What we are not getting rewarded for, is being open about how complex, messy and uncertain research results can be. For articulating all those pesky assumptions our inferences are based on – or even for worrying about them too much ourselves. For showing how variable our results are, depending on the models we choose apply to our data. For being transparent about all the decisions we make as part of our scientific workflow, at best based on reasoned and reasonable trade-offs between practical feasibility and maximum rigor rather then self-serving opportunism.
For spending the time, effort and money needed for collecting high-quality data when there are cheaper short-cuts available. For showing the results of our analyses that don’t fit so nicely with the story. For admitting that the data are simply too noisy to provide a clear signal. For taking the time that is necessary to complete research projects that adhere to FAIR principles, rather than just pumping out another publication. For doing the kind of theoretical and empirical grunt work that is needed to contribute to the accumulation of knowledge instead of just telling interesting “new” stories.
By no means universally, with large variations between different areas of study and between academic communities, and with incremental improvements over time. But still way too pervasively.