The most extreme such example is that everything that Barbara McClintock published for decades about jumping genes was questioned because it seemed like an impossible result. It got so bad that she stopped even trying to submit papers for publication anywhere after 1953. Then in the 1960s and 1970s the mechanics of transposons was discovered and she became hailed as a pioneer and was awarded the Nobel.
See https://en.wikipedia.org/wiki/Barbara_McClintock for more.
When we couldn't get a paper published, my old PI would always joke, "There's always BBRC!", referring to the journal biochemical and biophysical research communications. The impact factor was and still is quite low, and it would make the post docs bristle when he said it.
But I once asked him, why BBRC? and he explained that Aaron Ciechanover and Avram Herschko published their first paper on ubiquitinylation (how proteins get tagged for degradation in the proteasome) in that journal [1,2]. It won them a Nobel prize 26 years later and exploded into a huge field. You just never know sometimes.
[1] https://www.ncbi.nlm.nih.gov/m/pubmed/26325464/
[2] https://www.sciencedirect.com/science/article/pii/0006291X78...
(The BBRC text is paywalled sorry)
No problem, I think this is the paper you were looking for:
For a long time I thought this was due to poor presentation. Then one day it hit me: the reason boss/coworker doesn't get it is the same reason why he could not come up with the idea himself.
My point is that with time, you can look back at ideas that turned out to be great and recall how they were initially received.
Rejection is not an indicator for the merit of an idea.
This is an extreme example of that, and should stand as a cautionary tale that data should be believed especially when it is in conflict with theory. Rather than being rejected and ignored.
This is a terrible sentiment and it needs to stop spreading with these articles about Nobel rejections.
Noticing that your data is in conflict with theory is the best way of realizing that you have made a mistake. Go back to your analysis and work through it until you realize what you did wrong. If you can't find the mistake, then share it with your colleagues/PI, and get them to tell you what you did wrong. If all of their ideas don't explain your result, then you may have found an interesting finding, but you should probably triple check your work and then try to reproduce the finding in new data.
But it is a systemic failure that biologists broadly rejected a result out of hand that had been successfully replicated many times, at multiple labs, over decades. And the attitude that you describe is part of why it happened.
Data that does not fit in existing paradigms should be scrutinized with extra care, not rejected out of hand.
Carl Sagan.