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.
A review of methods to match building energy simulation models to measured data
2014639 citationsPaul Raftery, Marcus Keane et al.profile →
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 Marcus Keane'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 Marcus Keane with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marcus Keane more than expected).
This network shows the impact of papers produced by Marcus Keane. 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 Marcus Keane. The network helps show where Marcus Keane may publish in the future.
Co-authorship network of co-authors of Marcus Keane
This figure shows the co-authorship network connecting the top 25 collaborators of Marcus Keane.
A scholar is included among the top collaborators of Marcus Keane 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 Marcus Keane. Marcus Keane is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Schukat, Michael, et al.. (2019). Machine Learning Methods Applied to Building Energy Production and Consumption Prediction.. 236–247.2 indexed citations
Costa, Andrea, et al.. (2012). SWIMMING POOL HALL HVAC MODELLING, SIMULATION AND END OF SETBACK NEURAL NETWORK PREDICTION: A DETAILED CASE STUDY. Proceedings of SimBuild. 5(1). 104–111.1 indexed citations
O’Flynn, Brendan, et al.. (2010). Development of miniaturized wireless sensor nodes suitable for building energy management and modelling. Arrow@dit (Dublin Institute of Technology).5 indexed citations
15.
Pesch, Dirk, Shafique Ahmad Chaudhry, Cormac J. Sreenan, et al.. (2009). Efficient building management with IP-based wireless sensor network. Arrow@dit (Dublin Institute of Technology).6 indexed citations
16.
O’Donnell, James, Marcus Keane, & Vladimir Bazjanac. (2008). Specification of an Information Delivery Tool to Support Optimal Holistic Environmental and Energy Management in Buildings. University of North Texas Digital Library (University of North Texas). 3(1). 61–68.5 indexed citations
17.
Keane, Marcus, et al.. (2006). Specifiation Of An IFC Based Software Application To Support CFD Simulation.2 indexed citations
18.
O’Donnell, James, et al.. (2004). Specification and implementation of IFC based performance metrics to support building life cycle assessment of hybrid energy systems. University of North Texas Digital Library (University of North Texas). 1(1).10 indexed citations
19.
O’Donnell, James, et al.. (2004). BuildingPI: A future tool for building life cycle analysis. eScholarship (California Digital Library). 1(1).5 indexed citations
20.
Keane, Marcus, et al.. (1989). Simulating analogical mapping difficulties in recursion problems. 3–12.3 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.