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Song, M.; Kim, S.Y.; Zhang, G.; Ding, Y.; Chambers, T.: Productivity and influence in bioinformatics : a bibliometric analysis using PubMed central (2014)
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- Date
- 29. 1.2014 16:40:41
- Field
- Medizin
-
Ding, Y.: Scholarly communication and bibliometrics : Part 1: The scholarly communication model: literature review (1998)
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- Source
- International forum on information and documentation. 23(1998) no.2, S.20-29
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Ding, Y.: ¬A review of ontologies with the Semantic Web in view (2001)
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- Date
- 4. 7.2006 17:05:19
-
Ding, Y.: Applying weighted PageRank to author citation networks (2011)
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- Date
- 22. 1.2011 13:02:21
-
Ding, Y.; Zhang, G.; Chambers, T.; Song, M.; Wang, X.; Zhai, C.: Content-based citation analysis : the next generation of citation analysis (2014)
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- Date
- 22. 8.2014 16:52:04
-
Min, C.; Ding, Y.; Li, J.; Bu, Y.; Pei, L.; Sun, J.: Innovation or imitation : the diffusion of citations (2018)
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- Date
- 29. 9.2018 13:24:10
-
Liu, M.; Bu, Y.; Chen, C.; Xu, J.; Li, D.; Leng, Y.; Freeman, R.B.; Meyer, E.T.; Yoon, W.; Sung, M.; Jeong, M.; Lee, J.; Kang, J.; Min, C.; Zhai, Y.; Song, M.; Ding, Y.: Pandemics are catalysts of scientific novelty : evidence from COVID-19 (2022)
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- Abstract
- Scientific novelty drives the efforts to invent new vaccines and solutions during the pandemic. First-time collaboration and international collaboration are two pivotal channels to expand teams' search activities for a broader scope of resources required to address the global challenge, which might facilitate the generation of novel ideas. Our analysis of 98,981 coronavirus papers suggests that scientific novelty measured by the BioBERT model that is pretrained on 29 million PubMed articles, and first-time collaboration increased after the outbreak of COVID-19, and international collaboration witnessed a sudden decrease. During COVID-19, papers with more first-time collaboration were found to be more novel and international collaboration did not hamper novelty as it had done in the normal periods. The findings suggest the necessity of reaching out for distant resources and the importance of maintaining a collaborative scientific community beyond nationalism during a pandemic.
-
Ni, C.; Shaw, D.; Lind, S.M.; Ding, Y.: Journal impact and proximity : an assessment using bibliographic features (2013)
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- Source
- Journal of the American Society for Information Science and Technology. 64(2013) no.4, S.802-817
-
Hu, B.; Dong, X.; Zhang, C.; Bowman, T.D.; Ding, Y.; Milojevic, S.; Ni, C.; Yan, E.; Larivière, V.: ¬A lead-lag analysis of the topic evolution patterns for preprints and publications (2015)
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- Abstract
- This study applied LDA (latent Dirichlet allocation) and regression analysis to conduct a lead-lag analysis to identify different topic evolution patterns between preprints and papers from arXiv and the Web of Science (WoS) in astrophysics over the last 20 years (1992-2011). Fifty topics in arXiv and WoS were generated using an LDA algorithm and then regression models were used to explain 4 types of topic growth patterns. Based on the slopes of the fitted equation curves, the paper redefines the topic trends and popularity. Results show that arXiv and WoS share similar topics in a given domain, but differ in evolution trends. Topics in WoS lose their popularity much earlier and their durations of popularity are shorter than those in arXiv. This work demonstrates that open access preprints have stronger growth tendency as compared to traditional printed publications.
-
Zhai, Y.; Ding, Y.; Zhang, H.: Innovation adoption : broadcasting versus virality (2021)
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- Source
- Journal of the Association for Information Science and Technology. 72(2021) no.4, S.403-416
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Li, R.; Chambers, T.; Ding, Y.; Zhang, G.; Meng, L.: Patent citation analysis : calculating science linkage based on citing motivation (2014)
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- Abstract
- Science linkage is a widely used patent bibliometric indicator to measure patent linkage to scientific research based on the frequency of citations to scientific papers within the patent. Science linkage is also regarded as noisy because the subject of patent citation behavior varies from inventors/applicants to examiners. In order to identify and ultimately reduce this noise, we analyzed the different citing motivations of examiners and inventors/applicants. We built 4 hypotheses based upon our study of patent law, the unique economic nature of a patent, and a patent citation's market effect. To test our hypotheses, we conducted an expert survey based on our science linkage calculation in the domain of catalyst from U.S. patent data (2006-2009) over 3 types of citations: self-citation by inventor/applicant, non-self-citation by inventor/applicant, and citation by examiner. According to our results, evaluated by domain experts, we conclude that the non-self-citation by inventor/applicant is quite noisy and cannot indicate science linkage and that self-citation by inventor/applicant, although limited, is more appropriate for understanding science linkage.
