• Digital Humanities and Cultural Heritage
• DataverseNO close

The eMunch dataset contains correspondence metadata of 8.527 letters to and from the Norwegian painter Edvard Munch (1863-1944). The dataset is derived from the digital scholarly edition of Edvard Munch's Writings, eMunch.no, edited by Hilde Bøe, The Munch Museum, Oslo. The eMunch dataset is part of the NorKorr - Norwegian Correspondences project that aims to collect metadata from all correspondences in collections of Norwegian academic and cultural heritage institutions, project website on GitHub. A Python script was developed to parse the XML files on eMunch.no and supplementary data files (Excel spreadsheet with updated dates, CSV file with GeoNames IDs for places) and extract the following metadata: sender's name, receiver's name, place name, date, and letter ID in the scholarly edition. These metadata were then converted into the Correspondence Metadata Interchange Format (CMIF). The entire dataset has been integrated into the international CorrespSearch search service for scholarly editions of letters hosted by the Berlin-Brandenburg Academy of Sciences—link to the CorrespSearch website.

The dataset contains transcripts of parent-child communication over picture stimuli depicting motion events. The transcripts are partly-coded and transcribed in purpose of comparative analysis. The data were collected in Czech republic (Czech native speakers, n=25), Russia (Russian native speakers, n=25), and Germany (Russian-German bilinguals with Russian as a dominant language, n=22). The transcriptions are in Czech, Russian, and German. The data were collected in purpose of analysis of language-specific patterns while describing motion events. The transcripts are content-driven, without precise phonetic transcription. The transcript are based on audio recordings of parents and their children which are protected by privacy rules. Additional information about the original files can be provided by authors in accordance to the privacy policy.

1. List of occurrences of the Greek term Sarx in Josephus (ancient writer) 2. List of occurrences of the Greek term Sarx in Paul's Letters (ancient writer, New Testament) 3. List of occurrences of the Greek term Sarx in Philo (ancient writer) 4. Map over transition markers in the Greek Text (Paul's letter to the Galatians) 5. Map over transition markers in the Greek Text (Paul's first letter to the Corinthians)

This dataset is based on examples found in Kuteva et al. 2019: Kuteva, Tania, Bernd Heine, Bo Hong, Haiping Long, Heiko Narrog, and Seongha Rhee. 2019. World Lexicon of Gramaticalization (2nd ed.). Cambridge: Cambridge University Press. Kuteva et al.’s World Lexicon of Grammaticalization (2019) is an inventory of examples of morphological reanalysis observed across a sample of over 900 languages. The goal of Kuteva et al.’s inventory is to represent grammaticalization changes that are documented in multiple languages. The examples are cataloged as types defined by Source to Target shifts, such as Ablative > Partitive, that are attested in two or more languages of the sample. The inventory lists 526 Source > Target types. The purpose of this dataset is to explore how many Sources also serve as Targets, and to provide a broad semantic classification of the Sources. The classification is intended only for general description of patterns, and does not represent a precise assignment to mutually exclusive classes. Its purpose is to give a qualitative overview and thus does not lend itself to further quantitative analysis. This dataset is the basis for the analysis in Section 4 of this publication: Janda, Laura A. To Appear. “Morphological reanalysis: recycling old form to new function”, as part of Volume 3 Morphology & Syntax, Part 1 Morphology of The Wiley Blackwell Companion to Diachronic Linguistics, edited by Edith Aldridge, Anne Breitbarth, Katalin É. Kiss, Adam Ledgeway, Joe Salmons, and Alexandra Simonenko.

Dataset containing (5) GIS shapefiles which can be used to visualize a circumpolar overview map of geographical language speaker areas for Arctic Indigenous Peoples languages with additional attribute information about the languages. The language speaker areas show generally the maximum continuous areas where the Indigenous Peoples who spoke those languages lived in a historical context. The exact time range is defined specifically per region. Data from the languages and dialects shapefile was used to make language family and language family branch shapefiles. There is also a separate shapefile with some examples of innovative language revitalization in the region. There is a supporting shapefile of Arctic places to assist when visualizing the data. The 5 shapefiles can be used together or separately. All shapefiles are intended to be used as open resources for education and research.

