Precedence is a powerful human motivator, but how do you prove you are first?

Innovation is a key criteria in the evaluation of new work.  Why do something if it has already been done?  Today, the internet, patent, and entire academic system support the notion of precedence.  But even now big disputes break out over who was first. The gene editing CRISPR technology is a fascinating example [1].

In NIH grants, innovation is a major category. Being able to establish precedence can mean the difference between success and failure on your next proposal.  It is useful to say "nobody has ever tried this method before."  But most reviewers won't take your word for it.  So how do you prove that you are the first?  

Sysrev open access projects allow you to share your search and prove that you have reviewed the relevant literature.  Grant reviewers can easily look at your review, evaluate your search criteria, and confirm that you evaluated the resulting articles. They can even randomly check your actual review process and confirm to some degree of confidence that claims of precedence are correct.  

Survival RNNs in Cancer - a mini review to establish grant application novelty.

We did this recently for a grant applying advanced machine learning approaches to cancer survival modeling.  In our short review we actually found 2 publications already implementing many of the same ideas, albeit on a slightly different datasets [2,3].  Because the project is open access we shared the url in our grant and our reviewers could easily confirm that the idea was somewhat novel and that we had evaluated many of the pertinent examples.  

If you want to prove that you reviewed the literature for your next grant or paper an open access sysrev might work for you.  Getting started takes less than 5 minutes (blog.sysrev.com/getting-started). The success of your next project might depend on it!

References

[1] https://www.sciencemag.org/news/2017/02/how-battle-lines-over-crispr-were-drawn

[2] Interpretable Representation Learning for Healthcare via Capturing Disease Progression through Time. https://www.ncbi.nlm.nih.gov/pubmed/?term=31037221

[3] Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging. https://www.ncbi.nlm.nih.gov/pubmed/?term=31010833