Paul Saxman

561 citations
10 papers · 370 indexed · h-index 7
Topics
Biomedical Text Mining and Ontologies (4 papers)Bioinformatics and Genomic Networks (3 papers)Semantic Web and Ontologies (2 papers)
Partner nations
United StatesIndiaItaly

In The Last Decade

Paul Saxman

9 papers receiving 361 citations

Peers

Paul Saxman
Comparison fields: 5 of 103
  • Molecular Biology 167
  • Ecology 119
  • Pollution 57
  • Artificial Intelligence 43
  • Plant Science 28
Replace Vanessa Aguiar‐Pulido with:
Vanessa Aguiar‐Pulido United States
Dan Smith United Kingdom
Tim Booth United Kingdom
Ioanna Pagani United States
Nikolas Papanikolaou Greece
Kay Schallert Germany
Bart Mesuere Belgium
Piotr Gawron Luxembourg
X. B. Yang United States
Paul Saxman relative to Vanessa Aguiar‐Pulido United States Vanessa Aguiar‐Pulido's profile →
Citations per field
00.5×1.5×1.9×
Vanessa Aguiar‐Pulido · 1×
Citations per year

Countries citing papers authored by Paul Saxman

Since Specialization
Citations

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

Fields of papers citing papers by Paul Saxman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Paul Saxman

This figure shows the co-authorship network connecting the top 25 collaborators of Paul Saxman. A scholar is included among the top collaborators of Paul Saxman 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 Paul Saxman. Paul Saxman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

10 of 10 papers shown
#WorkIndexed citations
1 15
2 12
3
VISAGE: A Query Interface for Clinical Research.
45
4
User requirements for exploring a resource inventory for clinical research.
2
5 27
6 31
7
A novel system for rapidly identifying toxic chemicals.
0
8
Casepedia: a Web 2.0 case repository enabling collaborative learning.
1
9
The "Honest Broker" method of integrating interdisciplinary research data.
8
10 229

About Paul Saxman

Paul Saxman is a scholar working on Information Systems and Management, Health Information Management and Computer Science Applications, having authored 10 papers that have together received 370 indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (4 papers), Bioinformatics and Genomic Networks (3 papers) and Semantic Web and Ontologies (2 papers). The work is most often cited by research in Pollution (57 citations), Ecology (119 citations) and Information Systems and Management (20 citations). Paul Saxman has collaborated with scholars based in United States, India and Italy. Frequent co-authors include Terence L. Marsh, James H. Cole, James M. Tiedje, Guo‐Qiang Zhang, Sebastian Martini, Felix Eichinger, Remo Mueller, Matthias Kretzler, Nathan A. Johnson and H. V. Jagadish. Their work appears in journals such as Applied and Environmental Microbiology, BMC Bioinformatics and Journal of the American Medical Informatics Association.

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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