Search bioRxiv⌕ Search

bioRxiv · 10.1101/2020.12.06.413682

Scientific publications and COVID-19 "research pivots" during the pandemic: An initial bibliometric analysis

Abstract

An examination is presented of scientific research publication trends during the global coronavirus (COVID-19) pandemic in 2020. After reviewing the timing of the emergence of the pandemic in 2020 and the growth of governmental responses, available secondary sources are used to highlight impacts of COVID-19 on scientific research. A bibliometric analysis is then undertaken to analyze developments in COVID-19 related scientific publications through to October of 2020 by broad trends, fields, countries, and organizations. Two publication data sources are used: PubMed and the Web of Science. While there has been a massive absolute increase in PubMed and Web of Science papers directly focused on COVID-19 topics, especially in medical, biological science, and public health fields, this is still a relatively small proportion of publication outputs across all fields of science. Using Web of Science publication data, the paper examines the extent to which researchers across all fields of science have pivoted their research outputs to focus on topics related to COVID-19. A COVID-19 research pivot is defined as the extent to which the proportion of output in a particular research field has shifted to a focus on COVID-19 topics in 2020 (to date) compared with 2019. Significant variations are found by specific fields (identified by Web of Science Subject Categories). In a top quintile of fields, not only in medical specialties, biomedical sciences, and public health but also in subjects in social sciences and arts and humanities, there are relatively high to medium research pivots. In lower quintiles, including other subjects in science, social science, and arts and humanities, low to zero COVID-19 research pivoting is identified. In a new Appendix to the paper, an updated analysis is provided through to mid-April 2022. CitationShapira, P. "Scientific publications and COVID-19 "research pivots" during the pandemic: An initial bibliometric analysis," bioRxiv 2020.12.06.413682; doi: https://doi.org/10.1101/2020.12.06.413682 Version NotesVersion 1: Original paper, completed on December 6, 2020; posted at bioRxiv on December 7, 2020. Version 2: Minor grammar items corrected. Version 3: Updated bibliometric analysis through to mid-April 2022 added on April 29, 2022, as new Appendix 2.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Shapira, P.. 2020-12-07. Scientific publications and COVID-19 "research pivots" during the pandemic: An initial bibliometric analysis. https://doi.org/10.1101/2020.12.06.413682

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Evaluating Large Language Models as Tools to Navigate Researchers in Rapidly Evolving Research Landscapes: A Case Study in Cancer Drug Response Prediction

Large Language Models (LLMs) have emerged as promising tools for assisting researchers in automating and accelerating the synthesis of literature reviews. However, their reliability is a significant concern due to issues like factual inaccuracies and hallucinations. The key question is whether LLMs can reliably provide comprehensive, up-to-date overviews and analyses. This study evaluates the performance of three leading LLMs (OpenAI's ChatGPT, Google's Gemini, and DeepSeek) on the complex task of generating a comprehensive survey paper on deep learning for cancer Drug Response Prediction (DRP). By testing both standard and Deep Research (DR) / Deep Think (DT) modes of LLMs with prompts of varying detail, this paper assesses key academic dimensions, including reference management, content quality, and analytical depth. Key findings reveal that while DR modes of LLMs significantly improve reliability by eliminating hallucinations, performance variations exist across models and prompts. A trade-off between reference quantity and integration quality was observed, and even the best-performing models lacked the analytical depth of human experts, often requiring extensive human supervision. The study concludes that LLMs currently serve as powerful assistive tools but still cannot replace the critical validation and synthesis provided by human researchers. Choosing the best LLM to use depends on the task in hand, while several strategies can be implemented to improve the produced output.

scientific communication and education↗

Welfare concerns for mounted load carrying by working donkeys in Pakistan

Working donkeys (Equus asinus) are vital to peoples livelihoods. They are essential for carrying goods, however globally, overloading is one of the primary welfare concerns of working donkeys. We studied mounted load carrying by donkeys and associated factors in Pakistan. A cross-sectional study of donkey owners (n = 332) was conducted, and interviews were undertaken based on a questionnaire. Owners estimated that the median weight of their donkeys was 110kg (interquartile range (IQR) 100-120kg), and that they carried a median mounted load of 81.5kg (IQR 63-99kg). We found that 87.4% of donkeys carried a load above 50% of their bodyweight ratio (BWR), the median BWR carried was 77.1% (IQR 54.5-90.7%), and 25.3% of donkeys carried above 90% BWR. Donkeys that were loaded at more than 50% BWR were more likely to sit, compared to donkeys loaded with less weight (p=0.01). Donkeys working in peri-urban and urban areas were more likely to carry a greater BWR than donkeys working in rural areas (P<0.001), as were those carrying construction materials or bricks, compared to agricultural materials (p=0.004). Age (p=0.03) and breed (p=0.01) were also associated with carrying a higher weight. Overloading based on current recommendations (50% BWR) was common, with the majority (87.4%) of donkeys reported to carry more than the recommended 50% limit. This survey provides evidence of on-the-ground working practices and factors associated with mounted load carrying, which is critical for developing evidence-based recommendations for loading, in order to improve the welfare of working donkeys.

scientific communication and education↗