• DARIAH EU
• 02 engineering and technology close

The research group on four-dimensional research and communication of urban history (HistStadt4D) investigates and develops methods and technologies to transfer extensive repositories of historical photographs and their contextual information into a three-dimensional spatial model, with an additional temporal component. This will make content accessible to researchers and the public, via a 4D browser as well as a location-dependent augmented reality representation. Against this background, this article highlights users and requirements of both scholarly and touristic usage of digital information about urban history, in particular historical photographs.

Despite extensive research having been conducted on the subject, the problem of three-dimensional information systems for historical cities is actually still unresolved. In addition, commercially available software seems to be increasingly aiming at a quick development of unspecific urban settings, rather than at a metrically and perceptively faithful representation of reality. In this scenario, the SIUR 3D software (Sistema Informativo URbano tridimensionale) is based on a management structure that links an interactive, photorealistic and metrically reliable model of a city with a qualitative database of the historical, archaeological and material scope of an architectural part. Such application uses the Unity 3D game engine for geometrical models management and is equipped for online data sharing.

Despite the hype, the effective implementation of Open Science is hindered by several cultural and technical barriers. Researchers embraced digital science, use “digital laboratories” (e.g. research infrastructures, thematic services) to conduct their research and publish research data, but practices and tools are still far from achieving the expectations of transparency and reproducibility of Open Science. The places where science is performed and the places where science is published are still regarded as different realms. Publishing is still a post-experimental, tedious, manual process, too often limited to articles, in some contexts semantically linked to datasets, rarely to software, generally disregarding digital representations of experiments. In this work we present the OpenAIRE Research Community Dashboard (RCD), designed to overcome some of these barriers for a given research community, minimizing the technical efforts and without renouncing any of the community services or practices. The RCD flanks digital laboratories of research communities with scholarly communication tools for discovering and publishing interlinked scientific products such as literature, datasets, and software. The benefits of the RCD are show-cased by means of two real-case scenarios: the European Marine Science community and the European Plate Observing System (EPOS) research infrastructure. This work is partly funded by the OpenAIRE-Advance H2020 project (grant number: 777541; call: H2020-EINFRA-2017) and the OpenAIREConnect H2020 project (grant number: 731011; call: H2020-EINFRA-2016-1). Moreover, we would like to thank our colleagues Michele Manunta, Francesco Casu, and Claudio De Luca (Institute for the Electromagnetic Sensing of the Environment, CNR, Italy) for their work on the EPOS infrastructure RCD; and Stephane Pesant (University of Bremen, Germany) his work on the European Marine Science RCD. First Online 30 August 2019

This article describes how virtual research environments (VREs) offer new opportunities for researchers to analyse open data and to obtain new insights for policy making. Although various VRE-related initiatives are under development, there is a lack of insight into how VREs support collaborative open data analysis by researchers and how this might be improved, ultimately leading to input for policy making to solve societal issues. This article clarifies in which ways VREs support researchers in open data analysis. Seven cases presenting different modes of researcher support for open data analysis were investigated and compared. Four types of support were identified: 1) ‘Figure it out yourself', 2) ‘Leading users by the hand', 3) ‘Training to provide the basics' and 4) ‘Learning from peers'. The author provides recommendations to improve the support of researchers' open data analysis and to subsequently obtain new insights for policy making to solve societal challenges.

International audience; Academic dictionary writing is making greater and greater use of the TEI Guidelines’ dictionary module. And as increasing numbers of TEI dictionaries become available, there is an ever more palpable need to work towards greater interoperability among dictionary writing systems and other language resources that are needed by dictionaries and dictionary tools. In particular this holds true for the crucial role that statistical data obtained from language resources play in lexicographic workflow—a role that also has to be reflected in the model of the data produced in these workflows. Presenting a range of current projects, the authors address two main questions in this area: How can the relationship between a dictionary and other language resources be conceptualized, irrespective of whether they are used in the production of the dictionary or to enrich existing lexicographic data? And how can this be documented using the TEI Guidelines? Discussing a variety of options, this paper proposes a customization of the TEI dictionary module that tries to respond to the emerging requirements in an environment of increasingly intertwined language resources.

