Jeffrey Shaffer

433 citations
4 papers · 312 · h-index 4

Impact in

Papers in

Journals
Expert Systems with Applications (1 paper)INFORMS Journal on Applied Analytics (1 paper)Wiley eBooks (1 paper)CERN Document Server (European Organization for Nuclear Research) (1 paper)
Partner nations
United States

In The Last Decade

Jeffrey Shaffer

4 papers receiving 296 citations

Peers

Jeffrey Shaffer
Comparison fields: 5 of 76
  • General Social Sciences 35
  • Computer Vision and Pattern Recognition 61
  • Computer Science Applications 14
  • Artificial Intelligence 82
  • Management Information Systems 24
Replace Samir Passi with:
Samir Passi United States
Edi Surya Negara Indonesia
Brittany Fiore-Gartland United States
Annavarapu Chandra Sekhara Rao India
Ronal Watrianthos Indonesia
Chienting Lin United States
Nopasit Chakpitak Thailand
Ahmet Ayaz Türkiye
Il Young Choi South Korea
Patrick Bernard Washington United States
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Citations per field
00.5×1.5×2×2.5×
Samir Passi · 1×
Citations per year

Countries citing papers authored by Jeffrey Shaffer

Since Specialization
Citations

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

Fields of papers citing papers by Jeffrey Shaffer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 3 scholars most cited alongside Jeffrey Shaffer, 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 Jeffrey Shaffer Line = papers co-authored together Jeffrey Shaffer links everyone, so they are left out of the graph.

All Works

4 of 4 papers shown
#Work
1 2019183
2 201769
3
The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios
201746
4 201714

About Jeffrey Shaffer

Jeffrey Shaffer is a scholar working on Management Information Systems, Computer Vision and Pattern Recognition, General Social Sciences, Artificial Intelligence and Nuclear Energy and Engineering, having authored 4 papers that have together received 312 indexed citations. Recurring topics across this work include Data Visualization and Analytics (3 papers), Big Data and Business Intelligence (2 papers), Topic Modeling (1 paper) and Computational and Text Analysis Methods (1 paper). The work is most often cited by research in General Social Sciences (35 citations), Computer Vision and Pattern Recognition (61 citations), Computer Science Applications (14 citations), Artificial Intelligence (82 citations) and Management Information Systems (24 citations). Jeffrey Shaffer has collaborated with scholars based in United States. Frequent co-authors include Kaveh Bastani, Michael J. Fry and Jeffrey D. Camm. Their work appears in journals such as Expert Systems with Applications, INFORMS Journal on Applied Analytics, Wiley eBooks and CERN Document Server (European Organization for Nuclear Research).

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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