The top 10 (more than half of these have estimated citation counts over 100,000):

1. Deep residual learning for image recognition (2016, preprint 2015)
2. Analysis of relative gene expression data using real-time quantitative PCR and the 2–ΔΔCT method (2001)
3. Using thematic analysis in psychology (2006)
4. Diagnostic and Statistical Manual of Mental Disorders, DSM-5 (2013)
5. A short history of SHELX (2007)
6. Random forests (2001)
7. Attention is all you need (2017)
8. ImageNet classification with deep convolutional neural networks (2017)
9. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries (2020)
10. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries (2016)

The article went in-depth on some trends… but in brief:

• 1, 6, 7, 8 are all papers that are foundational to the current generation of deep learning/AI research, so naturally they got cited a lot. Among these 6 may be less relevant than the others, but random forest is still incredibly important as a method
• 2 and 5 were random (but extremely important) methods that got written into papers so ppl can cite them
• 9, 10 are extremely important cancer statistics/reviews that are cited by just about every and any cancer researcher
• 4 is the “psychiatry’s bible” and is meant to be a foundational work to this field of research
• 3 was meant to be a brief guideline for a psychology research method, but accidentally blew up in popularity

Link to the supplementary infomation of the top 25 papers. Note that this will open a link to download the Excel spreadsheet