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Collaborative work in NFDI
(2023)
The non-profit association National Research Data Infrastructure (NFDI) promotes science and research through a National Research Data Infrastructure. Its aim is to develop and establish an overarching research data management (RDM) for Germany and to increase the efficiency of the entire German science system. After a two-and-a-half year build up phase, the process of adding new consortia, each representing a different data domain, has ended in March 2023. NFDI now has 26 disciplinary consortia (and one additional basic service collaboration). Now the full extent of cross-consortial interaction is beginning to show.
Die erfolgreiche Wiederverwendung gesprochener Korpora muss fachspezifischen Evaluationskritierien genügen und erfordert daher eine flexible Korpusarchitektur, die durch multirepräsentationale (Verfügbarkeit eines akustischen Signals und einer Transliteration) und multisituationale Daten (Variabilität von Situationen bzw. Aufgaben) gekennzeichnet ist. Diese Kriterien werden in einer Fallstudie zur /eː/-Diphthongisierung polnischer Deutschlerner/-innen angewendet und diskutiert. Die Fallstudie repliziert die Ergebnisse der /eː/-Diphthongisierung bei Bildbenennungen von Nimz (2016). Vor der Wiederverwendung werden weitere fachspezifische Evaluationskriterien überprüft, wie Multisituationalität, Aufnahmequalitäten, Erweiterbarkeit, vorhandene Metadaten und vorhandene Dokumentation. Nach der Replikationsstudie werden die Herausforderungen für eine Umsetzung der Wiederverwendung bezüglich Datenmanagement, Workflows und Data Literacy in Forschungs- und Lehrkontexten diskutiert.
In this article, we examine the current situation of data dissemination and provision for CMC corpora. By that we aim to give a guiding grid for future projects that will improve the transparency and replicability of research results as well as the reusability of the created resources. Based on the FAIR guiding principles for research data management, we evaluate the 20 European CMC corpora listed in the CLARIN CMC Resource family, individuate successful strategies among the existing corpora and establish best practices for future projects. We give an overview of existing approaches to data referencing, dissemination and provision in European CMC corpora, and discuss the methods, formats and strategies used. Furthermore, we discuss the need for community standards and offer recommendations for best practices when creating a new CMC corpus.
This paper describes the TEI-based ISO standard 24624:2016 ‘Transcription of spoken language’ and other formats used within CLARIN for spoken language resources. It assesses the current state of support for the standard and the interoperability between these formats and with rele- vant tools and services. The main idea behind the paper is that a digital infrastructure providing language resources and services to researchers should also allow the combined use of resources and/or services from different contexts. This requires syntactic and semantic interoperability. We propose a solution based on the ISO/TEI format and describe the necessary steps for this format to work as an exchange format with basic semantic interoperability for spoken language resources across the CLARIN infrastructure and beyond.
Contents:
1. Vasile Pais, Maria Mitrofan, Verginica Barbu Mititelu, Elena Irimia, Roxana Micu and Carol Luca Gasan: Challenges in Creating a Representative Corpus of Romanian Micro-Blogging Text. Pp. 1-7
2. Modest von Korff: Exhaustive Indexing of PubMed Records with Medical Subject Headings. Pp. 8-15
3. Luca Brigada Villa: UDeasy: a Tool for Querying Treebanks in CoNLL-U Format. Pp. 16-19
4. Nils Diewald: Matrix and Double-Array Representations for Efficient Finite State Tokenization. Pp. 20-26
5. Peter Fankhauser and Marc Kupietz: Count-Based and Predictive Language Models for Exploring DeReKo. Pp. 27-31
6. Hanno Biber: “The word expired when that world awoke.” New Challenges for Research with Large Text Corpora and Corpus-Based Discourse Studies in Totalitarian Times. Pp. 32-35
The current state of the art for metadata provision allows for a very flexible approach, catering for the needs of different archives and communities, referring to common data category registries that describe the meaning of a data category at least to authors of metadata. Component models for metadata provisions are for example used by CLARIN and META-SHARE, but there is also an increased flexibility in other metadata schemas such as Dublin Core, which is usually not seen as appropriate for meaningful description of language resources.
Making resources available for others and putting this to a second use in other projects has never been more widely accepted as a sensible efficient way to avoid a waste of efforts and resources. However, when it comes to the details, there is still a vast number of problems. This workshop has aimed at being a forum to address issues and challenges in the concrete work with metadata for LRs, not restricted to a single initiative for archiving LRs. It has allowed for exchange and discussion and we hope that the reader finds the articles here compiled interesting and useful.
Measuring the quality of metadata is only possible by assessing the quality of the underlying schema and the metadata instance. We propose some factors that are measurable automatically for metadata according to the CMD framework, taking into account the variability of schemas that can be defined in this framework. The factors include among others the number of elements, the (re-)use of reusable components, the number of filled in elements. The resulting score can serve as an indicator of the overall quality of the CMD instance, used for feedback to metadata providers or to provide an overview of the overall quality of metadata within a repository. The score is independent of specific schemas and generalizable. An overall assessment of harvested metadata is provided in form of statistical summaries and the distribution, based on a corpus of harvested metadata. The score is implemented in XQuery and can be used in tools, editors and repositories.
The transfer of research data management from one institution to another infrastructural partner is all but trivial, but can be required, for instance, when an institution faces reorganization or closure. In a case study, we describe the migration of all research data, identify the challenges we encountered, and discuss how we addressed them. It shows that the moving of research data management to another institution is a feasible, but potentially costly enterprise. Being able to demonstrate the feasibility of research data migration supports the stance of data archives that users can expect high levels of trust and reliability when it comes to data safety and sustainability.
Datenmanagement wird durch die Forschungsföderungsorganisationen (etwa in Horizon 2020 der EU, die Allianz der deutschen Wissenschaftsorganisationen oder in DFG geförderten Projekten) mehr und mehr Teil der Forschungslandschaft. Für die Computerlinguistik ist das Forschungsdatenmanagement aber auch Teil des Forschungsgebietes: Datenmodellierung und Transformation für die nachhaltige Datenspeicherung gehören in den Bereich der Texttechnologie und Textlinguistik, ebenso die Modellierung der beschreibenden Daten zu Datensätzen.