Daniel Sawyer

54 papers receiving 704 citations

Peers

Daniel Sawyer
Comparison fields: 5 of 71
  • Geology 204
  • Instrumentation 104
  • Statistics, Probability and Uncertainty 109
  • Computer Vision and Pattern Recognition 272
  • Environmental Engineering 159
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Liping Yan China
Timothy J. Beberniss United States
David A. Ehrhardt United States
Gary A. Fleming United States
Dongliang Zheng China
Adrian A. Dorrington New Zealand
Sabbir Rangwala United States
Nan Gao China
Hong Zhao China
Alberto Vale Portugal
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Citations per year

Countries citing papers authored by Daniel Sawyer

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Sawyer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 56 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2015193
2 200966
3 201456
4 195855
5 201728
6 201727
7 201023
8 202022
9 201322
10 201720
11 202014
12 195714
13 201714
14 201414
15
The Calculation of CMM Measurement Uncertainty via The Method of Simulation by Constraints | NIST
199713
16 200112
17 201711
18
The Validation of CMM Task Specific Measurement Uncertainty Software | NIST
200311
19 201810
20 201910

About Daniel Sawyer

Daniel Sawyer is a scholar working on Mechanical Engineering, Geology, Computer Vision and Pattern Recognition, Biomedical Engineering and Environmental Engineering, having authored 56 papers that have together received 768 indexed citations. Recurring topics across this work include Advanced Measurement and Metrology Techniques (21 papers), 3D Surveying and Cultural Heritage (20 papers), Optical measurement and interference techniques (12 papers), Remote Sensing and LiDAR Applications (12 papers), Scientific Measurement and Uncertainty Evaluation (7 papers), Advanced Optical Sensing Technologies (7 papers), Advanced Sensor Technologies Research (6 papers) and Advanced X-ray and CT Imaging (4 papers). The work is most often cited by research in Geology (204 citations), Instrumentation (104 citations), Statistics, Probability and Uncertainty (109 citations), Computer Vision and Pattern Recognition (272 citations) and Environmental Engineering (159 citations). Daniel Sawyer has collaborated with scholars based in United States, China and Canada. Frequent co-authors include Bala Muralikrishnan, R. H. Rediker, Steven Phillips, Bruce R. Borchardt, William T. Estler, Vincent Lee, Geraldine S. Cheok, Luc Cournoyer, Craig M. Shakarji and Ling Wang. Their work appears in journals such as Journal of Research of the National Institute of Standards and Technology, Measurement, Precision Engineering, Measurement Science and Technology and Optics and Lasers in Engineering.

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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