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  • × author_ss:"Zhang, L."
  • × year_i:[2020 TO 2030}
  1. Zhang, L.; Lu, W.; Yang, J.: LAGOS-AND : a large gold standard dataset for scholarly author name disambiguation (2023) 0.02 
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    Abstract
    In this article, we present a method to automatically build large labeled datasets for the author ambiguity problem in the academic world by leveraging the authoritative academic resources, ORCID and DOI. Using the method, we built LAGOS-AND, two large, gold-standard sub-datasets for author name disambiguation (AND), of which LAGOS-AND-BLOCK is created for clustering-based AND research and LAGOS-AND-PAIRWISE is created for classification-based AND research. Our LAGOS-AND datasets are substantially different from the existing ones. The initial versions of the datasets (v1.0, released in February 2021) include 7.5 M citations authored by 798 K unique authors (LAGOS-AND-BLOCK) and close to 1 M instances (LAGOS-AND-PAIRWISE). And both datasets show close similarities to the whole Microsoft Academic Graph (MAG) across validations of six facets. In building the datasets, we reveal the variation degrees of last names in three literature databases, PubMed, MAG, and Semantic Scholar, by comparing author names hosted to the authors' official last names shown on the ORCID pages. Furthermore, we evaluate several baseline disambiguation methods as well as the MAG's author IDs system on our datasets, and the evaluation helps identify several interesting findings. We hope the datasets and findings will bring new insights for future studies. The code and datasets are publicly available.
    Date
    22. 1.2023 18:40:36
    Type
    a
  2. Zhang, L.: ¬The knowledge organization education within and beyond the master of library and information science (2023) 0.00 
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    Abstract
    By analyzing 63 English-speaking institutions that offer ALA-accredited master's programs in library and information studies, this research aims to explore the education for knowl­edge organization (KO) at different levels and across fields. This research examines the KO courses that are the required courses and elective courses in the MLIS programs, that are offered in other master's programs and graduate certificate programs, that are adapted to the undergraduate degree and certificate programs, and that are particularly developed for programs other than MLIS. The findings indicate that the great majority of MLIS programs still have a focus on or a significant component of knowl­edge organization as their required course and include the knowl­edge organization elective courses, particularly library cataloging and classification, on their curriculum. However, there is a variety of the offerings of KO related courses across the programs in an institution or in the same program across the institutions. It shows a promising trend that the traditional and new KO courses play an important role in many other programs, at different levels and across fields. With the conventional, adapted, or innovative content, these courses demonstrate that the principles and skills of knowl­edge organization are applicable to a wide variety of settings, can be integrated with other disciplinary knowl­edge and emerging technologies, and meet the needs of different career pathways and groups of learners.
    Type
    a
  3. Zhang, L.; Gou, Z.; Fang, Z.; Sivertsen, G.; Huang, Y.: Who tweets scientific publications? : a large-scale study of tweeting audiences in all areas of research (2023) 0.00 
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    Abstract
    The purpose of this study is to investigate the validity of tweets about scientific publications as an indicator of societal impact by measuring the degree to which the publications are tweeted beyond academia. We introduce methods that allow for using a much larger and broader data set than in previous validation studies. It covers all areas of research and includes almost 40 million tweets by 2.5 million unique tweeters mentioning almost 4 million scientific publications. We find that, although half of the tweeters are external to academia, most of the tweets are from within academia, and most of the external tweets are responses to original tweets within academia. Only half of the tweeted publications are tweeted outside of academia. We conclude that, in general, the tweeting of scientific publications is not a valid indicator of the societal impact of research. However, publications that continue being tweeted after a few days represent recent scientific achievements that catch attention in society. These publications occur more often in the health sciences and in the social sciences and humanities.
    Content
    Beitrag in: JASIST special issue on 'Who tweets scientific publications? A large-scale study of tweeting audiences in all areas of research'. Vgl.: https://asistdl.onlinelibrary.wiley.com/doi/10.1002/asi.24830.
    Type
    a
  4. Kulczycki, E.; Huang, Y.; Zuccala, A.A.; Engels, T.C.E.; Ferrara, A.; Guns, R.; Pölönen, J.; Sivertsen, G.; Taskin, Z.; Zhang, L.: Uses of the Journal Impact Factor in national journal rankings in China and Europe (2022) 0.00 
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    Abstract
    This paper investigates different uses of the Journal Impact Factor (JIF) in national journal rankings and discusses the merits of supplementing metrics with expert assessment. Our focus is national journal rankings used as evidence to support decisions about the distribution of institutional funding or career advancement. The seven countries under comparison are China, Denmark, Finland, Italy, Norway, Poland, and Turkey-and the region of Flanders in Belgium. With the exception of Italy, top-tier journals used in national rankings include those classified at the highest level, or according to tier, or points implemented. A total of 3,565 (75.8%) out of 4,701 unique top-tier journals were identified as having a JIF, with 55.7% belonging to the first Journal Impact Factor quartile. Journal rankings in China, Flanders, Poland, and Turkey classify journals with a JIF as being top-tier, but only when they are in the first quartile of the Average Journal Impact Factor Percentile. Journal rankings that result from expert assessment in Denmark, Finland, and Norway regularly classify journals as top-tier outside the first quartile, particularly in the social sciences and humanities. We conclude that experts, when tasked with metric-informed journal rankings, take into account quality dimensions that are not covered by JIFs.
    Type
    a

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