Keiko Niimi
About
In The Last Decade
Keiko Niimi
75 papers receiving 2.0k citations
Peers
Comparison fields: 5 of 89
- Pulmonary and Respiratory Medicine 1.5k
- Surgery 1.3k
- Gastroenterology 856
- Oncology 265
- Epidemiology 76
Countries citing papers authored by Keiko Niimi
This map shows the geographic impact of Keiko Niimi'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 Keiko Niimi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Keiko Niimi more than expected).
Fields of papers citing papers by Keiko Niimi
This network shows the impact of papers produced by Keiko Niimi. 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 Keiko Niimi. The network helps show where Keiko Niimi may publish in the future.
Co-authorship network of co-authors of Keiko Niimi
This figure shows the co-authorship network connecting the top 25 collaborators of Keiko Niimi. A scholar is included among the top collaborators of Keiko Niimi 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 Keiko Niimi. Keiko Niimi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 1 | |
| 4 | 4 | |
| 5 | 7 | |
| 6 | 胃癌の浸潤深さを予測するための高精度人工知能システム:従来の白色光イメージング,非拡大狭帯域イメージングおよびインジゴカルミン色素コントラストイメージングの有効性【JST・京大機械翻訳】 | 4 |
| 7 | 19 | |
| 8 | 11 | |
| 9 | 19 | |
| 10 | 34 | |
| 11 | 32 | |
| 12 | 67 | |
| 13 | 37 | |
| 14 | 47 | |
| 15 | 82 | |
| 16 | 5 | |
| 17 | 46 | |
| 18 | 3 | |
| 19 | 2 | |
| 20 | 28 |
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.