This dataset contains data on soil C and N stocks (from soil samples), charcoal weight, historical fire frequencies, year of last fire, bottom layer vegetation cover, topography, and woody cover from Trillemarka Nature reserve. RStudio, 4.1.1

Dataset description: This is a study of examples of Russian impersonal constructions with the modal word možno ‘can, be possible’ with and without the future copula budet ‘will be,’ i.e., možno + budet + INF and možno + INF. The data was collected in 2020-2021 from the old version of the Russian National Corpus (ruscorpora.ru). In the spreadsheet 01DataMoznoBudet, the data merges the results of four searches conducted to extract examples of sentences with the following construction types: možno + budet + INF.PFV, možno + budet + INF.IPFV, možno + INF.PFV and možno + INF.IPFV. The results for each search were downloaded, pseudorandomized, and the first 200 examples were manually annotated, based on the syntactic analyses given in the corpus. The syntactic and morphological categories used in the corpus are explained here: https://ruscorpora.ru/corpus/main. In the spreadsheet 01DataZavtraMoznoBudet, the data merges the results of four searches conducted to extract examples of sentences with the following structure: zavtra + možno + budet + INF.PFV, zavtra + možno + budet + INF.IPFV, zavtra + možno + INF.PFV and zavtra + možno + INF.IPFV. All of the examples (103 sentences) were imported to a spreadsheet and annotated manually, based on the syntactic analyses given in the corpus. The syntactic and morphological categories used in the corpus are explained here: https://ruscorpora.ru/corpus/main. Article abstract: This paper examines Russian impersonal constructions with the modal word možno ‘can, be possible’ with and without the future copula budet ‘will be,’ i.e., možno + budet + INF and možno + INF. My contribution can be summarized as follows. First, corpus-based evidence reveals that možno + INF constructions are vastly more frequent than constructions with copula. Second, the meaning of constructions without the future copula is more flexible: while the possibility is typically located in the present, the situation denoted by the infinitive may be located in the present or the future. Third, I show that the možno + INF construction is more ambiguous and can denote present, gnomic or future situations. Fourth, I identify a number of contextual factors that unambiguously locate the situation in the future. I demonstrate that such factors are more frequently used with the future copula, and thus motivate the choice between the two constructions. Finally, I illustrate the interpretations in a straightforward manner by means of schemas of the type used in cognitive linguistics.

Dataset abstract The dataset contains the ratings for a 100-split task performed by Russian learners of English. 272 Russian learners were subdivided into two groups. One group rated 25 English sentences containing the dative alternation taken from the British National Corpus (BNC), the other group rated 25 Russian sentences (translations of the English ones) with the ditransitive alternation. The English sentences with the double object construction were translated into Russian sentences with the equivalent recipient/dative-theme /accusative order, while the English sentences with the to-dative construction were translated with the equivalent theme/accusative-Recipient/dative order. The dataset contains information about the Participants (Age, Gender, Year of Study, Study domain), the test Sentences (the reference to the BNC, the sentences in both languages, the observed dative construction in the BNC, and its main verb), and the Ratings for each Sentence by each Participant. The replication R code is additionally shared together with the output as an html-file. Article abstract Ditransitive verbs include a “recipient” and a “theme” argument (in addition to the subject). The choice of putting one argument before the other (i.e., either recipient-theme, or theme-recipient) is associated with multiple discourse-pragmatic factors. Languages have different options to code the ditransitive construction. In English, a ditransitive verb can take two alternating patterns (“the dative alternation”): the Double Object Construction (DOC) (John gives Mary a book) and the to-dative construction (to-dative) (John gives a book to Mary). In Russian, theme and recipient are marked by accusative and dative, respectively. In addition, word order is flexible and either the accusative-marked theme (Pjotr dal knigu Marii), or the dative-marked recipient (Pjotr dal Marii knigu) can come first. This article reports on two sentence rating experiments (acceptability judgments) to test whether Russian learners of English transfer their preferences about the theme-recipient order in Russian to the ditransitive construction in English. A total of 272 Russian students were tested. Results for both tests showed a great variability in the ratings. A comparison of the ratings seems to suggest a small positive correlation, but no statistically significant relation was found between the order preferences in both languages. However, we found a small preference for the use of the to-dative, which we relate to the language acquisition process as proposed by Processability Theory

Formal analysis of the minimal computational complexity of verification algorithms for natural language quantifiers implies that different classes of quantifiers demand the engagement of different cognitive resources for their verification. In particular, sentences containing proportional quantifiers, e.g., ‘most’, provably require a memory component, whereas non-proportional quantifiers, e.g., ‘all’, ‘three’, do not. In an ERP study, we tested whether previously observed differences between these classes were modulated by memory load. Participants performed a picture-sentence verification task while they had to remember a string of 2 or 4 digits to be compared to a second string at the end of a trial. Relative to non-proportional quantifiers, proportional quantifiers elicited a sentence-internal sustained negativity. Additionally, an interaction in ERP signals between Digit-Load and Quantifier-Class was observed at the sentence-final word. Our results suggest that constraints on cognitive resources deployed during human sentence processing and verification are of the same nature as formal constraints on abstract machines. The present dataset contains behavioural and EEG data from both experiments, as well as analysis scripts for both data types in R and Matlab/FieldTrip.