#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
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.
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
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”.
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!