DOI: 10.57760/sciencedb.14702
title: Literature data set of open government data research at domestic and abroad up to 2022
title: 截至2022年国内外开放政府数据研究的文献数据集

description: In this study, two databases, CNKI and Web of Science (WOS), were selected as the literature sources of open government data at domestic and abroad respectively, and the search formula was constructed by combining the topics and keywords respectively. Since the data of the papers in 2023 is not complete, the papers in 2022 and before are selected as the object of analysis. Among them, CNKI restricts the type of literature to "academic journals", and filters "PKU, CSSCI" for analysing Chinese papers, and the search formula is: "主题AND关键词=政府数据开放 OR 政府开放数据 OR 开放政府数据 OR 政府数据公开". After data cleaning, we deleted the papers with incomplete content, lack of keywords, weak academic nature, and renamed papers, and obtained 618 Chinese papers that were more relevant to the theme of "开放政府数据" and the number of papers that were suitable; WOS chose the core collection of Web of Science (SCI-Expanded, SSCI, WOS selects Web of Science core collections (SCI-Expanded, SSCI, CPCI-S, CPCI-SSH, BKCI-SSH), restricts the types of documents to Article and Proceedings paper, and constructs the search formula by using the theme (TS): (TS="open* government data " OR TS="government open* data" OR TS="government data publish*" OR TS="government data publish*" OR TS="government data publish*" OR TS="government data publish*" OR TS="government data publish*"). TS="government data publish*") AND DT=(Article OR Proceedings Paper), we cleaned the data and manually deleted 62 papers published by Chinese authors in WOS, and obtained 547 English papers which were balanced with the number of Chinese papers and met the requirements.
description: In this study, two databases, CNKI and Web of Science (WOS), were selected as the literature sources of open government data at domestic and abroad respectively, and the search formula was constructed by combining the topics and keywords respectively. Since the data of the papers in 2023 is not complete, the papers in 2022 and before are selected as the object of analysis. Among them, CNKI restricts the type of literature to "academic journals", and filters "PKU, CSSCI" for analysing Chinese papers, and the search formula is: "主题AND关键词=政府数据开放 OR 政府开放数据 OR 开放政府数据 OR 政府数据公开". After data cleaning, we deleted the papers with incomplete content, lack of keywords, weak academic nature, and renamed papers, and obtained 618 Chinese papers that were more relevant to the theme of "开放政府数据" and the number of papers that were suitable; WOS chose the core collection of Web of Science (SCI-Expanded, SSCI, WOS selects Web of Science core collections (SCI-Expanded, SSCI, CPCI-S, CPCI-SSH, BKCI-SSH), restricts the types of documents to Article and Proceedings paper, and constructs the search formula by using the theme (TS): (TS="open* government data " OR TS="government open* data" OR TS="government data publish*" OR TS="government data publish*" OR TS="government data publish*" OR TS="government data publish*" OR TS="government data publish*"). TS="government data publish*") AND DT=(Article OR Proceedings Paper), we cleaned the data and manually deleted 62 papers published by Chinese authors in WOS, and obtained 547 English papers which were balanced with the number of Chinese papers and met the requirements.