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This paper describes a corpus of about 3,000 English literary texts with about 250 million words extracted from the Gutenberg project that span a range of genres from both fiction and non-fiction written by more than 130 authors (e.g., Darwin, Dickens, Shakespeare). Quantitative narrative analysis (QNA) is used to explore a cleaned subcorpus, the Gutenberg English Poetry Corpus (GEPC), which comprises over 100 poetic texts with around two million words from about 50 authors (e.g., Keats, Joyce, Wordsworth). Some exemplary QNA studies show author similarities based on latent semantic analysis, significant topics for each author or various text-analytic metrics for George Eliot’s poem “How Lisa Loved the King” and James Joyce’s “Chamber Music,” concerning, e.g., lexical diversity or sentiment analysis. The GEPC is particularly suited for research in Digital Humanities, Computational Stylistics, or Neurocognitive Poetics, e.g., as training and test corpus for stimulus development and control in empirical studies.

Digital data on tangible and intangible cultural assets is an essential part of daily life, communication and experience. It has a lasting influence on the perception of cultural identity as well as on the interactions between research, the cultural economy and society. Throughout the last three decades, many cultural heritage institutions have contributed a wealth of digital representations of cultural assets (2D digital reproductions of paintings, sheet music, 3D digital models of sculptures, monuments, rooms, buildings), audio-visual data (music, film, stage performances), and procedural research data such as encoding and annotation formats. The long-term preservation and FAIR availability of research data from the cultural heritage domain is fundamentally important, not only for future academic success in the humanities but also for the cultural identity of individuals and society as a whole. Up to now, no coordinated effort for professional research data management on a national level exists in Germany. NFDI4Culture aims to fill this gap and create a user-centered, research-driven infrastructure that will cover a broad range of research domains from musicology, art history and architecture to performance, theatre, film, and media studies. The research landscape addressed by the consortium is characterized by strong institutional differentiation. Research units in the consortium's community of interest comprise university institutes, art colleges, academies, galleries, libraries, archives and museums. This diverse landscape is also characterized by an abundance of research objects, methodologies and a great potential for data-driven research. In a unique effort carried out by the applicant and co-applicants of this proposal and ten academic societies, this community is interconnected for the first time through a federated approach that is ideally suited to the needs of the participating researchers. To promote collaboration within the NFDI, to share knowledge and technology and to provide extensive support for its users have been the guiding principles of the consortium from the beginning and will be at the heart of all workflows and decision-making processes. Thanks to these principles, NFDI4Culture has gathered strong support ranging from individual researchers to high-level cultural heritage organizations such as the UNESCO, the International Council of Museums, the Open Knowledge Foundation and Wikimedia. On this basis, NFDI4Culture will take innovative measures that promote a cultural change towards a more reflective and sustainable handling of research data and at the same time boost qualification and professionalization in data-driven research in the domain of cultural heritage. This will create a long-lasting impact on science, cultural economy and society as a whole.

This paper describes a corpus of about 3000 English literary texts with about 250 million words extracted from the Gutenberg project that span a range of genres from both fiction and non-fiction written by more than 130 authors (e.g., Darwin, Dickens, Shakespeare). Quantitative Narrative Analysis (QNA) is used to explore a cleaned subcorpus, the Gutenberg English Poetry Corpus (GEPC) which comprises over 100 poetic texts with around 2 million words from about 50 authors (e.g., Keats, Joyce, Wordsworth). Some exemplary QNA studies show author similarities based on latent semantic analysis, significant topics for each author or various text-analytic metrics for George Eliot's poem 'How Lisa Loved the King' and James Joyce's 'Chamber Music', concerning e.g. lexical diversity or sentiment analysis. The GEPC is particularly suited for research in Digital Humanities, Natural Language Processing or Neurocognitive Poetics, e.g. as training and test corpus, or for stimulus development and control. Comment: 27 pages, 4 figures

The Autumn School for Information Retrieval and Information Foraging (ASIRF) 2019 took place at Schloss Dagstuhl in Germany from September 22nd to 27th. The event featured eight lectures and tutorials from information retrieval experts and stood out due to the diversity of the participants, both regarding their cultural background and research. A varied social program complemented the scientific exchange.

