Search (121 results, page 1 of 7)

  • × year_i:[2020 TO 2030}
  • × language_ss:"e"
  1. Candela, G.: ¬An automatic data quality approach to assess semantic data from cultural heritage institutions (2023) 0.05 
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    Date
    22. 6.2023 18:23:31
  2. Geras, A.; Siudem, G.; Gagolewski, M.: Should we introduce a dislike button for academic articles? (2020) 0.04 
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    Date
    6. 1.2020 18:10:22
  3. Li, G.; Siddharth, L.; Luo, J.: Embedding knowledge graph of patent metadata to measure knowledge proximity (2023) 0.04 
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    Date
    22. 3.2023 12:06:55
  4. Wang, S.; Ma, Y.; Mao, J.; Bai, Y.; Liang, Z.; Li, G.: Quantifying scientific breakthroughs by a novel disruption indicator based on knowledge entities : On the rise of scrape-and-report scholarship in online reviews research (2023) 0.03 
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    Date
    22. 1.2023 18:37:33
  5. Yu, C.; Xue, H.; An, L.; Li, G.: ¬A lightweight semantic-enhanced interactive network for efficient short-text matching (2023) 0.03 
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    Date
    22. 1.2023 19:05:27
  6. Cerda-Cosme, R.; Méndez, E.: Analysis of shared research data in Spanish scientific papers about COVID-19 : a first approach (2023) 0.03 
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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
  7. Zhang, Y.; Wu, M.; Zhang, G.; Lu, J.: Stepping beyond your comfort zone : diffusion-based network analytics for knowledge trajectory recommendation (2023) 0.03 
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    Date
    22. 6.2023 18:07:12
  8. Luo, L.; Ju, J.; Li, Y.-F.; Haffari, G.; Xiong, B.; Pan, S.: ChatRule: mining logical rules with large language models for knowledge graph reasoning (2023) 0.03 
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    Date
    23.11.2023 19:07:22
  9. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.03 
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  10. Fugmann, R.: What is information? : an information veteran looks back (2022) 0.02 
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    Date
    18. 8.2022 19:22:57
  11. Butlin, P.; Long, R.; Elmoznino, E.; Bengio, Y.; Birch, J.; Constant, A.; Deane, G.; Fleming, S.M.; Frith, C.; Ji, X.; Kanai, R.; Klein, C.; Lindsay, G.; Michel, M.; Mudrik, L.; Peters, M.A.K.; Schwitzgebel, E.; Simon, J.; VanRullen, R.: Consciousness in artificial intelligence : insights from the science of consciousness (2023) 0.02 
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  12. Wong, K.; Walton, G.; Bailey, G.: Using information science to enhance educational preventing violent extremism programs (2021) 0.01 
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  13. Wiggers, G.; Verberne, S.; Loon, W. van; Zwenne, G.-J.: Bibliometric-enhanced legal information retrieval : combining usage and citations as flavors of impact relevance (2023) 0.01 
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  14. Dunsire, G.; Fritz, D.; Fritz, R.: Instructions, interfaces, and interoperable data : the RIMMF experience with RDA revisited (2020) 0.01 
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  15. Oh, D.-G.: Korean Decimal Classification (KDC) : its history, development, characteristics, and future prospect (2021) 0.01 
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  16. Holley, R.M.; Joudrey, D.N.: Aboutness and conceptual analysis : a review (2021) 0.01 
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    Abstract
    The purpose of this paper is to provide an overview of aboutness and conceptual analysis, essential concepts for LIS practitioners to understand. Aboutness refers to the subject matter and genre/form properties of a resource. It is identified during conceptual analysis, which yields an aboutness statement, a summary of a resource's aboutness. While few scholars have discussed the aboutness determination process in detail, the methods described by Patrick Wilson, D.W. Langridge, Arlene G. Taylor, and Daniel N. Joudrey provide exemplary frameworks for determining aboutness and are presented here. Discussions of how to construct an aboutness statement and the challenges associated with aboutness determination follow.
  17. Heng, G.; Cole, T.W.; Tian, T.(C.); Han, M.-J.: Rethinking authority reconciliation process (2022) 0.01 
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  18. Zhu, L.; Xu, A.; Deng, S.; Heng, G.; Li, X.: Entity management using Wikidata for cultural heritage information (2024) 0.01 
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  19. Morris, V.: Automated language identification of bibliographic resources (2020) 0.01 
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