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Edited by Christof Schöch, Julia Dudar and Evgeniia Fileva. With contributions by Joanna Byszuk, Julia Dudar, Evegniia Fileva, Andressa Gomide, Lisanne van Rossum, Christof Schöch, Artjoms Šeļa and Karina van Dalen-Oskam. The aim of this publication is to document and describe current, widespread research practices in CLS, based on a large collection of publications that have been published in this field over the last approximately ten years. The perspective of this survey is primarily descriptive: it aims to document current, widespread practices as the authors were able to observe them in the published literature. In this sense, the survey can also serve as an annotated bibliography of sorts and as a guide to further reading. Despite the fact that this survey is not intended as an introductory textbook, it can nevertheless also serve as an introduction to several research areas or issues that are prominent within CLS as well as to several key methodological concerns that are of importance when performing research in CLS.

Edited by Christof Schöch, Julia Dudar and Evgeniia Fileva. With contributions by Joanna Byszuk, Julia Dudar, Evegniia Fileva, Andressa Gomide, Lisanne van Rossum, Christof Schöch, Artjoms Šeļa and Karina van Dalen-Oskam. The aim of this publication is to document and describe current, widespread research practices in CLS, based on a large collection of publications that have been published in this field over the last approximately ten years. The perspective of this survey is primarily descriptive: it aims to document current, widespread practices as the authors were able to observe them in the published literature. In this sense, the survey can also serve as an annotated bibliography of sorts and as a guide to further reading. Despite the fact that this survey is not intended as an introductory textbook, it can nevertheless also serve as an introduction to several research areas or issues that are prominent within CLS as well as to several key methodological concerns that are of importance when performing research in CLS.

Test version of the Treviso catalogue.&nbsp

The workshop aims to share theoretical and practical knowledge about modeling data in the humanities and especially literary history in the paradigm of Linked Open Data, to introduce the syntax of the query language SPARQL, and to demonstrate the advantages of modeling and providing data as knowledge graphs. Have a look at our SPARQL tutorial Abstract of the workshop

The aim of this poster is to provide an overview of the work carried out in the CLS INFRA project and its conclusions for the field of Computational Literary Studies.

This poster reports on the experiences of launching a publisher-independent OA journal, the Journal of Computational Literary Studies (JCLS) and reflects the workflow established so far. It also discusses openess as a practice of opening up diversified communication spaces with regard to community involvement, peer reviewing, and the publishing workflow.

This forum, for the entire ADHO community, presents our Alliance's organizational structure, finances, committees, and professional activities from the point of view of the people who serve in it. In discussion with the community, we hope to make our shared organization more transparent, empower more people to engage with it, and foster a greater diversity of voices in our policies and governance.

In this poster, researchers from different projects present the integration of existingTEI-encoded corpora into a repository and analysis infrastructure, as well as the benefits of this integration. The focus is not on resource creation (corpus design or text encoding), but on infrastructure integration, dissemination and re-use of existing resources. The NFDI consortium Text+ seeks the integration of already existing resources, for example through the publication of corpora in repositories. We present the publication of the corpora of the European Literary Text Collection in the TextGrid Repository, discuss their characteristics and the advantages of this integration. The poster was accepted and presented at the DH2023 conference in Graz.

