“You see, in this job, the problem isn’t really finding the answers, it’s finding the questions.” (Yes Minister, Season 3, Episode 6) is a statement which probably is much more important to keep in mind when doing science compared to politics. As the impact of the ground-breaking advances in high-throughput molecular biology and the subsequent mind-blowing tsunami of publications hits your screen and desk, you are not alone with the suspicion your mental core was just not designed to keep up with it and is doomed for a meltdown.
The deluge of online data and information has transformed the role of researcher from that of an experimentalist to a mix of information scientist, reviewer and critic as well as having to be a skilled laboratory scientist. A researcher now has to develop superior skills in finding those relevant bits of data and information in the course of doing their experiments that will ensure that they are following the right path, making the right assumptions, properly testing their hypotheses and improving their odds that they will make significant discoveries.
Well, there is some truth in this. Allegedly, these days wet-lab scientists spent over 40% of their time at the computer. But it can’t be the right way to turn highly trained and talented experimentalists into computer-scientists, although handling massive amounts of complex data has become nearly as important as handling a pipette. Anything which makes the time spent at the keyboard more worthwhile while helping to plan and conduct better experiments is definitely a step in the right direction.
Quertle comes with an elegant, straightforward query-interface. According to my preliminary tests it seems to work fine for the task of finding literature that is relevant to you and your research.
Thanks to its’ approach called “Semantic Fingerprinting“, DocumentLens looks a bit more ambitious: It’s built by Praxeon, a Boston-based software company, on Amazon Web Services (AWS) and uses Web-based Distributed Authoring and Versioning (WebDAV).
DocumentLens is available to try-out for free and seems to have a couple of killer-features I always wanted, for example navigation tools to explore the results graphically by figures, chemical entities and subtopics. Since turning data to insight is a goal often voiced but rarely achieved, I hope the tools for deriving timelines and storylines from the data do what it says on the tin. I’ll see how my testing goes and keep you posted!