📅 2025-08-20 — Session: Comprehensive Review and Categorization of Academic Papers
🕒 03:00–04:10
🏷️ Labels: Academic Review, Data Science, NLP, Semantic Web, Machine Learning
📂 Project: Teaching
⭐ Priority: MEDIUM
Session Goal
The session aimed to systematically review and categorize a large batch of academic papers across various domains such as Data Science, Machine Learning, Semantic Web, and NLP. The objective was to determine the relevance of each paper to ongoing research projects and to decide whether to keep or discard them.
Key Activities
- Conducted a systematic cross-referencing of categories to identify common and unique elements for filtering and decision-making.
- Reviewed multiple batches of academic papers, categorizing them based on their relevance to specific research domains.
- Created thematic maps and partial maps of retained papers to visualize methodological clusters and research themes.
- Highlighted key papers and datasets that contribute to ongoing research efforts in areas like NLP, Earth observation, and historical economics.
- Analyzed CONICET publications to identify strategic insights related to authorship and affiliations.
Achievements
- Successfully categorized papers into ‘keep’ or ‘discard’ groups, enhancing the focus of ongoing research initiatives.
- Developed thematic maps that provide a visual representation of research clusters and methodological approaches.
- Identified strategic insights regarding international collaborations and co-affiliation patterns in fields like NLP and Semantic Web.
Pending Tasks
- Further exploration of visualization paths for understanding co-affiliations and clusters in NLP.
- Creation of a detailed map for the Semantic Web research network in Argentina.
- Continued refinement of thematic maps to include emerging research trends and clusters.