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Examining the representation of landscape and its emotional value in German-Swiss fiction around 1900

<p>This repository provides access to&nbsp;the main datasets used in Grisot, G &amp; Herrmann,&nbsp;J. B.&nbsp;(2023)&nbsp;<em>Examining the representation of landscape and its emotional value in German-Swiss fiction around 1900.</em>&nbsp;The data set includes the following item:</p> <p>(1) <em>all_entities_new.Rdata</em>: comprehensive&nbsp;list of labelled spatial entities;</p> <p>(2) <em>corpus_summary_table.xlsx</em>: a summary of the corpus content;</p> <p>(3) <em>corpus_clean_partitioned.zip</em>: the entire corpus after removing stopwords;</p> <p>(4) <em>sentiment_lexicons.RData</em>: the sentiment lexicons for German used in our analyses;</p> <p>(5) tables with details of various analyses (<em>proportions_spatial_entities.xlsx,&nbsp;proportions_spatial_entities_by_country.xlsx,&nbsp;Spatial_entities_in_Corpus_by_category.xlsx</em>);</p> <p>(6)&nbsp;<em>Sentiment_lexicons_info.xlsx</em>: information about the content of the sentment&nbsp;lexicons.</p> <p>The study for which this data were&nbsp;collected explored&nbsp;the representation and affective encoding of fictional space in a corpus of 125 Swiss literary prose texts of the 19th and early 20th Century written in German, offering thus a contribution to both spatial and affective literary studies.&nbsp;Motivated by questions about the iconic dichotomy between urban and rural in literary works&nbsp;&ndash; and in Swiss literature in particular&nbsp;&ndash; we used&nbsp;computational methods to examine the types of space encoded in German-Swiss literature as well as the affective encoding associated with these space types. Taking into account the complexity of cultural perceptions and representations of space across history, we explore questions about affective encoding of rural, urban, and natural fictional spaces, as well as about their&nbsp;role in the&nbsp;construction of &lsquo;Swiss&rsquo; national literature.</p>

ShareScore

40/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
8
Harmonization
4
Access
16
Reuse readiness
8
Engagement
4

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