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In this paper, we describe a data processing pipeline used for annotated spoken corpora of Uralic languages created in the INEL (Indigenous Northern Eurasian Languages) project. With this processing pipeline we convert the data into a loss-less standard format (ISO/TEI) for long-term preservation while simultaneously enabling a powerful search in this version of the data. For each corpus, the input we are working with is a set of files in EXMARaLDA XML format, which contain transcriptions, multimedia alignment, morpheme segmentation and other kinds of annotation. The first step of processing is the conversion of the data into a certain subset of TEI following the ISO standard ’Transcription of spoken language’ with the help of an XSL transformation. The primary purpose of this step is to obtain a representation of our data in a standard format, which will ensure its long-term accessibility. The second step is the conversion of the ISO/TEI files to a JSON format used by the “Tsakorpus” search platform. This step allows us to make the corpora available through a web-based search interface. As an addition, the existence of such a converter allows other spoken corpora with ISO/TEI annotation to be made accessible online in the future.
This paper presents the QUEST project and describes concepts and tools that are being developed within its framework. The goal of the project is to establish quality criteria and curation criteria for annotated audiovisual language data. Building on existing resources developed by the participating institutions earlier, QUEST develops tools that could be used to facilitate and verify adherence to these criteria. An important focus of the project is making these tools accessible for researchers without substantial technical background and helping them produce high-quality data. The main tools we intend to provide are the depositors’ questionnaire and automatic quality assurance, both developed as web applications. They are accompanied by a Knowledge base, which will contain recommendations and descriptions of best practices established in the course of the project. Conceptually, we split linguistic data into three resource classes (data deposits, collections and corpora). The class of a resource defines the strictness of the quality assurance it should undergo. This division is introduced so that too strict quality criteria do not prevent researchers from depositing their data.
This paper presents the QUEST project and describes concepts and tools that are being developed within its framework. The goal of the project is to establish quality criteria and curation criteria for annotated audiovisual language data. Building on existing resources developed by the participating institutions earlier, QUEST also develops tools that could be used to facilitate and verify adherence to these criteria. An important focus of the project is making these tools accessible for researchers without substantial technical background and helping them produce high-quality data. The main tools we intend to provide are a questionnaire and automatic quality assurance for depositors of language resources, both developed as web applications. They are accompanied by a knowledge base, which will contain recommendations and descriptions of best practices established in the course of the project. Conceptually, we consider three main data maturity levels in order to decide on a suitable level of strictness of the quality assurance. This division has been introduced to avoid that a set of ideal quality criteria prevent researchers from depositing or even assessing their (legacy) data. The tools described in the paper are work in progress and are expected to be released by the end of the QUEST project in 2022.
Standards in CLARIN
(2022)
This chapter looks at a fragment of the ongoing work of the CLARIN Standards Committee (CSC) on producing a shared set of recommendations on standards, formats, and related best practices supported by the CLARIN infrastructure and its participating centres. What might at first glance seem to be a straightforward goal has over the years proven to be rather complex, reflecting the robustness and heterogeneity of the emerging distributed digital research infrastructure and the various disciplines and research traditions of the language-based humanities that it serves and represents, and therefore part of the chapter reviews the various initiatives and proposals that strove to produce helpful standards-related guidance. The focus turns next to a subtask initiated in late 2019, its scope narrowed to one of the core activities and responsibilities of CLARIN backbone centres, namely the provision of data deposition services. Centres are obligated to publish their recom-mendations concerning the repertoire of data formats that are best suited for their research profiles. We look at how this requirement has been met by the particular centres and suggest that having centres maintain their information in the Standards Information System (SIS) is the way to improve on the current state of affairs.
This contribution presents the background, design and results of a study of users of three oral corpus platforms in Germany. Roughly 5.000 registered users of the Database for Spoken German (DGD), the GeWiss corpus and the corpora of the Hamburg Centre for Language Corpora (HZSK) were asked to participate in a user survey. This quantitative approach was complemented by qualitative interviews with selected users. We briefly introduce the corpus resources involved in the study in section 2. Section 3 describes the methods employed in the user studies. Section 4 summarizes results of the studies focusing on selected key topics. Section 5 attempts a generalization of these results to larger contexts.
DIL ist ein deutsch-italienisches Online-Fachwörterbuch der Linguistik. Es ist ein offenes Wörterbuch und mit diesem Beitrag wird für eine mögliche Zusammenarbeit, Kollaboration plädiert. DIL ist noch im Aufbau begriffen; zur Zeit ist nur die Sektion DaF komplett veröffentlicht, auch wenn andere Sektionen in Bearbeitung sind. Die Sektion LEX (Lexikographie), die zur Veröffentlichung ansteht, wird zusammen mit den wichtigsten Eigenschaften des Wörterbuches präsentiert.
FAIR-Prinzipien und Qualitätskriterien für Transkriptionsdaten. Empfehlungen und offene Fragen
(2022)
Dieser Beitrag behandelt die mittlerweile als Bestandteil guter wissenschaftlicher Praxis anerkannten FAIR-Prinzipien in Bezug auf die Transkription und Annotation gesprochener Sprache und multimodaler Interaktion. Forschungsdaten - und somit Transkriptionsdaten - sollen heute Findable, Accessible, Interoperable und Reusable sein. Der Beitrag versucht dementsprechend, empirische Methoden im Prozess der Digitalisierung und generische Prinzipien des digitalen Forschungsdatenmanagements zusammenzubringen, um für diesen Kontext einem operationalisierten Begriff der „FAIRness“ näher zu kommen und möglichst konkrete Empfehlungen aufzustellen. Der Beitrag sollte aber gleichzeitig zur Diskussion anregen, denn konkrete Anforderungen in Bezug auf das Forschungsdatenmanagement und die Datenqualität müssen auch im Rahmen der FAIR-Prinzipien von den Fachgemeinschaften selbst herausgearbeitet werden.
This article describes the development of the digital infrastructure at a research data centre for audio-visual linguistic research data, the Hamburg Centre for Language Corpora (HZSK) at the University of Hamburg in Germany, over the past ten years. The typical resource hosted in the HZSK Repository, the core component of the infrastructure, is a collection of recordings with time-aligned transcripts and additional contextual data, a spoken language corpus. Since the centre has a thematic focus on multilingualism and linguistic diversity and provides its service to researchers within linguistics and other disciplines, the development of the infrastructure was driven by diverse usage scenarios and user needs on the one hand, and by the common technical requirements for certified service centres of the CLARIN infrastructure on the other. Beyond the technical details, the article also aims to be a contribution to the discussion on responsibilities and services within emerging digital research data infrastructures and the fundamental issues in sustainability of research software engineering, concluding that in order to truly cater to user needs across the research data lifecycle, we still need to bridge the gap between discipline-specific research methods in the process of digitalisation and generic digital research data management approaches.
Towards Comprehensive Definitions of Data Quality for Audiovisual Annotated Language Resources
(2020)
Though digital infrastructures such as CLARIN have been successfully established and now provide large collections of digital resources, the lack of widely accepted standards for data quality and documentation still makes re-use of research data a difficult endeavour, especially for more complex resource types. The article gives a detailed overview over relevant characteristics of audiovisual annotated language resources and reviews possible approaches to data quality in terms of their suitability for the current context. Conclusively, various strategies are suggested in order to arrive at comprehensive and adequate definitions of data quality for this particular resource type.