-
Thelwall, M.; Kousha, K.; Abdoli, M.; Stuart, E.; Makita, M.; Wilson, P.; Levitt, J.: Why are coauthored academic articles more cited : higher quality or larger audience? (2023)
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- Date
- 22. 6.2023 18:11:50
-
Vakkari, P.; Järvelin, K.; Chang, Y.-W.: ¬The association of disciplinary background with the evolution of topics and methods in Library and Information Science research 1995-2015 (2023)
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- Date
- 22. 6.2023 18:15:06
-
Zhou, H.; Dong, K.; Xia, Y.: Knowledge inheritance in disciplines : quantifying the successive and distant reuse of references (2023)
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- Abstract
- How the knowledge base of disciplines grows, renews, and decays informs their distinct characteristics and epistemology. Here we track the evolution of knowledge bases of 19 disciplines for over 45 years. We introduce the notation of knowledge inheritance as the overlap in the set of references between years. We discuss two modes of knowledge inheritance of disciplines-successive and distant. To quantify the status and propensity of knowledge inheritance for disciplines, we propose two indicators: one descriptively describes knowledge base evolution, and one estimates the propensity of knowledge inheritance. When observing the continuity in knowledge bases for disciplines, we show distinct patterns for STEM and SS&H disciplines: the former inherits knowledge bases more successively, yet the latter inherits significantly from distant knowledge bases. We further discover stagnation or revival in knowledge base evolution where older knowledge base ceases to decay after 10 years (e.g., Physics and Mathematics) and are increasingly reused (e.g., Philosophy). Regarding the propensity of inheriting prior knowledge bases, we observe unanimous rises in both successive and distant knowledge inheritance. We show that knowledge inheritance could reveal disciplinary characteristics regarding the trajectory of knowledge base evolution and interesting insights into the metabolism and maturity of scholarly communication.
-
Cerda-Cosme, R.; Méndez, E.: Analysis of shared research data in Spanish scientific papers about COVID-19 : a first approach (2023)
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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
-
Krattenthaler, C.: Was der h-Index wirklich aussagt (2021)
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- Abstract
- Diese Note legt dar, dass der sogenannte h-Index (Hirschs bibliometrischer Index) im Wesentlichen dieselbe Information wiedergibt wie die Gesamtanzahl von Zitationen von Publikationen einer Autorin oder eines Autors, also ein nutzloser bibliometrischer Index ist. Dies basiert auf einem faszinierenden Satz der Wahrscheinlichkeitstheorie, der hier ebenfalls erläutert wird.
- Content
- Vgl.: DOI: 10.1515/dmvm-2021-0050. Auch abgedruckt u.d.T.: 'Der h-Index - "ein nutzloser bibliometrischer Index"' in Open Password Nr. 1007 vom 06.12.2021 unter: https://www.password-online.de/?mailpoet_router&endpoint=view_in_browser&action=view&data=WzM3NCwiZDI3MzMzOTEwMzUzIiwwLDAsMzQ4LDFd.
