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.
Pegasus, a workflow management system for science automation
2014517 citationsEwa Deelman, Gideon Juve et al.profile →
CyberShake: A Physics-Based Seismic Hazard Model for Southern California
2010357 citationsRobert Graves, T. H. Jordan et al.profile →
Author Peers
Peers are selected by citation overlap in the author's most active subfields.
citations ·
hero ref
Countries citing papers authored by P. J. Maechling
Since
Specialization
Citations
This map shows the geographic impact of P. J. Maechling'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 P. J. Maechling with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites P. J. Maechling more than expected).
This network shows the impact of papers produced by P. J. Maechling. 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 P. J. Maechling. The network helps show where P. J. Maechling may publish in the future.
Co-authorship network of co-authors of P. J. Maechling
This figure shows the co-authorship network connecting the top 25 collaborators of P. J. Maechling.
A scholar is included among the top collaborators of P. J. Maechling 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 P. J. Maechling. P. J. Maechling is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Maechling, P. J., et al.. (2020). Enhancing CyberShake Simulations for Engineering Applications. AGU Fall Meeting Abstracts. 2020.1 indexed citations
3.
Savran, William H., P. J. Maechling, Maximilian J. Werner, et al.. (2019). The Collaboratory for the Study of Earthquake Predictability Version 2 (CSEP2): Testing Forecasts that Generate Synthetic Earthquake Catalogs. Publication Database GFZ (GFZ German Research Centre for Geosciences). 12445.1 indexed citations
4.
Callaghan, S., P. J. Maechling, Christine Goulet, et al.. (2017). CyberShake Physics-Based PSHA in Central California. AGU Fall Meeting Abstracts. 2017.1 indexed citations
5.
Callaghan, S., P. J. Maechling, Christine Goulet, et al.. (2016). Expanding CyberShake Physics-Based Seismic Hazard Calculations to Central California. AGUFM. 2016.1 indexed citations
6.
Bielak, Jacobo, Ricardo Taborda, K. B. Olsen, et al.. (2016). Verification and Validation of High-Frequency (f max = 5 Hz) Ground Motion Simulations of the 2014 M 5.1 La Habra, California, earthquake. AGUFM. 2016.4 indexed citations
7.
Callaghan, S., et al.. (2015). Using CyberShake Workflows to Manage Big Seismic Hazard Data on Large-Scale Open-Science HPC Resources. 2015 AGU Fall Meeting. 2015.1 indexed citations
8.
Goulet, Christine, Ferran Silva, P. J. Maechling, S. Callaghan, & T. H. Jordan. (2015). The SCEC Broadband Platform: Open-Source Software for Strong Ground Motion Simulation and Validation. AGU Fall Meeting Abstracts. 2015.1 indexed citations
9.
Callaghan, S., et al.. (2014). Optimizing CyberShake Seismic Hazard Workflows for Large HPC Resources. 2014 AGU Fall Meeting. 2014.1 indexed citations
10.
Jordán, Tibor, et al.. (2013). Using the Averaging-Based Factorization to Assess CyberShake Hazard Models. AGU Fall Meeting Abstracts. 2013.3 indexed citations
11.
Jordan, T. H., et al.. (2013). Full-3D waveform tomography of Southern California crustal structure by using earthquake recordings and ambient noise Green's functions based on adjoint and scattering-integral methods. AGUFM. 2013.1 indexed citations
12.
Callaghan, S., P. J. Maechling, Robert Graves, et al.. (2010). Running On-Demand Strong Ground Motion Simulations with the Second-Generation Broadband Platform. AGU Fall Meeting Abstracts. 2010.1 indexed citations
13.
Ely, G., T. H. Jordan, Patrick Small, & P. J. Maechling. (2010). A Vs30-derived Near-surface Seismic Velocity Model. AGU Fall Meeting Abstracts. 2010.22 indexed citations
14.
Jordan, T. H., et al.. (2010). Full-3D Waveform Tomography for Southern California. AGU Fall Meeting Abstracts. 2010.3 indexed citations
15.
Maechling, P. J., T. H. Jordan, M. Liukis, & S. Callaghan. (2009). Developing Performance Measures for the CISN Earthquake Early Warning Testing Center. AGU Fall Meeting Abstracts. 2009.1 indexed citations
16.
Maechling, P. J., Ewa Deelman, & Yifeng Cui. (2009). Implementing Software Acceptance Tests as Scientific Workflows.. Parallel and Distributed Processing Techniques and Applications. 317–323.2 indexed citations
17.
Cui, Y., K. B. Olsen, Steven M. Day, et al.. (2006). Optimization and Scalability of an Large-scale Earthquake Simulation Application. AGUFM. 2006.1 indexed citations
18.
Olsen, K. B., J. B. Minster, Y. Cui, et al.. (2005). TeraShake: Strong Shaking in Los Angeles Expected From Southern San Andreas Earthquake. AGU Fall Meeting Abstracts. 2005.5 indexed citations
19.
Maechling, P. J., T. H. Jordan, B. Minster, Robert A. Moore, & Carl Kesselman. (2004). The SCEC Community Modeling Environment (SCEC/CME) - An Overview of its Architecture and Current Capabilities. AGU Fall Meeting Abstracts. 2004.1 indexed citations
20.
Juve, Gideon, et al.. (2003). Creating A Virtual Fault Database Using Ontologies. AGU Fall Meeting Abstracts. 2003.1 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.