This is the conference reader for the2nd Annual Conference of Computational Literary Studies (CCLS2023) in Würzburg, 22-23 June 2023. It contains peer-reviewed, complete and fully layouted versions of the presented papers that, however, are not the final versions yet. The final versions of record are due to be published in issue 1,2023 of the Journal of Computational Literary Studies (JCLS). The editors of this conference reader are listed above. Citation suggestion: [Authors]. 2023."[Paper title]", in: Conference Reader: 2nd Annual Conference of Computational Literary Studies (CCLS2023), edited by Christof Schöch, Élodie Ripoll, Evelyn Gius andPeer Trilcke. JCLS / Zenodo. https://doi.org/10.5281/zenodo.8093598. The titles and authors of the papers are listed below: Gender Depiction in Portuguese – Cláudia Freitas*, Diana Santos A Novel Approach for Identification and Linking of Short Quotations in Scholarly Texts and Literary Works – Frederik Arnold*, Robert Jäschke Automatic Topic-Guided Segmentation of Holocaust Survivor Testimonies – Eitan Wagner, Renana Keydar*, Amit Pinchevski, Omri Abend InvBERT: Reconstructing Text from Contextualized Word Embeddings by inverting the BERT pipeline – Kai Kugler*, Simon Münker, Johannes Höhmann, Achim Rettinger What do characters do? – Andrew Piper Need a Good Book about Privacy? Evaluating Dictionary-Based Corpus Query for Detecting the Topic of Privacy in Literary Texts – Erik Ketzan*, Jennifer Edmond, Carl Vogel The Authorship of Stephen King’s Books Written Under the Pseudonym “Richard Bachman”: A Stylometric Analysis – Vincent Neyt, Mike Kestemont, Dorothy Henriette Modrall Sperling* Extracting Geographical References from Finnish Literature. Fully Automated Processing of Plain-Text Corpora – Harri Kiiskinen*, Asko Nivala, Jasmine Westerlund, Juhana Saarelainen Stylistic History of the Hungarian Novel Based on Sentence Structures – Botond Szemes Why the Daisy sisters are different. a stylometric study on the oeuvre of Swedish author Henning Mankell and the Dutch translations of his work – Martje Wijers Translation-based connotation visualization for classical poetic Japanese vocabulary of the Kokin Wakashū ca. 905 – Xudong Chen*, Hilofumi Yamamoto, Bor Hodošček What's that Scary Sound? – Svenja Guhr*, Mark Algee-Hewitt Connecting the Dots – Leonard Konle*, Merten Kröncke, Simone Winko, Fotis Jannidis Computational approaches to opera libretti – Luca Giovannini*, Daniil Skorinkin

DHd-Workshop 7: „SPARQL für (digitale) Geisteswissenschaftler:innen – Querying Wikidata und die MiMoTextBase“, 13.03.23 Abstract Nicht nur in Kultur- und Gedächtnisinstitutionen, auch in DH-Projekten ist derzeit eine Zunahme des Linked Open Data-Paradigmas sichtbar. Wie können Daten im Sinne von „Open Data, Open Cultures“ offen, gut zugänglich, interoperabel vernetzt, maschinenlesbar und langfristig verfügbar dargeboten werden? Im Projekt „Mining and Modeling Text“ haben wir uns für die offene und kostenlose Software Wikibase entschieden, die einen eigenen SPARQL-Endpoint beinhaltet und neben Wikidata von einer wachsenden Anzahl an Forschungsprojekten verwendet wird. Der Workshop setzt es sich zum Ziel, theoretisches und praktisches Wissen zur Modellierung geisteswissenschaftlichen und speziell literaturgeschichtlichen Wissens in Form von Linked Open Data (LOD) zu vermitteln, Einblick in die Syntax der Abfragesprache SPARQL zu geben und den Mehrwert der Aufbereitung von Daten als Wissensgraphen in Anwendungsszenarien aufzuzeigen. Dabei liegt der Schwerpunkt auf der Vermittlung von SPARQL in theoretischen und praktischen Sessions. Teilnehmende sollen die Kompetenz erlangen, die Struktur von SPARQL zu verstehen und eigenständig Queries zu schreiben. Referenzen (Auswahl) Dengel, Andreas. 2012. Semantische Technologien: Grundlagen. Konzepte. Anwendungen. Heidelberg: Spektrum. DuCharme, Bob. 2013. Learning SPARQL. Sebastopol, United States: O’Reilly Media. http://ebookcentral.proquest.com/lib/uni-trier/detail.action?docID=1250020. Hinzmann, Maria, Anne Klee, Johanna Konstanciak, Julia Röttgermann, Christof Schöch, and Moritz Steffes. 2022. “MiMoTextBase Tutorial.” July 2022. https://mimotext.github.io/MiMoTextBase_Tutorial/. Sack, Harald, and Mehwish Alam. 2020. “Knowledge Graphs.” OpenHPI. 2020. https://open.hpi.de/courses/knowledgegraphs2020. Schöch, Christof. 2021. “Open Access Für Die Maschinen.” In Die Zukunft Des Kunsthistorischen Publizierens, edited by Maria Effinger and Hubertus Kohle, 79–94. arthistoricum.net. https://doi.org/10.11588/ARTHISTORICUM.663. Wikimedia Israel. 2020. “Wikidata Query Service Tutorial.” 2020. https://wdqs-tutorial.toolforge.org/. Zhao, Fudie. 2022. “A Systematic Review of Wikidata in Digital Humanities Projects.” Digital Scholarship in the Humanities, December, fqac083. https://doi.org/10.1093/llc/fqac083.

