Recently I wrote about two big reasons for scientists to have more accessible data so that they can stop reinventing the wheel. There is a third, more insidious form of reinvention that most ELN companies wouldn’t even dream of telling you about. That’s because they either don’t realize that it’s a problem (there are benefits to having a scientist help design an electronic laboratory notebook!), or they don’t have a solution.
“science only progresses if you build on the work of others, rather than continually reinventing it.”
Here’s the problem. Any ELN that follows 21CFR11 guidelines (see previous posts) has a major limitation – whenever you perform a search, your search results will only contain things that both match your search term and that you explicitly have been given permission to see. In other words, there may be plenty of notebooks in the system that match your search terms, but if you haven’t been invited to see them, you’ll never even know they exist.
Why is this a problem? Sir Isaac Newton said it best – “If I have seen further it is by standing on the shoulders of giants” – meaning that science only progresses if you build on the work of others, rather than continually reinventing it.
I’ve talked to hundreds of researchers at countless research organizations, academic, commercial, military, government – and they all agree that too many times they end up repeating an experiment (are you ready?) because they don’t know they already have the answer. This is by far the worst case of reinvention!
CERF is the only ELN that has a solution to this problem. It’s called “Find Experts”, and it works by searching every notebook, every file and document in the system – whether or not you have access to it. This doesn’t violate 21CFR11 because CERF doesn’t return the data (that would be a violation) – instead CERF returns the names of the people who submitted that data. Now CERF isn’t just helping you with your own science, it’s helping hook you up with your next potential collaborator, so that you can spend your time building on the work of others instead of endless repeating it. Lightning doesn’t have to strike often to make this sort of technology pay off, either – finding just one pre-optimized protocol for your particular organism and/or conditions, or finding the preliminary data that points out which experiments to do next – or which ones to not do next – could save you six months of time – and money.