#expbio 2015 ASPET Blogging: Who owns the data I generated and how do I transition from grad school/ postdoctoral scholar to independent scientist?

The lab notebook: you can't take it
with you.

The path to becoming an independent scientist is different for everyone but the training steps are similar. Graduate students pursue ideas originally proposed by their advisor, likely in a grant application. Learning and perfecting techniques takes time and many scribbled notes (hopefully) in a lab notebook. After reading a million papers and conducting dozens of lit reviews, trainees begin to come up with their own ideas. As a graduate student or postdoc, they learn to craft these ideas into full research projects. But the notes in the lab notebook, the ideas in that fellowship application, and the data presented at a poster session... who does it really belong to? And can trainees take it with them to their next position? Lynn Wecker, Distinguished Professor at the University of South Florida Morsani College of Medicine had answers for us during the ASPET Graduate Student-Postdoctoral Colloquium.

In most cases, the data generated in a university laboratory belongs to the university. This is true for some types of public (government) funding and from non-profit organizations. The grant mechanisms most trainees are familiar with are the R series grants from the National Institutes of Health (NIH), with the most common being the R01. Data generated using funds from R series grants are owned by the university that was awarded the grant. Public funds can also be distributed via U series contracts. The federal government owns data collected using U contract funds. Data generated using funding from a private company are owned by the company. Noticing a trend here? No matter how much blood, sweat, and tears are put into generating the data (sometimes literally), the trainee does not own it. But is that such a bad thing?

Owning data is about more than just possession. There are rights and obligations regarding how data is collected, stored, and archived. Universities, the government, and private companies have the resources to oversee and regulate data management whereas a trainee or sometimes even a principal investigator do not. However, just because a trainee does not own the data they collected does not mean that it cannot be used by them after they leave the lab. But trainees cannot assume anything when it comes to taking data or ideas out of the lab!

Universities have policies for data ownership so trainees should familiarize themselves with them. Communication is key when transitioning out of one lab into the next. Trainees and mentors need to openly discuss the expectations for trainees who would like to take data with them when they leave that lab. In this context, "data" means actual data, lab notebooks, and ideas born out of the advisor's projects even if conceived by the trainee. It is always best to openly discuss and then document the decisions so everyone is clear on what can go and what must stay. In general, even if the mentor says the trainee can take data the actual lab notebooks and raw data cannot leave the lab! Copies can be made with the permission of the mentor. Advisors have the right to decide that trainees cannot take any data, materials, or ideas and have them sign a document stating such.

When transitioning from graduate school to a postdoctoral position, bringing up the question of taking a project with them is good for trainees to ask during the interview process. This is in anticipation of transitioning from a postdoc to an independent scientist after the postdoc. Independent scientists must have their own project ideas and, ideally, preliminary data to use when applying for grants in their first non-trainee position. When looking for a postdoc, she says, trainees should choose a lab that does research in a complementary area rather than continuing the same line of research. To become an independent scientist, trainees need to stuff their “toolbox” with all of the methods and knowledge they can in order to be successful. In addition, Dr. Wecker stressed that an important part of both transition periods - grad school to postdoc and postdoc to independent scientist - is for the trainee to distinguish themselves from “the pack”.

Dr. Wecker mentioned several ways to become unique in the pool of trainees. There are many certificate programs, which can help trainees expand their area of knowledge and fill up their toolbox. A quick Google search showed me several such certificates including science policy, data science or nanotechnology, or teaching. The last link for a Coursera certificate is particularly interesting. Coursera provides free courses online facilitated by faculty at institutions around the United States including Stanford and Harvard among many others. There are other opportunities for trainees to expand their toolbox that aren't through formalized classes. For example, science blogging (hi there!), short-term industry or policy internships, and more!

In summary, Dr. Wecker emphasized that even though trainees do not own their data, communication with their mentor is very important to know which data they can use after leaving that lab. Prior to transitioning to a new position, trainees should make themselves stand out from the crowd!