Abstract
Towards Machine-Readable (Meta) Data and the FAIR Value for Artificial Intelligence Exploration of COVID-19 and Cancer Research Data.
Highlights
Specialty section: This article was submitted to Medicine and Public Health, a section of the journal Frontiers in Big Data
With the pandemic coming at an accelerating pace, a series of global research actions are being implemented to strive against the virus and its effects and to create data-driven investigations to support more agile responses to future events1
Cancer research is an excellent example of the adoption of the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles (Wilkinson et al, 2016) on precision oncology (Deist et al, 2020; Delgado and Llorente, 2020; Vesteghem et al, 2020) and major cancer data repositories, such as the NIH Cancer Research Data Commons, are gradually adhering to these principles
Summary
Specialty section: This article was submitted to Medicine and Public Health, a section of the journal Frontiers in Big Data. Several groups are implementing the FAIR data principles, fostering the use of standards, common metadata models, and ontologies to increase the interoperability and reusability of data in oncology projects (Martínez-García et al, 2020; Zong et al, 2020).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
