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.
Computation of phase equilibria by linear programming: A tool for geodynamic modeling and its application to subduction zone decarbonation
Countries citing papers authored by J. A. D. Connolly
Since
Specialization
Citations
This map shows the geographic impact of J. A. D. Connolly'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 J. A. D. Connolly with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites J. A. D. Connolly more than expected).
Fields of papers citing papers by J. A. D. Connolly
This network shows the impact of papers produced by J. A. D. Connolly. 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 J. A. D. Connolly. The network helps show where J. A. D. Connolly may publish in the future.
Co-authorship network of co-authors of J. A. D. Connolly
This figure shows the co-authorship network connecting the top 25 collaborators of J. A. D. Connolly.
A scholar is included among the top collaborators of J. A. D. Connolly 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 J. A. D. Connolly. J. A. D. Connolly is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Gerya, Taras, et al.. (2017). Modelling Earthquakes Using a Poro-Elastic Two-Phase Flow Formulation. AGUFM. 2017.1 indexed citations
10.
Vannucchi, P., et al.. (2016). Has Earth's Plate Tectonics Led to Rapid Core Cooling?. AGU Fall Meeting Abstracts. 2016.1 indexed citations
11.
Khan, Ajmir, Anne Pommier, & J. A. D. Connolly. (2013). On the Presence of a Titanium-Rich Melt-Layer in the Deep Lunar Interior. Lunar and Planetary Science Conference. 1272.2 indexed citations
Afonso, Juan Carlos, Javier Fullea, Yingjie Yang, et al.. (2011). A 3D multi-observable probabilistic inversion method for the compositional and thermal structure of the lithosphere and sublithospheric upper mantle. AGUFM. 2011.1 indexed citations
14.
Nakagawa, Takashi, Paul Tackley, Frédéric Deschamps, & J. A. D. Connolly. (2009). Influence of MORB bulk composition on 3-D spherical models of thermo-chemical mantle convection with self-consistently calculated mineral physics. Geochimica et Cosmochimica Acta. 73(13).1 indexed citations
Connolly, J. A. D. & Yury Podladchikov. (2004). Asthenospheric Melt Segregation and Channelization: The Influence of Differential Yielding and Disaggregation on Fluid Flow in Ductile Rocks. AGU Fall Meeting Abstracts. 2004.1 indexed citations
17.
Kerrick, D. M., J. A. D. Connolly, & K. Caldeira. (2003). Arc paleo-CO2 degassing revisited. EGS - AGU - EUG Joint Assembly. 14253.
18.
Boschi, Chiara, et al.. (2002). The Role of Serpentinization in Metasomatism, Carbonate Precipitation and Microbial Activity: Stable Isotope Constraints from the Lost City Vent Field (MAR 30°N). AGU Fall Meeting Abstracts. 2002.7 indexed citations
19.
Connolly, J. A. D., I. Memmi, Volkmar Trommsdorff, Marcello Franceschelli, & C. A. Ricci. (1994). Forward modeling of calc-silicate microinclusions and fluid evolution in a graphitic metapelite, Northeast Sardinia. American Mineralogist. 79. 960–972.19 indexed citations
20.
Larimer, J. W., et al.. (1981). Unusual Textures and Minerals in Enstatite Chondrites. Meteoritics and Planetary Science. 16. 346.2 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.