"Die Open-Access-Transformation des wissenschaftliches Publikationssystems öffnet neue institutionelle, von etablierten Verlagen unabhängige Organisationsformen und technische Gestaltungsspielräume. Dies hat zur Folge, dass Rollen, Logiken und Konventionen des wissenschaftlichen Publizierens derzeit neu verhandelt und womöglich sogar neu erfunden werden. Mit diesem Poster berichten wir über die Gründung eines Open-Access Journals, dem Journal for Computational Literary Studies (JCLS), dessen erstes Rolling Issue im Herbst 2022 erscheinen wird. Wir stellen dabei drei Felder (Community, Review und Workflow), auf denen sich aus unserer Sicht aktuell Entwicklungs- und Öffnungspotenziale im Publikationssystem bieten besonders heraus." Ein Beitrag zur 9. Tagung des Verbands "Digital Humanities im deutschsprachigen Raum" - DHd 2023 Open Humanities Open Culture.

"Nicht nur in Kultur- und Gedächtnisinstitutionen, auch in DH-Projekten ist derzeit eine Zunahme des Linked Open Data-Paradigmas sichtbar. Wie können Daten im Sinne von "Open Data, Open Cultures" offen, gut zugänglich, interoperabel vernetzt, maschinenlesbar und langfristig verfügbar dargeboten werden? Im Projekt "Mining and Modeling Text" haben wir uns für die offene und kostenlose Software Wikibase entschieden, die einen eigenen SPARQL-Endpoint beinhaltet und neben Wikidata von einer wachsenden Anzahl an Forschungsprojekten verwendet wird. Der Workshop setzt es sich zum Ziel, theoretisches und praktisches Wissen zur Modellierung geisteswissenschaftlichen und speziell literaturgeschichtlichen Wissens in Form von Linked Open Data zu vermitteln, Einblick in die Syntax der Abfragesprache SPARQL zu geben und den Mehrwert der Aufbereitung von Daten als Wissensgraph in Anwendungsszenarien aufzuzeigen. Dabei liegt der Schwerpunkt auf der Vermittlung von SPARQL in theoretischen und praktischen Sessions. Teilnehmende sollen die Kompetenz erlangen, die Struktur von SPARQL zu verstehen und eigenständig einfache Queries zu schreiben." Ein Beitrag zur 9. Tagung des Verbands "Digital Humanities im deutschsprachigen Raum" - DHd 2023 Open Humanities Open Culture.

Genre is, like authorship or time period, one of a number of fundamental categories allowing authors and readers as well as literary scholars to endow the vast field of literary production with some internal structure. Genre is not specific to literature, of course: whether we consider painting, music or cinema, genre as an intermediary category situated between individual works and an entire artform is always a relevant category. Unlike theme or style, literary genre is not in itself a level of analysis; rather, different levels of analysis such as theme or style can be used to describe and distinguish genres. The aim of this chapter is to introduce to the analysis of genre with quantitative, computational methods. First, the chapter will discuss what literary genres are and which aspects of genre may be studied quantitatively. Second, some issues relevant to building text collections for genre analysis are presented. Then, several example analyses are discussed, all based on the idea that computational genre analysis can be conducted from a contrastive perspective – that is, by comparing texts belonging to a genre of interest to texts from other, related genres.

