Data Collection and Network Analysis of Temporal Citation Data using the Dimensions research database
Moses Boudourides, Emily Koechel, and Mitali Kissenger
Data Collection and Network Analysis of Temporal Citation Data using the Dimensions research database
This workshop aims to introduce a new method for collecting and analyzing data on bibliographic citations focusing on the temporal, disciplinary, and various other attributes of publications. Our example will use the application programming interfaces (APIs) provided by Dimensions (https://app.dimensions.ai), an intelligence–powered, linked database of bibliometric and other research data.. We use Python (and natural language processing) to capture data structure for scholarly publications. We will provide instructions for how to utilize such APIs to fetch information on articles primarily.
In this workshop, participants will learn how to generate citation graphs and their partitions by fields of study (disciplines) and time (years of publication). These image maps are directed graphs with time stamps and labelled nodes. The time stamped data allows us to model the trajectories of underlying concepts and ideas over time as they traverse different fields of study. The general methodology explored here is also applicable using other bibliographical data archiving systems. In addition, the approach to temporal semantic analysis is generalizable beyond citation graphs and can be applied to other contexts including career, employment, and life course dynamics.