Research software supporting the publication “Leveraging Large Language Models to Classify and Inspect Defects in Reinforced Autoclaved Aerated Concrete (RAAC)”

Liu, Lixu (2025) Research software supporting the publication “Leveraging Large Language Models to Classify and Inspect Defects in Reinforced Autoclaved Aerated Concrete (RAAC)”.

Dataset Details

Data creator(s):

Creators	Email	ORCID
Liu, Lixu	l.liu.9@bham.ac.uk; lixu@verdemetrix.com	orcid.org/0009-0008-8011-496X

Research Data Type:

Software

DOI:

https://doi.org/10.25500/edata.bham.00001292

Publisher:

University of Birmingham

Funder:

Other

Keywords:

Python, PDF Downloader, Unpaywall API, Open Access, Research Tools, DOI, Academic Software, Scholarly Communication, Python, Automation

Managing organisational unit:

Colleges (2008 onwards) > College of Engineering & Physical Sciences

UoB School, Department or Institute:

School of Engineering, Department of Civil Engineering

Date:

29 May 2025

Additional Details

Project/Grant:	C-DICE Industrial Secondment
Primary language:	English
Data processing and preparation activities:	The script was developed using Python 3.10 and tested in a local environment. Dependencies were managed using pip and listed in requirements.txt. The code reads DOIs from a CSV file, queries the Unpaywall API, and downloads open-access PDFs to a local directory. Logging and error handling were implemented to track failed downloads, which are saved separately in rest_articles.csv. A notification sound was added to indicate completion using librosa and sounddevice.
Depositing User:	Lixu Liu
Date Deposited:	29 May 2025 12:54
Last Modified:	29 May 2025 14:21

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UnpaywallPDFDow ... der_Package.zip

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