• zbMATH
• Web of Science
• English Wikipedia
• Word Embedding: GloVe

The database SQL file: math_software.sql.

Project: WoSImporter

One can preprocess all files in BibTeX format by running BibTeXPreprocessor.java in the package cn.edu.bjut.ui.

> nohup preprocess-wos.sh ../dataset/WoS/papers > preprocess-wos.log 2>&1

The articles can be imported to the database by running ArticleBibTexImporter.java in the package cn.edu.bjut.ui. It is noteworthy that the parameters checkFlag and citedArticleFlag should be set to false. In this procedure, the DOI names of cited articles are pre-processed with the cleaning method in Xu et al. (2019).

> nohup ./import-wos.sh 0 WoS > import-wos.log 2>&1
> nohup ./import-wos-keyword WoS > import-wos-keyword.log 2>&1

Project: MathSoftware

The citing articles can be exported for Gaussian AT^credit model by running ToGaussianATCreditConverter.java in the pakcage cn.edu.bjut.converter.

The text-embedding-v4 model is employed to produce a 64-demensional embedding vector for each word.

> python .\extract_embedding.py --model text-embedding-v4 --dimension 64 --batch-size 10 --output text-embedding-v4-64.txt --resume .\math_software.word.vocab

Wikepedia dumps (version: 2026-03-01) should be downloaded in advance.

From downloaded English Wikipedia, the plain texts can be extracted by running the following codes. Please refer to gensim.scripts.segment for more detial.

> python -m gensim.scripts.segment_wiki -i -f enwiki-2026-03-01-p10p1147431.xml.bz2 -o enwiki-2026-03-01-p10p1147431.json.gz
> python -m gensim.scripts.segment_wiki -i -f enwiki-2026-03-01-p1147434p3987701.xml.bz2 -o enwiki-2026-03-01-p1147434p3987701.json.gz
> python -m gensim.scripts.segment_wiki -i -f enwiki-2026-03-01-p3987703p8213792.xml.bz2 -o enwiki-2026-03-01-p3987703p8213792.json.gz
> python -m gensim.scripts.segment_wiki -i -f enwiki-2026-03-01-p8213793p13295371.xml.bz2 -o enwiki-2026-03-01-p8213793p13295371.json.gz
> python -m gensim.scripts.segment_wiki -i -f enwiki-2026-03-01-p13295373p18816201.xml.bz2 -o enwiki-2026-03-01-p13295373p18816201.json.gz
> python -m gensim.scripts.segment_wiki -i -f enwiki-2026-03-01-p18816202p24038461.xml.bz2 -o enwiki-2026-03-01-p18816202p24038461.json.gz
> python -m gensim.scripts.segment_wiki -i -f enwiki-2026-03-01-p24038462p29075629.xml.bz2 -o enwiki-2026-03-01-p24038462p29075629.json.gz
> python -m gensim.scripts.segment_wiki -i -f enwiki-2026-03-01-p29075630p34204620.xml.bz2 -o enwiki-2026-03-01-p29075630p34204620.json.gz
> python -m gensim.scripts.segment_wiki -i -f enwiki-2026-03-01-p34204621p39293698.xml.bz2 -o enwiki-2026-03-01-p34204621p39293698.json.gz
> python -m gensim.scripts.segment_wiki -i -f enwiki-2026-03-01-p39293699p43920660.xml.bz2 -o enwiki-2026-03-01-p39293699p43920660.json.gz
> python -m gensim.scripts.segment_wiki -i -f enwiki-2026-03-01-p43920661p48725620.xml.bz2 -o enwiki-2026-03-01-p43920661p48725620.json.gz
> python -m gensim.scripts.segment_wiki -i -f enwiki-2026-03-01-p48725621p53857278.xml.bz2 -o enwiki-2026-03-01-p48725621p53857278.json.gz
> python -m gensim.scripts.segment_wiki -i -f enwiki-2026-03-01-p53857280p58693957.xml.bz2 -o enwiki-2026-03-01-p53857280p58693957.json.gz
> python -m gensim.scripts.segment_wiki -i -f enwiki-2026-03-01-p58693958p63265982.xml.bz2 -o enwiki-2026-03-01-p58693958p63265982.json.gz
> python -m gensim.scripts.segment_wiki -i -f enwiki-2026-03-01-p63265983p67638983.xml.bz2 -o enwiki-2026-03-01-p63265983p67638983.json.gz
> python -m gensim.scripts.segment_wiki -i -f enwiki-2026-03-01-p67638984p71810319.xml.bz2 -o enwiki-2026-03-01-p67638984p71810319.json.gz
> python -m gensim.scripts.segment_wiki -i -f enwiki-2026-03-01-p71810320p76318043.xml.bz2 -o enwiki-2026-03-01-p71810320p76318043.json.gz
> python -m gensim.scripts.segment_wiki -i -f enwiki-2026-03-01-p76318044p80915674.xml.bz2 -o enwiki-2026-03-01-p76318044p80915674.json.gz
> python -m gensim.scripts.segment_wiki -i -f enwiki-2026-03-01-p80915675p82539885.xml.bz2 -o enwiki-2026-03-01-p80915675p82539885.json.gz

