Developed and Optimized Abstract Retrieval Pipeline

• Day: 2025-02-08
• Time: 15:20 to 16:10
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: API, Abstract Retrieval, AI, Pipeline, Crossref, Semantic Scholar

Description

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.

Pending Tasks

• Further refine AI agent prompts for abstract analysis.
• Continue troubleshooting any remaining API connection issues.

Evidence

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