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.
The number of people with glaucoma worldwide in 2010 and 2020
Countries citing papers authored by Harry A. Quigley
Since
Specialization
Citations
This map shows the geographic impact of Harry A. Quigley'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 Harry A. Quigley with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Harry A. Quigley more than expected).
Fields of papers citing papers by Harry A. Quigley
This network shows the impact of papers produced by Harry A. Quigley. 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 Harry A. Quigley. The network helps show where Harry A. Quigley may publish in the future.
Co-authorship network of co-authors of Harry A. Quigley
This figure shows the co-authorship network connecting the top 25 collaborators of Harry A. Quigley.
A scholar is included among the top collaborators of Harry A. Quigley 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 Harry A. Quigley. Harry A. Quigley is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Korneva, Arina, et al.. (2020). Regional Mechanical Strains in Mouse Astrocytic Lamina and Peripapillary Sclera after Chronic IOP Elevation. Investigative Ophthalmology & Visual Science. 61(7). 996–996.2 indexed citations
7.
Schaub, Julie, et al.. (2020). Amyloid precursor protein transport block evaluation as a biomarker for experimental mouse glaucoma. Investigative Ophthalmology & Visual Science. 61(7). 2001–2001.
Arora, Karun, Joan L. Jefferys, Eugenio A. Maul, & Harry A. Quigley. (2012). Choroidal Thickness Increase Is Different among Angle-Closure Versus Open-Angle Eyes but Does Not Explain IOP Rise after Water Drinking. Investigative Ophthalmology & Visual Science. 53(14). 4173–4173.3 indexed citations
12.
Boote, Craig, Thomas Sørensen, Baptiste Coudrillier, et al.. (2010). Posterior Scleral Collagen Architecture in Normal and Glaucoma Human Eyes, as Determined Using Wide-Angle X-Ray Scattering. Investigative Ophthalmology & Visual Science. 51(13). 4900–4900.3 indexed citations
13.
Broman, Aimee Teo, Joanne Katz, Beatriz Muñoz, et al.. (2007). Estimating the Individual Rate of Progressive Visual Field Loss Among Subjects With Open Angle Glaucoma in Population-Based Cross-Sectional Studies. Investigative Ophthalmology & Visual Science. 48(13). 4448–4448.1 indexed citations
Ramulu, Pradeep Y., Harry A. Quigley, & David S. Friedman. (2007). A Randomized Controlled Trial to Increased Compliance With Glaucoma Follow-Up Visits. Investigative Ophthalmology & Visual Science. 48(13). 5577–5577.
Congdon, Nathan, et al.. (1996). Screening techniques for angle-closure glaucoma in rural Taiwan. 3(49). 179.14 indexed citations
18.
Quigley, Harry A., James M. Tielsch, Joanne Katz, & Alfred Sommer. (1996). The rate of progression in open-angle glaucoma estimated from cross-sectional prevalence of visual field damage in the Baltimore Eye Survey. Investigative Ophthalmology & Visual Science. 37(3).1 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.