Introduction
Over the last 10 years, the emergence and steady improvements of high-throughput technologies in the life science & biomedical fields generated massive amounts of data, providing an unprecedented data-based description of a human individual. The data explosion is expected to last in time, with a growth of 2 to 40 exabytes of genetics and genomics data produced in the next 10 years. The data life cycle management, which comprises the data handling, processing and preservation, of such rich personalised data sets is still challenging.
Besides, fostering interdisciplinary collaborations across mutually "untrusted" third parties (e.g., HPC computing centers, hospitals and industry) while preserving individual data privacy is a topic of friction and intense (computational) development.
In this forum, we would like to discuss the current state of the art of biomedical and other sensitive data, the inherent costs for compliance and standardization and how to preserve data privacy while expert intensive computational analyses are required within a HPC computing environment.
Key questions
Over the last 10 years, the emergence and steady improvements of high-throughput technologies in the life science & biomedical fields generated massive amounts of data, providing an unprecedented data-based description of a human individual. The data explosion is expected to last in time, with a growth of 2 to 40 exabytes of genetics and genomics data produced in the next 10 years. The data life cycle management, which comprises the data handling, processing and preservation, of such rich personalised data sets is still challenging.
Besides, fostering interdisciplinary collaborations across mutually "untrusted" third parties (e.g., HPC computing centers, hospitals and industry) while preserving individual data privacy is a topic of friction and intense (computational) development.
In this forum, we would like to discuss the current state of the art of biomedical and other sensitive data, the inherent costs for compliance and standardization and how to preserve data privacy while expert intensive computational analyses are required within a HPC computing environment.
Key questions
- How to avoid public leaks of sensitive data, protecting by the same time your reputation and that of your institution ?
- What are the best practices in sensitive data management ?
- What are the best practices in preserving patient data ?
- What are the HPC tools and technology to foster multi-disciplinary collaborations across several partners while preserving patient data security ?
- How can we efficiently manage cost for compliance and standardization ?
- How to make sense of very large biomedical data by harmonizing very disparate data sets from heterogeneous data source by respecting international institutional regulations regarding patients data security for the aim of Big Data analytics ?
