The data used in the paper "Novelpy, a python package to measure novelty and disruptiveness in science." (Pelletier & Wirtz 2022) This data is a sample of Pubmed Knowledge Graph (PKG) formatted to run novelty indicators using the package Novelpy. Each zip is a folder with a JSON file per year for the analysis. In each JSON file you can find information on a list of papers, here's an exhaustive list of information. 'authors_sample' : PMID: 7472417 a02_authorslist: list of authors of the paper with PMID 7472417, AID = Disambiguated ID year: 1995 'Citation_net_sample' : PMID: 7472417 refs_pmid_wos: list of PMIDs cited by 7472417 year: 1995 'Meshterms_sample' : PMID: 7472417 Mesh_year_category: list of meshterms and their year of creation year: 1995 'Ref_Journals_sample' : PMID: 7472417 c04_referencelist : The list of journals cited in the paper 7472417 and the year of publication of the cited paper year: 1995 'Title_abs_sample' : PMID: 7472417 ArticleTitle : The title of the article 7472417 a04_abstract : The abstract of the article 7472417 year: 1995

{"references": ["Xu, J., Kim, S., Song, M., Jeong, M., Kim, D., Kang, J., Rousseau, J.F., Li, X., Xu, W., Torvik, V.I. and Bu, Y., 2020. Building a PubMed knowledge graph. Scientific data, 7(1), pp.1-15."]}