Meta-research on COVID-19: An overview of the early trends

• URL: http://arxiv.org/abs/2106.02961v1
• Date: Sat, 5 Jun 2021 20:50:43 GMT
• Title: Meta-research on COVID-19: An overview of the early trends
• Authors: Giovanni Colavizza
• Abstract summary: pandemic has underlined the severity of known challenges in research and surfaced new ones. This review considers early trends emerging from meta-research on COVID-19.
• Score: 0.22843885788439797
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: COVID-19 is having a dramatic impact on research and researchers. The pandemic has underlined the severity of known challenges in research and surfaced new ones, but also accelerated the adoption of innovations and manifested new opportunities. This review considers early trends emerging from meta-research on COVID-19. In particular, it focuses on the following topics: i) mapping COVID-19 research; ii) data and machine learning; iii) research practices including open access and open data, reviewing, publishing and funding; iv) communicating research to the public; v) the impact of COVID-19 on researchers, in particular with respect to gender and career trajectories. This overview finds that most early meta-research on COVID-19 has been reactive and focused on short-term questions, while more recently a shift to consider the long-term consequences of COVID-19 is taking place. Based on these findings, the author speculates that some aspects of doing research during COVID-19 are more likely to persist than others. These include: the shift to virtual for academic events such as conferences; the use of openly accessible pre-prints; the `datafication' of scholarly literature and consequent broader adoption of machine learning in science communication; the public visibility of research and researchers on social and online media.

Related papers

• Human Behavior in the Time of COVID-19: Learning from Big Data [71.26355067309193]
  Since March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths. The pandemic has impacted and even changed human behavior in almost every aspect. Researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning.
  arXiv  Detail & Related papers  (2023-03-23T17:19:26Z)
• Accelerating COVID-19 research with graph mining and transformer-based learning [2.493740042317776]
  We present an automated general purpose hypothesis generation systems AGATHA-C and AGATHA-GP for COVID-19 research. Both systems achieve high-quality predictions across domains (in some domains up to 0.97% ROC AUC) in fast computational time. We show that the systems are able to discover on-going research findings such as the relationship between COVID-19 and oxytocin hormone.
  arXiv  Detail & Related papers  (2021-02-10T15:11:36Z)
• COVIDScholar: An automated COVID-19 research aggregation and analysis platform [0.0]
  As of October 2020, over 81,000 COVID-19 related scientific papers have been released, at a rate of over 250 per day. This has created a challenge to traditional methods of engagement with the research literature. We present an analysis of trends in COVID-19 research over the course of 2020.
  arXiv  Detail & Related papers  (2020-12-07T18:17:11Z)
• Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing [66.63200823918429]
  The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world. We used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues.
  arXiv  Detail & Related papers  (2020-07-22T18:02:39Z)
• A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19 [75.84689958489724]
  The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. As a powerful tool against COVID-19, artificial intelligence (AI) technologies are widely used in combating this pandemic. This survey presents medical and AI researchers with a comprehensive view of the existing and potential applications of AI technology in combating COVID-19.
  arXiv  Detail & Related papers  (2020-07-04T22:48:15Z)
• Visualising COVID-19 Research [4.664989082015335]
  We develop a novel automated theme-based visualisation method. It combines advanced data modelling of large corpora, information mapping and trend analysis. It provides a top-down and bottom-up browsing and search interface for quick discovery of topics and research resources.
  arXiv  Detail & Related papers  (2020-05-13T15:45:14Z)
• A Study of Knowledge Sharing related to Covid-19 Pandemic in Stack Overflow [69.5231754305538]
  Study of 464 Stack Overflow questions posted mainly in February and March 2020 and leveraging the power of text mining. Findings reveal that indeed this global crisis sparked off an intense and increasing activity in Stack Overflow with most post topics reflecting a strong interest on the analysis of Covid-19 data.
  arXiv  Detail & Related papers  (2020-04-18T08:19:46Z)
• Computer Vision For COVID-19 Control: A Survey [10.032488704661903]
  The COVID-19 pandemic has triggered an urgent need to contribute to the fight against an immense threat to the human population. Computer Vision, as a subfield of Artificial Intelligence, has enjoyed recent success in solving various complex problems in health care. This survey paper is intended to provide a preliminary review of the available literature on the computer vision efforts against COVID-19 pandemic.
  arXiv  Detail & Related papers  (2020-04-15T05:43:52Z)
• Rapidly Deploying a Neural Search Engine for the COVID-19 Open Research Dataset: Preliminary Thoughts and Lessons Learned [88.42878484408469]
  We present the Neural Covidex, a search engine that exploits the latest neural ranking architectures. This paper describes our initial efforts and offers a few thoughts about lessons we have learned along the way.
  arXiv  Detail & Related papers  (2020-04-10T17:12:29Z)
• Mapping the Landscape of Artificial Intelligence Applications against COVID-19 [59.30734371401316]
  COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization. We present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence to tackle many aspects of the COVID-19 crisis.
  arXiv  Detail & Related papers  (2020-03-25T12:30:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.