Web of Science (ENG)

Analysing authors and affiliations 

The Web of Science ‘Analyse Results’ tool offers various options for visualising and exploring research data. Here are some of the main functionalities and options you can utilise to gain more detailed insights into your research results: 

Analyse by category 

• ‘Authors’: this feature enables you to identify which authors have published the largest amount of papers on your research topic, which is useful for recognising specialists in a specific area and for assessing the influence of each author. 

• ‘Affiliations’: shows the main institutions or universities that are publishing on the topic, which enables you to identify the most active and influential research centres in a given area. 

• ‘Publication Year’: enables you to visualise the distribution of papers over time, helping to identify trends or periods of an increased volume of research activity. 

• ‘Keyword Analysis’: identifies the most common keywords in the paper, which helps you understand which are the most-addressed topics of the theme being researched.

Data visualisation 

• ‘TreeMap Chart’: shows the categories as squares of sizes proportional to the number of records in each of them. This chart is useful for visually identifying the most and least covered areas of research, which enables you to obtain a quick sense of the amount of research carried out on different topics within your area of study. 

• ‘Bar Chart’: displays data in a bar graph, which enables you to compare different categories side by side, which is ideal for comparing publication volumes or other quantitative data between categories, providing a clear and easy-to-interpret visualisation, which in turn makes it easier to identify trends and variations in the data 

These functionalities and visualisation options help you obtain a deeper understanding of your research data, enabling you to identify patterns, trends, and areas of interest in your area of study. In addition, you can use other tools to analyse the data, such as: 

• VOSviewer: a tool designed to create, visualise, and explore co-authorship networks, citations and co-occurrences of terms, which is especially useful in bibliometric analyses, enabling users to visualise and explore relationships between authors, publications, and research topics in an intuitive way. 

• Gephi: a software platform for visualising and exploring networks, which is widely used to analyse large data sets and create graphical representations of complex networks, making it easier to identify patterns and interconnections. 

• Tableau: a data visualisation tool that enables you to transform raw data into interactive visualisations and dashboards, which is useful for analysing large volumes of data and facilitates data-driven decision-making. 

• R and Python: both of which are powerful programming languages that offer specific libraries for data analysis and visualisation, such as ggplot2 in R and Matplotlib or Seaborn in Python. These languages are widely used in research for data manipulation and visualisation. 

• Microsoft Excel: although this is a basic tool, Excel can be very effective for analysing and visualising simple data with its graph and pivot table functionalities. It is an affordable option for many researchers. 

These tools complement the data visualisation functionalities and provide a more robust and comprehensive analysis of the data collected in your research.