ABSTRACT

Data visualization techniques proffer efficient means to organize and present data in graphically appealing forms which not only speeds up the process of decision making and pattern recognition but also enables decision makers to fully understand data insights and make informed decisions. Over time, with the rise in technological and computational resources, there has been an exponential increase in world's scientific knowledge. However, most of it lacks structure and cannot be easily categorized and imported into regular databases. This type of data is often termed as Dark Data. Data visualization techniques provide a promising solution to explore such data by allowing quick comprehension of information, discovery of emerging trends, identification of relationships and patterns etc. In this empirical research and study, we use the rich corpus of the PubMed comprising of more than 28 million citations for biomedical literature to explore and analyze lexical and textual biomedical dark data using Network and Citation visualization techniques. We use VOSviewer to construct bibliometric networks for studying relationships between different entities like scientific documents and journals, researchers, and, keywords and terms. We discuss some of the techniques and methodology used by VOSviewer to preprocess enormous datasets and construct large scalable networks efficiently. The paper concludes with a discussion of the limitations and future applications of network and citation visualizations.