Overview
Code Ocean is a Computational Science platform for life science R&D teams who want a fast and efficient way to start, scale, collaborate, and reproduce computational research. It helps Computational Scientists set up and scale their workflows, work closer together, and lets them support non-coding bench scientists with accessible, intuitive applications. Built on FAIR data principles, it helps avoid technical debt, improves data architecture, and improves organizational compliance and quality.
Typical users include: computational biologists, bioinformaticians, research IT, R&D leadership.
Example use cases include: data analysis, data management, bioinformatics pipelines, AI & machine learning, multiomics, image processing, cloud management, result provenance.
Key features
Compute Capsules: a shareable, traceable, reproducible encapsulation of the code, data, and environments used in computational research, version controlled and linked to the results they produce.
Pipelines: Connect, automate, parallelize, and scale computational work. Build with the visual editor to auto-generate Nextflow code, import from nf-core, or write your own.
Data: a single place to manage all data assets in the cloud and from any other source. Use in computational work while tracking lineage, ensuring reproducibility, and reducing duplication.
Lineage Graph: an immutable record of how Result Data is generated within Code Ocean, showing the source, data processing through Capsules and Pipelines, and the output.
Collections: for gathering and organizing Capsules, Pipelines, Apps, and Data by project or scientific area of interest to make them more visible, accessible, and usable by others.
Apps: browse a selection of ready-to-use bioinformatics Apps, and take advantage of functionality to transform computational work into No-Code Apps for others to use.
Admin Panel: a single pane of glass for all users, resources, and data in your deployment. Manage integrations, cost and compute, and environments from a unified management console.
API: enables programmatic access without using the user interface. Tap into core functionality to run Computations, create Data Assets, retrieve metadata, and more.
Please use Private Offer to purchase and please contact us at support@codeocean.com to start a private offer.
Highlights
- Full reproducibility: Code Ocean technology guarantees computational reproducibility
- No lock-in: built entirely on open-source software, anything can be exported at any time
- An immutable record of how result data are generated
Details
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Financing for AWS Marketplace purchases
Pricing
Dimension | Description | Cost/12 months |
|---|---|---|
User Seat | License to access and use Code Ocean software | $6,250.00 |
Vendor refund policy
We do not offer refunds. However, a trial period without commitment may be available in certain cases. Please reach out to sales for more information.
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Delivery details
Code Ocean VPC Architecture
Code Ocean VPC is installed in your AWS account. It can be installed into a new dedicated AWS VPC which follows AWS Well-Architected guidelines, or into an existing AWS VPC so that you can leverage existing AWS resources and easily align with company VPC guidelines and standards.
Code Ocean VPC deployments are managed with AWS CloudFormation infrastructure as code (IaC) service. A CloudFormation template is available for installation and upgrades and it provisions all AWS resources required to run Code Ocean in your AWS account. See our CloudFormation Deployment section.
Code Ocean is designed to enable secure cross-functional team collaboration while managing a large number of backend services, so you and your colleagues can focus on the research. The following diagram represents the most common system architecture for Code Ocean on AWS environments.
If you choose to install Code Ocean into its own dedicated AWS VPC the CloudFormation template will provision a new VPC across two availability zones with public and private subnets in your selected AWS region. The availability zones and CIDR blocks for VPC and subnets are configurable.
If you choose to install Code Ocean into an existing AWS VPC you can configure the CloudFormation template with the two availability zones and the private and public subnets to deploy to. Public subnets are required if you choose an internet-facing deployment. EC2
Code Ocean uses an HTTPS-only AWS application load balancer (ALB) to expose the system to users and to allow access to its internal Git server and Docker registry. The ALB can be internet-facing or internal for deployments behind a VPN.
CloudFormation Template (CFT)
AWS CloudFormation templates are JSON or YAML-formatted text files that simplify provisioning and management on AWS. The templates describe the service or application architecture you want to deploy, and AWS CloudFormation uses those templates to provision and configure the required services (such as Amazon EC2 instances or Amazon RDS DB instances). The deployed application and associated resources are called a "stack."
