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 transgene-encoded cell surface polypeptide for selection, in vivo tracking, and ablation of engineered cells
Countries citing papers authored by Mark A. Sherman
Since
Specialization
Citations
This map shows the geographic impact of Mark A. Sherman'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 Mark A. Sherman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark A. Sherman more than expected).
This network shows the impact of papers produced by Mark A. Sherman. 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 Mark A. Sherman. The network helps show where Mark A. Sherman may publish in the future.
Co-authorship network of co-authors of Mark A. Sherman
This figure shows the co-authorship network connecting the top 25 collaborators of Mark A. Sherman.
A scholar is included among the top collaborators of Mark A. Sherman 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 Mark A. Sherman. Mark A. Sherman 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.
Tissenbaum, Mike, Mark A. Sherman, Josh Sheldon, & Hal Abelson. (2018). From computational thinking to computational action: Understanding changes in computational identity through app inventor and the internet of things. International Conference of Learning Sciences. 3. 1657–1658.1 indexed citations
2.
Sherman, Mark A.. (2017). Eric Pyle, William Blake’s Illustrations for Dante’s Divine Comedy: A Study of the Engravings, Pencil Sketches and Watercolors. 50(3).
3.
Martin, Fred & Mark A. Sherman. (2015). A dual-major course emphasizing computer science and graphic design. Journal of computing sciences in colleges. 30(6). 24–31.1 indexed citations
4.
Sherman, Mark A. & Fred Martin. (2015). The assessment of mobile computational thinking. Journal of computing sciences in colleges. 30(6). 53–59.20 indexed citations
Sherman, Mark A., et al.. (2013). Impact of auto-grading on an introductory computing course. Journal of computing sciences in colleges. 28(6). 69–75.29 indexed citations
Afanassieff, Marielle, et al.. (2001). At least one class I gene in restriction fragment pattern-Y (Rfp-Y), the second MHC gene cluster in the chicken, is transcribed, polymorphic, and shows divergent specialization in antigen binding region. HAL (Le Centre pour la Communication Scientifique Directe).2 indexed citations
Sherman, Mark A.. (1999). Some Thoughts on Restoration, Reintegration and Justice in the Transnational Context. Fordham international law journal. 23(5). 1397.2 indexed citations
Sherman, Mark A.. (1991). United States International Drug Control Policy, Extradition, and the Rule of Law in Colombia. Nova law review. 15(2). 10.1 indexed citations
20.
Sherman, Mark A.. (1989). An Inquiry Regarding the International and Domestic Legal Problems Presented in United States v. Noriega. 20(2). 393.
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.