Leveraging cloud computing for scientific research can provide a valuable edge and help protect data from accidental loss. It can mean the difference between efficient data storage and a complete loss.
Scientific research is fundamentally about producing, collecting, and analyzing data. Researchers generate data from any number of sources during their experiments, collecting data on different test groups and variables, and those test groups can contain several subjects. Before long, the amount of data points a researcher generates, stores, and analyzes becomes staggering – often requiring petabytes of space. Not only are the space requirements large, but latency and bandwidth become important factors driving the amount of compute time required to complete the task, and compute time directly drives research costs. These factors drive acquiring a dedicated storage solution.
Furthermore, the loss of data collected during the experiment process could have catastrophic consequences. Experiments are expensive — both in infrastructure and labor — and funding is frequently limited. Having to redo work due to cases of data loss could eat up funding and potentially cripple a research project’s success. Data loss also impacts the ability to reproduce an experiment, hampering independent verification of the results.
The importance of efficient data storage in scientific research
Because of this, scientific researchers will want to store multiple copies of their data to ensure that if one of the copies of their data is corrupted or lost, all of their hard work will not have gone to waste. However, storing multiple data sets for comparison and reproducibility can be cost-prohibitive. Beyond this, scientific researchers will have data replication in the form of repeated experiments.
Scientists often repeat their experiments under different conditions or by their peers to ensure the validity of their findings. These factors further contribute to the sheer quantity of data produced during scientific experiments.
Thankfully, cloud data storage offers an elastic and scalable solution for scientific researchers to store their data conveniently, securely, and cost-effectively. People are likely familiar with cloud storage solutions like Google Drive and Dropbox, which are essentially remote servers managed by a third-party provider that users can access via the internet.
However, consumer-facing cloud storage solutions like these may not be suitable for enterprises that produce and store large amounts of data — including laboratories and universities. In these cases, a specialized enterprise cloud storage solution will be necessary to manage their data storage needs.
Why cloud storage is an ideal solution for scientific researchers
One of the main benefits of cloud storage for scientific researchers is that it is an incredibly scalable solution. When storing data on-premises, storage space is restricted. You must have a room to house the data storage infrastructure, and meet certain conditions in that room — such as temperature and humidity — to ensure that the equipment functions properly. Cloud storage outsources data storage to a third-party provider, which manages these needs.
Improves costs
In turn, this scalability allows cloud storage to be an incredibly cost-effective solution for research labs and institutions. When dealing with physical data storage like server rooms, organizations must purchase more storage than they actually need to allow for backups and contingencies, not overwork the servers, and not cause latency issues. Cloud solutions enable institutions to buy precisely as much storage as they need, and as the amount of data they need to store grows, they can purchase additional storage space.
Enables easy access
Cloud storage has also been praised for enabling easy access to data. Many cloud storage solutions offer a web portal that is convenient and easy for users to access and navigate. The latency tradeoff with cloud services can be overcome with high bandwidth, dedicated and secure links to compute resources, and high-speed caching services. It is also much faster for researchers working between locations to access their data remotely via the cloud than to travel to a data storage location.
Protects against data loss
Cloud data storage also serves as an efficient hedge against data loss in cases like natural disasters and cybersecurity threats. By storing data in a centralized location, such as a server room on- or off-premises, researchers are leaving it vulnerable to isolated incidents. For example, if a flood destroys a server room where the researcher’s only copy of their data is held, that data is lost. Similarly, ransomware attacks could cause data loss if a hacker targets a single centralized server. Data storage in the cloud ensures that multiple copies of the data are stored in different locations, significantly reducing its vulnerability to loss.
Facilitates collaboration
Finally, storing data in the cloud is also an essential step in facilitating collaboration between teams in different geographic locations. Effective scientific discovery requires the brightest minds from around the world to come together sharing ideas, hypotheses, and data. Cloud technology enables researchers to share these resources and collaborate data in real time, no matter where they are. Since the cloud can be accessed from virtually any device with an internet connection, teams can access this data anytime or anywhere they need to and keep up with instantaneous updates.
Adopting cloud computing for scientific research
Scientific researchers generate and analyze a vast amount of data as part of the discovery process. Cloud technologies present a practical, scalable, cost-effective solution, allowing researchers to store and access their data efficiently and safely. If your research institution or lab has not already embraced the new paradigm of cloud storage technology, now is the time to protect and preserve your data by investing in a cloud storage solution.
Sid Rao brings over two decades of technology leadership to his role as CEO of Positron Networks, where he is building a scientific computing collaboration with Boston University called Project Robbie. Project Robbie unlocks convenient, scalable, scientific computing for public research. Previously, he spent over a decade as Director and General Manager at AWS, where he pioneered real-time communication services and launched the Amazon Chime SDK. This technology now supports over 18 million daily active users across applications by Slack (Huddles), Intuit (TurboTax Live), and PayPal (customer service). During the COVID-19 pandemic, Sid scaled his services by an impressive 1,274% in just one week. He led an applied science team in developing cutting-edge machine learning models for image segmentation, tonal sentiment detection, packet loss concealment, and audio super-resolution.
