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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Birkholz, Julie M.; Börner, Ingo; Chambers, Sally; Cinková, Silvie; +19 Authors

    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.

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    We have explored gaps in teaching of research skills for computational literary studies to inform the CLS INFRA project’s own approach to training schools and chart the territory to gain broader insight into current CLS teaching practices. To understand supply we have manually annotated a sample of European university courses in Digital Humanities and summer school workshops. To index demand we set up an online survey to ask the community to evaluate a set of predetermined ‘skills’ based on its perceived future prospects in the field and teaching (1-5 scale response, 118 participants). The survey also offered a chance to observe the demographic structure of the CLS community. The prevalence of early career respondents indicates a new generational wave within computational literary studies. Participant gender was balanced, although introduction of variables such as career stage, self-reported proficiency, and discipline demonstrated skewness. Researchers who work in the field of CLS also report more experience in computational methods, which suggests that these go hand in hand in current practice. Despite the gap in skills education being more general in nature, we identified areas of heightened interest. These are the skills that make up the backbone of computational research: from designing the study to text collection, to multivariate analysis and statistical modeling. Survey responses reiterated that the current gap in schooling is quantitative rather than qualitative. Moreover, there was a consensus among participants that the institutionalized training of a new generation of researchers is instrumental to disciplinary advancement of CLS.

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    Authors: Edmond, Jennifer; Horsley, Nicola; Lehmann, Jörg; Priddy, Mike;

    This book is available as open access through the Bloomsbury Open programme and is available on www.bloomsburycollections.com. It is funded by Trinity College Dublin, DARIAH-EU and the European Commission.This book explores the challenges society faces with big data, through the lens of culture rather than social, political or economic trends, as demonstrated in the words we use, the values that underpin our interactions, and the biases and assumptions that drive us. Focusing on areas such as data and language, data and sensemaking, data and power, data and invisibility, and big data aggregation, it demonstrates that humanities research, focussing on cultural rather than social, political or economic frames of reference for viewing technology, resists mass datafication for a reason, and that those very reasons can be instructive for the critical observation of big data research and innovation.

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    Book . 2021 . 2022 . Peer-reviewed
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      Book . 2021 . 2022 . Peer-reviewed
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    This book is a collection of seventeen papers which describe the impact that the ARIADNE project and its successor, ARIADNEplus (2019-2022) have had on the archaeological community, both in Europe and further afield. Each case study has been contributed by organisations involved in the ARIADNE Infrastructure who cover many countries from across Europe as well as Argentina and Japan. These papers were originally presented at the CAA Conference in Krakow, April 2019 and cover aspects such as data management, application of standards and guidelines, the use of CIDOC-CRM and Open Data to name but a few.

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    Part of book or chapter of book . 2019
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    Authors: Wilcke, W.X.; de Boer, Viktor; van Harmelen, Frank;

    In recent years, there has been a growing interest from the Digital Humanities in knowledge graphs as data modelling paradigm. Already, many data sets have been published as such and are available in the Linked Open Data cloud. With it, the nature of these data has shifted from unstructured to structured. This presents new opportunities for data mining. In this work, we investigate to what extend data mining can contribute to the understanding of archaeological knowledge, expressed as knowledge graph, and which form would best meet the communities' needs. A case study was held which involved the user-driven mining of generalized association rules. Experiments have shown that the approach yielded mostly plausible patterns, some of which were seen as highly relevant by domain experts.

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    Conference object . 2017
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    This report addresses the ARIADNE project objectives and the framework of the impact evaluation, summarises the overall results, and gives recommendations for ARIADNE and other stakeholders with a focus on potential further advances.

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    Authors: Wilcke, W.X.;
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    Recent years have seen the emergence of graph-based Knowl-edge Bases build upon Semantic Web technologies, known as KnowledgeGraphs (KG). Popular examples are DBpedia and GeoNames. The formal system underlying these KGs provides inherent support for deductive reasoning. Growing popularity has exposed several limitations of thisability, amongst which are scalability and uncertainty issues, as well ascoping with heterogeneous, noisy, and inconsistent data. By supplementing this form of reasoning with Machine Learning algorithms, these hurdles are much more easily overcome. Of the existing research in this area,only a handful have been considering a Deep Neural Network. Moreover,only one of these studies has addressed the problem of hyper-parameteroptimization, albeit under specific conditions. To contribute to this areaof research, we propose a research design that will investigate a DeepNeural Network with optimized hyper-parameters for its effectiveness toperform link prediction on real-world KGs.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Birkholz, Julie M.; Börner, Ingo; Chambers, Sally; Cinková, Silvie; +19 Authors

    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.

