Web of Science Transportation Dataset (WST202201)

Published: 17 May 2022| Version 1 | DOI: 10.17632/tnfw2dh5nj.1
Istiak Ahmad,


The Web of Science Transportation Dataset (WST202201) was collected from the well-known academic database Web of Science. We collected in aggregate 21,446 research article abstracts in the English language from about 20 categories of academic disciplines in the Web of Science, such as transportation science and technology, engineering, environmental science, telecommunications, economics, computer science, business, and others. The collected article abstracts were limited to the publishing years 2000 to 2022. Excluded were publications including news items, corrections, book chapters, data papers, book reviews, letters, editorial materials, and others. Each document in the dataset has three attributes: Article Title, Abstract, and Publication Year. This dataset was built to discover parameters for the academic-focused aspects of transportation as part of our deep journalism approach and DeepJournal tool. The deep journalism approach uses big data, deep learning, and digital methods to discover and analyse cross-sectional multi-perspective information to enable better decision making and develop better instruments for academic, corporate, national, and international governance. We discovered a total of 49 transportation parameters from the academic dataset and grouped them into 6 macro-parameters: Policy, Planning & Sustainability; Transportation Modes; Logistics & SCM; Pollution; Technologies; and Modelling. The other two transportation datasets related to this dataset used in the deep journalism approach include the Guardian Transportation Dataset (GT202201: http://dx.doi.org/10.17632/yvxx6s5xhh.1) and the Traffic Technology Today Transportation Dataset (TTIT202201: http://dx.doi.org/10.17632/k4bgjwktyp.1). Further details of the dataset, its collection, and usage for deep journalism including detection of the multi-perspective parameters for transportation can be found in our article here: https://doi.org/10.3390/su14095711.



King Abdulaziz University


Computer Science, Transport, Natural Language Processing, Machine Learning, Deep Learning, Textual Analysis