Our Mission
The IBL Core aims to accelerate neuroscience by transforming how data is collected, processed, and shared, enabling researchers to focus on discovery while relying on a robust, scalable engineering backbone.
This mission builds on the original vision of the International Brain Laboratory to understand how the brain gives rise to behavior through coordinated, large-scale collaboration.
Achieving this requires integrating complex data across many regions and scales, an effort that cannot be accomplished by isolated laboratories.
Central to this approach is the Research Software Engineer (RSE) team.
As highlighted by Chapuis and Winter in The Transmitter, modern neuroscience depends on dedicated engineering expertise to develop reliable, reusable, and scalable software systems. RSEs bridge the gap between research and infrastructure, enabling reproducibility, collaboration, and the long-term sustainability of scientific workflows.
Building on its experience supporting both large-scale coordination and a diverse portfolio of smaller projects, the IBL Core RSE team is now expanding beyond its original scope. It applies its expertise to new collaborations across neuroscience, supporting a broader community while maintaining its commitment to open, reproducible, and collaborative research.
The Original Idea
The IBL was founded on a simple but transformative insight: the brain is too complex to be understood by individual laboratories working alone.
In 2016, a group of neuroscientists proposed a new model for neuroscience, inspired by large-scale collaborations in physics such as CERN. They argued that tackling brain-wide questions requires coordinated, international efforts built around shared goals, standardized methods, and open data.
This vision introduced several key principles that continue to guide the IBL Core today:
A shared, ambitious goal: building a brain-wide understanding of decision-making;
Tight integration of theory and experiment;
Standardization of tasks, hardware, and analysis for reproducibility;
Open science, with data and tools freely accessible;
A collaborative, distributed structure with a relatively flat hierarchy.
These ideas shaped a new kind of "virtual laboratory", a coordinated network of research groups working together as a single scientific entity.
Building for What Comes Next
The International Brain Laboratory was officially launched in 2017, bringing together laboratories across the world to work on a unified experimental and computational framework.
The initial phase of the collaboration focused on a large-scale, coordinated effort, the Brainwide Map, which established standardized experimental protocols and generated shared datasets across labs.
This was followed by a second phase involving a broader set of investigator-led projects and task forces, which diversified the scientific scope while relying on the same shared infrastructure.
Throughout these phases, engineering, and in particular the work of RSEs, became central. Supporting both large-scale coordination and diverse projects required robust data systems, scalable pipelines, and flexible tools that could adapt to different scientific needs.
A New Model for Neuroscience
IBL represents a shift from traditional, individual-lab research toward team-based, large-scale science.
By coordinating experiments, sharing data openly, and building common tools, the collaboration enables discoveries that would otherwise be out of reach.
Within this model, the IBL Core and its RSEs are not just support functions but core contributors to scientific progress. They design the infrastructure that enables collaboration, ensure that analyses are reproducible, and create tools that allow scientists to interact with increasingly complex datasets.
As the original IBL projects and task forces reach completion, the IBL Core is transitioning toward a broader role as an engineering and data platform for neuroscience, supporting new partners and projects beyond the founding collaboration.
This perspective reflects a broader shift in science, where software and data engineering are becoming as fundamental as experimental design.
