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High resolution 3D-scan of the runestone from Ervik in Harstad municipality, Troms and Finnmark county. The stone has been scanned using an Artec Spider 3D-scanner and post-processed in Artec Studio 15 Professional at the archaeology lab at AHR, UiT The Arctic University of Norway. Maximum error reported during the initial scanning is 0,3 mm. The maximum theoretical 3D point accuracy of the scanner is 0,05 mm with a resolution of 0,1 mm. The main model included in the dataset is a non-simplified mesh of 36,288,016 faces and 18,144,026 vertices. The second model has been carefully simplified using a shape deviation constraint of 0,01 mm and consists of a mesh built up of 7,044,110 faces and 3,522,073 vertices. The latter model is included for convenience as the primary one warrants large computing capabilities to be processed. Both models are delivered in the open Wavefront .obj format while the decimated one also is delivered as .fbx for convenience purposes. A number of pre-rendered ambient occlusion views are also included.

The replication data, supplemental data, and R code provide documentation for the article "Development of metaphorical production in learner language: A longitudinal perspective". Article abstract: This article details a longitudinal corpus-based exploration into the development of metaphorical production of L2 learners. The study tracks the progress of five secondary school pupils aged 13-17 in Norway, with the data consisting of texts written for end-of-semester exams: two texts per pupil over four consecutive academic years. The overall goal is to shed light on how metaphorical production changes as pupils progress through different semesters and grades in their school careers. To do so, three subordinate aims are addressed. First, the study investigates how metaphorical density varies over time, both for the group of pupils and for the individuals. In this regard, patterns for open-class versus closed-class metaphors across grade levels are also compared, to identify whether there is any particular level at which the use of the former overtakes the latter, as has been uncovered in previous research. A second aim is to examine the distribution of metaphor clusters over time, since clusters have been found to serve important discoursal functions and might therefore be expected to increase with improved proficiency over time. The third aim is to focus more closely on the identified metaphor clusters to explore the functions they serve in the written discourse of these language learners.

Abstract of the publication Geosteering of wells requires fast interpretation of geophysical logs which is a non-unique inverse problem. Current work presents a proof-of-concept approach to multi-modal probabilistic inversion of logs using a single evaluation of an artificial deep neural network (DNN). A mixture density DNN (MDN) is trained using the ”multiple-trajectory-prediction” (MTP) loss functions, which avoids mode collapse typical for traditional MDNs, and allows multi-modal prediction ahead of data. The proposed approach is verified on the real-time stratigraphic inversion of gamma-ray logs. The multi-modal predictor outputs several likely inverse solutions/predictions, providing more accurate and realistic solutions compared to a deterministic regression using a DNN. For these likely stratigraphic curves, the model simultaneously predicts their probabilities, which are implicitly learned from the training geological data. The stratigraphy predictions and their probabilities obtained in milliseconds from the MDN can enable better real-time decisions under geological uncertainties. This stratigraphy-realization dataset consists of randomly generated stratigraphic vertical depth functions b∗(x) which follow a known trend (here zero). They need to be combined with an offset well-log, e.g. gamma-ray log from the Geosteering World Cup: https://doi.org/10.18710/20VIVT. The training data consists of triples: a reference offset-well log which is trimmed randomly to a short section of 64 cells (32 feet TVD); a sample of b∗(x) with 32 points (32 feet); and an observed well-log corresponding to the first 16 feet of b∗(x), obtained using the code supplied in trajectories_data_set.py. The full training dataset, if read with overlap, contains 28 million samples, stored in train.nc. Additionally, we use a test dataset generated with the same rules containing 560 thousand samples, stored in test.nc. The dataset contains realizations of geological stratigraphic curves which were generated using the included script. This data is required to replicate results in Sergey Alyaev and Ahmed H. Elsheikh. "Direct multi-modal inversion of geophysical logs using deep learning." arXiv preprint arXiv:2201.01871 (2021).

