As a graduate student, I was lucky to be part of the Scale, Consumer and Lotic Ecosystem Rates (SCALER) project. This project was a collaborative effort among seven sites to understand the scale dependence of ecosystem rates. I have been working with the team since 2012. The work related to this project is the major part of my PhD dissertation. Participating in a big collaborative project is quite different from finishing a project by myself. I want to share a few things I learnt from this experience.
1. There are usually plenty of opportunities to pursue unique questions within a big project. Worries that one cannot have unique project himself/herself and is likely to scoop each other is often unnecessary. A standardized experiment carried out at multiple sites may seems to prevent the variety of research at the first glance. But in reality, big project like this results in very rich data sets. People have different expertise and interests. There are often enough niches for lots of different research questions. There was the worry among graduate students that we do not have enough differentiation and essentially repeat each other’s work. But that was never a big problem as the project moved on. In the end, we all have the opportunity to pursue our own interests within the frame of the big project.
2. Strong personal interests and motivation drive the progress. Working groups are great to spark ideas. But when it comes to actually doing the job, it is a strong personal desire and motivation that gets things done. As one of the professors in this project once told me, if you see an opportunity or interesting question, go ahead and do it. In the end, everyone will be happy about it.
3. You need to have a good sense of data because standardization and quality control are never perfect. I am lucky to have the chance to travel to most of the sites and do field work with my fellow graduate students. These experience, although seemingly unrelated to my modeling focus in this project, is extremely helpful. It enables me to have an idea of what the data should look like. It helps me form a sharper eye to spot the unreasonable and wrong. Plus, catching shrimps in tropical streams, driving ATVs in Taiga forest and hiking on the tundra with tons of mosquitos are very memorable and fun experience.
4. Lots of tedious work needs to be done and someone has to do it. A big project often requires a lot of simple but time consuming work such as formatting the data, standardizing file format, quality checking the data, calculating derived quantities from the raw data and matching different data sets. These jobs are tedious but they are the foundation of more advanced work. I always appreciate those who spent time tackling these time-consuming tasks. That motivates me to step up, do the job and be a good collaborator.
5. It is important to communicate early and openly. In a big project that involves lots of people, misunderstanding can easily occur. If you want to pursue a new idea using the common data set, it would be good to ask around and make sure you will not step on other people’s toes. When you have a new draft you want to send around, it would be good to ask around who is involved and be inclusive. If you disagree with how the data are used or how the analyses are done, let people know your concern early. In the end, it is always easy to solve problem when it is early.
6. A good collaborator is an open collaborator. Although I agree that people should have priority to their own data in a collaborative project, being overly protective essentially loses the most significant strength of collaboration. It is counter productive to claim very big territory without fully utilizing it. As one of my fellow graduate student in this project once said:” I only have so much time and I cannot do everything. So why not letting other people do it?” I am grateful to my (mostly) generous collaborators in SCALER. It makes the experience in this project very enjoyable.
These are what I learnt from participating in a big collaborative project. What is your experience? Please comment and share your thoughts!