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index.html
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<!DOCTYPE html>
<html>
<head>
<title>CMU Theses Semantic Map</title>
<style>
* {
margin: 0;
padding: 0;
box-sizing: border-box;
}
body {
font-family: Arial, sans-serif;
background-color: #FFFFF7;
color: rgb(50, 50, 50);
width: 100%;
min-width: 1600px;
}
/* Header styling */
.header {
background-color: #FFFFF7;
padding: 1rem;
display: flex;
justify-content: space-between;
align-items: center;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
width: 100%;
}
.logo {
height: 200px;
width: auto;
}
/* Banner section styling */
.banner-section {
width: 100%;
display: flex;
justify-content: center;
align-items: center;
padding: 2rem 0;
background-color: #FFFFF7;
}
.banner-logo {
max-height: 250px;
width: auto;
}
/* Headline styling */
.headline {
text-align: center;
padding: 2rem 0;
font-size: 2.5em;
font-weight: bold;
color: rgb(50, 50, 50);
background-color: #FFFFF7;
margin: 0 auto;
width: 100%;
}
/* Plot container */
.plot-container {
width: 1601px;
height: 1200px;
margin: 1rem auto;
background-color: #FFFFF7;
}
.plot-container iframe {
width: 100%;
height: 100%;
border: none;
overflow: hidden;
background-color: #FFFFF7;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
}
/* Description box styling */
.description-box {
background-color: #FFFFF7;
margin: 1rem auto;
padding: 2rem;
width: 1600px;
border-radius: 8px;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
border: 1px solid rgba(0, 0, 0, 0.2);
}
/* Markdown styling */
.prose {
max-width: none;
color: rgb(50, 50, 50);
}
.prose h1 {
font-size: 2.5em;
margin-top: 1em;
margin-bottom: 0.5em;
font-weight: bold;
}
.prose h2 {
font-size: 1.8em;
margin-top: 1.2em;
margin-bottom: 0.5em;
font-weight: bold;
}
.prose h3 {
font-size: 1.4em;
margin-top: 1em;
margin-bottom: 0.5em;
font-weight: bold;
}
.prose p {
margin-bottom: 1em;
line-height: 1.6;
font-size: 1.1em;
}
.prose ul {
margin-left: 1.5em;
margin-bottom: 1em;
list-style-type: disc;
}
.prose li {
margin-bottom: 0.5em;
line-height: 1.6;
font-size: 1.1em;
}
.prose em {
font-style: italic;
}
.prose strong {
font-weight: bold;
}
.prose hr {
margin: 2em 0;
border: 0;
border-top: 1px solid rgba(0, 0, 0, 0.1);
}
.prose code {
background-color: #f5f5f5;
padding: 0.2em 0.4em;
border-radius: 3px;
font-family: monospace;
font-size: 0.9em;
}
.prose a {
color: #0066cc;
text-decoration: none;
}
.prose a:hover {
text-decoration: underline;
}
/* responsive design */
@media (min-width: 1601px) {
body {
display: flex;
flex-direction: column;
align-items: center;
}
.header {
width: 1600px;
}
.banner-section {
width: 1600px;
}
.headline {
width: 1600px;
}
}
@media (max-width: 1600px) {
body {
overflow-x: auto;
}
}
</style>
<!-- Load dependencies in the correct order -->
<script src="https://cdnjs.cloudflare.com/ajax/libs/react/17.0.2/umd/react.production.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/react-dom/17.0.2/umd/react-dom.production.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/marked/4.2.12/marked.min.js"></script>
</head>
<body>
<!-- Header with logos -->
<div class="header">
<img src="img/CMU_Libraries_logo.png" alt="CMU Libraries" class="logo">
<img src="img/TRDA_logo.png" alt="TRDA" class="logo">
</div>
<div class="banner-section">
<img src="img/LDW_logo.png" alt="Banner Logo" class="banner-logo">
</div>
<!-- Added headline -->
<div class="headline">
Leveraging AI technology for knowledge exploration and discovery
</div>
<!-- Plot container -->
<div class="plot-container">
<iframe src="web_viz.html" scrolling="no"></iframe>
</div>
<div class="description-box">
<div id="description-content" class="prose max-w-none"></div>
</div>
<script src="https://cdn.jsdelivr.net/npm/marked/marked.min.js"></script>
<!-- Replace the existing script section -->
<script>
window.addEventListener('DOMContentLoaded', function () {
const markdownContent = `
# Explore the Universe of Graduate Research at CMU! 🚀
## Introducing the KiltHub Thesis Explorer 🔭
Ever wondered how your research connects to work across different departments? Curious about unexplored areas in your field? We're excited to introduce a new interactive visualization tool that maps the entire landscape of graduate research at CMU!
## What You'll See 👀
Imagine a star map, but instead of stars, you're looking at every graduate thesis in KiltHub. Each point represents a thesis, and the space between them shows how related their topics are – the closer together, the more similar their research themes. This creates a fascinating "galaxy" of CMU graduate research where you can:
- Discover unexpected connections between different fields
- Find potential collaborators across departments
- Identify unique research opportunities
- Explore the evolution of research themes across colleges
## How to Explore 🔍
### Interactive Features
- ***Zoom:*** Like Google Maps, but for research! Zoom in to explore specific research clusters or out to see the big picture
- ***Hover:*** Mouse over any point to see thesis details, including:
- Title
- College
- Research topics
- ***Filter:*** Click on college names to show or hide different departments
### Research Insights ⚛️
- Find clusters of related work across different colleges
- Spot gaps between fields that might inspire new research directions
- Identify potential interdisciplinary collaboration opportunities
- See how your research interests connect to other fields
## Behind the Scenes 🛠️
This visualization was created using state-of-the-art natural language processing techniques that transform thesis abstracts into a semantic map. Think of it as a GPS system for research topics – it captures the *semantic* meaning of the research, not just keywords, allowing it to show conceptual relationships between different works.
### Technical Details
**Feature Extraction**: Count vectorizer followed by Class-based TF-IDF transformation
**Embedding Model**: \`paraphrase-MiniLM-L6-v2\` from [sentence-transformers](https://huggingface.co/sentence-transformers) to generate 384-dimensional embeddings of thesis descriptions
**Clustering Method**: [HDBSCAN](https://hdbscan.readthedocs.io/en/latest/index.html) to assign points to clusters in the embedding space
**Dimensionality Reduction**: We used [UMAP](https://umap-learn.readthedocs.io/en/latest/) to reduce from 384 to 2 dimensions for visualization purposes
### Acknowledgements
We used [BERTopic](https://maartengr.github.io/BERTopic/) to develop the workflow for this project.
---------------
*This project is a collaboration between the University Libraries and the Tartan Research Data Alliance. Built with love for the Love Data Week 2025 and the CMU community.*
TRDA members: Chehak Arora and Alfredo González-Espinoza`;
// Configure marked
marked.setOptions({
breaks: true,
gfm: true
});
try {
// Parse markdown and insert into DOM
const parsedContent = marked.parse(markdownContent);
document.getElementById('description-content').innerHTML = parsedContent;
} catch (error) {
console.error('Error parsing markdown:', error);
document.getElementById('description-content').innerHTML = 'Error loading content';
}
});
</script>
<!-- MathJax for any mathematical notation
<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script>
<script>
MathJax.Hub.Config({
tex2jax: {
inlineMath: [['$', '$'], ['\\(', '\\)']],
displayMath: [['$$', '$$'], ['\\[', '\\]']],
processEscapes: true
}
});
</script>
-->
</body>
</html>