The purpose of this dataset is to provide information on all references to scholars and linguistic sources in the two editions of Joannes (János) Sajnovics' "Demonstratio idioma Ungarorum et Lapponum idem esse" (Hafniae, [1770]; Tyrnaviae, [1771]).

This dataset provides replication data for an article on differential object marking in early Slavonic. The article uses extensive treebank data from the PROIEL and TOROT treebanks to track the much-debated rise of the animacy category in Russian, which in this article will be analysed as a change from at least partly definiteness-driven differential object marking in Old Church Slavonic via constructionally conditioned variation in Old East Slavonic to fully fledged animacy subgender marking in late Middle Russian. The change is interesting from a methodological point of view as well, since it requires us to annotate data through an ongoing change, and also since conventional treebank annotation is not enough to capture the conditions of the observed variation and change: annotation for semantics and information structure is necessary too. The article describes and defends a conservative approach to annotation in the face of change: the analysis that fits the first attested stage of a change is retained as long as possible. R, 4.0.3

This dataset contains summary data for each of the 49 analyzed texts in a research accepted for publication in the Scandinavian Journal of Educational Research. The article investigates patterns of systematic metaphors used to characterize various aspects of the doctoral education period, based on analysis of dissertation acknowledgements (DAs) from doctoral dissertations across academic disciplines and written by researchers from four PhD programs offered by a Norwegian university. A discourse dynamics approach was applied to the data, allowing for the identification of metaphors employed about these topics followed by the categorization of the identified metaphors into broader categories. The resulting overview of the systematic metaphorical patterns in DAs provides empirical evidence concerning how doctoral researchers view their experiences, useful in mentoring situations as a starting point for addressing attitudes, beliefs and values about the various challenges and rewards involved in doctoral trajectories.

The dataset supports the research article "Salience-simplification strategy to markedness of causal subordinators: The case of “because” and “since” in argumentative essays". In total, the dataset marks features of 976 causal adverbial subordinations retrieved from student argumentative essays.Data points were extracted from three corpora. Specifically, all essays in NESSIE (Native English Speakers’ Similarly or Identically-prompted Essays, created by Xu Jiajin, 781 essays; 291,911 tokens) and argumentative essays in LOCNESS (the Louvain Corpus of Native English Essays, created by Granger, 323 essays; 230,138 tokens) were selected. Native argumentative essays from BAWE’s (British Academic Written English, created by Hilary Nesi) Arts and Humanities disciplinary group were chosen (512 essays; 1,360,932 tokens). In total, 1,616 essays comprising 1,882,981 tokens were examined. The dataset comprises 976 datapoints of causal subordinations conjoined by "because" and "since" in students' argumentative essays--488 data points of all "since" subordinations, and 488 randomly selected "because" subordinations. On these data points, ten contextual features that are potential predictors of people's choices between causal subordinators "because" and "since" were annotated. The ten contextual features annotated are "position", "separation", "embeddedness", "initial adverbials", "sub-clause", "de-ranking", "clause-length ratio", "hedging terms", "clausal relationship", and "bridging". Overall fourteen variables including ten contetual features are annotated: (1) "No." is the ID of each data point(this is one ID marker); (2) "subordinator" marks the logical subordinators (this categorical variable has two values: "because" and "since"); (3) "position" marks the logical adverbial clause positions compared with the main clause (this categorical variable has two values: "preposed" or "postposed"); (4) "sep" indicates whether a separating punctuation mark exists between the subordinate and main clauses(this categorical variable has two values: "YES" or "NO"); (5) "embeddedness" indicates whether a complex sentence is embedded in a larger comlex sentence(this categorical variable has two values: "YES" or "NO"); (6) "ini.adv" denotes whether an initial adverbial exists in the causal subordination(this categorical variable has two values: "YES" or "NO"); (7) "sub-clau" indicates whether the causal subordinate contains sub-clauses of any type(this categorical variable has two values: "YES" or "NO"); (8) "deranking" indicates whether the predicate of the subordinate clause is complete(this categorical variable has two values: "YES" or "NO"); (9) "sub.main.ratio" is the length ratio of the subordinate and main clauses in terms of word count (this numerical variable is converted into ln value for better interpretation); (10) "hedging" indicates whether a hedging term exists in the subordinate clause(this categorical variable has two values: "YES" or "NO"); (11) "clau.rel" denotes the interclausal relationships on the general level(this categorical variable has two values: "direct" or "indirect"); (12) "spc.clau.rel2" denotes the interclausal relationships on the secondary level(this categorical variable has five values: "im", "rm", "asst", "inpr", and "sugg"); (13) "bridging" indicates whether the subordinate clause contains any information referring back to the preceding clause(this categorical variable has two values: "YES" or "NO"); (14) "source" shows specific corpora the data points come from (this categorical variable has three values: "NESSIE", "LOCNESS", or "BAWE") ; This dataset was constructed to explore contextual features that discriminate between causal subordinators of "because" and "since" and to rank the effective features. AntConc, 3.5.8 R Language, 3.6.2 RStudio Team, 1.1.456

