The data we will end up with will provide us with the opportunity to tell the consensus story in many different ways - which is quite powerful. It means we can do a series of posts/papers about consensus, looking at it from a multitude of angles, all reinforcing the strengthening consensus idea. Some examples of what's possible:

1. Number of rejection papers versus number of endorsement papers per year
2. Number of rejection citations (measure of impact) versus endorsement citations
3. Total number of authors of endorsement papers vs rejection authors as a way to produce a head count
4. Total number of "new authors" each year endorsing the consensus, showing whether there are more new scientists on board with the consensus or whether there's a growing exodus towards denial
5. % of endorsements per year - how does it evolve, does the percentage change over time?
6. Breaking down explicit vs implicit endorsements, total amount and break down by category - what interesting stories come out of that analysis

So lots of options here, I'm sure there are plenty of other ways to skin this cat also. As we grind through the ratings, will provide the data as a live output so we can all load it into excel and have a play around with visuals.