
Sign up to save your podcasts
Or


This episode on Systems explores the challenges of cloud computing within the framework of biomedical research. Phil Bourne, Dean of the UVA School of Data Science, speaks with computational biologist and associate professor Nathan Sheffield about a paper they co-wrote on systemic issues from cloud platforms that do not support FAIRness, including platform lock-in, poor integration across platforms, and duplicated efforts for users and developers. They suggest instead prioritizing microservices and access to modular data in smaller chunks or summarized form. Emphasizing modularity and interoperability would lead to a more powerful Unix-like ecosystem of web services for biomedical analysis and data retrieval. The two discuss how funders, developers, and researchers can support microservices as the next generation of cloud-based bioinformatics.
From Cloud Computing to Microservices: Next Steps in FAIR Data and Analysis
https://pubmed.ncbi.nlm.nih.gov/36075919/
By UVA School of Data Science5
33 ratings
This episode on Systems explores the challenges of cloud computing within the framework of biomedical research. Phil Bourne, Dean of the UVA School of Data Science, speaks with computational biologist and associate professor Nathan Sheffield about a paper they co-wrote on systemic issues from cloud platforms that do not support FAIRness, including platform lock-in, poor integration across platforms, and duplicated efforts for users and developers. They suggest instead prioritizing microservices and access to modular data in smaller chunks or summarized form. Emphasizing modularity and interoperability would lead to a more powerful Unix-like ecosystem of web services for biomedical analysis and data retrieval. The two discuss how funders, developers, and researchers can support microservices as the next generation of cloud-based bioinformatics.
From Cloud Computing to Microservices: Next Steps in FAIR Data and Analysis
https://pubmed.ncbi.nlm.nih.gov/36075919/

90,762 Listeners

32,152 Listeners

172,074 Listeners

112,482 Listeners

6,064 Listeners

5,146 Listeners

29,223 Listeners

16,055 Listeners

28 Listeners

622 Listeners