Team Apeiron has received the following awards and nominations. Way to go!
We developed an user-friendly website for searching biodiversity information with atmosphere photos, visualized maps, informative paragraphs. Also there are links to further information for important features. We found out that there are many effective websites with enormous data,related to our project. But not quite accessible for every age and every requirement. We think it is important because we thought of many situations with different scenarios. About it’s working system, we got our data with innovative codes for labelling it. After labelling, we added it right below the information about endangered species. We are hoping that users can find information related to their studies. For short we want them to maintain the process of visualizing science.
We used Python, JavaScript and HTML programming languages and Wordpress for our project. We added JavaScript codes to the source code of our websites theme. To get the WorldView links and the coordinate data we wrote a python program and a requests module. Using python we separated text files from the coordinates and after we had separated coordinates we started getting the exact photos of the coordinates with the help of NASA WorldView.

We had so many problems along our path like having 710 GB data or code continuing additively but we found many solutions too. We used “Requests Module” to get photographs of the coordinate data from NASA’s website and used “BeautifulSoup Module” but it didn't work out because the website we were trying to take the data, had a different structure. Therefore, with some problems and of course their solutions, we had 2 different codes for different algorithms.
We took advantage of WorldView, Gbif, Tableau from the Space Apps resources. Starting with World View, we used it’s coordinate data input to take many links with python. The links gave us the atmosphere photos with time, coordinate, satellite labels. And we specialized our data with another code.

Second one, Gbif, we used it for the biodiversity side of our project. Which means, finding endangered species’ population with info of coordinates. Unfortunately Gbif gives a great dataset, so it required labelling too. Labelling json lists of it, we had two dataset of different sides, which lead us to usage of Tableau. For visualization of biodiversity and human aspects ,World View data, Tableau mixed both of them with “Data Source” and layer tools, therefore we had an informative “sheet”.
https://www.youtube.com/watch?v=cZd-FteangI&ab_channel=TeamApeiron