Phillip I. Good

1.9k total citations
32 papers, 1.2k citations indexed

About

Phillip I. Good is a scholar working on Molecular Biology, Physiology and Aging. According to data from OpenAlex, Phillip I. Good has authored 32 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Molecular Biology, 3 papers in Physiology and 3 papers in Aging. Recurrent topics in Phillip I. Good's work include Gene Regulatory Network Analysis (3 papers), Genetics, Aging, and Longevity in Model Organisms (3 papers) and Telomeres, Telomerase, and Senescence (3 papers). Phillip I. Good is often cited by papers focused on Gene Regulatory Network Analysis (3 papers), Genetics, Aging, and Longevity in Model Organisms (3 papers) and Telomeres, Telomerase, and Senescence (3 papers). Phillip I. Good collaborates with scholars based in United States and Canada. Phillip I. Good's co-authors include Yogendra P. Chaubey, James W. Hardin, Tim Hesterberg, James R. Smith, Margaret L. Heidrick, Julia W. Albright, Takashi Makinodan, Olivia M. Pereira‐Smith, C. P. Peter and Clifford E. Lunneborg and has published in prestigious journals such as Nature, Journal of the American Statistical Association and Technometrics.

In The Last Decade

Phillip I. Good

31 papers receiving 1.1k citations

Peers

Phillip I. Good
Comparison fields: 5 of 187
  • Molecular Biology 158
  • Statistics and Probability 142
  • Artificial Intelligence 111
  • Physiology 97
  • Cognitive Neuroscience 74
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Citations per field, relative to Phillip I. Good
Phillip I. Good · 1×
Citations per year, relative to Phillip I. Good
Phillip I. Good · 1×

Countries citing papers authored by Phillip I. Good

Since Specialization
Citations

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

Fields of papers citing papers by Phillip I. Good

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Phillip I. Good. 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 Phillip I. Good. The network helps show where Phillip I. Good may publish in the future.

Co-authorship network of co-authors of Phillip I. Good

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

All Works

20 of 20 papers shown
# Work Indexed citations
1 0
2 45
3
Common Errors in Statistics (and How to Avoid Them), 2nd Edition + Introduction to Statistics Through Resampling Methods and Microsoft Office Excel
3
4 112
5 7
6 11
7
Permutation, Parametric, and Bootstrap Tests of Hypotheses (Springer Series in Statistics)
57
8 2
9 37
10 2
11 314
12 78
13 11
14 37
15
Reduced humoral immune activity in long-lived old mice: an approach to elucidating its mechanisms.
59
16 3
17 3
18 30
19 4
20 36

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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