Upcoming Webinars

The Best Way to Leverage Public Omics Data Sets

In the era of big data and predictive modeling in life sciences, the integration and analysis of multi-omics datasets have emerged as powerful tools for deciphering the intricate molecular mechanisms underlying biological systems. 

In this webinar we will explore: 

  • The process of bringing multi-omic datasets from data generation to discovery
  • How you can improve the use of these datasets in organization and exploration
  • Challenges in accessing public datasets in academic and commercial settings 
  • Best practices for organizing datasets, standardized formatting and access methods for future use
  • Methods of exploration for discoveries that could empower scientists of various domains

On-Demand Webinars

FAIRR 101: Computational tools for FAIRR analytics

In the sea of life sciences tools, how do you maximize their capabilities and manage challenges to achieve Findable, Accessible, Interoperable, Reusable, and Reproducible results?

Join us for a webinar exploring:

  • Critical components of life science workflows responsible for reproducibility and adherence to FAIRR analytics (i.e. code,data,environment)
  • Current tools available for achieving reproducible and repeatable results
  • Common challenges seen in the use of these tools, and how to adopt best practices to overcome them
  • Building a personalized stack of computational tools to maximize FAIRR results in your workflow

From FAIR Data to FAIRR Analytics

In the rapidly evolving field of computational science, researchers face the dual challenge of managing large datasets and extracting meaningful insights from them. The principles of FAIR (Findable, Accessible, Interoperable and Reusable) have become integral to data management, but what does it take for the analysis that is performed on that data to be FAIR and Reproducible (FAIRR Analytics)?

Join us for a webinar examining nuances and practicalities of FAIR Data and FAIRR Analytics, and practical explorations how experimental results can be not only reproducible but reusable as well.

Breaking Barriers: Speed Up Time To Your Next Scientific Breakthrough

In this upcoming webinar, teams will learn how computational housekeeping can slow down scientific progress and how more efficient systems speed up time to new scientific breakthroughs.

Learn more about how to build more efficient systems for R&D and how to enable bench scientists to handle big data without building infrastructure from scratch. See how Code Ocean helps teams speed up onboarding, gain faster access to code, and decrease the time to execute workflows from months to minutes.

How to Build, Run, and Deploy Bioinformatics Apps to the Cloud in Minutes

In this on-demand webinar, bioinformaticians will learn how to build, run, and deploy apps to the cloud in minutes.

Learn more about how cloud deployment can be as easy as building on a local environment. You’ll see how to install custom software, how to package and run apps in the cloud, and how Code Ocean simplifies this process, including one-click deployment.

Automated Pipelines: A Simple Solution to Complex Workflows

In this on-demand session, biotech teams will learn about Code Ocean’s automated pipeline tool.

Learn how to connect multiple coding modules (Compute Capsules) in a drag-and-drop interface that any collaborator can use. No code required! We’ll also show you how to implement an RNA-seq workflow in a Code Ocean pipeline, from raw reads to counts matrix using FastQC, Cutadapt, Trim Galore, and STAR.

Empowering Biologists: The Future of Data Insights and Visualization

In this on-demand session, teams will learn how to transform pre-processed data to actionable insights, aligning all stakeholders along the way.

Gain insight into how an integrated, end-to-end bioinformatic process can look, as shown on single-cell RNA-seq data. You’ll also learn how to use your visualization tool of choice in a framework that’s continuous with your compute and data management systems (Shiny, with Docker, AWS, and Git).

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