Why did we invest in Code Ocean

What is Code Ocean?

Code Ocean is the first computational research laboratory of its kind, built for scientific collaboration and discovery. Basically, it is a unique DevOps & CloudOps platform for Computational Research, as well as a repository for Cloud Research Capsules (Code, Data, Cloud-Environments). The Code Ocean platform facilitates standardization of all research workflows and creates a leapfrog in computational research. The standardization enables researchers to perform Computational Research more efficiently, generate more consistent and repeatable results, and significantly accelerate scientific discovery. Currently, Code Ocean is focusing on Biopharma as its first vertical, where accelerated scientific discoveries lead to better treatment and cures and ultimately save countless lives!

Biology is becoming a huge consumer of Big data & Cloud

Over the last decade, the volume and complexity of the raw data generated by biology R&D labs (both academic and industrial) have been exponentially increasing.  Biologists today are analyzing large data sets which lead to many unmet needs - data integration, data management, scaling analyses for HPC, cloud access, bioinformatics and computational biology support, multi-step workflows, updated analysis software, and the ability to store, share, and publish data. Biotech startups are running experiments that are generating between 10-100 Terabytes of data!

Biologists vs. Bioinformaticians - The Divide Between Coders and Non-Coders 

The nature of biology R&D has fundamentally changed, which created an industry wide problem. Experiments are now performed by multi-disciplinary teams composed of wet-lab biologists  and computational biologists / bioinformaticians . Modern day biology experiments are composed of two phases: the Biological Research Phase (i.e. biologists in a wet-lab) and the Computational Research Phase (bioinformaticians). These two tribes have different skills and speak different languages, this has created silos at every biology R&D lab.

Lack of Tools

This divide is exacerbated by the lack of robust, accessible, enterprise-grade collaboration tools with great UX/UI. It is difficult for research teams to find good SaaS tools that are available to all members (coders and non-coders) or user-friendly data analytics software that allows biologists to perform high level Computational Biology workflows to quality control data. There is also a large and fragmented code universe. These conditions lead to significant inefficiencies.

Reproducibility Crisis in Biological & Computational Research

A reproducibility crisis has plagued the biology research space for the past decade. In a now famous paper, Nature Journal surveyed 1500 scientists. According to the survey, 60% of biologists failed to reproduce their own experimental results, and 75% failed to replicate the published results of others.

Increasing Complexity of Cloud Infra, Pace of Innovation, and Scarcity of Talent

The cloud is advancing at breakneck speed, including data, AI compute, and storage functions. Market demand, an explosion of data, as well as advancements in computing, networks, storage, and artificial intelligence are all key drivers. Every few months, the big cloud vendors release dozens of new services, compute and storage instances. NVIDIA releases new graphics cards. PaaS vendors and open source vendors add to the complexity and chaos. It's hard to keep up with this pace of innovation for software development and DevOps engineering teams, not to mention researchers. The cadence of new tools and cloud technologies overwhelms them. Furthermore, there is a critical shortage of DevOps and IT talent to take advantage of these new capabilities. Teams that want to combine cutting-edge cloud technologies with state-of-the-art research have a hard time doing so (and finding DevOps talent).

Developer Tools for Scientific Research - a new need

Over the past few years, cloud computing and "software eating the world" trends have fueled a growth in developer tools that make it easier and faster to create high quality, scalable commercial software. However, these trends did not fully extend to non-commercial software development and specifically to computational research software. Research tools and technologies - that allow researchers to adhere to best practices when developing software and leveraging the cloud - are lacking.

A Stellar team 

Code Ocean was founded by two 8200 colleagues - Simon, a PhD researcher at Tel-Aviv University and subsequently a Runway postdoctoral fellow at the Jacobs Technion-Cornell Institute, and Ram - a seasoned CTO with extensive experience building large scale systems and managing R&D teams.We met the team during due diligence for another investment  and instantly fell in love.

Code Ocean, we believe, is one of the few early-stage companies in this space to boast such an elite team and an industry-defining product. Having deep knowledge of the researchers' world paired with expertise in Software development life cycle (SDLC), Cloud and Software Development gained from many years of experience in leading software R&D organizations makes the team extremely strong. Their vision to project SDLC, cloud, and devOps beyond software development and into research workflows set them apart from the competition. 

A category defining product

CodeOcean has developed a first-of-its-kind platform that we believe can standardize Computational Research on the Cloud. Their central repository for containerized reproducible research containers, will enable (for biology initially, but then for other verticals as well) greater access, operational efficiency, reproducibility, and ultimately hypervelocity for scientific discoveries!