To date, BenchSci’s machine learning algorithm has decoded millions of Open Access scientific papers and over 3.7 million antibody products. However, OA papers only represent a subset of all scientific literature. Scientists generally use resources like Google or Pubmed, and are missing out on tools and other resources that publishers might offer. BenchSci therefore has initiated talks with publishers to make their content discoverable through BenchSci without a loss of readership or traffic to their sites.
Designing experiments is not an easy process. I can relate, having recently been at the bench myself before joining the BenchSci team. If your lab doesn’t already have an established set of protocols and reagents, then the next best resource is the scientific literature in your field. Not only does it take hours to comb through paper after paper to find exactly what you need, but I’m sure many scientists have experienced the frustration of finding a paper that contained the exact experiment you were looking to perform, only to find that the crucial details for the reagents you need to reproduce the experiment are absent.