Mary Childs

So Florian downloaded the data straight from the source and followed the instructions to create the one data set.

And then that's when we started getting the errors because variables were missing.

And then as we kept going through, we kept finding more variables that were being used in the regression but weren't necessarily included in the supposedly what is meant to be the raw data set.

Some variables are missing from the raw data set.

The authors seem to have used data in their analysis that they did not account for.

Not good.

They tell us the paper looks at 20 cartels and data about what types of crimes were happening and when, to see if cartels changed the types of crime they did after the government ramped up a big war on drugs.

Yeah, they found something in the first test they tried.

Over lunch, the cartel team starts puzzling through, like, how does this sort of thing even happen?

David Benatia, a professor on the team, says this is a robustness check that he would have tried if he had been the author.

At the end of the day, our researchers limp back into the auditorium to present what they'd all found.

So the way we like to finish is to give each team about one minute to tell us how your day went, the different challenges you face.

Maybe we can start from the beginning, move around.

For the 71 replicators in the Montreal game, 14 teams got to uphold science by double-checking some published work.

They spent a day coding with their friends and peers, learned some new coding hacks, and new ways to make choices in research.

And they'll get a little authorship credit on a meta paper in a real journal.

Maybe that makes a splash and everyone thinks they're brilliant.

Or maybe it makes a splash and everyone hates them.

And because Abel handles it from his position at the Institute for Replication, it doesn't feel so personal.

And the replicators have a little bit of insulation.