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Visualize synonym4/15/2023 With this in mind (and a full CT export at hand), we can convert data from the CT into an edge list – a data format that allows for representing network data in an easily readable way by listing two cities and the number of CT records in which both cities occur. Basically, a network is simply a collection of nodes and edges between some of these nodes. This is where we can benefit from network analysis. Only after having compiled all these data you would be able to visualise the networks around your city of interest. To make matters worse, even if you could manage to get a list of all people in question you would still have to go through each of them to find out which other places they worked or lived in. If you are familiar with using the CT search syntax, you might be able to express your information need in a complex query by formalising find all records of type „person“ where the biographical dates are within XXXX and YYYY and where the person was either active, lived, or studied in the city represented by record identifier XYZ. It would be a strenuous activity to look at all these records individually just to find out if a person lived there during the period of interest. 2, we can see that the number of people linked to a city can easily exceed some thousands. The absolute number of people that were active in a city (as currently shown in the web interface) is already a good indicator but does not allow for many conclusions. How many people were active in this city? Did they also work in other cities afterwards? Did they come back? How did this change over time? Imagine you are interested in the role a certain city played during the early 17th century. Although we might already get an idea of the putative importance of a city (in terms of the number of people and institutions that resided, worked, lived here), a key piece of information is missing: the relationship between a place and other places. However, this section of the website does not reflect all knowledge implicitly contained in these links. Figure 2: Related Records from a geographical entity in the CERL Thesaurus Figure 1: Geographical places linked to a person‘s record in the CERL ThesaurusĪdditionally, based on these hyperlinks, an overview of ‘Related Records’ is generated automatically for every geographical entity in the CT’s web interface. Links between CT records are displayed in the CT web interface as direct hyperlinks, allowing for an easy navigation between related records. Other CT records (as well as data sets outside of the CT) can be linked to these geographical places, e.g., as place of birth, place of death, or place of activity. The CERL Thesaurus (CT) contains nearly 1.5 million records, around 36,500 of which are geographical places such as London, Paris, or Stockholm. Links and relationships in the CERL Thesaurus This time, by creating networks of places from the CERL Thesaurus, we are able to illustrate both geographical places themselves as well as connections between them. In the following blog post, we add to these insights by describing another approach to visualising metadata. In our February 2021 blog post The value of visualisation in improving data quality: Mapping MEI, we already discussed valuable insights that can be gained by visualising data from CERL resources on a map.
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