David Stansby

6.9k total citations
33 papers, 591 citations indexed

About

David Stansby is a scholar working on Astronomy and Astrophysics, Molecular Biology and Artificial Intelligence. According to data from OpenAlex, David Stansby has authored 33 papers receiving a total of 591 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Astronomy and Astrophysics, 6 papers in Molecular Biology and 4 papers in Artificial Intelligence. Recurrent topics in David Stansby's work include Solar and Space Plasma Dynamics (30 papers), Astro and Planetary Science (17 papers) and Ionosphere and magnetosphere dynamics (13 papers). David Stansby is often cited by papers focused on Solar and Space Plasma Dynamics (30 papers), Astro and Planetary Science (17 papers) and Ionosphere and magnetosphere dynamics (13 papers). David Stansby collaborates with scholars based in United Kingdom, United States and France. David Stansby's co-authors include T. S. Horbury, Lorenzo Matteini, Denise Perrone, Anthony R. Yeates, Samuel T. Badman, Laura Berčič, R. D’Amicis, C. J. Owen, Georgios Nicolaou and David H. Brooks and has published in prestigious journals such as The Astrophysical Journal, Monthly Notices of the Royal Astronomical Society and Astronomy and Astrophysics.

In The Last Decade

David Stansby

33 papers receiving 524 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 Stansby United Kingdom 15 582 180 71 15 13 33 591
Samuel T. Badman United States 15 688 1.2× 220 1.2× 79 1.1× 22 1.5× 12 0.9× 36 698
Shuhong Yang China 16 788 1.4× 131 0.7× 61 0.9× 10 0.7× 12 0.9× 54 805
Yingna Su China 16 868 1.5× 231 1.3× 62 0.9× 23 1.5× 14 1.1× 51 884
Karin Dissauer Austria 13 445 0.8× 91 0.5× 70 1.0× 16 1.1× 14 1.1× 33 468
B. P. Filippov Russia 16 748 1.3× 203 1.1× 40 0.6× 14 0.9× 9 0.7× 85 773
L. Bone United Kingdom 5 569 1.0× 89 0.5× 72 1.0× 16 1.1× 12 0.9× 6 585
Q. M. Zhang China 14 611 1.0× 116 0.6× 37 0.5× 21 1.4× 7 0.5× 21 617
R. C. Colaninno United States 15 687 1.2× 186 1.0× 55 0.8× 8 0.5× 18 1.4× 25 698
S. Vargas Domínguez Colombia 13 413 0.7× 103 0.6× 74 1.0× 20 1.3× 7 0.5× 36 428
K. Dalmasse France 11 581 1.0× 136 0.8× 60 0.8× 11 0.7× 14 1.1× 18 588

Countries citing papers authored by David Stansby

Since Specialization
Citations

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

Fields of papers citing papers by David Stansby

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of David Stansby

This figure shows the co-authorship network connecting the top 25 collaborators of David Stansby. A scholar is included among the top collaborators of David Stansby 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 Stansby. David Stansby 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.
Ryan, Daniel F., Stuart Mumford, Nabil Freij, et al.. (2023). ndcube: Manipulating N-dimensional Astronomical Data inPython. The Journal of Open Source Software. 8(89). 5296–5296. 3 indexed citations
2.
Bastian, T. S., L. van Driel‐Gesztelyi, David M. Long, et al.. (2023). Understanding the Relationship between Solar Coronal Abundances and F10.7 cm Radio Emission. The Astrophysical Journal. 948(2). 121–121. 7 indexed citations
3.
Barnes, Will, Steven Christe, Nabil Freij, et al.. (2023). The SunPy Project: An interoperable ecosystem for solar data analysis. Frontiers in Astronomy and Space Sciences. 10. 7 indexed citations
4.
Owen, C. J., Daniel Verscharen, David Stansby, et al.. (2022). Radial evolution of thermal and suprathermal electron populations in the slow solar wind from 0.13 to 0.5 au : Parker Solar Probe Observations. arXiv (Cornell University). 27 indexed citations
5.
Stansby, David, Deborah Baker, David H. Brooks, & C. J. Owen. (2020). Directly comparing coronal and solar wind elemental fractionation. Springer Link (Chiba Institute of Technology). 9 indexed citations
6.
Němeček, Zdeněk, Jana Šafránková, F. Němec, et al.. (2020). What is the Solar Wind Frame of Reference?. The Astrophysical Journal. 889(2). 163–163. 25 indexed citations
7.
Stansby, David, Anthony R. Yeates, & Samuel T. Badman. (2020). pfsspy: A Python package for potential field source surface modelling. The Journal of Open Source Software. 5(54). 2732–2732. 60 indexed citations
8.
Barnes, Will, Mark C. M. Cheung, Monica Bobra, et al.. (2020). aiapy: A Python Package for Analyzing Solar EUV Image Data from AIA. The Journal of Open Source Software. 5(55). 2801–2801. 51 indexed citations
9.
Stansby, David, Laura Berčič, Lorenzo Matteini, et al.. (2020). Sensitivity of solar wind mass flux to coronal temperature. Astronomy and Astrophysics. 650. L2–L2. 2 indexed citations
10.
Perrone, Denise, R. D’Amicis, Rossana De Marco, et al.. (2020). Highly Alfvénic slow solar wind at 0.3 au during a solar minimum: Helios insights for Parker Solar Probe and Solar Orbiter. Astronomy and Astrophysics. 633. A166–A166. 26 indexed citations
11.
Woodham, L. D., T. S. Horbury, Lorenzo Matteini, et al.. (2020). Enhanced proton parallel temperature inside patches of switchbacks in the inner heliosphere. Astronomy and Astrophysics. 650. L1–L1. 39 indexed citations
12.
Stansby, David. (2020). In-situ Observations of the Sub-Alfvénic Solar Wind by Helios. Research Notes of the AAS. 4(4). 51–51. 1 indexed citations
13.
Stansby, David, Lorenzo Matteini, T. S. Horbury, et al.. (2019). The origin of slow Alfvénic solar wind at solar minimum. Monthly Notices of the Royal Astronomical Society. 492(1). 39–44. 28 indexed citations
14.
Stansby, David, Denise Perrone, Lorenzo Matteini, T. S. Horbury, & C. S. Salem. (2019). Alpha particle thermodynamics in the inner heliosphere fast solar wind. Springer Link (Chiba Institute of Technology). 25 indexed citations
15.
Matteini, Lorenzo, et al.. (2019). The rotation angle distribution underlying magnetic field fluctuations in the 1/ f range of solar wind turbulent spectra. 42(1). 16. 2 indexed citations
16.
Owens, M. J., Matthew Lang, Pete Riley, & David Stansby. (2019). Towards Construction of a Solar Wind “Reanalysis” Dataset: Application to the First Perihelion Pass of Parker Solar Probe. Solar Physics. 294(6). 4 indexed citations
17.
Stansby, David, et al.. (2019). HelioPy: Heliospheric and planetary physics library. Astrophysics Source Code Library. 2 indexed citations
18.
Stansby, David & T. S. Horbury. (2018). Number density structures in the inner heliosphere. Astronomy and Astrophysics. 613. A62–A62. 11 indexed citations
19.
Perrone, Denise, David Stansby, T. S. Horbury, & Lorenzo Matteini. (2018). Radial evolution of the solar wind in pure high-speed streams: HELIOS revised observations. Monthly Notices of the Royal Astronomical Society. 483(3). 3730–3737. 35 indexed citations
20.
Stansby, David. (2017). Helios Corefit Plasma Dataset. Zenodo (CERN European Organization for Nuclear Research). 1 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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