The Visual Display of Quantitative Information2002 · 1.1k citations
What are hit papers?
Paper score: A paper's citations measured against the top-1% bar of its own field and year: the square root of citations ÷ bar, so 1 means right at the bar and 2 means four times its citations. The bar blends the citation counts that enter the top 1% of the paper's subfields in its year (weighted 89.5%), of those subfields over all years (0.5%) and of its year across all fields (10%); a subfield and year with fewer than 400 papers takes its year's bar. Papers from 2026 are not scored yet: in an unfinished year, when a paper appeared matters more than how it is cited.
Hit paper: A paper cited at least 1.5× the top-1% bar of its field and year: a paper score of at least √1.5.
This map shows the geographic impact of Edward Mulrow'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 Edward Mulrow with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Edward Mulrow more than expected).
This network shows the impact of papers produced by Edward Mulrow. 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 Edward Mulrow. The network helps show where Edward Mulrow may publish in the future.
Co-authors
The 3 scholars most cited alongside Edward Mulrow, linked wherever they
have co-authored with each other. Click a name or a connecting line to browse the papers
they share.
Border = papers with Edward MulrowLine = papers co-authored togetherEdward Mulrow links everyone, so they are left out of the graph.
Edward Mulrow is a scholar working on Statistics and Probability, Ecological Modeling, Health, Signal Processing and Artificial Intelligence, having authored 5 papers that have together received 1.1k indexed citations. Recurring topics across this work include Data Analysis with R (1 paper), Statistical Methods in Clinical Trials (1 paper), Data Visualization and Analytics (1 paper), Data-Driven Disease Surveillance (1 paper), Species Distribution and Climate Change (1 paper), Health disparities and outcomes (1 paper), Data Management and Algorithms (1 paper) and Advanced Text Analysis Techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (348 citations), Statistics and Probability (80 citations), Human-Computer Interaction (51 citations), Geography, Planning and Development (41 citations) and Information Systems and Management (48 citations). Edward Mulrow has collaborated with scholars based in United States, Israel and Canada. Frequent co-authors include Kirk Marcus Wolter, Quentin Brummet and Heike Hofmann. Their work appears in journals such as Technometrics, Lecture notes in computer science, Journal of Data Science and Harvard Data Science Review.
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.