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.
Committee-Based Active Learning for Surrogate-Assisted Particle Swarm Optimization of Expensive Problems
This map shows the geographic impact of John Doherty'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 John Doherty with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Doherty more than expected).
This network shows the impact of papers produced by John Doherty. 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 John Doherty. The network helps show where John Doherty may publish in the future.
Co-authorship network of co-authors of John Doherty
This figure shows the co-authorship network connecting the top 25 collaborators of John Doherty.
A scholar is included among the top collaborators of John Doherty 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 John Doherty. John Doherty is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Tiwari, S. N., et al.. (2024). A review of liquid hydrogen aircraft and propulsion technologies. International Journal of Hydrogen Energy. 57. 1174–1196.89 indexed citations breakdown →
Ellis, Randy E., et al.. (2009). Applying PEST (parameter ESTimation) to improve parameter estimation and uncertainty analysis in WaterCAST models. Congress on Modelling and Simulation.4 indexed citations
Xu, Yuchun, et al.. (2008). Manufacturing Cost Modeling for Aircraft Wing. Research Portal (Queen's University Belfast).1 indexed citations
13.
Wang, Jian, Yuchun Xu, Juliana Early, et al.. (2008). Costing of Aluminium Process for Life Cycle. Research Portal (Queen's University Belfast).
14.
Doherty, John. (2008). Model predictive error : how it arises and how it can be accommodated. IAHS-AISH publication. 267–271.1 indexed citations
15.
Christensen, Steen & John Doherty. (2008). Using many pilot points and singular value decomposition in groundwater model calibration. IAHS-AISH publication. 235–239.2 indexed citations
16.
Christensen, Steen, Catherine Moore, & John Doherty. (2006). Comparison of stochastic and regression based methods for quantification of predictive uncertainty of model-simulated wellhead protection zones in heterogeneous aquifers. Queensland's institutional digital repository (The University of Queensland). 202–208.4 indexed citations
Lin, Zhi, D. E. Radcliffe, & John Doherty. (2004). Two-Stage Automatic Calibration and Predictive Uncertainty Analysis of a Semi-distributed Watershed Model. AGUFM. 2004.1 indexed citations
19.
Vesselinov, Velimir V., et al.. (2001). Analysis of uncertainty in model predictions of flow and transport in the Espanola Basin regional aquifer, Northern New Mexico. AGUFM. 2001.1 indexed citations
20.
Doherty, John. (1994). PEST: A Unique Computer Program for Model-independent Parameter Optimisation. 551.36 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.