| Title | Bibliometric dataset (1990-2019) concerning 35 city labels dealing with sustainable urbanism |
|---|---|
| ID_Doc | 42366 |
| Authors | Joss, S; Schraven, D; de Jong, M |
| Title | Bibliometric dataset (1990-2019) concerning 35 city labels dealing with sustainable urbanism |
| Year | 2022 |
| Published | |
| Abstract | This data article presents a tripartite dataset that formed the empirical basis for a comprehensive bibliometric analysis of the use of city labels denoting sustainable urbanism in the scientific literature (Schraven, 2021). The tripartite dataset was generated using the abstract and citation database Scopus (Elsevier). Dataset A lists 148 city labels denoting different approaches to urban planning and development. It was used to select 35 city labels that specifically address sustainable urbanism ('sustainable city', 'smart city', 'compact city' etc.). Dataset B references 11,337 journal and review articles spanning the period 1990-2019. All retrieved articles contain at least one of the 35 city labels in the title, abstract, and author keywords. This database was used to calculate the frequency of the selected city labels across time, and to analyze the co-occurrences of city labels. It was further used to calculate the future trajectory of scientific outputs using the Logistic Growth Model (LGM). Dataset C entails 22,820 author keywords extracted from across the 11,337 articles. This was used to analyze the co-occurrences of keywords with city labels. The data article describes the methods of data collection and curation, the analysis performed, and the potential for reusing the data for further research. The comprehensiveness of the bibliometric corpus - spanning three decades and 35 city labels - lends itself to further investigation of how sustainable urban development has evolved as a topic in the scientific literature since the 1990s. Furthermore, the robust methodology developed could be adapted to other scientific repositories and, indeed, other research problems and questions. (c) 2022 The Author(s). Published by Elsevier Inc. |
| https://doi.org/10.1016/j.dib.2022.107966 |
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