Browsing by Author "Sens, Irina"
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- Item3D@Technische Informationsbibliothek (TIB) : das Projekt PROBADO zeigt effiziente Möglichkeiten zur Suche in 3D-Daten. In: Web Science - die Zukunft des Internets(Hannover : Leibniz Universität Hannover, 2012) Blümel, Ina; Sens, Irinaimmer mehr digitale Wissensobjekte finden Eingang in Bibliotheken. innerhalb der Future Internet Initiative ist die TIB Mitglied der Arbeitsgruppe Internet of Contents, die Fragestellungen rund um die Erschließung und Nutzung von nichttextuellen, insbesondere audiovisuellen Daten in den Blick nimmt. Am Beispiel des Projekts PROBADO zeigen die Autorinnen, wie 3D-Modelle in die bibliothekarische Prozesskette integriert werden können.
- ItemAccess and preservation of digital research content: Linked open data services - A research library perspective(München : European Geosciences Union, 2016) Kraft, Angelina; Sens, Irina; Löwe, Peter; Dreyer, Britta[no abstract available]
- ItemInformationsbeschaffungs- und Publikationsverhalten von Wissenschaftlerinnen und Wissenschaftlern der natur- und ingenieurwissenschaftlichen Fächern : Auswertung einer Umfrage mit Schwerpunkt auf nicht-textuellen Materialien(Hannover : Technische Informationsbibliothek (TIB), 2017) Einbock, Joanna; Dreyer, Britta; Heller, Lambert; Kraft, Angelina; Niemeyer, Sandra; Plank, Margret; Schrenk, Philip; Sens, Irina; Struß, Julia; Tullney, Marco; Bernhofer, Carolin; Häfner, Peter[no abstract available]
- ItemKooperation oder Wettbewerb. Oder: Ist Kooperation der neue Wettbewerb?(Berlin : de Gruyter, 2018) Babion, Michaela; Bähr, Thomas; Sens, IrinaWissenschaftliche Bibliotheken haben eine lange Tradition zu kooperieren und scheinen, wenn man sich die folgenden Beispiele anschaut, prädestiniert für erfolgreiche Kooperationen auf nationaler und internationaler Ebene.
- ItemNFDI4Ing - the National Research Data Infrastructure for Engineering Sciences(Meyrin : CERN, 2020-09-25) Schmitt, Robert H.; Anthofer, Verena; Auer, Sören; Başkaya, Sait; Bischof, Christian; Bronger, Torsten; Claus, Florian; Cordes, Florian; Demandt, Évariste; Eifert, Thomas; Flemisch, Bernd; Fuchs, Matthias; Fuhrmans, Marc; Gerike, Regine; Gerstner, Eva-Maria; Hanke, Vanessa; Heine, Ina; Huebser, Louis; Iglezakis, Dorothea; Jagusch, Gerald; Klinger, Axel; Krafczyk, Manfred; Kraft, Angelina; Kuckertz, Patrick; Küsters, Ulrike; Lachmayer, Roland; Langenbach, Christian; Mozgova, Iryna; Müller, Matthias S.; Nestler, Britta; Pelz, Peter; Politze, Marius; Preuß, Nils; Przybylski-Freund, Marie-Dominique; Rißler-Pipka, Nanette; Robinius, Martin; Schachtner, Joachim; Schlenz, Hartmut; Schwarz, Annett; Schwibs, Jürgen; Selzer, Michael; Sens, Irina; Stäcker, Thomas; Stemmer, Christian; Stille, Wolfgang; Stolten, Detlef; Stotzka, Rainer; Streit, Achim; Strötgen, Robert; Wang, Wei MinNFDI4Ing brings together the engineering communities and fosters the management of engineering research data. The consortium represents engineers from all walks of the profession. It offers a unique method-oriented and user-centred approach in order to make engineering research data FAIR – findable, accessible, interoperable, and re-usable. NFDI4Ing has been founded in 2017. The consortium has actively engaged engineers across all five engineering research areas of the DFG classification. Leading figures have teamed up with experienced infrastructure providers. As one important step, NFDI4Ing has taken on the task of structuring the wealth of concrete needs in research data management. A broad consensus on typical methods and workflows in engineering research has been established: The archetypes. So far, seven archetypes are harmonising the methodological needs: Alex: bespoke experiments with high variability of setups, Betty: engineering research software, Caden: provenance tracking of physical samples & data samples, Doris: high performance measurement & computation, Ellen: extensive and heterogeneous data requirements, Frank: many participants & simultaneous devices, Golo: field data & distributed systems. A survey of the entire engineering research landscape in Germany confirms that the concept of engineering archetypes has been very well received. 