Supporting material for the research titled "Global inequity in climate change studies revealed by deep learning"

This supporting material contains the main data, pretrained models, embeddings, analysis code, and documentation associated with this study. The deposited files are described below. Data of climate change articles.xlsx This file contains the climate-change literature dataset used in the study, including bibliographic information and text fields required for subsequent analysis. 01embeddings.npy This file stores the document-level semantic embeddings generated from the climate-change literature corpus. all-MiniLM-L6-v2.zip This archive contains the pretrained all-MiniLM-L6-v2 model used for semantic representation and topic modeling. sciBERT.zip This archive contains the pretrained SciBERT model used for semantic relevance assessment of scientific literature. BERT NER.zip This archive contains the BERT-based named entity recognition model used for entity extraction from the literature corpus. SciBERT Screening and BERTopic Modeling.py This Python script contains the code used for SciBERT-based literature screening and BERTopic topic modeling in this study. README for BERTopic modelling.md This file provides a brief description of the uploaded materials, analysis workflow, and main parameter settings.