I don't have any evidence for any of these observations, but I think most of the keys to a successful collaboration need to be in place before it starts.
1. Make sure you (and they) have the time and energy to commit.
The fastest way for a collaboration to fail is for one (or all) of the collaborators to be overextended. If you barely have time to do your own research projects, how will you be able to be a fruitful collaborator? I regularly politely decline collaborations where the time ask is too great. Likewise, it might sound exciting to collaborate with a Nobel laureate, but if they don't have time to actually bring anything other than their name, is that what you want?
2. The best collaborations are when the different parties contribute meaningfully novel expertise or resources.
Almost all of my successful collaborations have been a match between two or more parties that have expertise or resources that the other parties just don't have. The access to something collaborators can't easily replace motivates them to pull their weight in the project.
- They are experts in a particular disease, you know how to use machine learning.
- You have interesting genome sequencing data, they know how to do de novo assembly.
- You have 50 participants with a rare condition, they have 10 more participants with that rare condition.
3. Make sure all parties are excited about the project.
I tend to avoid collaborations where the parties are not excited about the topic. I find that collaborators that just want to "pay the bills" so to speak struggle to deliver high quality products. The same goes for oneself. Is it worth it to work on a project that doesn't excite you just to get another publication?
4. Clearly agree on authorship and funding early.
This is key to avoiding drama later. Obviously, as the project goes on, things can change, but it's vital to have a baseline that everyone expects. One of the comments suggests that collaborations are frequently only productive when there is transfer of funds. That has not been my personal experience, although I admit that I have been privileged to conduct research in high resource settings.
The best collaborations, in my opinion, are when clearly defined deliverables are agreed upon at every meeting and the team members complete them or are agree to be accountable for why they can't be delivered. At the end of each meeting, we "go around the (often virtual) room" and agree to our "homework".
- By next meeting, Ian will write a draft for the introduction to the manuscript
- By next meeting, Nasir will commit the code for training the deep learning model to the Github repository
- By next meeting, Linda will write aim 3 for the R01 submission
Also, it's important to schedule the next meeting before concluding the current meeting. For bonus points, the meeting could automatically recur.
6. Don't be afraid to end a collaboration if it's not working.
At some level, collaboration is like dating. They don't all work out. If you or your collaborators aren't able to meet or aren't able to make progress on the deliverables, I think it is reasonable and necessary to end them. I've started collaborations where others weren't able to meet when they agreed multiple times, and that just had to be the end. Sometimes collaborations can just drift off into the night, but sometimes you need to just say "I'm very sorry, but I don't think I can continue this collaboration".