- Object
- h-index
- Source
- Mitteilungen der Deutschen Mathematiker-Vereinigung. 2021, H.3, S.124-128
-
Manley, S.: Letters to the editor and the race for publication metrics (2022)
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- Date
- 6. 4.2022 19:22:26
-
Yan, E.; Chen, Z.; Li, K.: Authors' status and the perceived quality of their work : measuring citation sentiment change in nobel articles (2020)
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Wu, C.; Yan, E.; Zhu, Y.; Li, K.: Gender imbalance in the productivity of funded projects : a study of the outputs of National Institutes of Health R01 grants (2021)
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-
Springer, M.: Erlahmt die Forschung? (2023)
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- Source
- Spektrum der Wissenschaft. 2023, H.3, S.30
-
Lorentzen, D.G.: Bridging polarised Twitter discussions : the interactions of the users in the middle (2021)
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- Date
- 20. 1.2015 18:30:22
-
Milard, B.; Pitarch, Y.: Egocentric cocitation networks and scientific papers destinies (2023)
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- Date
- 21. 3.2023 19:22:14
-
Ikae, C.; Savoy, J.: Gender identification on Twitter (2022)
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- Abstract
- To determine the author of a text's gender, various feature types have been suggested (e.g., function words, n-gram of letters, etc.) leading to a huge number of stylistic markers. To determine the target category, different machine learning models have been suggested (e.g., logistic regression, decision tree, k nearest-neighbors, support vector machine, naïve Bayes, neural networks, and random forest). In this study, our first objective is to know whether or not the same model always proposes the best effectiveness when considering similar corpora under the same conditions. Thus, based on 7 CLEF-PAN collections, this study analyzes the effectiveness of 10 different classifiers. Our second aim is to propose a 2-stage feature selection to reduce the feature size to a few hundred terms without any significant change in the performance level compared to approaches using all the attributes (increase of around 5% after applying the proposed feature selection). Based on our experiments, neural network or random forest tend, on average, to produce the highest effectiveness. Moreover, empirical evidence indicates that reducing the feature set size to around 300 without penalizing the effectiveness is possible. Finally, based on such reduced feature sizes, an analysis reveals some of the specific terms that clearly discriminate between the 2 genders.
-
Thelwall, M.; Kousha, K.; Abdoli, M.; Stuart, E.; Makita, M.; Wilson, P.; Levitt, J.: Do altmetric scores reflect article quality? : evidence from the UK Research Excellence Framework 2021 (2023)
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-
Järvelin, K.; Vakkari, P.: LIS research across 50 years: content analysis of journal articles : offering an information-centric conception of memes (2022)
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-
Thelwall, M.; Kousha, K.; Stuart, E.; Makita, M.; Abdoli, M.; Wilson, P.; Levitt, J.: In which fields are citations indicators of research quality? (2023)
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-
Delgado-Quirós, L.; Aguillo, I.F.; Martín-Martín, A.; López-Cózar, E.D.; Orduña-Malea, E.; Ortega, J.L.: Why are these publications missing? : uncovering the reasons behind the exclusion of documents in free-access scholarly databases (2024)
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- Abstract
- This study analyses the coverage of seven free-access bibliographic databases (Crossref, Dimensions-non-subscription version, Google Scholar, Lens, Microsoft Academic, Scilit, and Semantic Scholar) to identify the potential reasons that might cause the exclusion of scholarly documents and how they could influence coverage. To do this, 116 k randomly selected bibliographic records from Crossref were used as a baseline. API endpoints and web scraping were used to query each database. The results show that coverage differences are mainly caused by the way each service builds their databases. While classic bibliographic databases ingest almost the exact same content from Crossref (Lens and Scilit miss 0.1% and 0.2% of the records, respectively), academic search engines present lower coverage (Google Scholar does not find: 9.8%, Semantic Scholar: 10%, and Microsoft Academic: 12%). Coverage differences are mainly attributed to external factors, such as web accessibility and robot exclusion policies (39.2%-46%), and internal requirements that exclude secondary content (6.5%-11.6%). In the case of Dimensions, the only classic bibliographic database with the lowest coverage (7.6%), internal selection criteria such as the indexation of full books instead of book chapters (65%) and the exclusion of secondary content (15%) are the main motives of missing publications.
-
Dederke, J.; Hirschmann, B.; Johann, D.: ¬Der Data Citation Index von Clarivate : Eine wertvolle Ressource für die Forschung und für Bibliotheken? (2022)
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- Source
- B.I.T. Online. 25(2022) H.1, S.21-
-
Thelwall, M.; Thelwall, S.: ¬A thematic analysis of highly retweeted early COVID-19 tweets : consensus, information, dissent and lockdown life (2020)
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- Date
- 20. 1.2015 18:30:22
-
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)
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Asubiaro, T.V.; Onaolapo, S.: ¬A comparative study of the coverage of African journals in Web of Science, Scopus, and CrossRef (2023)
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