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- ItemWie FAIR sind unsere Metadaten? : Eine Analyse der Metadaten in den Repositorien des TIB-DOI-Services(Marburg : Philipps-Universität, 2021) Burger, Marleen; Cordts, Anette; Habermann, TedIm vorliegenden Erfahrungsbericht stellen wir eine Metadatenanalyse vor, welche die Metadatenqualität von 144 Repositorien des TIB-DOI-Service im Hinblick auf die Erfüllung der FAIR Data Principles, Konsistenz und Vollständigkeit untersucht. Im Ergebnis zeigt sich, dass der Fokus der untersuchten Repositorien schwerpunktmäßig auf der Auffindbarkeit der mit Metadaten beschriebenen Ressourcen liegt und im Gesamtdurchschnitt über die Metadaten-Pflichtfelder hinaus nur wenige weitere Metadaten angegeben werden. Insbesondere mit Blick auf eine angestrebte bessere Nachnutzbarkeit sowie eine stärkere Verknüpfung mit anderen in Beziehung stehenden persistenten Identifikatoren wie ORCID, ROR ID oder DOI-zu-DOI-Beziehungen mit zitierten oder zitierenden Ressourcen, bestehen noch ungenutzte Potenziale, die im Sinne einer offenen, zukunftsweisenden Wissenschaft erschlossen werden sollten. Dahingegen zeigt unsere Analyse auch einzelne Repositorien mit umfangreichen Metadaten als Best-Practice-Beispiele auf, an denen sich andere Repositorien orientieren können. Insgesamt ermöglicht die durchgeführte Metadatenanalyse die Ableitung von Handlungsempfehlungen zur passgenauen Beratung von Repositorien, die ihre Metadatenqualität verbessern möchten.
- ItemAnne Baillot: From Handwriting to Footprinting: Text and Heritage in the Age of Climate Crisis. Cambridge: Open Book Publishers, 2023, 179 Seiten, ISBN 978-1-80511-089-7, https://doi.org/10.11647/OBP.0355(Berlin : Walter de Gruyter, 2024-05-04) Schmeja, StefanReviewed Publication: Baillot Anne From Handwriting to Footprinting: Text and Heritage in the Age of Climate Crisis Cambridge Open Book Publishers 2023 179 Seiten ISBN 978-1-80511-089-7, https://doi.org/10.11647/OBP.0355
- ItemZertifizierung von Forschungsdatenrepositorien: Wege, Praxiserfahrungen und Perspektiven : 10. Workshop der DINI/nestor-AG Forschungsdaten(Marburg : Philipps-Universität, 2020) Recker, Jonas; Helbig, Kerstin; Neumann, JannaDie DINI/nestor-AG Forschungsdaten führte am 5. März 2020 einen Workshop zum Thema Zertifizierung von Forschungsdatenrepositorien[1] an der Universitätsbibliothek Leipzig durch. Motiviert war die Veranstaltung durch den Wunsch, Teilnehmer*innen einen Überblick über relevante Zertifizierungsverfahren zu geben und Vorteile einer Zertifizierung herauszustellen. Gleichzeitig diente die Veranstaltung dem Austausch über Anforderungen und Unterstützungsbedarfe seitens der Repositorien. Trotz einer Reihe von Corona-bedingten Absagen und Vortragsausfällen verfolgten insgesamt 50 Teilnehmende die Vorträge und nahmen an der lebhaften Breakoutsession teil. Dieser Beitrag bereitet die Informationen und Anregungen aus den Diskussionen auf und skizziert erste Lösungsansätze zum Abbau identifizierter Hürden.
