We recently got the chance to share with the Acoustical Society of America how our volunteers are doing and answering the question: can citizen science effectively help us in our research?
We took some of the subject frames that have almost been completed. The classifications that our volunteers provided were all averaged to synthesize the completed frame. These aggregated frames are later strung together to make the desired mapping of the vibrating steelpan. To test if these results were sufficient, they were compared to the same frame but completed by the research team. Here is what the side by side comparison looks like.
The ellipses here are the indicated antinode regions and in the center of these ellipses is the averaged number of frames. The left column is the aggregated frames using the classifications from the volunteers and the right column is the research team’s answer for the same frame. As you can see the location and size of these antinodes are almost identical, and the reported fringe count is satisfactory between the volunteers and research team.
These similarities suggest that the citizen science approach is an accurate way to analyze the data. So keep up this great work everybody and invite some friends because it is only a matter of time until we can make some interesting discoveries together.
We’re so thrilled to see our 1,000th volunteer registered tonight! We are so thankful for all the classifications that you have made to help us with our project.
Although there is nothing particularly significant about the round number 1000, it does make the math a little easier to understand: we have 1000 volunteers and a total of 13,557 classifications made – so on average our volunteers are make 13.557 classifications.
We estimate that each classification takes about a minute to complete – do you have 15 minutes to spare on doing science with us?
Last week we looked a little bit at the classification data to see what our volunteers have been doing to help us get through all the images we need examined. The figure above is a histogram of the volunteers and the classifications they have completed.
You can see that over all users, the average number of classifications done was was about 6.5 per user. As of last week there were over 1700 users who had done classifications, although that does not account for the same volunteer working sometimes logged in and sometimes not logged in, or any other case where the same person shows up as multiple users. (We believe those cases are rare.)
As is typical with citizen science project like this, there are a large number of volunteers who try one or a few images and then never come back. The next graph shows the statistics for users who have done 5 or more classifications for us.
As you can see, there were 547 users who, on average, did almost 17 classifications each! We are SO thankful for ALL our awesome volunteers, but these are the users who are truly pushing this project forward. These users make up 30% of all visitors to our project, but account for 79% of all the classifications!
If you are one of the 30% of users, THANK YOU. Keep up the great work!
We’re looking for more users to join the ranks of the super-classifiers. Please consider sharing this project with a friend or two and working with them in person for a bit.
Every little bit helps, even 17 classifcations a day. They really add up!
Last night I was at our local makerspace with another member who wanted to work on making classifications. We sat across from each other chatting while making progress on the project: in about 1.5 hours, we had combined to do 125 classifications. It was a very relaxing way to spend the evening yet also very productive!
If you have a chance this weekend, consider sharing this project with a friend or relative. Show them how you are contributing to our scientific research and ask them to be a part of it. Many people find that it is easier to get excited about doing citizen science if they are shown how by someone already participating.
How many registered volunteers could we reach by Monday? 1000? 1100? 1200?? Help us out by sharing our project not just online, but in person with the people you are closest to!
And if you are not already making classifications, grab a buddy and try to figure it out together. Make sure to tag some of your favorite images and talk about it on the Talk pages.
Thank you for doing science with us!
We’re so excited to see that today brought the first retirement of one of our images!! Thanks for all the classifications done by our volunteers! Keep up the great work!
We recently ran across a really interesting article from the Trinidad and Tobago Guardian online titled “Pan is Everywhere” detailing the many examples of the use of steelpan in various musical genres ranging from pop to jazz to classical music among many other genres.
Here’s a short playlist of recent country music tunes to feature steelpan:
Put these videos on while you make some classifications!
If you read our About page, we have example images of what second harmonic and third harmonic resonances look like. Those images were made for notes located at the 6 o’clock position on the diagram of the steelpan:
But most (all?) of the images we ask our volunteers to classify were of notes near the 9 o’clock to 10 o’clock positions. So the following is an example of the note in the upper left part of the image exhbiting primarily motion associated with the third harmonic resonance:
This was an example of an image that I saw when making classifications and initially marked as a second harmonic resonance. Later, I realized the pattern was third harmonic, so I went back and tagged it #thirdresonance.
We are really interested in finding second and third harmonic resonances. The steelpan tuners make sure to get the resonances of these notes as close as possible to being exactly harmonic – and we are looking for the connection between when the different resonances appear and the unique sound the steelpan makes.
What about you? What interesting patterns have you found in our images? Consider tagging your images in the Talk section so that we can discuss them with you! Thanks for your help – we really appreciate it!
Sometimes we come across interesting images that stand out from the majority as we are going through and making classifications.
The frame shown above is an example frame from shortly after the initial strike. What we are seeing here is that the initial wave traveling across the steelpan has seemingly not yet finished it’s initial crossing.
Part of what we are very interested in learning more about with this project is how mechanical energy is transferred from one note on the pan that was struck with a mallet to other neighboring notes. We are very interested in the time shortly after the strike – and it is neat to be able to capture the initial wave as it spreads across the steelpan.
Keep going with the classifications! We really appreciate all the help.
One of the great features of the Zooniverse platform is for users to leave notes about the images that they get shown for classification. There is a section in the Talk pages dedicated to these notes. One of our users noted the distinct antinode in the center of this image and left us a note about it.
Another use for the notes section is to leave questions for our team to try to clarify how to best make the classifications. Here is an example that generated a question:
The question was whether or not the fringes had to form complete circles (or ellipses) in order to “count” as a full fringe. This was a great question, because it was something we hadn’t considered in our testing of our project. We said that if the fringe goes more than halfway around the other fringes we would tend to count that as an additional fringe.
We now have over 600 volunteers and are adding more every day! Thanks for the help – keep it coming! Keep telling your friends and family about this chance to do science with us – it really helps when you spread the word!
See you on the Talk pages!
I recently discovered a series of youtube videos of musicians playing music on the ice of Lake Baikal in southern Siberia. Some people say that the music reminds them of the sound of a steelpan – what do you think?
Here’s an entire playlist of unique musical creations, which would be a great soundtrack for playing while making more Steelpan Vibration classifications: