Daniel Tan

1.4k total citations · 1 hit paper
8 papers, 1.1k citations indexed

About

Daniel Tan is a scholar working on Cognitive Neuroscience, Biomedical Engineering and Cellular and Molecular Neuroscience. According to data from OpenAlex, Daniel Tan has authored 8 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Cognitive Neuroscience, 5 papers in Biomedical Engineering and 4 papers in Cellular and Molecular Neuroscience. Recurrent topics in Daniel Tan's work include Muscle activation and electromyography studies (5 papers), Neuroscience and Neural Engineering (4 papers) and EEG and Brain-Computer Interfaces (4 papers). Daniel Tan is often cited by papers focused on Muscle activation and electromyography studies (5 papers), Neuroscience and Neural Engineering (4 papers) and EEG and Brain-Computer Interfaces (4 papers). Daniel Tan collaborates with scholars based in United States, Australia and Serbia. Daniel Tan's co-authors include Dustin J. Tyler, Matthew A. Schiefer, Michael W. Keith, Emily L. Graczyk, Jonathan P. Miller, Jennifer A. Sweet, Brian Dawson, Peter Peeling and Robert P. Anderson and has published in prestigious journals such as PLoS ONE, Science Translational Medicine and Journal of Neural Engineering.

In The Last Decade

Daniel Tan

8 papers receiving 1.1k citations

Hit Papers

A neural interface provides long-term stable natural touc... 2014 2026 2018 2022 2014 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
Daniel Tan United States 8 757 689 671 111 90 8 1.1k
Tim M. Bruns United States 20 295 0.4× 462 0.7× 327 0.5× 140 1.3× 54 0.6× 58 957
Matthew A. Schiefer United States 18 1.4k 1.8× 1.4k 2.0× 1.2k 1.8× 256 2.3× 67 0.7× 37 2.0k
Francesco M. Petrini Switzerland 19 1.5k 2.0× 1.2k 1.8× 1.2k 1.8× 202 1.8× 63 0.7× 31 2.1k
David T. Kluger United States 9 478 0.6× 357 0.5× 367 0.5× 42 0.4× 33 0.4× 10 643
Christian Éthier Canada 19 547 0.7× 684 1.0× 925 1.4× 229 2.1× 16 0.2× 31 1.3k
Edoardo D’Anna Switzerland 10 703 0.9× 580 0.8× 607 0.9× 80 0.7× 24 0.3× 14 931
Nikolaus Wenger Germany 13 323 0.4× 272 0.4× 191 0.3× 229 2.1× 86 1.0× 26 920
Aidan D. Roche Austria 16 774 1.0× 579 0.8× 359 0.5× 62 0.6× 26 0.3× 36 1.0k
Jacopo Rigosa Italy 13 582 0.8× 648 0.9× 496 0.7× 91 0.8× 18 0.2× 22 921
Paul Čvančara Germany 10 379 0.5× 309 0.4× 225 0.3× 65 0.6× 28 0.3× 21 625

Countries citing papers authored by Daniel Tan

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Tan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daniel Tan

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

All Works

8 of 8 papers shown
1.
Schiefer, Matthew A., et al.. (2018). Artificial tactile and proprioceptive feedback improves performance and confidence on object identification tasks. PLoS ONE. 13(12). e0207659–e0207659. 86 indexed citations
2.
Tan, Daniel, Dustin J. Tyler, Jennifer A. Sweet, & Jonathan P. Miller. (2015). Intensity Modulation: A Novel Approach to Percept Control in Spinal Cord Stimulation. Neuromodulation Technology at the Neural Interface. 19(3). 254–259. 18 indexed citations
3.
Sweet, Jennifer A., et al.. (2015). Paresthesia-Free High-Density Spinal Cord Stimulation for Postlaminectomy Syndrome in a Prescreened Population: A Prospective Case Series. Neuromodulation Technology at the Neural Interface. 19(3). 260–267. 44 indexed citations
4.
Tan, Daniel, et al.. (2015). Stability and selectivity of a chronic, multi-contact cuff electrode for sensory stimulation in human amputees. Journal of Neural Engineering. 12(2). 26002–26002. 134 indexed citations
5.
Schiefer, Matthew A., et al.. (2015). Sensory feedback by peripheral nerve stimulation improves task performance in individuals with upper limb loss using a myoelectric prosthesis. Journal of Neural Engineering. 13(1). 16001–16001. 185 indexed citations
6.
Tan, Daniel, et al.. (2014). A neural interface provides long-term stable natural touch perception. Science Translational Medicine. 6(257). 257ra138–257ra138. 571 indexed citations breakdown →
7.
Tan, Daniel, Matthew A. Schiefer, Michael W. Keith, Robert P. Anderson, & Dustin J. Tyler. (2013). Stability and selectivity of a chronic, multi-contact cuff electrode for sensory stimulation in a human amputee. 859–862. 17 indexed citations
8.
Tan, Daniel, Brian Dawson, & Peter Peeling. (2012). Hemolytic Effects of a Football-Specific Training Session in Elite Female Players. International Journal of Sports Physiology and Performance. 7(3). 271–276. 23 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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