The authors also acknowledge the support and collaboration of many other colleagues in their respective institutes, research communities and IT Infrastructures, together with the funding received by these from many different sources. These include but are not limited to the following: (i) The Worldwide LHC Computing Grid (WLCG) project is a global collaboration of more than 170 computing centres in 43 countries, linking up national and international grid infrastructures. Funding is acknowledged from many national funding bodies and we acknowledge the support of several operational infrastructures including EGI, OSG and NDGF/NeIC. (ii) EGI acknowledges the funding and support received from the European Commission and the many National Grid Initiatives and other members. EOSC-hub receives funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 777536. (iii) The work leading to these results has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 730941 (AARC2). (iv) Work on the development of ESGF's identity management system has been supported by The UK Natural Environment Research Council and funding from the European Union's Seventh Framework Programme for research, technological development and demonstration through projects IS-ENES (grant agreement no 228203) and IS-ENES2 (grant agreement no 312979). (v) Ludek Matyska and Michal Prochazka acknowledge funding from the RI ELIXIR CZ project funded by MEYS Czech Republic No. LM2015047. (vi) Scott Koranda acknowledges support provided by the United States National Science Foundation under Grant No. PHY-1700765. (vii) GÉANT Association on behalf of the GN4 Phase 2 project (GN4-2).The research leading to these results has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 731122(GN4-2). (viii) ELIXIR acknowledges support from Research Infrastructure programme of Horizon 2020 grant No 676559 EXCELERATE. (ix) CORBEL life science cluster acknowledges support from Horizon 2020 research and innovation programme under grant agreement No 654248. (x) Mirjam van Daalen acknowledges that the research leading to this result has been supported by the project CALIPSOplus under the Grant Agreement 730872 from the EU Framework Programme for Research and Innovation HORIZON 2020. (xi) EISCAT is an international association supported by research organisations in China (CRIRP), Finland (SA), Japan (NIPR), Norway (NFR), Sweden (VR), and the United Kingdom (NERC). This white-paper expresses common requirements of Research Communities seeking to leverage Identity Federation for Authentication and Authorisation. Recommendations are made to Stakeholders to guide the future evolution of Federated Identity Management in a direction that better satisfies research use cases. The authors represent research communities, Research Services, Infrastructures, Identity Federations and Interfederations, with a joint motivation to ease collaboration for distributed researchers. The content has been edited collaboratively by the Federated Identity Management for Research (FIM4R) Community, with input sought at conferences and meetings in Europe, Asia and North America.

o-bib. Das offene Bibliotheksjournal / Herausgeber VDB, Bd. 8 Nr. 4 (2021)

Der Beitrag vermittelt einen ��berblick ��ber die Publikationsoptionen der K��nstlerischen Forschung (Fokus: Gestaltung und Kunst) im deutschsprachigen Raum. Dabei wird zum einen auf Aspekte des Open Access eingegangen. Zum andern werden Herausforderungen angesprochen, die ��hnlich auch in anderen Disziplinen anzutreffen sind. Angesprochen wird die Anforderung genannt, mit und in Medien wie Bild, Klang, Video/Film oder Arrangements zu argumentieren, was Gestaltungsfreir��ume innerhalb der Publikationsmedien erfordert. Andere Schwierigkeiten zeigen sich bei der Aufnahme in ��bergeordnete Wissensportale. Nicht zuletzt spielt auch die Verf��gbarkeit von Publikationsservern und institutionellen Repositorien eine Rolle. The article gives an overview of publication options currently available to artistic research (focus: art and design) in the German-speaking countries. On the one hand, aspects of Open Access are addressed. On the other hand, challenges are mentioned that can also be found in other disciplines. Addressed is e.g. the need to argue with and in media such as image, sound, video/film, or arrangements. This requires flexibilities while designing the publication. Other difficulties become apparent when it comes to acceptance in overarching knowledge portals. Last but not least, the availability of publication servers and institutional repositories plays a role.

No abstract available.

Maya hieroglyphic script (300 BCE–1500 CE) is a semi-deciphered logographic and syllabic autochthonous writing system from the Americas and is one of the most significant writing traditions of the ancient world. Because of its incomplete state of decipherment, complexity and variation in graphematics, and partially lost lexicon, transliterations cannot be used within the encoding. The project Text Database and Dictionary of Classic Mayan approaches this challenge with an encoding strategy relying on stand-off markup, which is enriched with additional information sources. Using different formats (RDF, XML) and standards (CIDOC CRM, TEI P5), the inscriptions are encoded in a multilevel corpus: (1) a tei_all-compliant schema defining values and rules for the encoding of the text’s topological and structural features, (2) a “Sign Catalogue” for the classification of Maya hieroglyphs, and (3) the tool ALMAH (Annotator for the Linguistic analysis of MAya Hieroglyphs) for linguistic analyses. In this paper, we focus on the TEI schema and highlight our strategy for encoding hieroglyphs without using linguistic transliterations and transcriptions.