Dataset Information

Searching and analyzing patent-relevant Covid-19 information

ABSTRACT: The Covid-19 pandemic has prompted several institutions to offer free, dedicated websites and tools to foster research and access to urgently needed innovation by facilitating the search and analysis of information within the large amount of scientific and patent literature which was published in a very short period of time. This situation is clearly exceptional and challenging for patent information professionals searching for relevant disclosures at a given date in a reliable manner. This article provides an overview of search criteria and strategies, main databases and websites, number of publications, biological sequence information and experimental data sets covering Covid-19 findings within scientific and patent literature disclosed between January and August 2020. The analysis of non-patent literature has been focused on the identification, date assignment, disambiguation, and access to experimental data. The analysis of patent literature has been focused on the trends found within the earliest filed and published patent documents in representative jurisdictions worldwide. Some practical advice and strategies for technical, medical, or patentability assessment of Covid-19-related innovations across different information formats and resources are proposed.

SUBMITTER: Falciola L

PROVIDER: S-EPMC8769884 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:On the behest of the Office of Science and Technology Policy in the White House, six institutions, including ours, have created an open research dataset called COVID-19 Research Dataset (CORD-19) to facilitate the development of question-answering systems that can assist researchers in finding relevant research on COVID-19. As of May 27, 2020, CORD-19 includes more than 100,000 open access publications from major publishers and PubMed as well as preprint articles deposited into medRxiv, bioRxiv, and arXiv. Recent years, however, have also seen question-answering and other machine learning systems exhibit harmful behaviors to humans due to biases in the training data. It is imperative and only ethical for modern scientists to be vigilant in inspecting and be prepared to mitigate the potential biases when working with any datasets. This article describes a framework to examine biases in scientific document collections like CORD-19 by comparing their properties with those derived from the citation behaviors of the entire scientific community. In total, three expanded sets are created for the analyses: 1) the enclosure set CORD-19E composed of CORD-19 articles and their references and citations, mirroring the methodology used in the renowned "A Century of Physics" analysis; 2) the full closure graph CORD-19C that recursively includes references starting with CORD-19; and 3) the inflection closure CORD-19I, that is, a much smaller subset of CORD-19C but already appropriate for statistical analysis based on the theory of the scale-free nature of the citation network. Taken together, all these expanded datasets show much smoother trends when used to analyze global COVID-19 research. The results suggest that while CORD-19 exhibits a strong tilt toward recent and topically focused articles, the knowledge being explored to attack the pandemic encompasses a much longer time span and is very interdisciplinary. A question-answering system with such expanded scope of knowledge may perform better in understanding the literature and answering related questions. However, while CORD-19 appears to have topical coverage biases compared to the expanded sets, the collaboration patterns, especially in terms of team sizes and geographical distributions, are captured very well already in CORD-19 as the raw statistics and trends agree with those from larger datasets.

Dataset Information

Searching and analyzing patent-relevant Covid-19 information

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets