Home > Resources > Whitepapers > Evolving Considerations for Data Storage in Life Sciences

Evolving Considerations for Data Storage in Life Sciences

June 5, 2019

The latest lab instruments are driving the need for powerful IT resources in the life sciences, including data storage. Laboratory technologies are evolving rapidly. The latest instruments for life science research enable scientists to visualize highly detailed 3D models of protein molecules and assemble genomes in a fraction of the time they could in the past.New lab instruments are also producing tremendous amounts of data. For example, in the field of genomic research, nextgeneration sequencing (NGS) instruments can output 1 TB of data per hour. Cryo-electron microscopy (cryo-EM) is another burgeoning field that can produce massive data volumes. With cryo-EM, researchers study protein molecules at cryogenic temperatures, taking numerous images of each molecule and constructing 3D models. Researchers then use those models to study cancer at a molecular level and develop strategies for combating emerging viruses, such as Zika. The cryo-EM workflow can generate 5 TB of data in 24 hours.

Spotlight

Videology, Inc.

Videology (videologygroup.com) is a leading software provider for converged TV and video advertising. By simplifying big data, we empower marketers and media companies to make smarter advertising decisions to fully harness the value of their audience across screens. Our math and science-based technology enables our customers to manage, measure and optimize digital video and TV advertising to achieve the best results in the converging media landscape.

OTHER WHITEPAPERS

Data Strategy in Connected Business Models

whitePaper | November 25, 2022

In our contemporary environment, data and inherent data strategies constitute vital sources in pursuing and developing customer-centric business models while supporting collaboration between different organizations in creating customer value.

2022 EDUCAUSE Horizon Report® Data and Analytics Edition

whitePaper | September 16, 2022

With the 2022 data and analytics edition, we further expand our series of Horizon Reports to focus on an emerging area of practice that is driving institutional decision-making and strategic planning for the future—the trends, technologies, and practices that are shaping the world of postsecondary data and analytics. Based on a methodology that grounds the findings in the perspectives and expertise of a panel of leaders in higher education data and analytics, in this report we summarize the panel’s input on the major trends shaping higher education, including panelists’ reflections on the implications of this research for the future of higher education for particular institutional roles.

atos-operational-acceptance-testing

whitePaper | March 12, 2020

Operational Acceptance Testing (OAT) is the penultimate phase of Acceptance Testing in a Software Testing Life cycle and is the last defence line between a software development project and deployment of software on production. FFor decedes, Operational Acceptance has been undermined and misunderstood. Where User Acceptance has been written about and hailed as a final phase in testing before production. User Acceptance is but one side of the coin, Operational Acceptance is the other.

Do No Harm Guide Centering Accessibility in Data Visualization

whitePaper | December 29, 2022

Every day we are inundated with tables, graphs, charts, and maps explaining everything, including the unemployment rate, COVID-19 vaccination rates, baseball home run launch velocities, and our investment portfolios. When made well, data visualizations can help readers and users find insights and make discoveries. When made poorly, they obfuscate, mislead, or make it difficult for people to use them effectively.

Data Governance Is Valuable Moving to an Offensive Strategy

whitePaper | October 20, 2022

Data governance defines how data should be gathered and used within an organization. Download this whitepaper to understand how it's linked to business success.

The Evolution of Data 3.0

whitePaper | October 17, 2022

Today, massive amounts of data are collected, processed, and stored for a range of analytical purposes around the world. Every customer, device, transaction, email, and image leaves a data trail. At present, this data is growing too big, changing too fast, and becoming hyper distributed. The traditional ways of doing integration and analytics are no longer viable or scalable. It is not feasible to create millions of data pipelines and to continue moving large amounts of raw data to a data lake or a centralized data warehouse.

Evolving Considerations for Data Storage in Life Sciences