Version release notes
Additional details
Usage instructions
Resources
Vendor resources
Support
Vendor support
Customers can report issues at any time (24x7x365) by sending an email to support@codeocean.com . Code Ocean will make commercially reasonable efforts to respond during normal business hours, based on the severity of the issues, which determines the initial response and target resolution times. For more information about SLAs and support tiers, please contact us at support@codeocean.com .
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Standard contract
Customer reviews
Collaborative research workflows have become fully reproducible and streamline peer reviews
What is our primary use case?
On a day-to-day basis, I use Code Ocean to run, validate, and share code in a fully reproducible environment, mostly when working with data analysis and research-oriented code, where results need to be reproducible.
One of the main use cases for Code Ocean was for reproducible research and secure code execution, along with collaboration. I used it for packaging code, datasets, and environment configurations so results can be reproduced exactly by others, making it very useful for peer reviews and validation.
How has it helped my organization?
Code Ocean has resulted in an overall improvement in collaboration reliability in my organization, helping us improve reproducibility and audit requirements, which are essential for some of our research-heavy or regulated workflows or tasks, and has also shortened review cycles and increased confidence in the shared results.
Our review cycles have been reduced by up to 20%, and while some improvements cannot be measured in metrics, the overall reproducibility and audit requirements have also been improved, allowing us to spend less time debugging environment issues and more time focusing on analysis and results.
Overall, there is improvement in our return on investment. We don't have to go through all the long review cycles, and most of our extra efforts involved in managing access and improving environment consistency have been reduced, which has removed excess efforts that we needed to put in and allowed us to spend less time debugging environment issues.
What is most valuable?
The best feature of Code Ocean is the compute capsule concept, which bundles code, data, dependencies, and instructions into a single reproducible unit, allowing for one-click execution. This enables anyone with access to rerun experiments, code runs, and pipelines without worrying about setup, versioning, and tracking, which are also very valuable.
The capsule concept and versioning in Code Ocean improve collaboration reliability significantly, allowing teams to spend less time debugging environment issues and more time focusing on analysis and results.
What needs improvement?
There is not much to dislike about Code Ocean, but I think the compute resources can sometimes be limited for very large or long-running workloads, and more flexible options or scaling compute may be beneficial.
I chose eight because Code Ocean can still be made a bit better. There is not much to dislike, but it lacks some flexibility for heavier jobs, and the compute resources can be difficult to manage for larger workloads.
For how long have I used the solution?
I have been using Code Ocean for almost two years.
What do I think about the stability of the solution?
Code Ocean is quite stable.
What do I think about the scalability of the solution?
Code Ocean's scalability meets all our requirements.
How are customer service and support?
The customer support for Code Ocean is good. I have not needed to reach out much, but it is good.
How would you rate customer service and support?
Positive
Which solution did I use previously and why did I switch?
We did not use a different solution before.
What was our ROI?
Overall, there is improvement in our return on investment. We don't have to go through all the long review cycles, and most of our extra efforts involved in managing access and improving environment consistency have been reduced, which has removed excess efforts that we needed to put in and allowed us to spend less time debugging environment issues.
Which other solutions did I evaluate?
I am not entirely sure if we evaluated other options before choosing Code Ocean.
What other advice do I have?
I would highly recommend Code Ocean, as it is not something optional. Once you start using it, you will love it. Code Ocean is an excellent platform for collaborative and reproducible computing, particularly if your work involves sharing code, data, and results in a reliable and auditable way. I gave this product a rating of 8.
Which deployment model are you using for this solution?
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
No better way to ensure that yoru computational analysis is transparent, reproducible and reusable
An efficient way to share your work with colleagues and the world
Encourage best practices and ensure efficient collaboration within the team
GPUs can be expensive, not a Code Ocean thing but still be aware