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    We have explored gaps in teaching of research skills for computational literary studies to inform the CLS INFRA project’s own approach to training schools and chart the territory to gain broader insight into current CLS teaching practices. To understand supply we have manually annotated a sample of European university courses in Digital Humanities and summer school workshops. To index demand we set up an online survey to ask the community to evaluate a set of predetermined ‘skills’ based on its perceived future prospects in the field and teaching (1-5 scale response, 118 participants). The survey also offered a chance to observe the demographic structure of the CLS community. The prevalence of early career respondents indicates a new generational wave within computational literary studies. Participant gender was balanced, although introduction of variables such as career stage, self-reported proficiency, and discipline demonstrated skewness. Researchers who work in the field of CLS also report more experience in computational methods, which suggests that these go hand in hand in current practice. Despite the gap in skills education being more general in nature, we identified areas of heightened interest. These are the skills that make up the backbone of computational research: from designing the study to text collection, to multivariate analysis and statistical modeling. Survey responses reiterated that the current gap in schooling is quantitative rather than qualitative. Moreover, there was a consensus among participants that the institutionalized training of a new generation of researchers is instrumental to disciplinary advancement of CLS.

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    Authors: Edmond, Jennifer; Horsley, Nicola; Lehmann, Jörg; Priddy, Mike;

    This book is available as open access through the Bloomsbury Open programme and is available on www.bloomsburycollections.com. It is funded by Trinity College Dublin, DARIAH-EU and the European Commission.This book explores the challenges society faces with big data, through the lens of culture rather than social, political or economic trends, as demonstrated in the words we use, the values that underpin our interactions, and the biases and assumptions that drive us. Focusing on areas such as data and language, data and sensemaking, data and power, data and invisibility, and big data aggregation, it demonstrates that humanities research, focussing on cultural rather than social, political or economic frames of reference for viewing technology, resists mass datafication for a reason, and that those very reasons can be instructive for the critical observation of big data research and innovation.

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    This book is a collection of seventeen papers which describe the impact that the ARIADNE project and its successor, ARIADNEplus (2019-2022) have had on the archaeological community, both in Europe and further afield. Each case study has been contributed by organisations involved in the ARIADNE Infrastructure who cover many countries from across Europe as well as Argentina and Japan. These papers were originally presented at the CAA Conference in Krakow, April 2019 and cover aspects such as data management, application of standards and guidelines, the use of CIDOC-CRM and Open Data to name but a few.

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      Part of book or chapter of book . 2019
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    Authors: Wilcke, W.X.; de Boer, Viktor; van Harmelen, Frank;

    In recent years, there has been a growing interest from the Digital Humanities in knowledge graphs as data modelling paradigm. Already, many data sets have been published as such and are available in the Linked Open Data cloud. With it, the nature of these data has shifted from unstructured to structured. This presents new opportunities for data mining. In this work, we investigate to what extend data mining can contribute to the understanding of archaeological knowledge, expressed as knowledge graph, and which form would best meet the communities' needs. A case study was held which involved the user-driven mining of generalized association rules. Experiments have shown that the approach yielded mostly plausible patterns, some of which were seen as highly relevant by domain experts.

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    This report addresses the ARIADNE project objectives and the framework of the impact evaluation, summarises the overall results, and gives recommendations for ARIADNE and other stakeholders with a focus on potential further advances.

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    Authors: Wilcke, W.X.;
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    Conference object . 2016
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    Recent years have seen the emergence of graph-based Knowl-edge Bases build upon Semantic Web technologies, known as KnowledgeGraphs (KG). Popular examples are DBpedia and GeoNames. The formal system underlying these KGs provides inherent support for deductive reasoning. Growing popularity has exposed several limitations of thisability, amongst which are scalability and uncertainty issues, as well ascoping with heterogeneous, noisy, and inconsistent data. By supplementing this form of reasoning with Machine Learning algorithms, these hurdles are much more easily overcome. Of the existing research in this area,only a handful have been considering a Deep Neural Network. Moreover,only one of these studies has addressed the problem of hyper-parameteroptimization, albeit under specific conditions. To contribute to this areaof research, we propose a research design that will investigate a DeepNeural Network with optimized hyper-parameters for its effectiveness toperform link prediction on real-world KGs.

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