Then, the statistics for single terms and term pairs can be obtained by running the following code (cf. Project WikipediaTool).

> java -jar WikipediaTool.jar -t 0 -i ..\Wikipedia\ -o ..\Wikipedia-token\ > tokenization.log
> java -jar WikipediaTool.jar -t 1 -i ..\Wikipedia-token\ -o ..\Wikipedia-token-cleaned\ > cleaning.log
> java -jar WikipediaTool.jar -t 2 -d ..\data\math_software.word.vocab -w 10 -i ..\Wikipedia-token-cleaned\ -o ..\data\Wikipedia\ > extraction-terms.log
 
> java -Xmx26g -jar WikipediaTool.jar -t 3 -d ..\data\math_software.word.vocab -w 10 -l 0 -u 5000 -i ..\Wikipedia-token-cleaned\ -o ..\Wikipedia-statistics\ > counting0-5000.log
> java -Xmx26g -jar WikipediaTool.jar -t 3 -d ..\data\math_software.word.vocab -w 10 -l 5000 -u 10000 -i ..\Wikipedia-token-cleaned\ -o ..\Wikipedia-statistics\ > counting5000-10000.log
> java -Xmx26g -jar WikipediaTool.jar -t 3 -d ..\data\math_software.word.vocab -w 10 -l 10000 -u 20000 -i ..\Wikipedia-token-cleaned\ -o ..\Wikipedia-statistics\ > counting10000-20000.log
> java -Xmx26g -jar WikipediaTool.jar -t 3 -d ..\data\math_software.word.vocab -w 10 -l 20000 -u 30000 -i ..\Wikipedia-token-cleaned\ -o ..\Wikipedia-statistics\ > counting20000-30000.log
> java -Xmx26g -jar WikipediaTool.jar -t 3 -d ..\data\math_software.word.vocab -w 10 -l 30000 -u 40000 -i ..\Wikipedia-token-cleaned\ -o ..\Wikipedia-statistics\ > counting30000-40000.log
> java -Xmx26g -jar WikipediaTool.jar -t 3 -d ..\data\math_software.word.vocab -w 10 -l 40000 -u 86000 -i ..\Wikipedia-token-cleaned\ -o ..\Wikipedia-statistics\ > counting40000-86000.log
 
> java -jar WikipediaTool.jar -t 4 -d ..\Wikipedia-statistics\ -o ..\data\Wikipedia\ > merging.log

