Data visualization techniques proffer efficient means to organize and present data in graphically appealing formats. Data visualization techniques provide a promising solution to explore such data by allowing quick comprehension of information.
Data visualization techniques proffer efficient means to organize and present
data in graphically appealing formats, 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 the 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, the discovery of emerging trends, identification
of relationships and patterns, etc. In this empirical research study, we use
the rich corpus of PubMed comprising of more than 30 million citations from
biomedical literature to visually explore and understand the underlying
key-insights using various information visualization techniques. We employ a
natural language processing based pipeline to discover knowledge out of the
biomedical dark data. The pipeline comprises of different lexical analysis
techniques like Topic Modeling to extract inherent topics and major focus
areas, Network Graphs to study the relationships between various entities like
scientific documents and journals, researchers, and, keywords and terms, etc.
With this analytical research, we aim to proffer a potential solution to
overcome the problem of analyzing overwhelming amounts of information and
diminish the limitation of human cognition and perception in handling and
examining such large volumes of data.