Overview
This page provides access to open-source tools, datasets, and computational resources developed for research in computational communication science and education. These resources support transparency, reproducibility, and collaborative research.
Research Tools
The Invisible Lab
Research lab focusing on invisible information, knowledge diversity, and digital communication.
- Website: invisible.info
- Research on invisible information and knowledge gaps
- Tools for measuring information diversity
PictoPercept
Capturing invisible biases through pairwise visual wikisurveys.
- Website: pictopercept.ai
- Visual perception research tool
- Bias measurement through comparative judgments
Facemeasure
Democratizing facial measurements for researchers.
- Website: facemeasure.com
- Open-access facial measurement tool
- Research applications in social perception
GitHub Repositories
Open-source code and replication materials available on GitHub:
- Shadowbans Detection - Computational prediction of shadowbans
- Research Code - Replication materials for published papers
- Teaching Materials - Workshops and tutorials
Data Sources
Research projects often utilize:
- Social media data (Twitter, Reddit, Facebook)
- Educational assessment data
- Digital analytics and web data
- Survey and experimental data
- Text corpora and linguistic datasets
Note: Specific datasets are shared alongside publications when permitted by data sharing agreements and IRB protocols.
Using Research Code
Most research code is provided in Python and R. Basic workflow:
Python Environment Setup
# Create virtual environment
python -m venv research_env
source research_env/bin/activate
# Install requirements
pip install -r requirements.txt
Running Analyses
import pandas as pd
import numpy as np
from research_tools import analysis
# Load data
data = pd.read_csv('data/research_data.csv')
# Run analysis
results = analysis.run_analysis(data)
Contact
For questions about data or tools, please contact: saurabh.khanna@uva.nl std_dev = np.std(data_array) min_value = np.min(data_array) max_value = np.max(data_array)
#### Display summary statistics:
Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod
tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam,
quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo
consequat `print`.
```python
print(f"Mean: {mean}")
print(f"Median: {median}")
print(f"Standard Deviation: {std_dev}")
print(f"Minimum Value: {min_value}")
print(f"Maximum Value: {max_value}")
Description of simulation parameters
| Parameter | Value | Language | Time period | Description |
|---|---|---|---|---|
| $\alpha$ | $1/2$ | French | 1930–1954 | Tempor dolor in |
| $\lambda$ | $e/2$ | French | 1930–1954 | Fugiat sint occaecat |
| $\gamma$ | $\ln(3)$ | Spanish | 1833–1954 | Duis officia deserunt |
| $\omega$ | $10^{-4}$ | Italian | 1930–1994 | Excepteur et dolore magna aliqua |
| $\sigma$ | $1.5$ | Portuguese | 1990–2023 | Lorem culpa qui |
| $\chi^2$ | $\pi^2$ | Portuguese | 1990–2023 | Labore et dolore |