Computational Literary Studies Infrastructure, funded by the Horizon2020 grant scheme, is a four-year, pan-European project that aims to unify the diverse landscape of computational text analysis, in terms of available texts, tools, methods, practices and so forth, within its growing international user community. The project started out in February 2021, meaning that it has been underway for just over a year. In our poster we discuss the various deliverables and activities that have come out of the CLS INFRA project in its first quarter to give an idea of its impact in practice.

Julia Röttgermann*; Maria Hinzmann*; Katharina Dietz; Henning Gebhard; Anne Klee; Johanna Konstanciak; Christof Schöch; Moritz Steffes: "Mining and Modeling Spaces and Places for Literary History as Linked Open Data" (Slides, PDF). DH2022, 25-29 July 2022, Tokyo, Japan and Fully Online (Zoom).

Computational Literary Studies Infrastructure, funded by the Horizon2020 grant scheme, is a four-year, pan-European project that aims to unify the diverse landscape of computational text analysis, in terms of available texts, tools, methods, practices and so forth, within its growing international user community. The project started out in February 2021, meaning that it has been underway for just over a year. In our poster we discuss the various deliverables and activities that have come out of the CLS INFRA project in its first quarter to give an idea of its impact in practice.&nbsp

First Progress Report for COST Action "Distant Reading for European Literary History" (CA16204).&nbsp

Second Progress Report of COST Action "Distant Reading for European Literary History" (CA16204).&nbsp

The aim of this poster is to provide an overview of the principal objectives of the newly started H2020 Computational Literary Studies (CLS) project- https://www.clsinfra.io. CLS is a infrastructure project works to develop and bring together resources of high-quality data, tools and knowledge to aid new approaches to studying literature in the digital age. Conducting computational literary studies has a number of challenges and opportunities from multilingual and bringing together distributing information. At present, the landscape of literary data is diverse and fragmented. Even though many resources are currently available in digital libraries, archives, repositories, websites or catalogues, a lack of standardisation hinders how they are constructed, accessed and the extent to which they are reusable (Ciotti 2014). CLS project aims to federate these resources, with the tools needed to interrogate them, and with a widened base of users, in the spirit of the FAIR and CARE principles (Wilkinson et al. 2016). The resulting improvements will benefit researchers by bridging gaps between greater- and lesser- resourced communities in computational literary studies and beyond, ultimately offering opportunities to create new research and insight into our shared and varied European cultural heritage. Rather than building entirely new resources for literary studies, the project is committed to exploiting and connecting the already-existing efforts and initiatives, in order to acknowledge and utilize the immense human labour that has already been undertaken. Therefore, the project builds on recently- compiled high-quality literary corpora, such as DraCor and ELTeC (Fischer et al. 2019, Burnard et al. 2021, Schöch et al. in press), integrates existing tools for text analysis, e.g. TXM, stylo, multilingual NLP pipelines (Heiden 2010, Eder et al. 2016), and takes advantage of deep integration with two other infrastructural projects, namely the CLARIN and DARIAH ERICs. Consequently, the project aims at building a coherent ecosystem to foster the technical and intellectual findability and accessibility of relevant data. The ecosystem consists of (1) resources, i.e. text collections for drama, poetry and prose in several languages, (2) tools, (3) methodological and theoretical considerations, (4) a network of CLS scholars based at different European institutions, (5) a system of short-term research stays for both early career researchers and seasoned scholars, (6) a repository for training materials, as well as (7) an efficient dissemination strategy. This is achieved through a collaboration between participating institutions: Institute of Polish Language at the Polish Academy of Sciences, Poland; University of Potsdam, Germany; Austrian Academy of Sciences, Austria; National University of Distance Education, Spain; École Normale Supérieure de Lyon, France; Humboldt University of Berlin, German; Charles University, Czech Republic; Digital Research Infrastructure for the Arts and Humanities, France; Ghent Centre for Digital Humanities, Ghent University, Belgium; Belgrade Centre for Digital Humanities, Serbia; Huygens Institute for the History of the Netherlands (Royal Netherlands Academy of Arts and Sciences), Netherlands; Trier Center for Digital Humanities, Trier University, Germany; Moore Institute, National University of Ireland Galway, Ireland; This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101004984. References Ciotti, Fabio. 2014. „Digital literary and cultural studies: the state of the art and perspectives“.Between4/8, 1-17.https://doi.org/10.13125/2039-6597/1392. Borgman, Christine. 2010. Scholarship in the Digital Age : Information, Infrastructure, andthe Internet. Cambridge, Mass & London: MIT Press. See https://www.dariah.euandhttps://www.clarin.eu. Burnard, Lou, Christof Schöch, and Carolin Odebrecht. 2021. „In search of comity: TEI fordistant reading“.Journal of the Text Encoding Initiative. https://doi.org/10.4000/jtei.3500. Eder, M., Rybicki, J. and Kestemont, M. 2016. Stylometry with R: a package forcomputational text analysis.R Journal, 8(1): 107-21.https://journal.r-project.org/archive/2016/RJ-2016-007/index.html Fischer, Frank, Ingo Börner, Matthias Göbel, Andrea Hechtl, Christopher Kittel, P. Miling, andPeer Trilcke. 2019. „Programmable Corpora: Introducing DraCor, an Infrastructure for theResearch on European Drama“. InBook of Abstractsof the Digital Humanities Conference2019. Utrecht: ADHO. Heiden, Serge. 2010. The TXM Platform: Building Open-Source Textual Analysis SoftwareCompatible with the TEI Encoding Scheme. In24th PacificAsia Conference on Language,Information and Computation(pp. 10 p.). Sendai, Japon.Retrieved fromhttp://halshs.archivesouvertes.fr/docs/00/54/97/64/PDF/paclic24_sheiden.pdf Schöch, Christof, Tomaz Erjavec, Roxana Patras, and Diana Santos (in press). „Creatingthe European Literary Text Collection (ELTeC): Challenges and Perspectives”.ModernLanguages Open. Wilkinson, Mark D., Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, MylesAxton, Arie Baak, Niklas Blomberg. 2016. „The FAIR Guiding Principles for Scientific DataManagement and Stewardship“.Scientific Data 3(1).https://doi.org/10.1038/sdata.2016.18.

