(Dataset) Web Accessibility Index vs Latin American Artificial Intelligence Index
Description
The "Web Accessibility Index vs Latin American Artificial Intelligence Index" dataset combines metrics on web accessibility in Latin American universities with indicators related to the development and implementation of artificial intelligence (AI) in the region. This dataset enables the analysis of correlations between digital accessibility and AI progress, facilitating the identification of gaps, trends, and areas of opportunity to promote inclusive and sustainable digital transformation in Latin America's academic and technological fields.
Files
Steps to reproduce
### Steps to Reproduce 1. **Data Collection** - Gather data on web accessibility metrics for universities in Latin America using recognized accessibility evaluation tools or reports (e.g., WAVE, WCAG compliance scores). - Collect indicators from the Latin American Artificial Intelligence Index, such as AI research output, funding, and implementation initiatives. 2. **Data Preparation** - Clean and preprocess the data to ensure consistency (e.g., standardize formats, remove duplicates, handle missing values). - Align both datasets by common identifiers such as country, institution, or region to enable effective comparison. 3. **Data Integration** - Merge the accessibility and AI datasets using shared keys, such as country or institution name. - Create new columns or features if necessary to enable detailed comparative analysis (e.g., calculate averages, generate ratios). 4. **Analysis** - Perform descriptive statistics to understand the distribution and key metrics of both datasets. - Use visualization tools (e.g., heatmaps, scatter plots) to identify trends or correlations between web accessibility and AI indicators. 5. **Validation** - Cross-check data points for accuracy by consulting original sources or additional references. - Use statistical methods (e.g., correlation analysis, regression models) to confirm significant relationships between variables. 6. **Documentation** - Record the methodologies and tools used during the data collection and analysis process. - Provide clear descriptions and metadata for the dataset to ensure reproducibility. 7. **Sharing and Collaboration** - Share the dataset through a data repository or collaboration platform (e.g., GitHub, Kaggle). - Include a README file with instructions for replication and details of the analysis process. 8. **Optional Extensions** - Explore additional factors such as economic, social, or technological indicators to enrich the analysis. - Use advanced AI techniques, such as clustering or machine learning, to uncover hidden patterns or insights.