Inspiration
The Harvard University Herbaria include six collections and more than five million specimens of algae, bryophytes, fungi, and vascular plants. Together they form one of the largest university herbarium collections in the world, and the third largest herbarium in the United States. With their state-of-the art research laboratories and world class libraries, the HUH have been a centerpiece of biodiversity science since the early 1800s.
We wanted to revamp their current website for many reasons:
- Educational outreach: Helping students, amateur botanists, and the general public learn about plant diversity and taxonomy
- Community engagement: Making scientific collections more approachable encourages public interest in botany and plant conservation
- Access: Using less technical terminology that can be intimidating for non-specialists
What it does
Our project makes learning about the names, genus, subgenus, their occurrences, and their distribution easier for beginners who just want to explore the herbarium. It offers beginner-friendly search functions that don't require knowledge of scientific names, these learners can use a simple graph navigation to look through the terms, definitions, and images.
How we built it
We first created some rough sketches on the whiteboard to draw up ideas. Attended the herbarium talk and understood the need for a revamp of the website after talking to the mentors individually. We then used our tech stack (HTML, CSS, JavaScript, D3, Google Gemini, BootStrap) to implement our project.
Challenges we ran into
- we had a hard time fixing the code since it had many errors
Accomplishments that we're proud of
- resolving team disagreements between one another
- picking a project and topic in a timely fashion
- integrating AI into our program files
What we learned
Working efficiently with external api's such as GBIF Harvard Herbarium and a couple other public datasets. Leveraging external javascript libraries like d3.js for our use case
What's next for Hack the Herbaria (Fungi Edition)
Add more features, more visualization tools, expand it to a larger subset of the dataset
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