Surprising Resilience of Science During a Global Pandemic: A Large-Scale Descriptive Analysis
<|reference_start|>Surprising Resilience of Science During a Global Pandemic: A Large-Scale Descriptive Analysis: The COVID-19 pandemic profoundly impacted people globally, yet its effect on scientists and research institutions has yet to be fully examined. To address this knowledge gap, we use a newly available bibliographic dataset covering tens of millions of papers and authors to investigate changes in research activity and collaboration during this period. Employing statistical methods, we analyze the pandemic's disruptions on the participation, productivity, and collaborations of researchers at the top 1,000 institutions worldwide based on historical productivity, taking into account variables such as geography, researcher seniority and gender, and field of study. Our findings reveal an unexpected trend: research activity and output significantly increased in the early stages of the pandemic, indicating a surprising resilience in the scientific community. However, by the end of 2022, there was a notable reversion to historical trends in research participation and productivity. This reversion suggests that the initial spike in research activity was a short-lived disruption rather than a permanent shift. As such, monitoring scientific outputs in 2023 and beyond becomes crucial. There may be a delayed negative effect of the pandemic on research, given the long time horizon for many research fields and the temporary closure of wet labs. Further analysis is needed to fully comprehend the factors that underpin the resilience of scientific innovation in the face of global crises. Our study provides an initial comprehensive exploration up to the end of 2022, offering valuable insights into how the scientific community has adapted and responded over the course of the pandemic.<|reference_end|>
@article{ahrabian2024surprising,
    title={Surprising Resilience of Science During a Global Pandemic: A Large-Scale
  Descriptive Analysis},
    author={Kian Ahrabian, Casandra Rusti, Ziao Wang, Jay Pujara, Kristina Lerman},
    journal={arXiv preprint arXiv:2409.07710},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.07710},
    primaryClass={cs.SI cs.DL}
}