95% of the research groups identify themselves with at least one of the NFDI4Ing archetypes. NFDI4Ing plans to further coordinate its engagement along the gateways provided by the DFG classification of engineering research areas. Consequently, NFDI4Ing will support five community clusters. In addition, an overarching task area will provide seven base services to be accessed by both the community clusters and the archetype task areas. Base services address quality assurance & metrics, research software development, terminologies & metadata, repositories & storage, data security & sovereignty, training, and data & knowledge discovery. With the archetype approach, NFDI4Ing’s work programme is modular and distinctly method-oriented. With the community clusters and base services, NFDI4Ing’s work programme remains firmly user-centred and highly integrated. NFDI4Ing has set in place an internal organisational structure that ensures viability, operational efficiency, and openness to new partners during the course of the consortium’s development. NFDI4Ing’s management team brings in the experience from two applicant institutions and from two years of actively engaging with the engineering communities. Eleven applicant institutions and over fifty participants have committed to carrying out NFDI4Ing’s work programme. Moreover, NFDI4Ing’s connectedness with consortia from nearby disciplinary fields is strong. Collaboration on cross-cutting topics is well prepared and foreseen. As a result, NFDI4Ing is ready to join the National Research Data Infrastructure.
- ItemPROBADO 3D - Integration von 3D-Objekten in Digitale Bibliotheken. Ein Dienstleistungsangebot für Architektur und Ingenieurwesen zur Erschließung und Bereitstellung von Multimediadokumenten(2011) Blümel, Ina; Sens, Irina[no abstract available]
- ItemTowards an Open Research Knowledge Graph(Zenodo, 2018) Auer, Sören; Blümel, Ina; Ewerth, Ralph; Garatzogianni, Alexandra; Heller,, Lambert; Hoppe, Anett; Kasprzik, Anna; Koepler, Oliver; Nejdl, Wolfgang; Plank, Margret; Sens, Irina; Stocker, Markus; Tullney, Marco; Vidal, Maria-Esther; van Wezenbeek, WilmaThe document-oriented workflows in science have reached (or already exceeded) the limits of adequacy as highlighted for example by recent discussions on the increasing proliferation of scientific literature and the reproducibility crisis. Despite an improved and digital access to scientific publications in the last decades, the exchange of scholarly knowledge continues to be primarily document-based: Researchers produce essays and articles that are made available in online and offline publication media as roughly granular text documents. With current developments in areas such as knowledge representation, semantic search, human-machine interaction, natural language processing, and artificial intelligence, it is possible to completely rethink this dominant paradigm of document-centered knowledge exchange and transform it into knowledge-based information flows by representing and expressing knowledge through semantically rich, interlinked knowledge graphs. The core of the establishment of knowledge-based information flows is the distributed, decentralized, collaborative creation and evolution of information models, vocabularies, ontologies, and knowledge graphs for the establishment of a common understanding of data and information between the various stakeholders as well as the integration of these technologies into the infrastructure and processes of search and knowledge exchange in the research library of the future. By integrating these information models into existing and new research infrastructure services, the information structures that are currently still implicit and deeply hidden in documents can be made explicit and directly usable. This revolutionizes scientific work because information and research results can be seamlessly interlinked with each other and better mapped to complex information needs. As a result, scientific work becomes more effective and efficient, since results become directly comparable and easier to reuse. In order to realize the vision of knowledge-based information flows in scholarly communication, comprehensive long-term technological infrastructure development and accompanying research are required. To secure information sovereignty, it is also of paramount importance to science – and urgency to science policymakers – that scientific infrastructures establish an open counterweight to emerging commercial developments in this area. The aim of this position paper is to facilitate the discussion on requirements, design decisions and a minimum viable product for an Open Research Knowledge Graph infrastructure. TIB aims to start developing this infrastructure in an open collaboration with interested partner organizations and individuals.
- ItemYear One and Beyond. ChemRxiv(ChemRxiv, 2018) Kidd, Rich; Koch, Wolfram; Milne, James; Sens, Irina; Tegen, Sarah; Wilson, Emma; Henderson, Darla; Brennan, MarshallIn this post, the ChemRxiv Governing Board and Management Team describe the achievements of the past year and detail some of our plans for the future of ChemRxiv.