Dataset Information

Trends in Diatom Research Since 1991 Based on Topic Modeling.

ABSTRACT: Diatoms are fundamental carbon sources in a wide range of aquatic food webs and have the potential for wide application in addressing environmental change. Understanding the evolution of topics in diatom research will provide a clear and needed guide to strengthen research on diatoms. However, such an overview remains unavailable. In this study, we used Latent Dirichlet Allocation (LDA), a generative model, to identify topics and determine their trends (i.e., cold and hot topics) by analyzing the abstracts of 19,000 publications from the Web of Science that were related to diatoms during 1991-2018. A total of 116 topics were identified from a Bayesian model selection. The hot topics (diversity, environmental indicator, climate change, land use, and water quality) that were identified by LDA indicated that diatoms are increasingly used as indicators to assess water quality and identify modern climate change impacts due to intensive anthropogenic activities. In terms of cold topics (growth rate, culture growth, cell life history, copepod feeding, grazing by microzooplankton, zooplankton predation, and primary productivity) and hot topics (spatial-temporal distribution, morphology, molecular identification, gene expression, and review), we determined that basic studies on diatoms have decreased and that studies tend to be more comprehensive. This study notes that future directions in diatom research will be closely associated with the application of diatoms in environmental management and climate change to cope with environmental challenges, and more comprehensive issues related to diatoms should be considered.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC6722707 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Trends in Diatom Research Since 1991 Based on Topic Modeling.

Zhang Yun Y Tao Juan J Wang Jun J Ding Liuyong L Ding Chengzhi C Li Yanling Y Zhou Qichao Q Li Dunhai D Zhang Hucai H

Microorganisms 20190724 8

Diatoms are fundamental carbon sources in a wide range of aquatic food webs and have the potential for wide application in addressing environmental change. Understanding the evolution of topics in diatom research will provide a clear and needed guide to strengthen research on diatoms. However, such an overview remains unavailable. In this study, we used Latent Dirichlet Allocation (LDA), a generative model, to identify topics and determine their trends (i.e., cold and hot topics) by analyzing th ...[more]

PMID: 31344825

Similar Datasets

Project description:Disease ecology is a rapidly growing subdiscipline, and mammals and their parasites feature prominently in both historical and more recent research efforts. Nevertheless, the diversity of topics explored, and those not well explored, has not been systematically assessed. We conducted a systematic review of the published scientific literature in disease ecology of mammals and subjected the collection of original and review articles identified to a topic modeling approach, which is based on the words used in the published texts and their contexts (i.e., the frequency and strength of their semantic relationships with one another). In addition to concept maps identifying the most prominent research themes, we identified eight (not mutually exclusive) subcategories of studies, including experimental, theoretical, comparative, behavioral, immunological-microbiological, biogeographic-macroecological, vector-focused (e.g., mosquitoes), and disturbance-focused. The most prominent themes arising in review papers included the ecology of zoonotic diseases transmitted from non-human mammals, comparisons of pathogen prevalence between mammalian species, and pathogen discovery-disease surveillance studies, particularly of marine mammals and bats. For the original articles, the most prominent themes included ecology of rodent-transmitted viral and bacterial diseases and the population biology of zoonotic hosts. Most studies used comparative or descriptive approaches to investigate mammal-pathogen-disease relationships at a local scale, focusing on vector-borne diseases. Experimental, modeling, immunological, and behavioral approaches were strikingly underrepresented. Topics of strong conceptual importance, but that are underrepresented in the current literature, include: 1) the effects of the population density of mammalian hosts, and manipulations of density, on pathogen transmission; 2) macroecological studies that quantify effects of mammalian host species on parasite abundance and prevalence; and 3) effects of climate change on physiological and behavioral processes relevant to mammal-parasite interactions.

Project description:BackgroundPractice-based research networks (PBRNs) have been recognized as essential laboratories and mechanisms for developing primary care research. This scoping review aims to examine and map the features and development trends of productivity, research categories, and methods in original primary care research conducted by global PBRNs between 1991 and 2023.MethodsWe have assembled an interdisciplinary team that will undertake this scoping review, following the framework developed by Arksey and O'Malley. Targeted literature includes original primary care research conducted by PBRNs, published from January 1, 1991, to December 31, 2023. An integrated search strategy will gather publications from 3 electronic databases (PubMed, Web of Science, and Embase), 16 major primary health care journals, and 364 relevant organizations. Two experienced researchers will independently screen the titles, keywords, and abstracts of all references and extract data regarding eight key elements. Disagreements between the reviewers will be resolved through group discussions, moderated by a third reviewer. Articles to be included will (1) be conducted in the primary care context, (2) be led by PBRNs, (3) provide a full report of original research, and (4) be published in a peer-reviewed journal between the aforementioned dates in any language. Exclusions encompass reviews, letters, commentaries, case reports, and conference papers. Final data will be displayed using tables and charts according to different conceptual categories.DiscussionThis scoping review is one of the initial attempts to delineate the development trends and features of primary care research conducted by PBRNs. This study will provide reference information for researchers in countries/regions that are building their research infrastructure and capacity in general practice, family medicine, and primary care.Systematic review registrationRegistered in OSF on July 25, 2022 ( https://osf.io/zgv9c ).

