Kim Williams

60 papers receiving 402 citations

Peers

Kim Williams
Comparison fields: 5 of 120
  • Small Animals 115
  • Architecture 20
  • Visual Arts and Performing Arts 35
  • Theoretical Computer Science 8
  • Clinical Psychology 113
Replace Peter Brown with:
Peter Brown United Kingdom
Verónica Pérez‐Cabezas Spain
Charles A. Lantz United States
Sarah M. Kreidler United States
Chunmei Lin United States
Christian Thiele Germany
T. Roy United States
Brian Gordon United States
Susan Fraser United Kingdom
Andrew P. Kurmis Australia
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Citations per field
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Citations per year

Countries citing papers authored by Kim Williams

Since Specialization
Citations

This map shows the geographic impact of Kim Williams'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 Kim Williams with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kim Williams more than expected).

Fields of papers citing papers by Kim Williams

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kim Williams. 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 Kim Williams. The network helps show where Kim Williams may publish in the future.

Co-authors

The 25 scholars most cited alongside Kim Williams, 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 Kim Williams Line = papers co-authored together Kim Williams links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 66 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200161
2 201047
3 201438
4 201836
5 201624
6 201619
7 201719
8
Microsoft® .NET Remoting
200217
9 201612
10 199911
11 201510
12 20188
13 20198
14 20087
15
Symmetry in Architecture
19997
16 20157
17 20157
18 20196
19 20026
20 19946

About Kim Williams

Kim Williams is a scholar working on Visual Arts and Performing Arts, Architecture, Small Animals, Archeology and Clinical Psychology, having authored 66 papers that have together received 434 indexed citations. Recurring topics across this work include Architecture and Art History Studies (17 papers), Architecture and Computational Design (11 papers), Veterinary Orthopedics and Neurology (5 papers), Historical Art and Architecture Studies (4 papers), Architecture, Modernity, and Design (3 papers), Historical and Architectural Studies (3 papers), Eating Disorders and Behaviors (3 papers) and Renaissance and Early Modern Studies (2 papers). The work is most often cited by research in Small Animals (115 citations), Architecture (20 citations), Visual Arts and Performing Arts (35 citations), Theoretical Computer Science (8 citations) and Clinical Psychology (113 citations). Kim Williams has collaborated with scholars based in Italy, United States and Australia. Frequent co-authors include Josie Geller, Suja Srikameswaran, Natasha J. Olby, Emily H. Griffith, Michael J. Ostwald, Christopher L. Mariani, Karen R. Muñana, Peter J. Early, Joe Fenn and Scott McLean. Their work appears in journals such as Nexus Network Journal, Journal of Veterinary Internal Medicine, European Eating Disorders Review, Emergency Medicine Australasia and British Journal of Dermatology.

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.

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