2022, Plank, Margret, Krause, Noreen, Beutnagel, Britta

[No abstract available]

2020, Recker, Jonas, Helbig, Kerstin, Neumann, Janna

Die 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.

2022, Peng, Ge, Lacagnina, Carlo, Downs, Robert R., Ganske, Anette, Ramapriyan, Hampapuram K., Ivánová, Ivana, Wyborn, Lesley, Jones, Dave, Bastin, Lucy, Shie, Chung-lin, Moroni, David F.

Open-source science builds on open and free resources that include data, metadata, software, and workflows. Informed decisions on whether and how to (re)use digital datasets are dependent on an understanding about the quality of the underpinning data and relevant information. However, quality information, being difficult to curate and often context specific, is currently not readily available for sharing within and across disciplines. To help address this challenge and promote the creation and (re)use of freely and openly shared information about the quality of individual datasets, members of several groups around the world have undertaken an effort to develop international community guidelines with practical recommendations for the Earth science community, collaborating with international domain experts. The guidelines were inspired by the guiding principles of being findable, accessible, interoperable, and reusable (FAIR). Use of the FAIR dataset quality information guidelines is intended to help stakeholders, such as scientific data centers, digital data repositories, and producers, publishers, stewards and managers of data, to: i) capture, describe, and represent quality information of their datasets in a manner that is consistent with the FAIR Guiding Principles; ii) allow for the maximum discovery, trust, sharing, and reuse of their datasets; and iii) enable international access to and integration of dataset quality information. This article describes the processes that developed the guidelines that are aligned with the FAIR principles, presents a generic quality assessment workflow, describes the guidelines for preparing and disseminating dataset quality information, and outlines a path forward to improve their disciplinary diversity.

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, Erik

The 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.

2021, Burger, Marleen, Cordts, Anette, Habermann, Ted

Im 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.

2022, Tavakoli, Mohammadreza, Faraji, Abdolali, Vrolijk, Jarno, Molavi, Mohammadreza, Mol, Stefan T., Kismihók, Gábor

Attaining those skills that match labor market demand is getting increasingly complicated, not in the last place in engineering education, as prerequisite knowledge, skills, and abilities are evolving dynamically through an uncontrollable and seemingly unpredictable process. Anticipating and addressing such dynamism is a fundamental challenge to twenty-first century education. The burgeoning availability of data, not only on the demand side but also on the supply side (in the form of open educational resources) coupled with smart technologies, may provide a fertile ground for addressing this challenge. In this paper, we propose a novel, Artificial Intelligence (AI) driven approach to the development of an open, personalized, and labor market oriented learning recommender system, called eDoer. We discuss the complete system development cycle starting with a systematic user requirements gathering, and followed by system design, implementation, and validation. Our recommender prototype (1) derives the skill requirements for particular occupations through an analysis of online job vacancy announcements

2022, Franken, Julian, Birukou, Aliaksandr, Eckert, Kai, Fahl, Wolfgang, Hauschke, Christian, Lange, Christoph

Persistent identification of entities plays a major role in the progress of digitization of many fields. In the scholarly publishing realm there are already persistent identifiers (PID) for papers (DOI), people (ORCID), organisation (GRID, ROR), books (ISBN) but there is no generally accepted PID system for scholarly events such as conferences or workshops yet. This article describes the relevant use cases that motivate the introduction of persistent identifiers for conferences. The use cases were mainly derived from interviews, discussions with experts and their previous work. As primary stakeholders who are involved in the typical conference event life cycle researchers, conference organizers, and data consumers were identified. The resulting list of use cases illustrates how PIDs for conference events will improve the current situation for these stakeholders and help with problems they are facing today.

2021, Chamanara, Javad, Gaikwad, Jitendra, Gerlach, Roman, Algergawy, Alsayed, Ostrowski, Andreas, König-Ries, Birgitta

Obtaining fit-to-use data associated with diverse aspects of biodiversity, ecology and environment is challenging since often it is fragmented, sub-optimally managed and available in heterogeneous formats. Recently, with the universal acceptance of the FAIR data principles, the requirements and standards of data publications have changed substantially. Researchers are encouraged to manage the data as per the FAIR data principles and ensure that the raw data, metadata, processed data, software, codes and associated material are securely stored and the data be made available with the completion of the research.

2021, Peng, Ge, Downs, Robert R., Lacagnina, Carlo, Ramapriyan, Hampapuram, Ivánová, Ivana, Moroni, David, Wei, Yaxing, Larnicol, Gilles, Wyborn, Lesley, Goldberg, Mitch, Schulz, Jörg, Bastrakova, Irina, Ganske, Anette, Bastin, Lucy, Khalsa, Siri Jodha S., Wu, Mingfang, Shie, Chung-Lin, Ritchey, Nancy, Jones, Dave, Habermann, Ted, Lief, Christina, Maggio, Iolanda, Albani, Mirko, Stall, Shelley, Zhou, Lihang, Drévillon, Marie, Champion, Sarah, Hou, C. Sophie, Doblas-Reyes, Francisco, Lehnert, Kerstin, Robinson, Erin, Bugbee, Kaylin

Knowledge about the quality of data and metadata is important to support informed decisions on the (re)use of individual datasets and is an essential part of the ecosystem that supports open science. Quality assessments reflect the reliability and usability of data. They need to be consistently curated, fully traceable, and adequately documented, as these are crucial for sound decision- and policy-making efforts that rely on data. Quality assessments also need to be consistently represented and readily integrated across systems and tools to allow for improved sharing of information on quality at the dataset level for individual quality attribute or dimension. Although the need for assessing the quality of data and associated information is well recognized, methodologies for an evaluation framework and presentation of resultant quality information to end users may not have been comprehensively addressed within and across disciplines. Global interdisciplinary domain experts have come together to systematically explore needs, challenges and impacts of consistently curating and representing quality information through the entire lifecycle of a dataset. This paper describes the findings of that effort, argues the importance of sharing dataset quality information, calls for community action to develop practical guidelines, and outlines community recommendations for developing such guidelines. Practical guidelines will allow for global access to and harmonization of quality information at the level of individual Earth science datasets, which in turn will support open science.

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.