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.
Multi-trait analysis of genome-wide association summary statistics using MTAG
2017538 citationsPatrick Turley, Raymond K. Walters et al.Nature Geneticsprofile →
Author Peers
Peers are selected by citation overlap in the author's most active subfields.
citations ·
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Countries citing papers authored by David Cesarini
Since
Specialization
Citations
This map shows the geographic impact of David Cesarini'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 David Cesarini with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Cesarini more than expected).
This network shows the impact of papers produced by David Cesarini. 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 David Cesarini. The network helps show where David Cesarini may publish in the future.
Co-authorship network of co-authors of David Cesarini
This figure shows the co-authorship network connecting the top 25 collaborators of David Cesarini.
A scholar is included among the top collaborators of David Cesarini 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 David Cesarini. David Cesarini is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Okbay, Aysu, Robbee Wedow, Edward Kong, et al.. (2017). GWAS of educational attainment: phase 3-main results. Behavior Genetics. 47(6). 699–700.2 indexed citations
7.
Turley, Patrick, Raymond K. Walters, Omeed Maghzian, et al.. (2017). Multi-trait analysis of genome-wide association summary statistics using MTAG. Nature Genetics. 50(2). 229–237.538 indexed citations breakdown →
Benjamin, Daniel J., Andrew Caplin, David Cesarini, Kevin Thom, & Patrick Turley. (2015). Smoking, Genes, and Health: Evidence from the Health and Retirement Study.1 indexed citations
10.
Briggs, Joseph, David Cesarini, Erik Lindqvist, & Robert Östling. (2015). Windfall Gains and Stock Market Participation. SSRN Electronic Journal.1 indexed citations
11.
Östling, Robert, Erik Lindqvist, David Cesarini, & Joseph Briggs. (2015). Wealth and Stock Market Participation: Estimating the Causal Effect From Swedish Lotteries. RePEc: Research Papers in Economics.5 indexed citations
12.
Chabris, Christopher F., James J. Lee, David Cesarini, Daniel J. Benjamin, & David Laibson. (2015). The Fourth Law of Behavior Genetics. Current Directions in Psychological Science. 24(4). 304–312.191 indexed citations
Rietveld, Cornelius A., Philipp Koellinger, Daniel J. Benjamin, et al.. (2013). Are SNPs associated with educational attainment also associated with cognitive function?. Queensland's institutional digital repository (The University of Queensland).1 indexed citations
Beauchamp, Jonathan, David Cesarini, Magnus Johannesson, et al.. (2011). Molecular Genetics and Economics. The Journal of Economic Perspectives. 25(4). 57–82.88 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.