- ItemPersistent identification of instruments(London : Ubiquity Press, 2020) Stocker, M.; Darroch, L.; Krahl, R.; Habermann, T.; Devaraju, A.; Schwardmann, U.; D’onofrio, C.; Häggström, I.Instruments play an essential role in creating research data. Given the importance of instruments and associated metadata to the assessment of data quality and data reuse, globally unique, persistent and resolvable identification of instruments is crucial. The Research Data Alliance Working Group Persistent Identification of Instruments (PIDINST) developed a community-driven solution for persistent identification of instruments which we present and discuss in this paper. Based on an analysis of 10 use cases, PIDINST developed a metadata schema and prototyped schema implementation with DataCite and ePIC as representative persistent identifier infrastructures and with HZB (Helmholtz-Zentrum Berlin für Materialien und Energie) and BODC (British Oceanographic Data Centre) as representative institutional instrument providers. These implementations demonstrate the viability of the proposed solution in practice. Moving forward, PIDINST will further catalyse adoption and consolidate the schema by addressing new stakeholder requirements.
- ItemFAIR Convergence Matrix: Optimizing the Reuse of Existing FAIR-Related Resources(Cambridge, MA : MIT Press, 2020) Sustkova, Hana Pergl; Hettne, Kristina Maria; Wittenburg, Peter; Jacobsen, Annika; Kuhn, Tobias; Pergl, Robert; Slifka, Jan; McQuilton, Peter; Magagna, Barbara; Sansone, Susanna-Assunta; Stocker, Markus; Imming, Melanie; Lannom, Larry; Musen, Mark; Schultes, ErikThe FAIR principles articulate the behaviors expected from digital artifacts that are Findable, Accessible, Interoperable and Reusable by machines and by people. Although by now widely accepted, the FAIR Principles by design do not explicitly consider actual implementation choices enabling FAIR behaviors. As different communities have their own, often well-established implementation preferences and priorities for data reuse, coordinating a broadly accepted, widely used FAIR implementation approach remains a global challenge. In an effort to accelerate broad community convergence on FAIR implementation options, the GO FAIR community has launched the development of the FAIR Convergence Matrix. The Matrix is a platform that compiles for any community of practice, an inventory of their self-declared FAIR implementation choices and challenges. The Convergence Matrix is itself a FAIR resource, openly available, and encourages voluntary participation by any self-identified community of practice (not only the GO FAIR Implementation Networks). Based on patterns of use and reuse of existing resources, the Convergence Matrix supports the transparent derivation of strategies that optimally coordinate convergence on standards and technologies in the emerging Internet of FAIR Data and Services.
- ItemGraf, Dorothee; Fadeeva, Yuliya; Falkenstein-Feldhoff, Katrin (Hrsg.): Bücher im Open Access: ein Zukunftsmodell für die Geistes- und Sozialwissenschaften? Opladen: Verlag Barbara Budrich, 2020. ISBN: 978-3-8474-2460-4, Paperback, 39,90 €. Auch Open Access: https://doi.org/10.17185/duepublico/72237(Berlin ; New York : de Gruyter, 2021) Schmeja, StefanRezension zu Graf, Dorothee; Fadeeva, Yuliya; Falkenstein-Feldhoff, Katrin (Hrsg.): Bücher im Open Access: ein Zukunftsmodell für die Geistes- und Sozialwissenschaften? Opladen: Verlag Barbara Budrich, 2020. ISBN: 978-3-8474-2460-4
- ItemScientific publishing sanctions in response to the Russo-Ukrainian war(Chichester : Wiley, 2022) Nazarovets, Maryna; Teixeira da Silva, Jaime A.The Russian invasion of Ukraine is negatively affecting the development of the Ukrainian academy, now and in the foreseeable future. Different academic stakeholders around the world have reacted differently to this war, some imposing sanctions against Russia and/or providing aid to Ukraine. Some scientific publishers have partially or temporarily suspended sales and marketing of products and services to research organizations in Russia and Belarus. The issue of banning publication in international journals by authors from Russian institutions remains controversial and needs to be carefully considered by various stakeholders. © 2022 The Authors. Learned Publishing published by John Wiley & Sons Ltd on behalf of ALPSP
