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.
Best Practices for Scientific Computing
2014395 citationsGreg Wilson, D. A. Aruliah et al.PLoS Biologyprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Kathryn Huff'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 Kathryn Huff with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kathryn Huff more than expected).
This network shows the impact of papers produced by Kathryn Huff. 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 Kathryn Huff. The network helps show where Kathryn Huff may publish in the future.
Co-authorship network of co-authors of Kathryn Huff
This figure shows the co-authorship network connecting the top 25 collaborators of Kathryn Huff.
A scholar is included among the top collaborators of Kathryn Huff 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 Kathryn Huff. Kathryn Huff is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Huff, Kathryn, Matthew Gidden, Robert Carlsen, et al.. (2015). Fundamental Concepts in the Cyclus Fuel Cycle Simulator Framework.. arXiv (Cornell University).1 indexed citations
10.
Carlsen, Robert, et al.. (2014). Cyclus v1.0.0. Figshare.
Huff, Kathryn, et al.. (2014). Extensions to the cyclus ecosystem in support of market-driven transition capability LLNL-PROC-656426. Transactions of the American Nuclear Society. 111. 245–248.1 indexed citations
13.
Wilson, Greg, D. A. Aruliah, C. Titus Brown, et al.. (2014). Best Practices for Scientific Computing. PLoS Biology. 12(1). e1001745–e1001745.395 indexed citations breakdown →
14.
Huff, Kathryn. (2013). An Integrated Used Fuel Disposition and Generic Repository Model for Fuel Cycle Analysis. PhDT.1 indexed citations
15.
Huff, Kathryn, et al.. (2012). Key Processes and Parameters in a Generic Clay Disposal System Model. Transactions of the American Nuclear Society. 107. 208–211.2 indexed citations
16.
Huff, Kathryn & T.H. Bauer. (2012). Numerical calibration of an analytical generic nuclear repository heat transfer model. Transactions of the American Nuclear Society. 106. 260–263.1 indexed citations
17.
Gidden, Matthew, Paul Wilson, Kathryn Huff, & Robert Carlsen. (2012). Once-through benchmarks with CYCLUS, a modular, open-source fuel cycle simulator. Transactions of the American Nuclear Society. 107. 264–266.1 indexed citations
18.
Scopatz, Anthony, Paul Romano, Paul Wilson, & Kathryn Huff. (2012). PyNE: Python for nuclear engineering. Transactions of the American Nuclear Society. 107. 985–987.16 indexed citations
19.
Huff, Kathryn, Paul Wilson, & Matthew Gidden. (2011). Open architecture and modular paradigm of CYCLUS , a fuel cycle simulation code. Transactions of the American Nuclear Society. 104. 183–184.2 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.