I love computers and multimedia, it’s what I do, but there’s a lot more things I’m interested in that I don’t necessarily get to delve into in my free time. Ecology, astronomy, and history, are all things I don’t get a chance to study in college, nor are they things I was exceptional in back in secondary school, but I am none the less extremely fascinated by them – this is where being a citizen scientist comes in.
Citizen science by its most basic definition is public participation in scientific research – it takes many different forms ranging from stargazing to butterfly catching to transcribing. The one I have come to have direct experience with is known as Zooniverse.
Zooniverse is a website for citizen scientists operated by the Citizen Science Alliance. It is home to some of the world’s biggest, most successful, and most popular projects to do with citizen science.
The site focuses on many different kinds of projects, initially I planned on helping with a project under the languages category, after all I’m studying – and failing – Spanish, but when there was nothing there to work on I let out a sigh of relief and delved into something I’m really interested in – nature. My fascination with nature comes not from years playing in my back garden which teaming with wild-life but from watching too many David Attenborough documentaries & successfully getting an A2 I leaving cert biology.
The first project I helped with was called ‘Mapping Change’. The project imagines a public atlas, which is searchable, showing where plants, fungi, and animals have been found & collected. The map focuses across many different American ecosystems in the midwest, and also represents moments in history before key changes occurred to the environment, climate, and landscape, notably from extensive European settlement at the time.
My job was to transcribe the data from hand-written museum specimen labels to map biodiversity. I had to note if there was more than a single primary label present, I then had to draw a rectangle around the primary label to tag it, and finally I transcribed the scientific name of the plant, the collectors name and number, and date of collection from said label, I did this for just over a dozen plants.
The process might sound a bit boring, but I found it quite interesting. Because I’m interested in botany it was just cool to just see these preserved pieces in the first place, they are from a natural history museum after all. I also learned how far data preservation techniques have come, there are some plants labels from the 1800s and they would always be in scrawled handwriting, missing certain things, difficult to discern, and tended to contain misspellings more frequently, compared to many from around the 1950s, which are almost always perfect with their primary labels being written with a typewriter, and then there were some from as recently as the 90’s that were printed from a computer and had a larger wealth of information about each plant than anything prior.
I felt like I was doing something important, and that may also have been part of the fun. This is work that takes beyond nothing for me to do and will go on to benefit people for a long time, even if it’s in a minute way, that’s why after an hour or so working on it I felt like I hadn’t lost any time.
While I can’t imagine using data of these old plants myself in any of my work I found it enjoyable from both a historical and an ecological perspective, and the fact that it’s going to be mapped ticks a third box of buzz for me, because I love me some maps.
The transcribing itself was good practice for preserving & managing data. It gave me an idea of the kinds of challenges I may stumble upon, such as hard-to-read handwriting and difficult to understand misspellings, on top of that as I mentioned it really showed me how far we’ve come in preserving information. The project also gave me ideas on how I might display things through time on a map, something I struggled with using Google MyMaps when I created my digital artefact.
I did over a dozen of these before pouncing on to a new project ‘Camera CATalogue’. Every year, Panthera’s motion-activated cameras collect hundreds of thousands of wildlife images.
It became my job to analyse these photos to identify the animals shown, letting them track wild cat population trends over time and determine what conservation actions are needed to better protect them.
The first thing you’re greeted with is a random image that was triggered by the cameras I mentioned, along with a list of animals to the right
I found this interesting as it was a similar project in the sense that they were both about preserving and understanding nature but it was so different in that I was looking at live, recent specimen, and I was tagging what they were myself, rather than just transcribing what someone had written decades ago.
On the right-hand side of the photo that I was inspecting was a list of 50 animals, along with 4 more options that may have triggered the camera that did indeed pop up a few times (human, fire, vehicle, nothing).
Now I’m no expert on wild animals in Angola or South Africa, but the project was so user-friendly and required nothing but clicking on my part. When I assumed it was a spotted hyena it just asked me how many were in the photo, which side of the animal was faced towards me, and what I would rate the picture! There were a couple of cool things in this project that I found really helpful – the sample image of that type of animal, as seen on the image there, allowed me to compare images to make sure I had chosen the right animal, but the ‘often confused with’ section, which showed animals that frequently get confused with one another, was even better, especially when it came to differentiating the four or five different type of deer-like animals, as I reckon most of us don’t know the difference between a Reedbok, an Impala, and a Steenbok.
I found this whole project quite exciting as I had just finished transcribing these primary tags from old plants and now I was in a sense creating these primary tags myself! I ended up doing over 50 of these
I actually had a great time doing it because it was easy, fun, exciting, and most of all I knew it was helping – more so than my transcribing project. While my transcribing will go on to help future botanists and other plant folk there was a sense of truly helping endangered animals here as ultimately their goal is to determine what conservation actions are needed to better protect these species that I would label, notably the cats. I’ve also learned to appreciate nature that bit more, from seeing these animals just hanging out.
The user interface is what I find truly fantastic. When I think of people categorising animals I don’t think of them doing it so intuitively, it sounds painstaking, slow, and as if it requires prior knowledge, the emphasis on preview images for animals and an ‘often confused with’ section as I mentioned is so minute but something that made it so much better. These simple bits of instinctive and helpful GUI can go a long way when it comes to submitting information and I’m definitely inspired.
I wrapped up my time on Zooniverse doing a couple more projects but there wasn’t much new I gathered from either – one was ‘Focus on Wildlife Cleveland Metro Park Statistics’. This project was nearly identical to the project I just wrote about, except that instead of tagging animals across Africa I was mostly just tagging woodland creatures in some of Ohio’s extensive nature preserves. I enjoyed working on it as it hit a bit close to home, being in temperate woodlands like Ireland, as opposed to the hot African desert, it felt like I was peeking in at animals that are all around us right now, it has me wishing I was in the scouts looking for beasts in the woods.
Something quite intuitive that I noticed was that it didn’t just take a simple still image but actually took a burst of 3 across a small space of time that you could cycle through or play like an animated gif – this was fantastic as in a lot of the African pictures I would see just the butt of an animal, not enough to discern what it was, with the multiple frames I would frequently see an animal hop fully into the second or third frame.
Finally I spent a small bit of time on ‘Arizona BatWatch’ purely out of curiosity as I spent a lot of time in Arizona and bats are just pretty cool dudes.
All I had to do was watch a 20-second clip, discern how many bats were on screen at any given time and simply mark what behaviours I saw from a small list – e.g. flying inside, outside, bumping. In the space of 15-minutes, I did around a dozen, and it helped scientists learn about the behaviour of these bats!
It was the only project I did that focused on the subject’s behaviour and not just it’s existence or basic properties, the fact that I could slow down time in the video, as slow as 25% of the original speed, helped a lot, in fact it was a necessity watching something as sporatic as a bat. For me this highlighted how much more we can understand with digital tools – there’s no way someone could know some of these bats habits without being able to slow down time like I can now.
These last two projects cemented the idea that when it comes to us as humans interpreting visual information video tends to work to best over images – even in some cases such as with the bats when the video is of a far inferior quality it was easier for me to understand what’s going on more than if I stared at a HD colorized image. I will definitely be taking this into account with my work in the future.
After my time on Zooniverse I feel like doing some citizen scientist work should be mandatory for students young & old, to bring humanitarian and scientific issues into their life while also giving them something to work on and a whole world to appreciate. I’ve also been inspired by the projects frequently simple and intuitive GUI, which will definitely help when it comes to my projects in digital curation & data management.