Data on sharing practices of software artifacts and source code for reproducible research

These data belong to an analysis on current practices of authors in open scientific journals in regard to code availability indications, FAIR principles applied to code and algorithms. Results show disciplinary differences of code availability in scholarly publications over the past years. Data have been collected from queries using search engines as Pubmed and Scopus as well as open data. Data was exported as or manually noted in comma-separated spreadsheets from Scopus and PMC and further processed via Python (vers. 3.8.3) available in Jupyter lab (vers. 3.2.5). Statistical analysis was carried out using pandas (vers. 1.4.2) method for pearson correlation. Plots were compiled using matplotlib (vers. 3.5.0), pandas (1.3.4), and seaborn (0.13.1). A detailed description and the accompanying discussion of results can be read in the related publication (reference to be added upon final acceptance).