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  • × year_i:[2020 TO 2030}
  • × type_ss:"a"
  1. Rötzer, F.; Engel, B.: Corona-App : Datenspende mit langer Vorgeschichte (2020) 0.02 
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    Abstract
    Öffentlich war bisher zur Bewältigung der Corona-Krise nur eine Tracing App angekündigt worden. Letzte Woche dann überraschte das Robert-Koch-Institut mit einer App, die Gesundheitsdaten an die Bundesbehörde übermitteln soll. Geplant waren sogenannte Datenspenden seit Langem.
  2. Reichmann, G.; Schlögl, C.: Möglichkeiten zur Steuerung der Ergebnisse einer Forschungsevaluation (2021) 0.01 
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    Abstract
    Ein Leistungsvergleich zwischen den (ehemaligen) Instituten für Informationswissenschaft der Universitäten Düsseldorf und Graz auf Basis der Forschungsleistung für einen Zeitraum von zehn Jahren zeigt, wie sehr die Ergebnisse einer Forschungsevaluation gesteuert werden können. Durch die Wahl "geeigneter" Indikatoren gelingt es - je nach Wunsch - entweder das eine oder das andere Institut an die erste Stelle zu bringen. Hält man sich dagegen an das wissenschaftliche Gebot der Unparteilichkeit, führt dies im hier gezeigten Anwendungsbeispiel zu gemischten Ergebnissen.
  3. Eyert, F.: Mathematische Wissenschaftskommunikation in der digitalen Gesellschaft (2023) 0.01 
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    Source
    Mitteilungen der Deutschen Mathematiker-Vereinigung. 2023, H.1, S.22-25
  4. Colombi, C.: Bibliothekarische Fachsystematiken am Deutschen Archäologischen Institut : 180 Jahre Wissensordnung (2023) 0.01 
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  5. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.01 
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  6. Brettschneider, P.: Urheberrecht (2023) 0.01 
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    Abstract
    In einer digitalen Wissensgesellschaft kommt dem Urheberrecht eine herausragende Bedeutung zu. Es ist - wie auch das Datenschutzrecht (s. Kapitel F 4 Informationelle Selbstbestimmung und Informationsfreiheit) - Kernbestandteil eines Kommunikationsrechts, das die Weitergabe und Nutzung von Informationen regelt. Daher mag es paradox klingen, dass das Urheberrecht Informationen - wie auch Ideen oder (wissenschaftliche) Theorien - überhaupt nicht schützt.1 Vielmehr greift sein Schutz erst, wenn sich Information z. B. in Form eines gedruckten oder digitalen Textes oder auch einer Rede manifestiert. Dieser richtet sich dann aber nicht auf den Informationsgehalt an sich, sondern seine Verkörperung in Form eines originellen und individuellen Werkes (s. Abschnitt 2 und 3).
    Imprint
    Berlin : DeGruyter
  7. Ockenfeld, M.: Informationswissenschaft - Bandbreite und Ausrichtungen in der DACH-Region (2020) 0.01 
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    Abstract
    Auf Initiative von Vivian Petras (Humboldt-Universität zu Berlin) als Gastgeberin und Dirk Lewandowski (Hochschule für Angewandte Wissenschaften Hamburg) veranstaltete der Hochschulverband Informationswissenschaft am 3. Februar 2020 einen Workshop in den Räumen des Instituts für Bibliotheks- und Informationswissenschaft (IBI) in Berlin. Anliegen dieses und gleichartiger künftig geplanter Treffen ist es, Standorte informationswissenschaftlicher Forschung und Lehre zu vernetzen und sich besser kennen zu lernen. Angesichts zahlreicher Emeritierungen und Neuberufungen an den Hochschulen sowie dem Generationenwechsel in außeruniversitären Einrichtungen der Forschungsinfrastruktur ist dies, wie die Beteiligung von etwa 60 Personen bewies, eine von vielen begrüßte Initiative. Der Hochschulverband Informationswissenschaft (HI) versteht sich nicht - wie der Name suggerieren könnte - als Professorenbund, erklärte Lewandowski bei der Einführung, sondern als Zusammenschluss aller, die in Wissenschaft und Forschung in der Informationswissenschaft tätig sind. Zu diesem ersten Workshop waren insgesamt sieben Vorträge - aus der KIBA, zwei Universitäten, zwei Hochschulen sowie zwei außeruniversitären Einrichtungen - eingeladen.
    Footnote
    Bericht über einen Workshop des Hochschulverbandes Informationswissenschaft am 3. Februar 2020 in den Räumen des Instituts für Bibliotheks- und Informationswissenschaft (IBI) in Berlin.
