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1. Children and the COVID-19 Pandemic

   https://doi.org/10.17603/ds2-gha4-x973  

   Lefkowitz, Zoe; Villarreal, Melissa; Peek, Lori; Fothergill, Alice (January 2024, Designsafe-CI)

   This project explores how children and youth below the age of 18 sought to help others during the COVID-19 pandemic. We used the data included in this publication to answer research questions such as “How did children in the U.S. help others and themselves during the COVID-19 pandemic?” and “What issues were children in the U.S. concerned about during the COVID-19 pandemic?” This project includes a data dictionary and a dataset that summarizes a unique collection of 115 news articles focused on the helping behaviors and key concerns of children in the U.S. during the pandemic. The articles appeared in print or online news sources between 2020 and 2023. We searched for media coverage using terms such as “kids,” “help,” “volunteer,” “actions,” “pandemic,” and “COVID-19.” Over time we refined and added additional search terms based on emergent themes such as “raising money,” “making personal protective equipment,” and “helping with homework.” We limited our searches by language (English), geography (the United States), and time (an article had to be published between January 2020, when the virus was first detected in the U.S., and November 2023, when we ended our searches for the dataset). When we identified news coverage that fit our definition of helping behaviors, we saved a PDF of the article (all PDFs are available upon request from the PI). Information included in this dataset is summarized as follows: (1) article citation and link; (2) article synopsis; (3) information on the child or children featured in the article; (4) summary of key helping behaviors or other actions taken by children during the pandemic; (5) information on who children were trying to help or what type of change they were attempting to influence; (6) quotes from children or youth; and (7) notations of photos, videos, or links to additional resources. The envisioned audience for this data includes social science and public health researchers, journalists, and policy makers with an interest in children and the pandemic, specifically, or disasters and altruism, more broadly. 

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2. Literature-Based Discovery to Elucidate the Biological Links between Resistant Hypertension and COVID-19

   https://doi.org/10.3390/biology12091269  

   Kartchner, David; McCoy, Kevin; Dubey, Janhvi; Zhang, Dongyu; Zheng, Kevin; Umrani, Rushda; Kim, James J.; Mitchell, Cassie S. (September 2023, Biology)

   Multiple studies have reported new or exacerbated persistent or resistant hypertension in patients previously infected with COVID-19. We used literature-based discovery to identify and prioritize multi-scalar explanatory biology that relates resistant hypertension to COVID-19. Cross-domain text mining of 33+ million PubMed articles within a comprehensive knowledge graph was performed using SemNet 2.0. Unsupervised rank aggregation determined which concepts were most relevant utilizing the normalized HeteSim score. A series of simulations identified concepts directly related to COVID-19 and resistant hypertension or connected via one of three renin–angiotensin–aldosterone system hub nodes (mineralocorticoid receptor, epithelial sodium channel, angiotensin I receptor). The top-ranking concepts relating COVID-19 to resistant hypertension included: cGMP-dependent protein kinase II, MAP3K1, haspin, ral guanine nucleotide exchange factor, N-(3-Oxododecanoyl)-L-homoserine lactone, aspartic endopeptidases, metabotropic glutamate receptors, choline-phosphate cytidylyltransferase, protein tyrosine phosphatase, tat genes, MAP3K10, uridine kinase, dicer enzyme, CMD1B, USP17L2, FLNA, exportin 5, somatotropin releasing hormone, beta-melanocyte stimulating hormone, pegylated leptin, beta-lipoprotein, corticotropin, growth hormone-releasing peptide 2, pro-opiomelanocortin, alpha-melanocyte stimulating hormone, prolactin, thyroid hormone, poly-beta-hydroxybutyrate depolymerase, CR 1392, BCR-ABL fusion gene, high density lipoprotein sphingomyelin, pregnancy-associated murine protein 1, recQ4 helicase, immunoglobulin heavy chain variable domain, aglycotransferrin, host cell factor C1, ATP6V0D1, imipramine demethylase, TRIM40, H3C2 gene, COL1A1+COL1A2 gene, QARS gene, VPS54, TPM2, MPST, EXOSC2, ribosomal protein S10, TAP-144, gonadotropins, human gonadotropin releasing hormone 1, beta-lipotropin, octreotide, salmon calcitonin, des-n-octanoyl ghrelin, liraglutide, gastrins. Concepts were mapped to six physiological themes: altered endocrine function, 23.1%; inflammation or cytokine storm, 21.3%; lipid metabolism and atherosclerosis, 17.6%; sympathetic input to blood pressure regulation, 16.7%; altered entry of COVID-19 virus, 14.8%; and unknown, 6.5%. 

