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The emergency medicine facing the challenge of open science

AuthorsSixto-Costoya, A.; Aleixandre-Benavent, Rafael ; Lucas-Dominguez, Rut; Vidal-Infer, Antonio
KeywordsOpen Access
Raw dat
Data sharing
Supplementary material
Data reuse
Sustainable development goals
Emergency medicine
Issue Date2020
PublisherMultidisciplinary Digital Publishing Institute
CitationData 5(2): 28 (2020)
Abstract[Background]: The availability of research datasets can strengthen and facilitate research processes. This is specifically relevant in the emergency medicine field due to the importance of providing immediate care in critical situations as the very current Coronavirus (COVID-19) Pandemic is showing to the scientific community. This work aims to show which Emergency Medicine journals indexed in Journal Citation Reports (JCR) currently meet data sharing criteria.
[Methods]: This study analyzes the editorial policies regarding the data deposit of the journals in the emergency medicine category of the JCR and evaluates the Supplementary material of the articles published in these journals that have been deposited in the PubMed Central repository.
[Results]: It has been observed that 19 out of the 24 journals contained in the emergency medicine category of Journal Citation Reports are also located in PubMed Central (PMC), yielding a total of 5983 articles. Out of these, only 9.4% of the articles contain supplemental material. Although second quartile journals of JCR emergency medicine category have quantitatively more articles in PMC, the main journals involved in the deposit of supplemental material belong to the first quartile, of which the most used format in the articles is pdf, followed by text documents.
[Conclusion]: This study reveals that data sharing remains an incipient practice in the emergency medicine field, as there are still barriers between researchers to participate in data sharing. Therefore, it is necessary to promote dynamics to improve this practice both qualitatively (the quality and format of datasets) and quantitatively (the quantity of datasets in absolute terms) in research.
DescriptionThis article belongs to the Special Issue Data Reuse for Sustainable Development Goals.
Publisher version (URL)https://doi.org/10.3390/data5020028
Appears in Collections:(INGENIO) Artículos
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