Research organizations are facing challenges when it comes to implementing artificial intelligence (AI) in scientific research. Despite the buzz surrounding AI in recent years, its adoption within these organizations is not as widespread as one might expect.
One of the main reasons for this is the staffing dilemma faced by research organizations. While they have employed scientists who can write experiment code, these individuals may lack deep expertise in engineering. On the other hand, machine learning platforms require technical users who are well-versed in command-line interface, git, docker, APIs, and version control. These skills are crucial for conducting large-scale machine learning experiments, managing extensive datasets, collaborating with non-technical domain experts, and ensuring transparent and replicable science.
However, there is currently a scarcity of skilled machine learning engineers, dedicated DevOps teams, and personnel with a strong foundation in computer science within research organizations. This creates a paradox where organizations struggle to handle complex datasets that pre-trained neural networks cannot process, while their researchers lack the technical expertise to train and deploy machine learning models effectively. Conversely, engineers and developers with the necessary training may not be familiar with scientific research topics and processes.
Traditionally, research organizations have deployed teams of research scientists and data scientists to work in parallel. While this approach may have short-term advantages, it often leads to inefficiencies in the allocation of time and resources. As a result, research organizations find themselves unable to fully leverage the functionality of data models or technical platforms, hindering their adoption of AI.
The Allen Institute, an independent nonprofit bioscience and medical research institute, has recognized these challenges and aims to address them. Committed to open science, the institute makes all its data and resources publicly available for external researchers and institutions to access and use. One of their projects, called OpenScope, focuses on sharing complex neuronal recording pipelines with the neuroscience community.
To overcome the limitations of their on-premises High-Performance Computing cluster and enable the sharing of computational processes with the broader neuroscience community, Dr. Jerome Lecoq, an associate investigator for the Allen Institute, turned to Code Ocean. Code Ocean is a reproducible computational science platform that offers efficiency gains and enables the reallocation of time and resources to higher-leverage activities.
Implementing Code Ocean has resulted in a 400% increase in workflow efficiency for the Allen Institute, empowering researchers to accelerate the pace of discoveries. With features such as fine-grained metadata management, data organization in Collections, and automated data provenance, Code Ocean assists large organizations like the Allen Institute to operate effectively at scale. The platform also integrates with external data warehouse providers and machine learning platforms, further enhancing its functionality.
The Allen Institute’s partnership with Code Ocean has not only allowed them to reap the benefits of foundational technologies but also reinforced industry-standard best practices. The institute has successfully used machine learning on a broad scale, handling petabyte-scale data and running thousands of computations. By providing a single pane of glass that integrates various tools and addressing the specialized needs of large organizations, Code Ocean has played a crucial role in optimizing the Allen Institute’s research processes.
Overall, the challenges faced by research organizations in implementing AI in scientific research are being addressed through innovative platforms like Code Ocean. By streamlining workflows and enabling the sharing of computational processes, these platforms are bringing efficiency and scalability to the field of scientific research. With continued advancements in AI implementation, research organizations can unlock the full potential of machine learning and accelerate scientific discoveries for the benefit of society.
