A Glimpse of the First Eight Months of the COVID-19 Literature on Microsoft Academic Graph: Themes, Citation Contexts, and Uncertainties.
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ABSTRACT:
SUBMITTER: Chen C
PROVIDER: S-EPMC8025977 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Since the relaunch of Microsoft Academic Services (MAS) 4 years ago, scholarly communications have undergone dramatic changes: more ideas are being exchanged online, more authors are sharing their data, and more software tools used to make discoveries and reproduce the results are being distributed openly. The sheer amount of information available is overwhelming for individual humans to keep up and digest. In the meantime, artificial intelligence (AI) technologies have made great strides and the cost of computing has plummeted to the extent that it has become practical to employ intelligent agents to comprehensively collect and analyze scholarly communications. MAS is one such effort and this paper describes its recent progresses since the last disclosure. As there are plenty of independent studies affirming the effectiveness of MAS, this paper focuses on the use of three key AI technologies that underlies its prowess in capturing scholarly communications with adequate quality and broad coverage: (1) natural language understanding in extracting factoids from individual articles at the web scale, (2) knowledge assisted inference and reasoning in assembling the factoids into a knowledge graph, and (3) a reinforcement learning approach to assessing scholarly importance for entities participating in scholarly communications, called the saliency, that serves both as an analytic and a predictive metric in MAS. These elements enhance the capabilities of MAS in supporting the studies of science of science based on the GOTO principle, i.e., good and open data with transparent and objective methodologies. The current direction of development and how to access the regularly updated data and tools from MAS, including the knowledge graph, a REST API and a website, are also described.
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Project description:OpCitance contains all the sentences from 2 million PubMed Central open-access (PMCOA) articles, with 137 million inline citations annotated (i.e., the "citation contexts"). Parsing out the references and citation contexts from the PMCOA XML files was non-trivial due to the diversity of referencing style. Only 0.5% citation contexts remain unidentified due to technical or human issues, e.g., references unmentioned by the authors in the text or improper XML nesting, which is more common among older articles (pre-2000). PubMed IDs (PMIDs) linked to inline citations in the XML files compared to citations harvested using the NCBI E-Utilities differed for 70.96% of the articles. Using an in-house citation matcher, called Patci, 6.84% of the referenced PMIDs were supplemented and corrected. OpCitance includes fewer total number of articles than the Semantic Scholar Open Research Corpus, but OpCitance has 160 thousand unique articles, a higher inline citation identification rate, and a more accurate reference mapping to PMIDs. We hope that OpCitance will facilitate citation context studies in particular and benefit text-mining research more broadly.
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Project description:Citations measure the importance of a publication, and may serve as a proxy for its popularity and quality of its contents. Here we study the distributions of citations to publications from individual academic institutions for a single year. The average number of citations have large variations between different institutions across the world, but the probability distributions of citations for individual institutions can be rescaled to a common form by scaling the citations by the average number of citations for that institution. We find this feature seems to be universal for a broad selection of institutions irrespective of the average number of citations per article. A similar analysis for citations to publications in a particular journal in a single year reveals similar results. We find high absolute inequality for both these sets, Gini coefficients being around 0.66 and 0.58 for institutions and journals respectively. We also find that the top 25% of the articles hold about 75% of the total citations for institutions and the top 29% of the articles hold about 71% of the total citations for journals.
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Project description:Several network features and information retrieval methods have been proposed to elucidate the structure of citation networks and to detect important nodes. However, it is difficult to retrieve information related to trends in an academic field and to detect cutting-edge areas from the citation network. In this paper, we propose a novel framework that detects the trend as the growth direction of a citation network using network representation learning(NRL). We presume that the linear growth of citation network in latent space obtained by NRL is the result of the iterative edge additional process of a citation network. On APS datasets and papers of some domains of the Web of Science, we confirm the existence of trends by observing that an academic field grows in a specific direction linearly in latent space. Next, we calculate each node's degree of trend-following as an indicator called the intrinsic publication year (IPY). As a result, there is a correlation between the indicator and the number of future citations. Furthermore, a word frequently used in the abstracts of cutting-edge papers (high-IPY paper) is likely to be used often in future publications. These results confirm the validity of the detected trend for predicting citation network growth.
