Shivom, a new blockchain project, was created to source and store genomic data to allow scientists to expand their research and come up with customized medicines that benefit those in need.
In order to prompt more users to donate their genomic data, Shivom has a reward system that offers them financial compensation, as well as access to the research that their data has been used in. This ecosystem of users is based on OmiX tokens, which can be traded online and internally stored. Due to this, the Shivom platform essentially functions as on open marketplace for researchers, who are able to benefit from the data collected and shared on the platform in a secure manner.
Why This Trend Is Growing
- Blockchain-enabled Genomic Data Storage
- The use of blockchain technology to securely store and share genomic data presents disruptive innovation opportunities for enhanced research collaboration and personalized medicine development.
- Incentivized Genomic Data Donation
- The implementation of reward systems, such as financial compensation and access to research, can encourage individuals to donate their genomic data, expanding the available data pool for scientific advancements.
- Open Marketplace for Genomic Research
- The development of platforms like Shivom creates an open marketplace where researchers can efficiently access and utilize genomic data, fostering collaboration and accelerating medical breakthroughs.
Industries Being Reshaped
- Healthcare and Life Sciences
- The healthcare and life sciences industry can leverage blockchain-enabled genomic data storage to drive advances in research, drug development, and personalized medicine.
- Blockchain Technology
- The blockchain industry can explore disruptive innovation opportunities by offering secure and transparent genomic data storage solutions, creating new applications for the technology.
- Data Science and Analytics
- The data science and analytics industry can benefit from the vast amount of genomic data available on open marketplaces, enabling deeper insights and predictive modeling for healthcare research.