Project: GaussianATModelWithCredit

> java -Xmx20g -jar GaussianATCreditModel.jar -sf -D 50 -e resources/glove.2024.wikigiga.50d.zip -n 50 -sc 7 -fb data/math_software/10/math_software -K 10
> java -Xmx20g -jar GaussianATCreditModel.jar -sf -D 50 -e resources/glove.2024.wikigiga.50d.zip -n 50 -sc 7 -fb data/math_software/20/math_software -K 20
> java -Xmx20g -jar GaussianATCreditModel.jar -sf -D 50 -e resources/glove.2024.wikigiga.50d.zip -n 50 -sc 7 -fb data/math_software/30/math_software -K 30
> java -Xmx20g -jar GaussianATCreditModel.jar -sf -D 50 -e resources/glove.2024.wikigiga.50d.zip -n 50 -sc 7 -fb data/math_software/40/math_software -K 40
> java -Xmx20g -jar GaussianATCreditModel.jar -sf -D 50 -e resources/glove.2024.wikigiga.50d.zip -n 50 -sc 7 -fb data/math_software/50/math_software -K 50
> java -Xmx20g -jar GaussianATCreditModel.jar -sf -D 50 -e resources/glove.2024.wikigiga.50d.zip -n 50 -sc 7 -fb data/math_software/60/math_software -K 60
> java -Xmx20g -jar GaussianATCreditModel.jar -sf -D 50 -e resources/glove.2024.wikigiga.50d.zip -n 50 -sc 7 -fb data/math_software/70/math_software -K 70
> java -Xmx20g -jar GaussianATCreditModel.jar -sf -D 50 -e resources/glove.2024.wikigiga.50d.zip -n 50 -sc 7 -fb data/math_software/80/math_software -K 80
> java -Xmx20g -jar GaussianATCreditModel.jar -sf -D 50 -e resources/glove.2024.wikigiga.50d.zip -n 50 -sc 7 -fb data/math_software/90/math_software -K 90
> java -Xmx20g -jar GaussianATCreditModel.jar -sf -D 50 -e resources/glove.2024.wikigiga.50d.zip -n 50 -sc 7 -fb data/math_software/100/math_software -K 100

> java -Xmx20g -jar GaussianATCreditModel.jar -sf -D 256 -e resources/qwen3.7-embeddings.zip -n 50 -sc 7 -fb data/math_software/10/math_software -K 10
> java -Xmx20g -jar GaussianATCreditModel.jar -sf -D 256 -e resources/qwen3.7-embeddings.zip -n 50 -sc 7 -fb data/math_software/20/math_software -K 20
> java -Xmx20g -jar GaussianATCreditModel.jar -sf -D 256 -e resources/qwen3.7-embeddings.zip -n 50 -sc 7 -fb data/math_software/30/math_software -K 30
> java -Xmx20g -jar GaussianATCreditModel.jar -sf -D 256 -e resources/qwen3.7-embeddings.zip -n 50 -sc 7 -fb data/math_software/40/math_software -K 40
> java -Xmx20g -jar GaussianATCreditModel.jar -sf -D 256 -e resources/qwen3.7-embeddings.zip -n 50 -sc 7 -fb data/math_software/50/math_software -K 50
> java -Xmx20g -jar GaussianATCreditModel.jar -sf -D 256 -e resources/qwen3.7-embeddings.zip -n 50 -sc 7 -fb data/math_software/60/math_software -K 60
> java -Xmx20g -jar GaussianATCreditModel.jar -sf -D 256 -e resources/qwen3.7-embeddings.zip -n 50 -sc 7 -fb data/math_software/70/math_software -K 70
> java -Xmx20g -jar GaussianATCreditModel.jar -sf -D 256 -e resources/qwen3.7-embeddings.zip -n 50 -sc 7 -fb data/math_software/80/math_software -K 80
> java -Xmx20g -jar GaussianATCreditModel.jar -sf -D 256 -e resources/qwen3.7-embeddings.zip -n 50 -sc 7 -fb data/math_software/90/math_software -K 90
> java -Xmx20g -jar GaussianATCreditModel.jar -sf -D 256 -e resources/qwen3.7-embeddings.zip -n 50 -sc 7 -fb data/math_software/100/math_software -K 100

The following three diversity indicators are calculated: (1) Rao-Stirling (Rao, 1982; Stirling, 2007), DIV (Leydesdorff et al., 2019), and Diversity (Mutz, 2022).