I have done research with unique datasets. Often with such samples, there are many different questions you can ask. I have had very little success taking the dataset and simultaneously working on multiple problems, instead I find working serially more productive. There aren’t many times I have seen ‘splitting up’ samples or ideas end up working well.
My impression is that isn’t true in other fields, such as those with big labs. I wonder what the difference is?