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.
CNS Myelin and Sertoli Cell Tight Junction Strands Are Absent in Osp/Claudin-11 Null Mice
1999583 citationsAlexander Gow, Cherie M. Southwood et al.Cellprofile →
Author Peers
Peers are selected by citation overlap in the author's most active subfields.
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This map shows the geographic impact of John Danias'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 John Danias with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Danias more than expected).
This network shows the impact of papers produced by John Danias. 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 John Danias. The network helps show where John Danias may publish in the future.
Co-authorship network of co-authors of John Danias
This figure shows the co-authorship network connecting the top 25 collaborators of John Danias.
A scholar is included among the top collaborators of John Danias 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 John Danias. John Danias is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Melin, Amanda, Rachel A. Munds, Michael J. Montague, et al.. (2020). Intraocular pressure, optic nerve appearance, and posterior pole pathology in a large cohort of free-ranging rhesus macaques. Investigative Ophthalmology & Visual Science. 61(7). 4784–4784.1 indexed citations
Panagis, Lampros, et al.. (2012). Changes to Retinal Complement Gene Expression Induced by Elevated Intraocular Pressure in the Microbead Model of Glaucoma in Mice. Investigative Ophthalmology & Visual Science. 53(14). 3865–3865.1 indexed citations
11.
Naymagon, Steven, et al.. (2007). Quantitative Evaluation of CNS Neuronal Loss in Glaucomatous DBA/2 Mice. Investigative Ophthalmology & Visual Science. 48(13). 3663–3663.1 indexed citations
Reichstein, David A., et al.. (2005). Visualization of Retinal Ganglion Cell Apoptosis in Aging Glaucomatous DBA/2J Mice. Investigative Ophthalmology & Visual Science. 46(13). 1259–1259.1 indexed citations
14.
Filippopoulos, Theodoros, Akihisa Matsubara, John Danias, et al.. (2005). Accuracy and Sensitivity of Two Non–Invasive Tonometers for the Measurement of Intraocular Pressure (IOP) in the Mouse. Investigative Ophthalmology & Visual Science. 46(13). 1257–1257.1 indexed citations
15.
Ren, Liang, et al.. (2005). Quantitation of Mouse RGCs by Direct Labeling With Antibodies to Neurofilament, PGP 9.5, Osteopontin, and Brn3 Compared to Retrograde Labeling With Aminostilbamidine. Investigative Ophthalmology & Visual Science. 46(13). 1262–1262.1 indexed citations
Danias, John, et al.. (2002). Measurement of Intraocular Pressure (IOP) in the Mouse Eye Using an Impact Probe. Investigative Ophthalmology & Visual Science. 43(13). 4057–4057.1 indexed citations
19.
Shen, Fran, et al.. (2002). Changes Of Glutamate Dehyrogenase (GDH) Activity In The Retina Of Rats With Short-term Intraocular Pressure (IOP) Elevation. Investigative Ophthalmology & Visual Science. 43(13). 4092–4092.1 indexed citations
20.
Gow, Alexander, Cherie M. Southwood, Milena Pariali, et al.. (1999). CNS Myelin and Sertoli Cell Tight Junction Strands Are Absent in Osp/Claudin-11 Null Mice. Cell. 99(6). 649–659.583 indexed citations breakdown →
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.