Crowdsourcing: Make no bones about it

This leap frogs a few Doing the dirty job posts, but I think the following story is worth sharing and compelling enough to stand on its own.

Right now, I have extracted DNA from the collected scats, analysed the genetic information, and moved on to washing out the scats for prey remains to determine the diet of leopard cats in Singapore. Most of the remains are hair, bones, feathers, scales and exoskeleton.

A day in the office: typical crime scene.

However, here comes the problem: I belong to the generation of biologists with comparative anatomy and mophology swapped out from our curriculum in favour of cell biology and molecular genetics — therefore, not too good at identifying animal bits at all. To compensate though, I did a fair bit of reading up on hair and skeleton before starting.

Bones are a little more tricky than hair (which I have a reference collection of), but excellent diagrams help a lot. Every now and then, strange things pop up, but are usually relatively easy to resolve.

After more than 30 samples, something odd appeared that I have yet to encounter. It was a bone fragment that looked like a product of two fused bones. Unfortunately, it was not complete, otherwise it may have been easier to identify. So there it started: the case of the mystery bone.

I had my suspicions of what it may be, but I also needed to know where to look and had no comparative material for skeletons. The Raffles Museum of Biodiversity Research collection is closed to prepare for the move to its new premises, and I did not want to be a nuisance to the collections manager. So, knowing that Twitterverse is full of accomplished workers in the field of science, I decided to try crowdsourcing to get more experienced/expert opinion in bone identification:

In a nutshell, I must say I was humbled by the responses and the helpful scientists on twitter.  Full story and final reveal on the mysterious bone below.

N.B.: Some tweets may have been lost if tags have been changed or if they are of a different time series.

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