| S-EPMC5962067 | biostudies-literature
Project description:Background: In recent years, due to its complexity and relevance, academic performance has become a controversial research topic within the health and educational field. The main purposes of this study were to analyze the links between publications and authors via citation networks, to identify the different research areas and to determine the most cited publications. Methods: The publication search was performed through the Web of Science database, using the term "Academic Performance" for a time interval from 1952 to 2019. The software used to analyze the publications was the Citation Network Explorer. Results: We found a total of 16,157 publications with 35,213 citations generated in the network, and 2018 had the highest number of publications of any year. The most cited publication was published in 2012 by Richardson et al. with a citation index score of 352. By using the clustering function, we found nine groups related to different areas of research in this field: health, psychology, psychosociology, demography, physical activity, sleep patterns, vision, economy, and delinquency. Conclusions: The citation network showed the main publications dealing with the different factors that affect academic performance, and it was determined that psychological and psychosocial factors were the most relevant.
| S-EPMC7432077 | biostudies-literature
Project description:New sources of citation data have recently become available, such as Microsoft Academic, Dimensions, and the OpenCitations Index of CrossRef open DOI-to-DOI citations (COCI). Although these have been compared to the Web of Science Core Collection (WoS), Scopus, or Google Scholar, there is no systematic evidence of their differences across subject categories. In response, this paper investigates 3,073,351 citations found by these six data sources to 2,515 English-language highly-cited documents published in 2006 from 252 subject categories, expanding and updating the largest previous study. Google Scholar found 88% of all citations, many of which were not found by the other sources, and nearly all citations found by the remaining sources (89-94%). A similar pattern held within most subject categories. Microsoft Academic is the second largest overall (60% of all citations), including 82% of Scopus citations and 86% of WoS citations. In most categories, Microsoft Academic found more citations than Scopus and WoS (182 and 223 subject categories, respectively), but had coverage gaps in some areas, such as Physics and some Humanities categories. After Scopus, Dimensions is fourth largest (54% of all citations), including 84% of Scopus citations and 88% of WoS citations. It found more citations than Scopus in 36 categories, more than WoS in 185, and displays some coverage gaps, especially in the Humanities. Following WoS, COCI is the smallest, with 28% of all citations. Google Scholar is still the most comprehensive source. In many subject categories Microsoft Academic and Dimensions are good alternatives to Scopus and WoS in terms of coverage.
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Project description:BackgroundLimited research has evaluated the relative citation ratio (RCR), a novel measure of research productivity. Accordingly, there remains a minimal understanding of its practical value relative to established metrics such as the h-index. Here, we examined correlations between the mean and weighted RCR scores and the h-index and explore the influence of academic rank, career duration, PhD acquisition, and fellowship training on these metrics.MethodsData regarding the academic rank (e.g. assistant professor, associate professor, professor, or "other"), career duration, degrees, fellowship training, and research yield were collected for 1018 academic ophthalmologists practicing in the southern United States of America. The iCite and Scopus databases were utilized to quantify research yield via calculations of mean and weighted RCR, and h-index, respectively.ResultsSignificant correlations were observed between the h-index and the mean (ρ = 0.62, P < 0.001) and weighted RCR (ρ = 0.84, P < 0.001). Advanced academic rank was associated with increased indices values. In a subset of ophthalmologists excluding members of the "other" category, career duration was moderately correlated with h-index (ρ = 0.45, P < 0.001), and weakly correlated with mean (ρ = 0.14, P < 0.001) and weighted (ρ = 0.26, P < 0.001) RCR. PhD and fellowship acquisition were associated with increased research yield.ConclusionThe findings suggest that the RCR is an effective measure of research yield, while resolving deficiencies present in the h-index. Further research remains to characterize the RCR's value relative to other established markers of research productivity.
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Project description:The primary purpose of this study is to identify academic cultural themes in the popular television sitcom The Big Bang Theory in order to enhance doctoral students' awareness and acquisition of Ph.D. competencies through formalized and explicit instruction. The secondary aim is to assess the impact of the selected academic themes on doctoral students' acquisition of Ph.D. competencies in an English for Academic Conversation (EAC) classroom. Drawing upon the concept of thematic learning instruction, a qualitative research method involving six clusters of Ph.D. competency reference framework developed by Durette and others was adopted to identify the academic cultural themes depicted by the sitcom's main characters. This is followed by evaluating the effectiveness of an EAC course in fostering learners' Ph.D. competencies using selected identified academic cultural themes. The result showed that the sitcom's main characters demonstrate the personal and professional skills commonly possessed by a competent academic as an individual or group. This is evidenced as all the thirty-four identified skills traverse the six clusters of Ph.D. competencies devised by Durette and colleagues in 2016. Also, the impact assessment results revealed that the course fostered learners' Ph.D. competencies as they shared knowledge, past experience, and action plans of every selected academic theme. This work contributes to existing knowledge of doctoral competencies vital to promoting competency-based Ph.D. programs in higher education.
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