Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Gene Ontology: tool for the unification of biology
200030.2k citationsJudith A. Blake, Allan Peter Davis et al.profile →
Countries citing papers authored by Janan T. Eppig
Since
Specialization
Citations
This map shows the geographic impact of Janan T. Eppig'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 Janan T. Eppig with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Janan T. Eppig more than expected).
This network shows the impact of papers produced by Janan T. Eppig. 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 Janan T. Eppig. The network helps show where Janan T. Eppig may publish in the future.
Co-authorship network of co-authors of Janan T. Eppig
This figure shows the co-authorship network connecting the top 25 collaborators of Janan T. Eppig.
A scholar is included among the top collaborators of Janan T. Eppig 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 Janan T. Eppig. Janan T. Eppig is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Bello, Susan M., Allan Peter Davis, Thomas C. Wiegers, et al.. (2011). Waiting for a Robust Disease Ontology: A Merger of OMIM and MeSH as a Practical Interim Solution..2 indexed citations
5.
Ramakrishnan, C. R., William A. Baumgartner, Judith A. Blake, et al.. (2010). Building the Scientific Knowledge Mine (SciKnowMine1): a Community-driven Framework for Text Mining Tools in Direct Service to Biocuration. Malta. Language Resources and Evaluation.5 indexed citations
6.
Bult, Carol J., Judith A. Blake, James A. Kadin, et al.. (2007). P4-S The Mouse Genome Informatics Database: An Integrated Resource for Mouse Genetics and Genomics.. Journal of Biomolecular Techniques JBT. 18(1). 2–2.2 indexed citations
Näf, Dieter, Debra M. Krupke, John P. Sundberg, Janan T. Eppig, & Carol J. Bult. (2002). The Mouse Tumor Biology Database: a public resource for cancer genetics and pathology of the mouse.. The Mouseion at the JAXlibrary (Jackson Laboratory). 62(5). 1235–40.40 indexed citations
11.
Hill, David P., Allan Peter Davis, Joel E. Richardson, et al.. (2001). PROGRAM DESCRIPTION. Genomics. 74(1). 121–128.41 indexed citations
Abbott, Catherine M., Robert D. Blank, Janan T. Eppig, et al.. (1992). Mouse Chromosome 4. Mammalian Genome. 3(S1). S55–S64.18 indexed citations
20.
Eppig, Janan T.. (1991). Mouse DNA clones and probes. Mammalian Genome. 1(S1). S332–S432.6 indexed citations
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.