Im Umgang mit dem stetig wachsenden 'digitalen Kulturerbe' bietet die Weiterentwicklung der systematischen Datenerschließung und Wissensrepräsentation bisher nicht ausgeschöpfte Potentiale für die Literaturgeschichtsschreibung. Vor diesem Hintergrund werden im Projekt "Mining and Modeling Text" (MiMoText) quantitative Methoden der Informationsextraktion ('Mining') und Datenmodellierung ('Modeling') ineinander verschränkt, um ein literaturgeschichtliches Informationssystem aufzubauen. Die Transferierbarkeit in andere Domänen wird berücksichtigt. Zentrales Anliegen ist es, den Bereich der quantitativen Methoden zur Extraktion, Modellierung und Analyse geisteswissenschaftlich relevanter Informationen aus umfangreichen Textsammlungen weiterzuentwickeln und aus interdisziplinärer (geistes-, informatik- und rechtswissenschaftlicher) Perspektive zu erforschen. Ein Beitrag zur 8. Tagung des Verbands "Digital Humanities im deutschsprachigen Raum" - DHd 2022 Kulturen des digitalen Gedächtnisses.

In den Computational Literary Studies (CLS) werden statistische Distinktivitätsmaße eingesetzt, um Features zu bestimmen, die charakteristisch für eine Textgruppe im Vergleich mit einer anderen Textgruppe sind. Allerdings erweisen sich die meisten vorhandenen Tools als ungeeignet, wenn Nutzer:innen ihre Analysen anpassen und eigene Parametereinstellungen vornehmen oder bestimmte Datenformate nutzen wollen. Um den Einsatz relevanter Maße für die kontrastive Textanalyse zu erleichtern und das Bewusstsein für die Vielfalt der Maße zu schärfen, entwickeln wir ein Python-Paket mit dem Namen pydistinto. Mithilfe von pydistinto können Nutzer:innen auch mit geringen Programmier- und Statistikkenntnissen zwei Textkorpora mit unterschiedlichen Maßen miteinander vergleichen, und in einem fortgeschrittenen Modus auch die Eigenschaften und Leistungsfähigkeit der unterschiedlichen Maße empirisch ermitteln und gegenüberstellen. Durch Tabellen und Abbildungen werden in dem geplanten Poster vor allem die folgenden Aspekte unseres Pakets vorgestellt: die Möglichkeiten der Vorverarbeitung der Textdaten, die implementierten Distinktivitätsmaße und die Visualisierung der kontrastiven Analyseergebnisse. Ein Beitrag zur 8. Tagung des Verbands "Digital Humanities im deutschsprachigen Raum" - DHd 2022 Kulturen des digitalen Gedächtnisses.

