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.
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
2020573 citationsMichael Roberts, Derek Driggs et al.Research Explorer (The University of Manchester)profile →
Countries citing papers authored by Christian Etmann
Since
Specialization
Citations
This map shows the geographic impact of Christian Etmann'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 Christian Etmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christian Etmann more than expected).
Fields of papers citing papers by Christian Etmann
This network shows the impact of papers produced by Christian Etmann. 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 Christian Etmann. The network helps show where Christian Etmann may publish in the future.
Co-authorship network of co-authors of Christian Etmann
This figure shows the co-authorship network connecting the top 25 collaborators of Christian Etmann.
A scholar is included among the top collaborators of Christian Etmann 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 Christian Etmann. Christian Etmann is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans 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.