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.
The Definition of Standard ML
19971.1k citationsRobin Milner, Robert Harper et al.profile →
Countries citing papers authored by David MacQueen
Since
Specialization
Citations
This map shows the geographic impact of David MacQueen'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 MacQueen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David MacQueen more than expected).
This network shows the impact of papers produced by David MacQueen. 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 MacQueen. The network helps show where David MacQueen may publish in the future.
Co-authorship network of co-authors of David MacQueen
This figure shows the co-authorship network connecting the top 25 collaborators of David MacQueen.
A scholar is included among the top collaborators of David MacQueen 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 MacQueen. David MacQueen is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
MacQueen, David, Robert Harper, & John Reppy. (2020). The history of Standard ML. Proceedings of the ACM on Programming Languages. 4(HOPL). 1–100.4 indexed citations
2.
MacQueen, David, D. K. Peterson, & W. H. Wright. (2006). Computationally Efficient Models of Flow-through Affinity-ba sed Assays. TechConnect Briefs. 2(2006). 581–584.1 indexed citations
3.
Kleebe, Hans‐Joachim, Giuliano Gregori, Florence Babonneau, et al.. (2006). Evolution of C-rich SiOC ceramics. Part I. Characterization by Integral Spectroscopic Techniques : Electron Energy-Loss Spectroscopy, High Resolution TEM and Energy-Filtered TEM. International Journal of Materials Research (formerly Zeitschrift fuer Metallkunde). 97.1 indexed citations
4.
MacQueen, David. (2002). Should ML be Object-Oriented?. Formal Aspects of Computing. 13(3-5). 214–232.3 indexed citations
MacQueen, David & Luca Cardelli. (1998). Proceedings of the 25th ACM SIGPLAN-SIGACT symposium on Principles of programming languages.14 indexed citations
8.
Milner, Robin, Robert Harper, David MacQueen, & Mads Tofte. (1997). The Definition of Standard ML (Revised). Research at the University of Copenhagen (University of Copenhagen).594 indexed citations breakdown →
Harper, Robert, Bruce F. Duba, & David MacQueen. (1993). Typing first-class continuations in ML. Journal of Functional Programming. 3(4). 465–484.38 indexed citations
MacQueen, David. (1990). A higher-order type system for functional programming. Addison-Wesley Longman Publishing Co., Inc. eBooks. 353–367.4 indexed citations
Milner, Robin, Robert Harper, & David MacQueen. (1986). Standard ML: Report ECS-LFCS-86-2.25 indexed citations
18.
Kahn, Gilles, David MacQueen, & Gordon Plotkin. (1984). Semantics of Data Types: International Symposium Sophia-Antipolis, France, June 27-29, 1984. Proceedings. Medical Entomology and Zoology.2 indexed citations
Burstall, R. M., David MacQueen, & Donald Sannella. (1980). HOPE. 136–143.201 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.