The findings reported here have been obtained in the framework of CLS INFRA’s Work Package 3 concerned with “Methodological Considerations of Computational Literary Studies” (WP3). The overarching objective of WP3 is to identify, document and show-case current shared practices in CLS research. This objective supports several purposes, among them guiding infrastructure development, defining training opportunities, and consolidating the CLS community. Specifically, one can deduct infrastructure requirements from such findings and feed them to other work packages within the project, in order to ensure that decisions taken when designing a research infrastructure for the CLS community are informed by these requirements.1Also, such a documentation of current shared practices can help design a useful training programme both for scholars who are newly entering the field and for more experienced researchers. In addition, it can help consolidate the CLS community by making shared research practices visible. Within WP3, the first step to identify, document and show-case best practices in CLS research is to capture the current state of the art in terms of widespread research practices in CLS. Task 3.1 on the “Baseline Methodological User Needs Analysis” is devoted to this aim.

The aim of this poster is to provide an overview of the principal objectives of the newly started H2020 Computational Literary Studies (CLS) project- https://www.clsinfra.io. CLS is a infrastructure project works to develop and bring together resources of high-quality data, tools and knowledge to aid new approaches to studying literature in the digital age. Conducting computational literary studies has a number of challenges and opportunities from multilingual and bringing together distributing information. At present, the landscape of literary data is diverse and fragmented. Even though many resources are currently available in digital libraries, archives, repositories, websites or catalogues, a lack of standardisation hinders how they are constructed, accessed and the extent to which they are reusable (Ciotti 2014). CLS project aims to federate these resources, with the tools needed to interrogate them, and with a widened base of users, in the spirit of the FAIR and CARE principles (Wilkinson et al. 2016). The resulting improvements will benefit researchers by bridging gaps between greater- and lesser- resourced communities in computational literary studies and beyond, ultimately offering opportunities to create new research and insight into our shared and varied European cultural heritage. Rather than building entirely new resources for literary studies, the project is committed to exploiting and connecting the already-existing efforts and initiatives, in order to acknowledge and utilize the immense human labour that has already been undertaken. Therefore, the project builds on recently- compiled high-quality literary corpora, such as DraCor and ELTeC (Fischer et al. 2019, Burnard et al. 2021, Schöch et al. in press), integrates existing tools for text analysis, e.g. TXM, stylo, multilingual NLP pipelines (Heiden 2010, Eder et al. 2016), and takes advantage of deep integration with two other infrastructural projects, namely the CLARIN and DARIAH ERICs. Consequently, the project aims at building a coherent ecosystem to foster the technical and intellectual findability and accessibility of relevant data. The ecosystem consists of (1) resources, i.e. text collections for drama, poetry and prose in several languages, (2) tools, (3) methodological and theoretical considerations, (4) a network of CLS scholars based at different European institutions, (5) a system of short-term research stays for both early career researchers and seasoned scholars, (6) a repository for training materials, as well as (7) an efficient dissemination strategy. This is achieved through a collaboration between participating institutions: Institute of Polish Language at the Polish Academy of Sciences, Poland; University of Potsdam, Germany; Austrian Academy of Sciences, Austria; National University of Distance Education, Spain; École Normale Supérieure de Lyon, France; Humboldt University of Berlin, German; Charles University, Czech Republic; Digital Research Infrastructure for the Arts and Humanities, France; Ghent Centre for Digital Humanities, Ghent University, Belgium; Belgrade Centre for Digital Humanities, Serbia; Huygens Institute for the History of the Netherlands (Royal Netherlands Academy of Arts and Sciences), Netherlands; Trier Center for Digital Humanities, Trier University, Germany; Moore Institute, National University of Ireland Galway, Ireland

Computational Literary Studies Infrastructure, funded by the Horizon2020 grant scheme, is a four-year, pan-European project that aims to unify the diverse landscape of computational text analysis, in terms of available texts, tools, methods, practices and so forth, within its growing international user community. The project started out in February 2021, meaning that it has been underway for just over a year. In our poster we discuss the various deliverables and activities that have come out of the CLS INFRA project in its first quarter to give an idea of its impact in practice.

