, & Itamar Ronen. (2007). Diffusion tensor spectroscopy and imaging of the arcuate fasciculus. NeuroImage. 39(1). 1–9. 55 indexed citations
13.
Upadhyay, Jaymin, Mathieu Ducros, Tracey A. Knaus, et al.. (2006). Function and Connectivity in Human Primary Auditory Cortex: A Combined fMRI and DTI Study at 3 Tesla. Cerebral Cortex. 17(10). 2420–2432. 48 indexed citations
14.
Ronen, Itamar, Kâmil Uǧurbil, & Dae‐Shik Kim. (2005). How does DWI correlate with white matter structures?. Magnetic Resonance in Medicine. 54(2). 317–323. 15 indexed citations
15.
Kim, Dae‐Shik. (2005). The Cutting Edge of fMRI and High‐Field fMRI. International review of neurobiology. 66. 147–166. 2 indexed citations
16.
Lehéricy, Stéphane, Mathieu Ducros, Pierre‐François Van de Moortele, et al.. (2004). Diffusion tensor fiber tracking shows distinct corticostriatal circuits in humans. Annals of Neurology. 55(4). 522–529. 441 indexed citations
17.
Goebel, Rainer, Alard Roebroeck, Dae‐Shik Kim, & Elia Formisano. (2003). Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping. Magnetic Resonance Imaging. 21(10). 1251–1261. 501 indexed citations breakdown →
18.
Duong, Timothy Q., Dae‐Shik Kim, Kâmil Uǧurbil, & Seong‐Gi Kim. (2000). Spatiotemporal dynamics of the BOLD fMRI signals: Toward mapping submillimeter cortical columns using the early negative response. Magnetic Resonance in Medicine. 44(2). 231–242. 7 indexed citations
19.
Galuske, Ralf A. W., Dae‐Shik Kim, Eero Ċastrén, & Wolf Singer. (2000). Differential effects of neurotrophins on ocular dominance plasticity in developing and adult cat visual cortex. European Journal of Neuroscience. 12(9). 3315–3330. 29 indexed citations
20.
Galuske, Ralf A. W., Dae‐Shik Kim, Eero Ċastrén, H. Thoenen, & Wolf Singer. (1996). Brain‐derived Neurotrophic Factor Reverses Experience‐dependent Synaptic Modifications in Kitten Visual Cortex. European Journal of Neuroscience. 8(7). 1554–1559. 82 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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