According to James Joyce, mistakes are the portals of discovery. Good science means making mistakes. I recently read a great article that encourages just that; researchers to be open and honest about their scientific mistakes. It recognizes that information borne from mistakes helps shape science.
The art is to not keep quiet about them because this actually risks leaving others to [unknowingly] repeat them. Tracking scientific mistakes for others to learn from, and looking at opportunities to share knowledge, is essential. This IS collaboration, a premise which beats at the very heart of science.
R&D generates high value data assets – the lifeblood of every laboratory. To avoid duplication and maximize scarce time and resources, quality data must flow through this knowledge ecosystem for it to thrive. Enterprise analytics require close collaboration to ensure metadata is captured alongside high context information, an ontology and its provenance. The intelligence of the community as they interpret and challenge the data must be captured alongside the experimental conclusion. Smart social tools, including tagging and commenting must be there ‘close-to-the-data’ to enable connectedness.
Only if there is high context – that includes the mistakes and false paths taken can we generate a useful Big Data asset. Competitive advantage is not going to be driven by choice of Big Data analytics alone but by the quality and provenance of data the analytics has to work on. We are all in a race to achieve the highest context, highest value distributed datasets to make data reusable. So when it comes to scientific mistakes, once is an understandable [and oft valuable] learning curve. Twice or more? Well, that risks looking distinctly careless and costly.