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?
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?
Public Cloud
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
What do you like best about the product?
Very accessible for users with deep or shallow computational expertise
An efficient way to share your work with colleagues and the world
Encourage best practices and ensure efficient collaboration within the team
What do you dislike about the product?
Always watch out for the cloud costs!
GPUs can be expensive, not a Code Ocean thing but still be aware
What problems is the product solving and how is that benefiting you?
Computational analysis of large-scale radiological and genomics data for heathcare
Code Ocean is a platform for building and growing effective computational teams
What do you like best about the product?
Easy to use, develop and share functional code that is web executable without any devops or software engineering requirements.
What do you dislike about the product?
If you have a local and cloud setup, it is difficult to move back and forth
What problems is the product solving and how is that benefiting you?
I have built my team operation around Code Ocean. We generated template capsules that provide 90% of each analysis type and data scientists do the remaning 10% per analysis. We created standards, speed and effective output. It was easy to deliver to biolgoists for use without any software engineering. I loved that I was able to capture all input and output in databases with no effort. Finally, every plot and every analysis is tracked and is reproducible.
reproducible, professional data science deliverables
What do you like best about the product?
CodeOcean provides data science teams with a centralized, managed, remote cloud workspace to not only develop and test new analyses using your favorite IDE (Posit Studio, VSCode, Jupyter), but to reliably and effortlessly deliver the outputs of those analyses to stakeholders in a way that is 100% reproducible, traceable, shareable, and reusable.
What do you dislike about the product?
CodeOcean may not be a good organizational fit for teams that already have cloud/dev-ops engineers, or who are developing analytic data analysis software products for web or mobile platforms.
What problems is the product solving and how is that benefiting you?
CodeOcean allows data scientists on my team to do their normal day-to-day work in a familiar IDE environment, while abstracting (and hiding) away nearly all of the cloud, git, and container devops work required for their results to be reproducible and reusable. This yields savings in time and effort, not to mention avoiding frustrations with otherwise native cloud computing environments. CodeOcean compute capsules capture everything about a data analysis necessary to return at a later date, obtain consistent results, and make updates while not losing track of output versioning and provenance.
A Convenient Platform for Collaborative Coding
What do you like best about the product?
Code Ocean provides a user-friendly interface, allowing multiple users to work together, locally or remotely, on coding projects seamlessly.
What do you dislike about the product?
The performance may occasionally lag when handling large or resource-intensive projects.
What problems is the product solving and how is that benefiting you?
Code Ocean solves the problem of collaborative coding by providing a platform that promotes effective teamwork, enhances accessibility, supports various programming languages, and improves productivity.
