Willow Outhwaite of outlined the trouble they have had obtaining accurate data from online animal trading websites. After looking through some of the marketplaces it became clear that web-scraping was impossible. Good data would have to be harvested manually. However these data sets aren't just useful to track costs and market flow. These data sets could be used to train AI to do the job. This is however somewhat more complex than 'hotdog' or 'not hotdog'.

What it does

Fauna Lens allows users to participate in a gamified online community that offers some rewards in return for data entry. A user can enter data from animal sale forums and rank up among their animal loving peers. If the animal name matches an endangered or threatened species that entry is escalated. Another aspect of the site is higher ranked and professional users can verify data as being accurate and add it to the AI training data set.

How we built it

Our application is written in Vue, using a bootstrap UI library. Our data model was developed around the idea that simpler data can be entered more accurately by laymen users. The back end is using Airtable to store user entries.

Challenges we ran into

As a team of two we really had to focus on getting core functionality and having a limited scope that we could deliver on. We initially sought out to solve the entire pipeline from user interaction, data scraping, AI, down to analytics views. This was not feasible within our limitations. However building a live, functioning app where real people can start contributing was not just possible, but what we have achieved.

Accomplishments that we're proud of

Our community engagement mechanisms are absolutely the soul of the project. Allowing for high visibility when a user finds a potentially endangered animal being traded is not just rewarding for the user, but part of the 'thrill of the hunt'. Our small group (literally the smallest size you can call a group) has created something that can not only increase awareness, but allow people to participate in the elimination of animal trafficking. Engagement is key in making progress on real world issues, and this project has the potential to move the needle on this in a substantial way.

What we learned

I learned a lot about how ubiquitous online animal sales are. I had no idea this was happening with such regularity. I also have never worked with Vue. Tryston also hadn't realized how popular online animal sales were and in the process seeing some animals that were threatened or endangered increased the urgent need for something like this.

What's next for Fauna Lens

We would also love to allow different causes to add campaigns and incentives specific to their needs. So another interested party can ask for a specific data set from our platform, and offer users perks of any kind. Starting to build the AI that ingests the data sets we are producing would be nice. Allowing the data to be queried using GraphQL for scientists looking for patterns in the markets. Our original idea encompassed a whole data pipeline from users, all the way to advanced analytic interfaces. Providing for more professional use of the data we are collecting would be a great next step.

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