Das Abstract ist Teil der vDHd Konferenz 2021 "Experimente", einer durch die Community des Verbandes "Digital Humanities im deutschsprachigen Raum" e.V. organisierten, virtuellen Konferenz. Die Abstracts haben kein Peer-Review-Verfahren durchlaufen.

Das Abstract ist Teil der vDHd Konferenz 2021 "Experimente", einer durch die Community des Verbandes "Digital Humanities im deutschsprachigen Raum" e.V. organisierten, virtuellen Konferenz. Die Abstracts haben kein Peer-Review-Verfahren durchlaufen.

There is a set of statistical measures developed mostly in corpus and computational linguistics and information retrieval, known as keyness measures, which are generally expected to detect textual features that account for differences between two texts or groups of texts. These measures are based on the frequency, distribution, or dispersion of words (or other features). Searching for relevant differences or similarities between two text groups is also an activity that is characteristic of traditional literary studies, whenever two authors, two periods in the work of one author, two historical periods or two literary genres are to be compared. Therefore, applying quantitative procedures in order to search for differences seems to be promising in the field of computational literary studies as it allows to analyze large corpora and to base historical hypotheses on differences between authors, genres and periods on larger empirical evidence. However, applying quantitative procedures in order to answer questions relevant to literary studies in many cases raises methodological problems, which have been discussed on a more general level in the context of integrating or triangulating quantitative and qualitative methods in mixed methods research of the social sciences. This paper aims to solve these methodological issues concretely for the concept of distinctiveness and thus to lay the methodological foundation permitting to operationalize quantitative procedures in order to use them not only as rough exploratory tools, but in a hermeneutically meaningful way for research in literary studies. Based on a structural definition of potential candidate measures for analyzing distinctiveness in the first section, we offer a systematic description of the issue of integrating quantitative procedures into a hermeneutically meaningful understanding of distinctiveness by distinguishing its epistemological from the methodological perspective. The second section develops a systematic strategy to solve the methodological side of this issue based on a critical reconstruction of the widespread non-integrative strategy in research on keyness measures that can be traced back to Rudolf Carnap’s model of explication. We demonstrate that it is, in the first instance, mandatory to gain a comprehensive qualitative understanding of the actual task. We show that Carnap’s model of explication suffers from a shortcoming that consists in ignoring the need for a systematic comparison of what he calls the explicatum and the explicandum. Only if there is a method of systematic comparison, the next task, namely that of evaluation can be addressed, which verifies whether the output of a quantitative procedure corresponds to the qualitative expectation that must be clarified in advance. We claim that evaluation is necessary for integrating quantitative procedures to a qualitative understanding of distinctiveness. Our reconstruction shows that both steps are usually skipped in empirical research on keyness measures that are the most important point of reference for the development of a measure of distinctiveness. Evaluation, which in turn requires thorough explication and conceptual clarification, needs to be employed to verify this relation. In the third section we offer a qualitative clarification of the concept of distinctiveness by spanning a three-dimensional conceptual space. This flexible framework takes into account that there is no single and proper concept of distinctiveness but rather a field of possible meanings depending on research interest, theoretical framework, and access to the perceptibility or salience of textual features. Therefore, we shall, instead of stipulating any narrow and strict definition, take into account that each of these aspects – interest, theoretical framework, and access to perceptibility – represents one dimension of the heuristic space of possible uses of the concept of distinctiveness. The fourth section discusses two possible strategies of operationalization and evaluation that we consider to be complementary to the previously provided clarification, and that complete the task of establishing a candidate measure successfully as a measure of distinctiveness in a qualitatively ambitious sense. We demonstrate that two different general strategies are worth considering, depending on the respective notion of distinctiveness and the interest as elaborated in the third section. If the interest is merely taxonomic, classification tasks based on multi-class supervised machine learning are sufficient. If the interest is aesthetic, more complex and intricate evaluation strategies are required, which have to rely on a thorough conceptual clarification of the concept of distinctiveness, in particular on the idea of salience or perceptibility. The challenge here is to correlate perceivable complex features of texts such as plot, theme (aboutness), style, form, or roles and constellation of fictional characters with the unperceived frequency and distribution of word features that are calculated by candidate measures of distinctiveness. Existing research did not clarify, so far, how to correlate such complex features with individual word features. The paper concludes with a general reflection on the possibility of mixed methods research for computational literary studies in terms of explanatory power and exploratory use. As our strategy of combining explication and evaluation shows, integration should be understood as a strategy of combining two different perspectives on the object area: in our evaluation scenarios, that of empirical reader response and that of a specific quantitative procedure. This does not imply that measures of distinctiveness, which proved to reach explanatory power in one qualitative aspect, should be supposed to be successful in all fields of research. As long as evaluation is omitted, candidate measures of distinctiveness lack explanatory power and are limited to exploratory use. In contrast with a skepticism that has sometimes been expressed from literary scholars with regard to the relevance of computational literary studies on proper issues of the humanities, we believe that integrating computational methods into hermeneutic literary studies can be achieved in a way that reaches higher explanatory power than the usual exploratory use of keyness measures, but it can only be achieved individually for concrete tasks and not once and for all based on a general theoretical demonstration., See also the publisher version here, accessible via personal request: https://zenodo.org/record/5707377

