Leveraging Large Language Models for Automated Scalable Development of Open Scientific Databases

2026-03-07

AI Summary

This work proposes the first unified, scalable, and domain-agnostic automated framework to address the time-consuming and error-prone nature of manual collection and curation of scientific literature. By integrating keyword-based retrieval, parallel harvesting from multiple API sources, and domain-specific filtering via customized large language model (LLM) prompts, the framework efficiently constructs open scientific databases. Evaluated on agricultural and crop yield tasks, the generated database achieves a 90% overlap with a small expert-curated benchmark, demonstrating high fidelity while substantially reducing manual effort. These results validate the framework’s effectiveness and practical utility for accelerating open science initiatives across diverse research domains.

Technology Category

• Data Mining & Knowledge Management: Knowledge Acquisition from the Web
• Humans and AI: Crowd Sourcing and Human Computation
• Application Domains: Humanities & Computational Social Science

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

With the exponential increase in online scientific literature, identifying reliable domain-specific data has become increasingly important but also very challenging. Manual data collection and filtering for domain-specific scientific literature is not only time-consuming but also labor-intensive and prone to errors and inconsistencies. To facilitate automated data collection, the paper introduces a web-based tool that leverages Large Language Models (LLMs) for automated and scalable development of open scientific databases. More specifically, the tool is based on an automated and unified framework that combines keyword-based querying, API-enabled data retrieval, and LLM-powered text classification to construct domain-specific scientific databases. Data is collected from multiple reliable data sources and search engines using a parallel querying technique to construct a combined unified dataset. The dataset is subsequently filtered using LLMs queried with prompts tailored for each keyword-based query to extract the relevant data to a scientific query of interest. The approach was tested across a set of variable keyword-based searches for different domain-specific tasks related to agriculture and crop yield. The results and analysis show 90\% overlap with small domain expert-curated databases, suggesting that the proposed tool can be used to significantly reduce manual workload. Furthermore, the proposed framework is both scalable and domain-agnostic and can be applied across diverse fields for building scalable open scientific databases.

Problem

• scientific literature
• domain-specific data
• automated data collection
• open scientific databases
• data extraction

Innovation

• Large Language Models
• Automated Database Construction
• Domain-Specific Data Filtering
• Scalable Scientific Databases
• Prompt-Based Text Classification