This dataset compiles selected sentences from the MCVF and ARTFL-FRANTEXT corpora containing lexical items that evoke the Reveal Secret frame (as described in the ASFALDA French FrameNet) from the 13th-20th centuries. The data are semantically annotated, and are used in a research project on changes in the use of the metonymic argument alternations MEDIUM FOR SPEAKER and TOPIC FOR INFORMATION.

This dataset concerns the data for the article that covers the topic of future tense meanings in Russian. Abstract: The relationship between future time and future tense forms in Russian is complex. The forms traditionally attributed to the future tense in certain cases do not refer to future time. Those cases have been previously presented as a list and/or attributed to the sphere of modality. In this article, we suggest a data-driven approach applied to the spectrum of meanings of Russian future tense forms. We analyzed corpus data and discovered that 44% of perfective future forms and 22% of imperfective future forms do not unambiguously express future time meaning. Among the non-future time meanings that Russian future tense forms can express are Gnomic, Performative, Implicative, Hypothetical, Alternation, and Stable scenario. Furthermore, we propose that the meanings of the future tense constitute a radial category. Future time reference is the prototypical meaning of the future tense. The remaining meanings comprise extensions connected to the prototypical meaning. We describe the radial category with reference to Langacker’s (2008) model of tense and potentiality. Additionally, we explore the interaction of future tense and modality.

This dataset contains digitized sales ledgers from Gumpert's bookshop in Gothenburg, one of the most prominent bookshops in Sweden during the nineteenth century. Like most major bookshops at the time, Gumpert���s bookshop primarily sold books on credit, and consequently, the bookshop had to keep a record detailing all the returning customers��� purchases. In most cases each customer was assigned a page in the sales ledgers and the purchases were listed in chronological order. The customers��� name, profession or title, and residence location were scribbled on top of the page. The Gumpert ledgers are comprised of 48 volumes, one volume per year from 1870 to 1917, each containing between 900 and 1,200 pages. They record nearly a million purchases. The present dataset contains material from 1870���1900 and consists of photographs of the original documents (in .jpg format) as well as transcriptions of a 10 % sample of the ledgers from 1879���1890 (in .txt /.pdf /.xlsx format), listing in all over 18,000 purchases. The dataset was created in association with the Scandinavian Moment in World Literature project and formed an important empirical source for my doctoral dissertation ��Modern Reading: Swedish Book Consumption during the late nineteenth century�� and my article Hansen, H. (2017). Buying and Borrowing Books: Book Consumption In Late Nineteenth-Century Sweden (pp 121-154). Papers of The Bibliographical Society of Canada, 54(1-2). https://doi.org/10.33137/pbsc.v54i1-2.25614.