Wow, last Monday you reached 1.5 million classifications. Thank you.
After feedback from a number of volunteers, we’ve decided to change (nearly all) the workflows so you can classify images of M. tuberculosis growing using the new Zooniverse mobile app.
Until recently the Zooniverse app, which is available for Android and iOS, could only handle very simple Citizen Science projects requiring a simple yes/no. They have just updated the app so it can handle multi-answer questions, which is what BashTheBug needs, so we are testing BashTheBug on mobile devices and would love your thoughts and comments.
Our second calendar year and it has been a busy one. Highlights of the year include
- reaching a million classifications!
- meeting a volunteer for the first time
- having our logo crocheted
- being inspiration for some textiles
- appearing on an Oxford Sparks podcast
- featuring in the Bacterial World exhibition at the Oxford Museum of Natural History
- appearing as part of a series of talks on TB
- having 15,000 volunteers try BashTheBug in one day after the Zooniverse emailed everyone
- showing BashTheBug to the public at events at Oxford Brookes and the Oxford Museum of Natural History.
In 2019 our first scientific papers will appear about how BashTheBug is helping the CRyPTIC project create an accurate dataset of the antibiotic susceptibility of thousands of clinical TB samples collecting around the globe and how we can use this to infer what genetic variation confers resistance. Watch this space!
Gemma Hall has crocheted a woolly bug in the process of being bashed! Love it.
If anyone wants to craft any bugs, whether being bashed or not, we’d be very happy to post images and Tweet about it!
Thanks to the hard work and persistence of all our volunteer scientists, BashTheBug reached one million classifications around noon on Tuesday 2 October!
In total, we’ve done 974,283 classifications. Which means ONE MILLION isn’t too far away.
Until now we have simply sent all the images of each M. tuberculosis sample growing on each and every of the 14 antibiotics out to be classified by the citizen scientists. A while ago we realised that some images are “easy” in the sense that all the volunteers we show it to all give exactly the same answer. So, with a bit of work behind the scenes, we’ve written some computer code that can detect where the wells are in each image and then measure the amount of growth in each well. Now, it is isn’t perfect; it can be confused by small amounts of growth and artefacts like air bubbles and shadows, but it does mean we can confidently filter out the relatively easy images, thereby allowing us to only send the more challenging cases to you, our volunteers.