  8. Ibrahim, G.M.; Taylor, M.: Krebszellen manipulieren Neurone : Gliome (2023) 0.01 
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    Source
    Spektrum der Wissenschaft. 2023, H.10, S.22-24
  9. Rieger, F.: Lügende Computer (2023) 0.01 
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    Date
    16. 3.2023 19:22:55
  10. Menzel, S.; Schnaitter, H.; Zinck, J.; Petras, V.; Neudecker, C.; Labusch, K.; Leitner, E.; Rehm, G.: Named Entity Linking mit Wikidata und GND : das Potenzial handkuratierter und strukturierter Datenquellen für die semantische Anreicherung von Volltexten (2021) 0.01 
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    Abstract
    Named Entities (benannte Entitäten) - wie Personen, Organisationen, Orte, Ereignisse und Werke - sind wichtige inhaltstragende Komponenten eines Dokuments und sind daher maßgeblich für eine gute inhaltliche Erschließung. Die Erkennung von Named Entities, deren Auszeichnung (Annotation) und Verfügbarmachung für die Suche sind wichtige Instrumente, um Anwendungen wie z. B. die inhaltliche oder semantische Suche in Texten, dokumentübergreifende Kontextualisierung oder das automatische Textzusammenfassen zu verbessern. Inhaltlich präzise und nachhaltig erschlossen werden die erkannten Named Entities eines Dokuments allerdings erst, wenn sie mit einer oder mehreren Quellen verknüpft werden (Grundprinzip von Linked Data, Berners-Lee 2006), die die Entität eindeutig identifizieren und gegenüber gleichlautenden Entitäten disambiguieren (vergleiche z. B. Berlin als Hauptstadt Deutschlands mit dem Komponisten Irving Berlin). Dazu wird die im Dokument erkannte Entität mit dem Entitätseintrag einer Normdatei oder einer anderen zuvor festgelegten Wissensbasis (z. B. Gazetteer für geografische Entitäten) verknüpft, gewöhnlich über den persistenten Identifikator der jeweiligen Wissensbasis oder Normdatei. Durch die Verknüpfung mit einer Normdatei erfolgt nicht nur die Disambiguierung und Identifikation der Entität, sondern es wird dadurch auch Interoperabilität zu anderen Systemen hergestellt, in denen die gleiche Normdatei benutzt wird, z. B. die Suche nach der Hauptstadt Berlin in verschiedenen Datenbanken bzw. Portalen. Die Entitätenverknüpfung (Named Entity Linking, NEL) hat zudem den Vorteil, dass die Normdateien oftmals Relationen zwischen Entitäten enthalten, sodass Dokumente, in denen Named Entities erkannt wurden, zusätzlich auch im Kontext einer größeren Netzwerkstruktur von Entitäten verortet und suchbar gemacht werden können
    Source
    Qualität in der Inhaltserschließung. Hrsg.: M. Franke-Maier, u.a
  11. Siegel, M.: Maschinelle Übersetzung (2023) 0.01 
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    Imprint
    Berlin : DeGruyter
  12. Bischoff, M.: KI lernt die Sprache der Mathematik (2020) 0.01 
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    Content
    Verweis auf: Lample, G., Charton, F.: Deep learning for symbolic mathematics. ArXiv: 11912.01412, 2019.
  13. Kleineberg, M.: Klassifikation (2023) 0.01 
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    Berlin : DeGruyter
  14. Eibl, M.; Haupt, J.; Kahl, S.; Taubert, S.; Wilhelm-Stein, T.: Audio- und Musik-Retrieval (2023) 0.01 
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  15. Michel, A.; Gäde, M.; Wittich, A.; Tappenbeck, I.: Informationsdidaktik (2023) 0.00 
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  16. Yang, F.; Zhang, X.: Focal fields in literature on the information divide : the USA, China, UK and India (2020) 0.00 
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    Date
    13. 2.2020 18:22:13
  17. Cerda-Cosme, R.; Méndez, E.: Analysis of shared research data in Spanish scientific papers about COVID-19 : a first approach (2023) 0.00 
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    Abstract
    During the coronavirus pandemic, changes in the way science is done and shared occurred, which motivates meta-research to help understand science communication in crises and improve its effectiveness. The objective is to study how many Spanish scientific papers on COVID-19 published during 2020 share their research data. Qualitative and descriptive study applying nine attributes: (a) availability, (b) accessibility, (c) format, (d) licensing, (e) linkage, (f) funding, (g) editorial policy, (h) content, and (i) statistics. We analyzed 1,340 papers, 1,173 (87.5%) did not have research data. A total of 12.5% share their research data of which 2.1% share their data in repositories, 5% share their data through a simple request, 0.2% do not have permission to share their data, and 5.2% share their data as supplementary material. There is a small percentage that shares their research data; however, it demonstrates the researchers' poor knowledge on how to properly share their research data and their lack of knowledge on what is research data.
    Date
    21. 3.2023 19:22:02
  18. Guo, T.; Bai, X.; Zhen, S.; Abid, S.; Xia, F.: Lost at starting line : predicting maladaptation of university freshmen based on educational big data (2023) 0.00 
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    Date
    27.12.2022 18:34:22
  19. Dogtas, G.; Ibitz, M.-P.; Jonitz, F.; Kocher, V.; Poyer, A.,; Stapf, L.: Kritik an rassifizierenden und diskriminierenden Titeln und Metadaten : Praxisorientierte Lösungsansätze (2022) 0.00 
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  20. Geras, A.; Siudem, G.; Gagolewski, M.: Should we introduce a dislike button for academic articles? (2020) 0.00 
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    Date
    6. 1.2020 18:10:22

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