Jacob T. VanderPlas
- Astronomy and Astrophysics top 10%
- Computer Networks and Communications
- Information Systems top 10%
- Information Systems and Management top 10%
- Artificial Intelligence
- Co-authors
- Andrew J. ConnollyAlexander GrayŽeljko IvezićAriel RokemKarthik RamDavid W. HoggA. A. ArendtDaniela Huppenkothen
- Topics
- Astronomy and Astrophysical Research (3 papers)Scientific Computing and Data Management (3 papers)Research Data Management Practices (2 papers)
- Journals
- Proceedings of the National Academy of SciencesJournal of Machine Learning ResearchProceedings of the VLDB Endowment
- Partner nations
- United StatesNetherlandsAustralia
In The Last Decade
Jacob T. VanderPlas
12 papers receiving 238 citations
Peers
Comparison fields: 5 of 85
- Astronomy and Astrophysics 60
- Computer Networks and Communications 45
- Information Systems 45
- Information Systems and Management 45
- Artificial Intelligence 33
Countries citing papers authored by Jacob T. VanderPlas
This map shows the geographic impact of Jacob T. VanderPlas'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 Jacob T. VanderPlas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jacob T. VanderPlas more than expected).
Fields of papers citing papers by Jacob T. VanderPlas
This network shows the impact of papers produced by Jacob T. VanderPlas. 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 Jacob T. VanderPlas. The network helps show where Jacob T. VanderPlas may publish in the future.
Co-authorship network of co-authors of Jacob T. VanderPlas
This figure shows the co-authorship network connecting the top 25 collaborators of Jacob T. VanderPlas. A scholar is included among the top collaborators of Jacob T. VanderPlas 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 Jacob T. VanderPlas. Jacob T. VanderPlas is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 9 | |
| 2 | 59 | |
| 3 | 3 | |
| 4 | 21 | |
| 5 | 32 | |
| 6 | 49 | |
| 7 | 38 | |
| 8 | 1 | |
| 9 | Megaman: scalable manifold learning in python | 14 |
| 10 | 1 | |
| 11 | AstroML: Machine learning and data mining in astronomy | 5 |
| 12 | Squeezing a Big Orange into Little Boxes: The AscotDB System for Parallel Processing of Data on a Sphere. | 11 |
About Jacob T. VanderPlas
Jacob T. VanderPlas is a scholar working on Instrumentation, Information Systems and Management and Computer Science Applications, having authored 12 papers that have together received 243 indexed citations. Recurring topics across this work include Astronomy and Astrophysical Research (3 papers), Scientific Computing and Data Management (3 papers) and Research Data Management Practices (2 papers). The work is most often cited by research in Information Systems and Management (45 citations), Instrumentation (21 citations) and Astronomy and Astrophysics (60 citations). Jacob T. VanderPlas has collaborated with scholars based in United States, Netherlands and Australia. Frequent co-authors include Andrew J. Connolly, Alexander Gray, Željko Ivezić, Ariel Rokem, Karthik Ram, David W. Hogg, A. A. Arendt, Daniela Huppenkothen, Magdalena Bałazińska and James M. McQueen. Their work appears in journals such as Proceedings of the National Academy of Sciences, Journal of Machine Learning Research and Proceedings of the VLDB Endowment.
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.