Jake Vanderplas

91.0k total citations · 1 hit paper
23 papers, 1.7k citations indexed

About

Jake Vanderplas is a scholar working on Artificial Intelligence, Astronomy and Astrophysics and Instrumentation. According to data from OpenAlex, Jake Vanderplas has authored 23 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 7 papers in Astronomy and Astrophysics and 6 papers in Instrumentation. Recurrent topics in Jake Vanderplas's work include Astronomy and Astrophysical Research (6 papers), Stellar, planetary, and galactic studies (5 papers) and Computational Physics and Python Applications (5 papers). Jake Vanderplas is often cited by papers focused on Astronomy and Astrophysical Research (6 papers), Stellar, planetary, and galactic studies (5 papers) and Computational Physics and Python Applications (5 papers). Jake Vanderplas collaborates with scholars based in United States and Argentina. Jake Vanderplas's co-authors include Andrew J. Connolly, Željko Ivezić, Alexander Gray, Bhuvnesh Jain, Arvind Satyanarayan, Brian Granger, Dominik Moritz, Jeffrey Heer, Kanit Wongsuphasawat and Scott Sievert and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Astrophysical Journal Supplement Series and The Astronomical Journal.

In The Last Decade

Jake Vanderplas

20 papers receiving 1.6k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Understanding the Lomb–Scargle Periodogram

2018 689 citations Jake Vanderplas The Astrophysical Journal Supplement Series profile →

Peers

Countries citing papers authored by Jake Vanderplas

Since Specialization

Citations

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

Fields of papers citing papers by Jake Vanderplas

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jake Vanderplas

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

All Works

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20 of 20 papers shown

#	Work	Indexed citations
1	Altair: Interactive Statistical Visualizations for Python 2018 ·The Journal of Open Source Software ·Jake Vanderplas, Brian Granger, Jeffrey Heer, Dominik Moritz, Kanit Wongsuphasawat, Arvind Satyanarayan, (unknown), (unknown), (unknown), Scott Sievert	126
2	Understanding the Lomb–Scargle Periodogram breakdown → 2018 ·The Astrophysical Journal Supplement Series ·Jake Vanderplas	689
3	Hierarchical Probabilistic Models for Group Anomaly Detection 2018 ·Research Showcase @ Carnegie Mellon University (Carnegie Mellon University) ·Liang Xiong, Barnabás Póczos, Jeff Schneider, Andrew J. Connolly, Jake Vanderplas	27
4	A Practical Taxonomy of Reproducibility for Machine Learning Research 2018 ·Rachael Tatman, Jake Vanderplas, (unknown)	20
5	From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning 2018 ·Astronomy and Computing ·Juan Cabral, B. Sánchez, (unknown), S. Gurovich, Pablo M. Granitto, Jake Vanderplas	18
6	approxposterior: Approximate Posterior Distributions in Python 2018 ·The Journal of Open Source Software ·D. P. Fleming, Jake Vanderplas	3
7	The Journal of Open Source Software 2017 ·Figshare ·Arfon M. Smith, Lorena A. Barba, George Githinji, Melissa Gymrek, Kathryn Huff, Daniel S. Katz, Christopher R. Madan, (unknown), Kevin M. Moerman, Kyle E. Niemeyer, Pjotr Prins, Karthik Ram, Ariel Rokem, (unknown), Jake Vanderplas	78
8	Data Science mit Python 2017 ·Jake Vanderplas	0
9	gatspy: Version 0.3 Feature Release 2016 ·Zenodo (CERN European Organization for Nuclear Research) ·Jake Vanderplas, (unknown), (unknown), Peter K. G. Williams, Brett M. Morris	3
10	mst_clustering: Clustering via Euclidean Minimum Spanning Trees 2016 ·The Journal of Open Source Software ·Jake Vanderplas	6
11	In Defense Of Extreme Openness 2016 ·Zenodo (CERN European Organization for Nuclear Research) ·Jake Vanderplas	0
12	Python data science handbook essential tools for working with data 2016 ·CERN Document Server (European Organization for Nuclear Research) ·Jake Vanderplas	165
13	gatspy: v0.2 Feature Release 2015 ·Zenodo (CERN European Organization for Nuclear Research) ·Jake Vanderplas	2
14	gatspy: General tools for Astronomical Time Series in Python 2015 ·Zenodo (CERN European Organization for Nuclear Research) ·Jake Vanderplas	22
15	supersmoother: Efficient Python Implementation of Friedman's SuperSmoother 2015 ·Zenodo (CERN European Organization for Nuclear Research) ·Jake Vanderplas	2
16	Statistics, Data Mining, and Machine Learning in Astronomy 2014 ·Princeton University Press eBooks ·Željko Ivezić, Andrew J. Connolly, Jake Vanderplas, Alexander Gray	205
17	Statistics, Data Mining, and Machine Learning in Astronomy 2014 ·Princeton University Press eBooks ·Alexander Gray, Željko Ivezić, Andrew J. Connolly, Jake Vanderplas	96
18	Introduction to astroML: Machine learning for astrophysics 2012 ·arXiv (Cornell University) ·Jake Vanderplas, Andrew J. Connolly, Željko Ivezić, (unknown)	165
19	CLASSIFICATION OF STELLAR SPECTRA WITH LOCAL LINEAR EMBEDDING 2011 ·The Astronomical Journal ·Scott F. Daniel, Andrew J. Connolly, Jeff Schneider, Jake Vanderplas, Liang Xiong	26
20	Tests of modified gravity with dwarf galaxies 2011 ·Journal of Cosmology and Astroparticle Physics ·Bhuvnesh Jain, Jake Vanderplas	46

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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