- ItemSentence, Phrase, and Triple Annotations to Build a Knowledge Graph of Natural Language Processing Contributions - A Trial Dataset(Beijing : National Science Library, Chinese Academy of Sciences, 2021) D’Souza, Jennifer; Auer, SörenThis work aims to normalize the NlpContributions scheme (henceforward, NlpContributionGraph) to structure, directly from article sentences, the contributions information in Natural Language Processing (NLP) scholarly articles via a two-stage annotation methodology: 1) pilot stage—to define the scheme (described in prior work); and 2) adjudication stage—to normalize the graphing model (the focus of this paper). We re-annotate, a second time, the contributions-pertinent information across 50 prior-annotated NLP scholarly articles in terms of a data pipeline comprising: contribution-centered sentences, phrases, and triple statements. To this end, specifically, care was taken in the adjudication annotation stage to reduce annotation noise while formulating the guidelines for our proposed novel NLP contributions structuring and graphing scheme. The application of NlpContributionGraph on the 50 articles resulted finally in a dataset of 900 contribution-focused sentences, 4,702 contribution-information-centered phrases, and 2,980 surface-structured triples. The intra-annotation agreement between the first and second stages, in terms of F1-score, was 67.92% for sentences, 41.82% for phrases, and 22.31% for triple statements indicating that with increased granularity of the information, the annotation decision variance is greater. NlpContributionGraph has limited scope for structuring scholarly contributions compared with STEM (Science, Technology, Engineering, and Medicine) scholarly knowledge at large. Further, the annotation scheme in this work is designed by only an intra-annotator consensus—a single annotator first annotated the data to propose the initial scheme, following which, the same annotator reannotated the data to normalize the annotations in an adjudication stage. However, the expected goal of this work is to achieve a standardized retrospective model of capturing NLP contributions from scholarly articles. This would entail a larger initiative of enlisting multiple annotators to accommodate different worldviews into a “single” set of structures and relationships as the final scheme. Given that the initial scheme is first proposed and the complexity of the annotation task in the realistic timeframe, our intra-annotation procedure is well-suited. Nevertheless, the model proposed in this work is presently limited since it does not incorporate multiple annotator worldviews. This is planned as future work to produce a robust model. We demonstrate NlpContributionGraph data integrated into the Open Research Knowledge Graph (ORKG), a next-generation KG-based digital library with intelligent computations enabled over structured scholarly knowledge, as a viable aid to assist researchers in their day-to-day tasks. NlpContributionGraph is a novel scheme to annotate research contributions from NLP articles and integrate them in a knowledge graph, which to the best of our knowledge does not exist in the community. Furthermore, our quantitative evaluations over the two-stage annotation tasks offer insights into task difficulty.
- Item“You say potato, I say potato” Mapping Digital Preservation and Research Data Management Concepts towards Collective Curation and Preservation Strategies(Bath : Digital Curation Centre, 2020) Lindlar, Michelle; Rudnik, Pia; Jones, Sarah; Horton, LaurenceThis paper explores models, concepts and terminology used in the Research Data Management and Digital Preservation communities. In doing so we identify several overlaps and mutual concerns where the advancements of one professional field can apply to and assist another. By focusing on what unites rather than divides us, and by adopting a more holistic approach we advance towards collective curation and preservation strategies.
- ItemCharacterization and classification of semantic image-text relations(Berlin : Springer Nature, 2020) Otto, C.; Springstein, M.; Anand, A.; Ewerth, R.The beneficial, complementary nature of visual and textual information to convey information is widely known, for example, in entertainment, news, advertisements, science, or education. While the complex interplay of image and text to form semantic meaning has been thoroughly studied in linguistics and communication sciences for several decades, computer vision and multimedia research remained on the surface of the problem more or less. An exception is previous work that introduced the two metrics Cross-Modal Mutual Information and Semantic Correlation in order to model complex image-text relations. In this paper, we motivate the necessity of an additional metric called Status in order to cover complex image-text relations more completely. This set of metrics enables us to derive a novel categorization of eight semantic image-text classes based on three dimensions. In addition, we demonstrate how to automatically gather and augment a dataset for these classes from the Web. Further, we present a deep learning system to automatically predict either of the three metrics, as well as a system to directly predict the eight image-text classes. Experimental results show the feasibility of the approach, whereby the predict-all approach outperforms the cascaded approach of the metric classifiers.