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Addressing Research Challenges

Dany Mukesha

16 December 2025

Source: vignettes/addressing_research_challenges.Rmd
addressing_research_challenges.Rmd

CiteAnalyzer specifically addresses several challenges that researchers face:

1. Google Scholar Blocking Prevention

The package includes built-in rate limiting and retry mechanisms to prevent Google Scholar from blocking requests:

# The ExtractScholarData function automatically handles:
# - Rate limiting between requests
# - Retry attempts on failures
# - User agent rotation
# - Request timeouts

2. Data Reproducibility

All analysis functions return structured S4 objects that can be saved and shared:

# Save scholar data for reproducibility
saveRDS(scholar_data, "scholar_analysis.rds")

# Load later for consistent results
loaded_data <- readRDS("scholar_analysis.rds")

3. Comprehensive Metric Calculation

Beyond standard metrics, CiteAnalyzer provides: - m-index (normalized h-index by career years) - Citation per paper ratios - Field-weighted citation impact estimates - Journal impact factor estimation

4. Integration with Bioconductor Workflows

The package uses Bioconductor S4 classes and integrates with other Bioconductor packages:

# Works with BiocParallel for large-scale analysis
# library(BiocParallel)
# results <- bplapply(scholar_ids, function(id) {
#     ExtractScholarData(id, max_publications = 20)
# })

Session Information