📅 2025-02-08 — Session: Developed and Optimized Abstract Retrieval Pipeline
🕒 15:20–16:10
🏷️ Labels: API, Abstract Retrieval, AI, Pipeline, Crossref, Semantic Scholar
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to develop and optimize a pipeline for retrieving and analyzing abstracts using various APIs and AI agents.
Key Activities
- Guide Creation: Developed a comprehensive guide on retrieving abstracts using CrossRef, PubMed, and Semantic Scholar APIs, including setup and code examples.
- API Comparison: Conducted a detailed comparison between CrossRef and Semantic Scholar APIs to aid in selecting the best tool for scholarly metadata retrieval.
- Pipeline Optimization: Designed a dual-layer data pipeline for literature screening, leveraging CrossRef for broad coverage and Semantic Scholar for citation analysis.
- Troubleshooting: Addressed network issues related to CrossRef API connections.
- Workflow Update: Updated the research paper processing pipeline to enhance data ingestion and abstract screening.
- AI Integration: Developed structured instructions for AI agents to improve abstract analysis, focusing on hypothesis, motivation, methods, results, and conclusions.
- LLM Priming: Reflected on effective priming strategies for large language models to enhance AI response quality.
Achievements
- Successfully created a detailed guide and workflow for abstract retrieval and analysis.
- Enhanced the research pipeline with AI integration for improved abstract screening.
- Developed insights on API selection and AI priming strategies.