📅 2025-02-08 — Session: Optimized Abstract Retrieval and Analysis Pipeline
🕒 15:20–16:10
🏷️ Labels: API, Abstracts, AI, Data Pipeline, Crossref, Semantic Scholar
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The goal of this session was to optimize the retrieval and analysis of research abstracts using various APIs and AI agents.
Key Activities
- Reviewed a guide on retrieving abstracts using CrossRef, PubMed, and Semantic Scholar APIs.
- Compared CrossRef Metadata API and Semantic Scholar API to select the best option for scholarly metadata retrieval.
- Planned a dual-layer pipeline for literature screening in economics using CrossRef and Semantic Scholar.
- Troubleshot network issues with the CrossRef API.
- Updated the research paper processing pipeline for data ingestion and abstract screening.
- Reflected on the role of abstracts in research production and proposed a hybrid approach for screening.
- Developed AI agent instructions for optimal abstract analysis and insights on priming techniques for LLMs.
Achievements
- Established a comprehensive workflow for abstract retrieval and integration.
- Enhanced AI agent instructions for abstract analysis, focusing on clarity, specificity, and completeness.
Pending Tasks
- Further refine the AI agent prompts for improved abstract evaluation.
- Implement the dual-layer pipeline for broader academic data gathering.
Outcome
The session resulted in a refined approach to abstract retrieval and analysis, leveraging both API capabilities and AI enhancements.