via retraction watch we learn:

A Bijan Ahvazi has been working at the USPTO since at least 2008, and today a source confirmed that it was the same person who was the subject of last October’s ORI report. Ahvazi was found to have faked five different images in three different papers, two of which have been retracted.

The Notice of ORI finding appeared in October of 2014.

Based on the report of an investigation conducted by the National Institutes of Health (NIH) and additional analysis by ORI in its oversight review, ORI found that Dr. Bijan Ahvazi, former Director of the Laboratory of X-ray Crystallography, National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), NIH, engaged in research misconduct in research supported by the Intramural Program at NIAMS, NIH.

The Notice shows that the offenses for which Ahvazi was convicted date to 2004 and 2006. One doesn’t have to assume that much to figure out that he was busted and then had to look for a new job somewhere between 2006 and 2008. It took until 2014 for his fraud to come to light via the official ORI mechanisms. Presumably, although we don’t know for sure, the investigation was confidential up until it reached its formal conclusions which may have permitted him to avoid telling the US Patent and Trade Office about his little whoopsie? I dunno, do you think the USPTO would hire a data fraud as a patent examiner if they knew about it? One thinks not.

p.s. apparently a co-author of this data faker died under bizarre circumstances in 2003.

Odyssey is pondering review articles today. That led to a question from Dr. Becca about the ideal ratio of reviews and primary research articles.

I am not a fan of authors publishing essentially the same review in multiple journals. Nor am I a fan of the incrementally updated review published every year or two. And I am really not fond of burgeoning subfields where everyone spits out a me-too review which then outnumber the primary research articles!

So, my views on this question are likely more negative than average.