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Examples of the use of databases

Below we briefly discuss a number of examples of the use of databases in literary, cultural and linguistic research at the VU Faculty of Humanities. In almost all cases they involve a combination of source data (frequently obtained through archival research) or textual data and analytical data inferred from these sources.

Reception of Bilderdijk   

This database, which was commissioned by the Bilderdijk Museum, contains a systematic inventory of texts about the poet Willem Bilderdijk (fl. 1780 - 1910) and reviews of his work. The resulting catalogue was intended to be an instrument for further research into BIlderdijk.
Example    Data structure

The social embedding of the authorship of Jan Vos (1610-1667)  

In her PhD research, Nina Geerdink examines the social anchoring and sociability of seventeenth-century authorship in the Dutch Republic. Authorial representation is the starting point: how did seventeenth-century authors represent themselves in their poetry? And how did they use their self-representation to acquire or consolidate a social position? The project focuses on the social poetry of Amsterdam-based poet Jan Vos, investigating to what extent the construction and representation of his cultural, social and religious identity in his poems can be connected with the various jobs he had in Amsterdam: Fox worked as a glazier, but had a second job as a theatre director. His career as a poet, glazier and theatre director saw him frequently working for the city government, but he also maintained contacts in (other) cultural and Catholic networks - Vos was not a member of the public church, but of the Catholic Church.
All data related to the collected works of Jan Vos are now available in an Access database, which allows users to quickly find certain information (such as the title of that one poem he wrote then and then, or the date of birth or occupation of a recipient of one of his poems). On the other hand, the database has made it possible to ask questions about the corpus that could not have been answered without a database, or without a lot of counting, such as 'How many poems did Vos write for Catholics?'and 'Are the poems Vos writes for the city regents different than his poems for other poets?'. Answering these questions is important for the study, because it allows researchers to map the network of the authors (recipients also belong to their network), and look into their relationships to the people in those networks (What kinds of poem were written for these people? How many poems were written for them? How long were these poems?
Example 1    Example 2    Data structure

The Correspondence of Elizabeth Stuart

In her doctoral research, Nadine Akkerman made an inventory of all known letters to and from the English Queen Elizabeth Stuart from the period 1632-1642. An important aim of this research was the publication of an index for these letters (The Correspondence or Elizabeth Stuart, Queen of Bohemia, Volume I and II, published by Oxford University Press between 2011 and 2015), which was to serve as a tool for further research. She used the inventory herself to analyse Elizabeth Stuart's relational network, allowing her to answer questions such as "who wrote what, to whom and how?' These answers could then be used to answer other questions such as 'Was Elizabeth  successful as a politically active woman?' and 'What was her role in the Thirty Years' War?'.
For this study Nadine Akkerman used a database to store the huge amount of data related to the inventorized letters and the people in question (senders and receivers) in a consistent manner.  During the annotation process, it was necessary to distinguish 700 different people and to quickly look up whether and where these people had previously been treated in scientific literature. It was also very  useful to have an archive to look up whether  a certain transcription had already been checked against the original yet. In addition, it was useful for the substantive aspect of the study that the letters could be selected on the basis of various characteristics (whether they were written by a secretary or by Elizabeth herself; the colour of the sealing wax (for dating the letters)). Finally, the database was used to create the index for the letters.
Example    Data structure

Discourse competence

Mike Hannay and Elena Martínez Caro are investigating what it means to be a competent language user. Their study focuses on discourse competence and particularly on how learners of English as a second language begin sentences in written texts. Does the writer give information that can be used to interpret his or her message at the start of the sentence? In that case, they might put the subject first (This book was a great success), or circumstantial information about time and place (Last week in London we came up with another plan), or the opinion of the writer (Fortunately, this didn’t end in a disaster). Or is the start of the sentence used to make a link with the preceding text? Then the sentence might contain elements that indicate a contrast (On the other hand, we should also consider other options) or a conclusion (to conclude,the results are unreliable). Questions in this study include the following: How complex is the start of the sentence? Do language learners use all patterns that are available in English? And what does this say about the development of the language competence of the learners? Do certain differences in the way that language learners start their sentences depend  on their mother tongue?
For this study they compiled a database of English texts written by students with Dutch, Spanish and English as their native language (some of the texts came from the ICLE corpus). A number of features were tagged in every sentence in the database, including the inflected form and the meaning of each element at the start of the sentence. The database makes it possible to quickly see which structures are used at the start of sentences, for example. It is also possible to do targeted searches for specific constructions, such as “presentative” constructions and inverted constructions, which are often problematic for language learners. In addition to searching the data, users can also quickly count how often certain constructions occur, and whether or not they occur in combination with other specific variables (e.g. an overview of the frequency of certain sentence constructions in each mother tongue).
Sample input screen
More information on this study can be found in Hannay & Caro (2008).

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