This blog post lays out arguments for why we should build and support organizations and/or businesses that offer technical scientific consulting. Many of the ideas here arose from discussions with the people at the Open Ephys and Miniscope projects, with Open Ephys users and other scientists at conferences over the last ~two years.
To quickly summarize the main points:
- Not all technical training makes one a better scientist.
- We mistakenly act like wasting student/postdoc time is cheap. Both in terms of their career development, but also in terms of money.
- Seemingly small risks and points of friction in experiments lead to large costs.
- Technological expertise has value that should be budgeted in grants and paid for.
- Spending money to reduce time, effort, and experimental risk is worth it.
Neuroscience has in large parts become so technically challenging that single researchers can not be expected to fully understand all the technical foundations of their experiments. In many cases this increasing specialization causes little friction: Cognitive scientists can use fMRI scanners without understanding the underlying physics, and systems neuroscientists can record from single neurons without understanding how an instrumentation amplifier works.
This is not universally true though. As soon as technical complexity is not tidily packed away in a box, but touches other parts of the experiment, the amount of expertise required to do science becomes overwhelming to any single scientist and friction occurs. The list of examples is endless: using the wrong grounding scheme could mean no data while mice touch the reward spout: missed opportunity to analyze the reward response. Or high frequency noise due to shielding issues: half the units can not be sorted correctly. Or the connector only kinda fits – ‘just make sure its plugged in fully before recording’: 15% of experiments have no synchronization signal. Or ‘Superglue works well enough’ – 20% of implants fail after a few weeks. Or wrong statistical methods, or missing controls, or wrong virus serotype, or buffer, etc etc. – some of these problems can lead to huge delays, or worse, wrong scientific inferences. I find that we often massively underestimate how expensive it is to waste time on such technical issues, especially if they could be avoided by bringing in people that are already experts in identifying and solving them.
Simple math: The NIH minimum monthly stipend for a postdoc (4yrs experience) in the US right now is $4,563. Something as simple as having the wrong glue / or a non functioning injector, and figuring it out after two months when the 2nd round of experiments fail costs around 10k. If the experiment involves other expensive stuff (it always does) the cost can easily be 2-10x that. The opportunity cost, missing deadlines and compounded career implications of such delays are bigger still.
In sum, technical issues often hold up projects for weeks/months or make projects fail altogether, and avoiding/resolving these should be a high priority and is worth money. Instead, neuroscience is still stuck with a mentality where everyone is expected to be an expert in all techniques, or there is some mythical ‘postdoc from the lab down the hall’ that can help for free. Establishing scientific consulting as a viable and appropriately valued career path could resolve this friction and make Neuroscience as a whole more productive.
It is important to distinguish areas where in-depth technical training might make one a better scientist from ones where it does not. This depends on the specific area and is highly personal. Expecting electrophysiologists to understand electrical engineering concepts makes sense and will likely make them better scientists. However, also expecting them to be mechanical engineers will not necessarily make them better scientists, but will certainly make them waste time when they spend months chasing down vibrations on their customized recording rig.
Companies that sell tools are resolving parts of these issues already, by providing support and in some cases training to scientists, but this is usually tied to recently having purchased a tool, and rarely deals with the overwhelming majority of issues that occur at interfaces between tools. For example, while your microscope company will help you set up your two-photon, they won’t integrate the many other systems (e.g. behavioral, ephys) that it takes to run a modern experiment, or consult on how to optimize head-posts, windows and indicator expression. This is not necessarily because they are opposed to branching out in this direction, but because many labs are currently unwilling, or unable to pay for such consulting services, either because they believe that students or postdocs can or should figure it out for free, that industry consultants should work for post-doc salaries, or because their funding agencies pay for equipment, but not for consulting.
There is also the point that the work of consultants will likely be of higher quality if they are impartial with regards to what methods are best, instead of being on the payroll of a company that also makes money out of selling a specific tool. It therefore seems unambiguous that independent consulting organizations would be able to fill a currently large gap in how we transfer expertise between organizations/labs/companies in order to improve scientific discovery.
In sum, a cultural shift that increases the role of scientific technical consulting as a professional career path will be good for science, good for trainees, and good for funding agencies seeking to increase the impact of each dollar they grant.