All persons listed have actively contributed to the Text+ proposal. In addition, thanks go to Alexander Czmiel, Sonja Friedrichs, Anne Klammt, Wolfgang Klein, Frank Michaelis, Stefan Schmunk, Alexander Steckel and Roberta Toscano. Text+ aims to develop a research data infrastructure for Humanities disciplines and beyond whose primary research focus is on language and text. Text+ will be flexible, scalable, and thus open for different discipline-specific requirements. By offering easy access to high quality research data, Text+ will support a maximum of methodological diversity, which in turn is a prerequisite for innovative and transdisciplinary research. Text+ focuses on Collections, Lexical Resources and Editions. These data domains have a long tradition of research and are linked to mature methodological paradigms that require distinctive but also cross-disciplinary practices of data generation, curation and management. The three types of research data are indispensable for a wide range of Humanities disciplines, including, but not limited to, Classical Philology, Linguistics, Literary Studies, Social and Cultural Anthropology, Non-European Cultures, Jewish Studies and Religious Studies, Philosophy, and language- and text-based research in the Social and Political Sciences. From the outset, 26 data centres will participate in Text+ that are technically sound and that are highly regarded in their fields of specialisation. They will provide data, tools, and services for the analysis and re-use of research data across a broad range of disciplines. By grouping data, tools, and services into thematic clusters, an optimal bundling is achieved. There are 34 institutions participating in Text+ that represent the communities addressed by Text+ as broadly as possible: research libraries, universities, Digital Humanities data centres as well as members of the Union of German Academies of Arts and Sciences and of the Leibniz Society. In addition, leading computing centres ensure robust and persistent operation of services for a distributed research data infrastructure. The high level of interest in Text+ is not only evidenced by the substantial in-kind contributions by the Text+ partner institutions, but is also documented by the more than 120 research-driven user stories and by the large number of letters of support from the communities of interest participating in Text+. At the heart of the governance structure are three scientific coordination committees for the data domains and one for the infrastructure. Their task is to continuously evaluate the portfolio of data, tools and services and to promote its further development according to the priorities of the participating disciplines in coordination with the infrastructure providers. The research data management strategy of Text+ is the core instrument for achieving the main objectives of Text+ in the NFDI context. It paves the way for the integration of data, tools and services into an infrastructure that meets relevant standards and implements the FAIR and CARE principles.