Month: June 2021

In sports and training there is a concept of “becoming an athlete before starting sports career”. We hear a lot about very successful prodigies who start to specialize in particular sport (or other activity) early on. But for professional athletes and definitely most of the people it is essential to develop strong base of general physical preparedness known as GPP.

Today we’ve been reminded that similar General Professional Preparedness is important at work too:

This list is excellent, and applicable to wide range of professionals. For science undergrads, PhD students, and postdocs we can have extended list (on top of the linked above):

• Experimental design and agile troubleshooting
• Not-taking in meetings and running efficient lab notebook
• Collaboration on publications (especially as “last” author or the most interested party)
• Conflict resolution and negotiation skills
• Writing skills (including writing/publishing alone or just without PI)
• Mentoring skills (including sponsorship skills)
• Teaching (formal and informal, for all levels: senior colleagues and juniors mentees)
• Presenting skills (including job interviews)
• Searching for jobs (starting at undergrad level, e.g. writing CV, cover letter, reaching out to PhD advisers, finding the right fit)
• Searching for employees and interviewing candidates
• Asking for help, and efficiently seeking advice/support
• How to pitch (cover letters to editors, funders etc) and deal with rejections
• Basic employment skills

All these skills are “taught” during scientist’s career, but almost never formalized or emphasized by the supervisors. For example, writing is taught through editing and excessive use of red ink. Mentoring is almost never taught, and just passed along as bad (or good) habits from person to person. Learning how to search for a job is almost never part of the academic process as well. A lot of these skills, however, are discussed especially on Workplace StackExchange.

Academic groups, especially PIs, have no time and inclination for most of this work, and perhaps consider it to be extra-curricular activities. However, these skills are really the core of any professional performance. If PIs are not willing to invest personal time into teaching these skills, they should at least emphasize that these skills can’t be acquired without practice and focus, yet still essential.

Previously we introduced “light” version of Paired Sciencing: just teaming up with a colleague in a quiet conference room and focusing on tasks at hand, not necessarily shared work.

There is also extended version, where actual science is done in pair. Activities can include: design of experiment, writing a grant, actual benchwork or microscope alignment, and many more.

In academia we often work alone on our tasks, occasionally meeting to discuss results or plans. That creates huge distance between design and implementation of the plan. Working in pair with trusted colleague will ensure that obvious mistakes are caught early on. This is especially important in biological experiments when protocols can take days to finish.

Work in science, especially at “high” level (whatever it means to you) produces limited number of valued artifacts. Mostly they include peer-reviewed papers, invited reviews, fellowships and grants, PhD theses, papers and posters at conferences.

Recently the community started to emphasize pre-prints as a valuable output of scientific process. Datasets are getting attention as “artifacts” that can be published, similar to protocols.

However, that is not enough. Much of the knowledge goes unnoticed, unrecorded, unorganized, and unchecked because we don’t value a lot of artifacts. Papers that reproduce published work are not valued and rarely noticed. Work and experience of technicians is not valued because of lack of formats to make it visible. Troubleshooting work that requires expertise and patience is not valued, because it is mostly done in the silence of the lab.

Making new formats for recording artifacts of scientific process will make it easier to show work and achievements and highlight what practices are accepted by the community.

We can compare it to software development. In the recent past the only value was in LOC – lines of code, namely KLOC as in “1000s of lines of code”. Then came time of Test-Driven Development, where (over-eagerly sometimes) most valuable became “test coverage %”. World of Agile brings forward “stories” that needs to be filled as quickly as possible. DevOps people value uptime and latency metrics. Startup companies value user base size and acquisition rate. None of these metrics (artifacts) are perfect, but they provide range of possible goals to aim for.

Software engineers have tons of metrics and artifacts they can produce that potentially capture value and represent useful work. Scientists have far fewer.

Modern science need to start producing – and valuing – more artifacts. We are currently learning to better acknowledge work of Core Centers / technology providers; role of open-source software; role of pre-prints and open peer-review; role of open-access publications. We need to highlight value of other items such as:

• technical articles
• assembly instructions for custom hardware that might not be publishable
• first-hand experimental protocols and checklists from techs/students who actually do the work
• meta-scientific articles and tools (how to make sciencing better)
• records of collaboration and technical assistance/consulting
• record of upgrading or reviving old protocols/tools/equipment
• work to make other experiments possible (e.g. building workstations or experimental setuplets)
• experiment design (perhaps through pre-registration)
• teaching and mentoring examples

The only requisite, really, is that these artifacts need to be preserved, potentially archived (so personal blogs are out 🙂

Every lab (or PI) should be able to define a set of valuable artifacts and find medium to make them public. It is OK to care about different things, but right now there is no shared set of such artifacts, or the list is too short. By elevating some of these artifacts we can fine-tune the research process to deliver value faster and more efficiently.