It is essential that we develop effective systems for the management and preservation of digital heritage data. This chapter outlines the key issues surrounding access, sharing and curation, and describes current efforts to establish research infrastructures in a number of countries. It aims to provide a detailed overview of the issues involved in the creation, ingest, preservation and dissemination of 3D datasets in particular. The chapter incorporates specific examples from past and present Archaeology Data Service (ADS) projects and highlights the recent work undertaken by the ADS and partners to specify standards and workflows in order to aid the preservation and reuse of 3D datasets.

Digital archives of memory institutions are typically concerned with the cataloguing of artefacts of artistic, historical, and cultural value. Recently, new forms of citizen participation in cultural heritage have emerged, producing a wealth of material spanning from visitors' experiential feedback on exhibitions and cultural artefacts, to digitally mediated interactions like the ones happening on social media platforms. Citizen curation is proposed in the context of the European project SPICE - Social Participation, Cohesion, and Inclusion through Cultural Engagement - as a methodology for producing, collecting, interpreting, and archiving people's responses to cultural objects, with the aim of favouring the emergence of multiple, sometimes conflicting viewpoints, and motivating users and memory institutions to reflect upon them. We argue that citizen curation urges to rethink the nature of computational infrastructures supporting data management of memory institutions, bringing novel challenges that include issues of distribution, authoritativeness, interdependence, privacy, and rights management. To approach these issues, we survey relevant literature towards a distributed, Linked Data infrastructure, with a focus on identifying the roles and requirements involved in such an infrastructure. We show how existing research can contribute significantly in facing the challenges raised by citizen curation, and discuss challenges and opportunities from the socio-technical standpoint.

This paper is about data in the humanities. Most of my colleagues in literary and cultural studies would not necessarily speak of their objects of study as “data.” If you ask them what it is they are studying, they would rather speak of books, paintings and movies; of drama and crime fiction, of still lives and action painting; of German expressionist movies and romantic comedy. They would mention Denis Diderot or Toni Morrison, Chardin or Jackson Pollock, Fritz Lang or Diane Keaton. Maybe they would talk about what they are studying as texts, images, and sounds. But rarely would they consider their objects of study to be “data.” However, in the humanities just as in other areas of research, we are increasingly dealing with “data.” With digitization efforts in the private and public sectors going on around the world, more and more data relevant to our fields of study exists, and, if the data has been licensed appropriately, it is available for research. The digital humanities aim to raise to the challenge and realize the potential of this data for humanistic inquiry. As Christine Borgman has shown in her book on Scholarship in the Digital Age, this is as much a theoretical, methodological and social issue as it is a technical issue. Indeed, the existence of all this data raises a host of questions, some of which I would like to address here. For example: What is the relation between the data we have and our objects of study? – Does data replace books, paintings and movies? In what way can data be said to be representations of them? What difference does it make to analyze the digital representation or version of a novel or a painting instead of the printed book, the manuscript, or the original painting? What types of data are there in the humanities, and what difference does it make? – I will argue that one can distinguish two types of data, “big” data and “smart” data. What, then, does it mean to deal with big data, or smart data, in the humanities? What new ways of dealing with data do we need to adopt in the humanities? – How is big data and smart data being dealt with in the process of scholarly knowledge generation, that is when data is being created, enriched, analyzed and interpreted?

Within the EU funded project “INCEPTION – Inclusive Cultural Heritage in Europe through 3D semantic modelling”, the use and application of H-BIM data is focused at. The project realizes innovation in 3D modelling of cultural heritage through an inclusive approach for time-dynamic 3D reconstruction of built and social environments.

We propose a morphologically informed model for named entity recognition, which is based on LSTM-CRF architecture and combines word embeddings, Bi-LSTM character embeddings, part-of-speech (POS) tags, and morphological information. While previous work has focused on learning from raw word input, using word and character embeddings only, we show that for morphologically rich languages, such as Bulgarian, access to POS information contributes more to the performance gains than the detailed morphological information. Thus, we show that named entity recognition needs only coarse-grained POS tags, but at the same time it can benefit from simultaneously using some POS information of different granularity. Our evaluation results over a standard dataset show sizable improvements over the state-of-the-art for Bulgarian NER. Comment: named entity recognition; Bulgarian NER; morphology; morpho-syntax