Tag: AWS Batch

One of the biggest scientific achievements of the twenty-first century was the completion of the Human Genome Project and the publication of a draft human genome. The project took over 13 years to complete and remains one of the largest private-public international collaborations ever. Advances since in sequencing technologies, computational hardware, and novel algorithms reduced the time it takes to produce a human genome assembly to only a few days, at a fraction of the cost. This made using the human genome draft for precision and personalized medicine more achievable. In this blog, we demonstrate how to do a genome assembly in the cloud in a cost-efficient manner using ARM-based AWS Graviton2 instances.

On February 9, a new object successfully began to orbit Mars: an uncrewed spacecraft called the Hope Probe. The mission has already returned the first image of Mars, taken by Hope’s Emirates eXploration Imager from an altitude of 24,700 km. Led by the Mohammed Bin Rashid Space Centre (MBRSC), the Hope Probe is the first interplanetary mission for the United Arab Emirates, the fifth country in history to reach the red planet. It will also be the first spacecraft to capture a complete picture of the Martian atmosphere and its layers during different times of the day and different seasons for one complete Martian year. Once data transmitted by the Hope Probe reaches the scientific teams on Earth, MBRSC will use AWS advanced technologies to process and analyze the vast amounts of data and imagery to help researchers better understand the Martian atmosphere and its layers.

The cloud can help researchers process complex workloads, store and analyze enormous amounts of data, collaborate globally, and accelerate research and innovation. For research IT, Amazon Web Services (AWS) can help build scalable, cost-effective, and flexible environments while still maintaining the governance and guardrails for security and compliance. Following best practices, AWS allows for centralized management of resources, improved security and compliance of research workloads, and can save costs and accelerate innovation. What are some common questions from research IT customers?

Governments, like the state government of California, are in the midst of a transition to a new way of delivering vital information, services and programs using technology and the cloud. Government organizations are adopting approaches pioneered in the technology industry, including user-centered design, agile development, data science, modular contracting, and the use of modern technology platforms. Many of these governments, like the state of California, are using Amazon Web Services (AWS) to respond quickly and scale to battle unprecedented challenges, like COVID-19, and help them quickly make decisions about how to protect their constituents.

The breadth of challenges that can be addressed by overhead imagery is broad and continues to grow as new and improved sensors are deployed. To make the best use of this data, you need to have high-quality training data—data that you know is true (often called ground truth data) so that your algorithms can learn from it. A lack of this high-quality labeled training data continues to impede progress in many areas of remote sensing analytics, including machine learning. Two of the SpaceNet collaborators, Capella Space and AWS, are providing access to a unique dataset to help foster innovation in geospatial-based artificial intelligence. Learn more about Earth observation data, the SpaceNet 6 Challenge, and available datasets.

Using AWS and open-source software, Fred Hutch built a single, user-friendly, browser-based solution, Motuz, to streamline and simplify upload of large quantities of data. Motuz helped Fred Hutch de-silo its data and make it shareable and accessible.

The rapidly changing and dynamic global health situation has impacted the lives of many people including researchers at universities and institutions worldwide. Many academic institutions are migrating to remote operations. Researchers are processing data, collaborating online, and trying to maintain labs remotely. Amazon and AWS are responding to these events in support of our communities and deploying resources and technology to enable remote learning and home working.

The botany department at the University of British Columbia (UBC) and the UBC Data Science Institute are working together to research the evolution and genetic makeup of sunflowers – a critical crop in addressing global food security.

The Centre for Genomic Pathogen Surveillance (CGPS) is based at the Wellcome Genome Campus, Cambridge and The Big Data Institute, University of Oxford in the United Kingdom. Much of its work involves collaborating with laboratories around the world to enhance genomic surveillance by using big data, engineering, training, and genomic capacity building. Ultimately, the Centre hopes to enable the linking and real-time interpretation of data globally to track pathogens and antimicrobial resistance at an affordable rate. Typically, spikes in cost for research are a common challenge for laboratories. With the cloud, the team wanted to mitigate their costs, and particularly those of their partners in low and middle-income countries, by exploring the Amazon Web Services (AWS) Cloud’s pay-as-you-go infrastructure.

Scientists at NC State University’s North Carolina Institute for Climate Studies (NCICS) work with large datasets and complex computational analysis. Traditionally, they did their work using on-premises computational resources. As different projects were stretching the limits of those systems, NCICS decided to explore cloud computing. As part of the Amazon Sustainability Data Initiative, we invited Jessica Mathews, Jared Rennie, and Tom Maycock to share what they learned from using AWS for climate research. As they considered exploring the cloud to support their work, the idea of leaving the comfort of the local environment was a bit scary. And they had questions: How much will it cost? What does it take to deploy processing to the cloud? Will it be faster? Will the results match what they were getting with their own systems? Here is their story and what they learned.