James Bradbury

19.6k total citations · 1 hit paper
7 papers, 1.0k citations indexed

About

James Bradbury is a scholar working on Artificial Intelligence, Atmospheric Science and Global and Planetary Change. According to data from OpenAlex, James Bradbury has authored 7 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 2 papers in Atmospheric Science and 2 papers in Global and Planetary Change. Recurrent topics in James Bradbury's work include Natural Language Processing Techniques (4 papers), Topic Modeling (4 papers) and Climate variability and models (2 papers). James Bradbury is often cited by papers focused on Natural Language Processing Techniques (4 papers), Topic Modeling (4 papers) and Climate variability and models (2 papers). James Bradbury collaborates with scholars based in United States. James Bradbury's co-authors include Bruce T. Anderson, Cameron P. Wake, Katharine Hayhoe, Art Degaetano, Tara J. Troy, Lifeng Luo, David W. Wolfe, Thomas G. Huntington, Justin Sheffield and Mark D. Schwartz and has published in prestigious journals such as Climate Dynamics, Mitigation and Adaptation Strategies for Global Change and arXiv (Cornell University).

In The Last Decade

James Bradbury

7 papers receiving 927 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.

Past and future changes in climate and hydrological indicators in the US Northeast

2006 719 citations Katharine Hayhoe, Cameron P. Wake et al. Climate Dynamics profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
James Bradbury United States	6	549	292	280	211	140	7	1.0k
Pia Papadopol Canada	13	833 1.5×	603 2.1×	293 1.0×	272 1.3×	260 1.9×	17	1.3k
Ian A. Nalder Canada	11	657 1.2×	435 1.5×	216 0.8×	234 1.1×	131 0.9×	13	1.0k
Gregory L. Johnson United States	11	557 1.0×	424 1.5×	320 1.1×	133 0.6×	83 0.6×	16	983
Monica Petri Netherlands	3	319 0.6×	147 0.5×	215 0.8×	143 0.7×	86 0.6×	4	794
Phillip A. Pasteris United States	4	798 1.5×	540 1.8×	350 1.3×	259 1.2×	216 1.5×	11	1.3k
Guillermo N. Murray‐Tortarolo Mexico	14	878 1.6×	232 0.8×	92 0.3×	398 1.9×	101 0.7×	25	1.2k
Miriam Machwitz Germany	19	523 1.0×	156 0.5×	163 0.6×	492 2.3×	71 0.5×	30	1.1k
Cletah Shoko South Africa	22	552 1.0×	98 0.3×	249 0.9×	620 2.9×	142 1.0×	65	1.2k
Travis Nauman United States	18	426 0.8×	153 0.5×	134 0.5×	425 2.0×	189 1.4×	38	1.3k
Clemente Gallardo Spain	17	769 1.4×	539 1.8×	120 0.4×	177 0.8×	77 0.6×	36	1.1k

Countries citing papers authored by James Bradbury

Since Specialization

Citations

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

Fields of papers citing papers by James Bradbury

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of James Bradbury

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

All Works

Sort: Min cites: Since: Top N: Style:

7 of 7 papers shown

1.

Hashimoto, Kazuma, et al.. (2019). A High-Quality Multilingual Dataset for Structured Documentation Translation. 116–127. 6 indexed citations

2.

Karpinski, Stefan, et al.. (2018). On Machine Learning and Programming Languages. UCL Discovery (University College London). 6 indexed citations

3.

McCann, Bryan, James Bradbury, Caiming Xiong, & Richard Socher. (2017). Towards the ImageNet-CNN of NLP: Pretraining Sentence Encoders with Machine Translation. Neural Information Processing Systems. 6285–6296. 1 indexed citations

4.

Gu, Jiatao, James Bradbury, Caiming Xiong, Victor O. K. Li, & Richard Socher. (2017). Non-Autoregressive Neural Machine Translation. arXiv (Cornell University). 55 indexed citations

5.

Bradbury, James & Richard Socher. (2016). MetaMind Neural Machine Translation System for WMT 2016. 264–267. 10 indexed citations

6.

Hayhoe, Katharine, Cameron P. Wake, Bruce T. Anderson, et al.. (2007). Regional climate change projections for the Northeast USA. Mitigation and Adaptation Strategies for Global Change. 13(5-6). 425–436. 205 indexed citations

7.

Hayhoe, Katharine, Cameron P. Wake, Thomas G. Huntington, et al.. (2006). Past and future changes in climate and hydrological indicators in the US Northeast. Climate Dynamics. 28(4). 381–407. 719 indexed citations breakdown →

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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