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.
This map shows the geographic impact of StataCorp'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 StataCorp with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites StataCorp more than expected).
This network shows the impact of papers produced by StataCorp. 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 StataCorp. The network helps show where StataCorp may publish in the future.
StataCorp is a scholar working on Statistics and Probability, Management Science and Operations Research, Infectious Diseases, Organic Chemistry and Surgery, having authored 16 papers that have together received 1.3k indexed citations. Recurring topics across this work include Statistics Education and Methodologies (3 papers), Probability and Statistical Research (2 papers) and demographic modeling and climate adaptation (2 papers). The work is most often cited by research in Health (121 citations), Economics and Econometrics (249 citations), General Health Professions (216 citations), Statistics and Probability (68 citations) and Clinical Psychology (140 citations). Their work appears in journals such as Medical Entomology and Zoology.
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.