I'm really interested to know how you transitioned. I'm a recent graduate and I'm planning to pursue a graduate degree, and I'm overly concerned with rules, which provide structure but also seem to constrict my learning and work.
- Understand what the rules are, why they are in place, and to whom they matter. Some are there to keep you from hurting yourself, some to keep you from hurting those on your level or below, some to keep your advisor from abusing you, and a lot to make sure you act in the best interest of the University via minimizing its liability, etc. You don't need to follow all of them all the time, but have some real justification when you do go off course, don't call attention to it, and don't screw anyone else over.
- For your research, you need a dual-prong strategy of making sure you check off the boxes for others, i.e. actually doing what your advisor got funded to do (she is on the hook for this for most grants), on time and under budget as well; then, pour all the rest of your efforts into researching what you are interested in, with an eye towards building your skill sets for the future and learning how to learn alone. Read widely enough to know the range of options for a solution space and deeply enough to know what has actually been tried in your field, and not just what has succeeded. Teach yourself stuff that your advisors don't know. The payoff from a broad familiarity with programming, statistics and discrete mathematics is large, especially if they're under-utilized in your field, and these skills are widely transferable. Furthermore experimenting/exploration through computing is often super cheap and the cost of failure is low, unlike destructive analysis of your physical samples.
- Keep your datasets small enough that you can master them. In the natural sciences, many scientists just want more and more data but getting and maintaining data is labor-intensive and has a huge opportunity cost. You don't always learn a lot of transferable skills that way unless you want to be a lab tech forever. The major exception is when obtaining data is primarily observation and measurement, which are really important to building intuition and just noticing subtleties.