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3. COVID-19 knowledge deconstruction and retrieval: an intelligent bibliometric solution

   https://doi.org/10.1007/s11192-023-04747-w  

   Wu, Mengjia; Zhang, Yi; Markley, Mark; Cassidy, Caitlin; Newman, Nils; Porter, Alan (May 2023, Scientometrics)

   Covid-19 has been an unprecedented challenge that disruptively reshaped societies and brought a massive amount of novel knowledge to the scientific community. However, as this knowledge flood has surged, researchers have been disadvantaged by not having access to a platform that can quickly synthesize rapidly emerging information and link the expertise it contains to established knowledge foundations. Aiming to fill this gap, in this paper we propose a research framework that can assist scientists in identifying, retrieving, and understanding Covid-19 knowledge from the ocean of scholarly articles. Incorporating Principal Component Decomposition (PDC), a knowledge model based on text analytics, and hierarchical topic tree analysis, the proposed framework profiles the research landscape, retrieves topic-specific knowledge and visualizes knowledge structures. Addressing 127,971 Covid-19 research papers from PubMed, our PCD topic analysis identifies 35 research hotspots, along with their correlations and trends. The hierarchical topic tree analysis further segments the knowledge landscape of the whole dataset into clinical and public health branches at a macro level. To supplement this analysis, we also built a knowledge model from research papers on vaccinations and fetched 92,286 pre-Covid publications as the established knowledge foundation for reference. The hierarchical topic tree analysis results on the retrieved papers show multiple relevant biomedical disciplines and four future research topics: monoclonal antibody treatments, vaccinations in diabetic patients, vaccine immunity effectiveness and durability, and vaccination-related allergic sensitization. 

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4. YouTube and science: models for research impact

   https://doi.org/10.1007/s11192-022-04574-5  

   Shaikh, Abdul Rahman; Alhoori, Hamed; Sun, Maoyuan (February 2023, Scientometrics)

   Video communication has been rapidly increasing over the past decade, with YouTube providing a medium where users can post, discover, share, and react to videos. There has also been an increase in the number of videos citing research articles, especially since it has become relatively commonplace for academic conferences to require video submissions. However, the relationship between research articles and YouTube videos is not clear, and the purpose of the present paper is to address this issue. We created new datasets using YouTube videos and mentions of research articles on various online platforms. We found that most of the articles cited in the videos are related to medicine and biochemistry. We analyzed these datasets through statistical techniques and visualization, and built machine learning models to predict (1) whether a research article is cited in videos, (2) whether a research article cited in a video achieves a level of popularity, and (3) whether a video citing a research article becomes popular. The best models achieved F1 scores between 80% and 94%. According to our results, research articles mentioned in more tweets and news coverage have a higher chance of receiving video citations. We also found that video views are important for predicting citations and increasing research articles’ popularity and public engagement with science. 

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5. Building an Open Resources Repository for COVID-19 Research

   https://doi.org/10.2478/dim-2020-0012  

   Hu, Tao; Guan, Weihe Wendy; Zhu, Xinyan; Shao, Yuanzheng; Liu, Lingbo; Du, Jing; Liu, Hongqiang; Zhou, Huan; Wang, Jialei; She, Bing; et al (July 2020, Data and Information Management)

   null (Ed.)

   Abstract The COVID-19 outbreak is a global pandemic declared by the World Health Organization, with rapidly increasing cases in most countries. A wide range of research is urgently needed for understanding the COVID-19 pandemic, such as transmissibility, geographic spreading, risk factors for infections, and economic impacts. Reliable data archive and sharing are essential to jump-start innovative research to combat COVID-19. This research is a collaborative and innovative effort in building such an archive, including the collection of various data resources relevant to COVID-19 research, such as daily cases, social media, population mobility, health facilities, climate, socioeconomic data, research articles, policy and regulation, and global news. Due to the heterogeneity between data sources, our effort also includes processing and integrating different datasets based on GIS (Geographic Information System) base maps to make them relatable and comparable. To keep the data files permanent, we published all open data to the Harvard Dataverse ( https://dataverse.harvard.edu/dataverse/2019ncov ), an online data management and sharing platform with a permanent Digital Object Identifier number for each dataset. Finally, preliminary studies are conducted based on the shared COVID-19 datasets and revealed different spatial transmission patterns among mainland China, Italy, and the United States. 

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