Project description:Background Clinical research publications have become the dominant source and basis of clinical evidence-based decision-making. Exploring the type and quantity of clinical research publications in the PubMed database is useful for clarifying the changing trends of clinical research development in recent years. Therefore, a longitudinal analysis of the type and quantity of clinical research publications in the PubMed database over three decades was conducted. Methods The PubMed database was searched to retrieve clinical research according to the type and year of publication from January 1, 1991 to December 31, 2020. The research types were classified as primary and secondary literature. Results A total of 1,078,404 primary literatures were retrieved and the constituent proportions were ranked from high to low as case report/series (27.54%), randomized clinical trials (RCTs) (23.62%), cohort studies (21.05%), cross-sectional studies (17.49%), case control studies (9.15%), non-RCTs (1.01%), and pragmatic clinical trials (PCTs) (0.15%). Correspondingly, 1,302,173 secondary literatures were retrieved and ranked as narrative review (70.88%), systematic review (15.02%), systematic review and meta-analyses (13.89%), traditional meta-analyses (4.48%), expert consensus (2.31%), guidelines (1.49%), scoping reviews (0.68%), net meta-analyses (0.40%), and umbrella reviews (0.04%). The average annual growth rate for the primary literature was 10.28%, and ranked from high to low as PCTs (83.68%), cohort studies (17.74%), cross-sectional studies (17.61%), non-RCTs (12.11%), case control studies (8.86%), RCTs (7.68%), case report/series (7.51%); while that for the secondary literature was 10.57%, and ranked from high to low as net meta-analyses (48.97%), umbrella reviews (47.09%), scoping reviews (41.92%), systematic reviews and meta-analyses (33.44%), systematic reviews (33.05%), traditional meta-analyses (12.49%), expert consensuses (9.22%), narrative review (8.72%), and guidelines (2.82%). Conclusion Both the composition and number of clinical studies changed significantly from 1991 to 2020. Based on the trend, the case report/series, case control study, and narrative review are on the decline, while cohort study, cross-sectional study, systematic reviews, and systematic review and meta-analysis literature have increased. To improve the quality of clinical evidence, we recommend RCT and cohort study give priority to access to allocated research resources in future. Supplementary Information The online version contains supplementary material available at 10.1186/s40001-022-00717-9.

Project description:BackgroundCOVID-19 vaccines are one of the most effective preventive strategies for containing the pandemic. Having a better understanding of the public's conceptions of COVID-19 vaccines may aid in the effort to promptly and thoroughly vaccinate the community. However, because no empirical research has yet fully explored the public's vaccine awareness through sentiment-based topic modeling, little is known about the evolution of public attitude since the rollout of COVID-19 vaccines.ObjectiveIn this study, we specifically focused on tweets about COVID-19 vaccines (Pfizer, Moderna, AstraZeneca, and Johnson & Johnson) after vaccines became publicly available. We aimed to explore the overall sentiments and topics of tweets about COVID-19 vaccines, as well as how such sentiments and main concerns evolved.MethodsWe collected 1,122,139 tweets related to COVID-19 vaccines from December 14, 2020, to April 30, 2021, using Twitter's application programming interface. We removed retweets and duplicate tweets to avoid data redundancy, which resulted in 857,128 tweets. We then applied sentiment-based topic modeling by using the compound score to determine sentiment polarity and the coherence score to determine the optimal topic number for different sentiment polarity categories. Finally, we calculated the topic distribution to illustrate the topic evolution of main concerns.ResultsOverall, 398,661 (46.51%) were positive, 204,084 (23.81%) were negative, 245,976 (28.70%) were neutral, 6899 (0.80%) were highly positive, and 1508 (0.18%) were highly negative sentiments. The main topics of positive and highly positive tweets were planning for getting vaccination (251,979/405,560, 62.13%), getting vaccination (76,029/405,560, 18.75%), and vaccine information and knowledge (21,127/405,560, 5.21%). The main concerns in negative and highly negative tweets were vaccine hesitancy (115,206/205,592, 56.04%), extreme side effects of the vaccines (19,690/205,592, 9.58%), and vaccine supply and rollout (17,154/205,592, 8.34%). During the study period, negative sentiment trends were stable, while positive sentiments could be easily influenced. Topic heatmap visualization demonstrated how main concerns changed during the current widespread vaccination campaign.ConclusionsTo the best of our knowledge, this is the first study to evaluate public COVID-19 vaccine awareness and awareness trends on social media with automated sentiment-based topic modeling after vaccine rollout. Our results can help policymakers and research communities track public attitudes toward COVID-19 vaccines and help them make decisions to promote the vaccination campaign.

Dataset Information

Trends in Diatom Research Since 1991 Based on Topic Modeling.

Publications

Trends in Diatom Research Since 1991 Based on Topic Modeling.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets