Cloud Computing and Digital Transformation in the Public Sector

Description

This dataset contains structured bibliographic records related to cloud computing, digital transformation, and electronic government/public sector innovation. The file compiles metadata from scholarly publications, including article title, source link, publication year, publication type, journal or publication title, author names, DOI, PDF link, open access status, Google Scholar identifier, citation count, snippet, and abstract. The dataset is designed to support bibliometric analysis, systematic mapping, and literature review processes focused on the adoption and impact of cloud-based technologies in government and public administration contexts. It enables researchers to examine publication trends, citation patterns, document types, thematic evolution, and the intellectual structure of the field. The data can be reused for descriptive analyses, co-occurrence analysis of terms, citation analysis, and evidence synthesis on how cloud computing contributes to digital government transformation, service modernization, and public sector innovation.

Steps to reproduce

1. Define the research scope focused on cloud computing, digital transformation, and e-government in the public sector. 2. Construct search queries using relevant keywords (e.g., “cloud computing”, “digital transformation”, “e-government”, “public sector innovation”). 3. Retrieve bibliographic records from academic databases such as Scopus, Web of Science, and Google Scholar. 4. Export metadata including title, authors, year, source, DOI, abstract, citation count, and access links in CSV or spreadsheet format. 5. Merge datasets from different sources and remove duplicates based on DOI, title similarity, and author names. 6. Clean and standardize the data (e.g., normalize author names, unify journal titles, verify DOIs, and correct missing values). 7. Filter records based on inclusion criteria (e.g., relevance to public sector, availability of abstracts, publication type). 8. Organize variables into a structured dataset suitable for bibliometric analysis. 9. Perform descriptive and bibliometric analyses (e.g., publication trends, citation analysis, keyword co-occurrence). 10. Validate the dataset by cross-checking a sample of records with original database sources to ensure accuracy and consistency.

Institutions

• Universidad Autónoma del Perú

  Lima Province, Lima
• Universidad Continental

  Junin, Huancayo

Categories

Licence