Building Shared Understanding Across Disciplines
Interdisciplinary collaboration is at the heart of EBRAINS, bringing together neuroscientists, engineers, computer scientists, clinicians, and many other specialists. However, we sometimes assume we’re talking about the same thing, only to discover later that we’re not.
This guide provides practical approaches for building a shared understanding across disciplines, particularly when jargon, assumptions, and knowledge structures differ significantly.
Why This Matters
Different fields construct knowledge differently. What counts as “evidence” in clinical medicine may differ from computer science. Publication norms, authorship expectations, and even basic terminology vary widely. Taking time to build shared understanding prevents costly misunderstandings down the line.
Building a Shared Glossary Across Disciplines
A glossary of key terms is essential for smooth interdisciplinary collaboration, especially in international teams where jargon, assumptions, and knowledge structures differ.
Step 1: Collect Key Terms (Brainstorm)
Identify discipline-specific terms that might confuse or be misunderstood.
Brainstorm Terms
Ask each team member to list 5–10 essential terms from their field related to the project. Use a shared space like a collaborative whiteboard or document.
Identify Boundary Objects
Ask: What models, data types, diagrams, or tools do we all refer to—even if we interpret them differently?
Examples of boundary objects in EBRAINS:
- “Model” (mathematical model vs. conceptual model vs. 3D brain model)
- “Data” (raw data vs. processed data vs. metadata)
- “Simulation” (in silico modeling vs. virtual environment vs. statistical simulation)
- “Atlas” (anatomical reference vs. probabilistic map vs. coordinate system)
Step 2: Apply the “ELI5 Technique”
ELI5 stands for “Explain Like I’m 5” — create accessible definitions that anyone (even outside academia) can understand.
Write Simple Definitions
For each term, the person who contributed it writes a definition as if explaining to a curious 5-year-old or someone outside your field.
Avoid: “A spiking neural network implements biologically plausible neuronal dynamics with temporal coding.”
Better: “A spiking neural network is like a computer that works more like a brain—individual units send quick pulses to each other rather than continuous signals.”
Test Understanding
Others repeat what they think they’ve understood and ask for further clarifications. This iterative process ensures genuine comprehension.
Use Analogies and Examples
Where possible, provide concrete examples or analogies from everyday life.
Step 3: Define Boundary Objects
Boundary objects are key terms that can be interpreted differently across disciplines but are robust enough to be useful for collaboration.
For each boundary object, document:
- Common term: What everyone calls it
- Discipline-specific meanings: How different fields interpret it
- Agreed project definition: What it means for your collaboration
- When clarification is needed: Situations where the difference matters
Step 4: Visualize Connections with Concept Maps
Concept maps look like simple flow charts: key terms are connected with lines that describe relationships between terms or ideas.
Choose a Tool
Options include:
- yEd, Miro, Lucidchart (specialized tools)
- PowerPoint, Google Slides (simple and accessible)
- Whiteboard (physical or digital)
Map Together
Involve the whole team in mapping during a live session. This collaborative process is as valuable as the output.
Show Relationships
Connect terms with labeled arrows showing how they relate:
- “is a type of”
- “produces”
- “requires”
- “influences”
- “measured by”
Iterate
Concept maps are living documents. Update them as understanding evolves.
Step 5: Build and Maintain the Glossary Document
Create a Central Document
Use a format accessible to all team members:
- Shared markdown file in GitLab
- EBRAINS Notes document
- Wiki page in your Collaboratory space
Structure It Clearly
Organize alphabetically or by topic/domain. Include:
- The term
- ELI5 definition
- Technical definition (if needed)
- Discipline-specific interpretations
- Examples
- Related terms
Make It Active
Reference the glossary in meetings and documentation. When confusion arises, add the term to the glossary.
Use for Onboarding
New team members should review the glossary as part of their onboarding. It’s one of the most efficient ways to get up to speed.
Update Regularly
Schedule periodic reviews (e.g., quarterly) to add new terms and refine existing definitions.
Acknowledging Disciplinary Differences
Beyond terminology, take time to discuss:
How Knowledge is Constructed
Different fields have different:
- Epistemologies: What counts as valid knowledge?
- Methodologies: What are acceptable research methods?
- Evidence standards: What level of proof is required?
- Validation approaches: How do you know if you’re right?
Research Organization Cultures
Even within the same discipline, different institutions may have:
- Different publication expectations
- Varied approaches to authorship and credit
- Different timelines and rhythms (academic vs. industry)
- Varying levels of openness vs. confidentiality
Communication Styles
Be aware that:
- Some fields prize conciseness; others value thoroughness
- Some cultures emphasize directness; others prefer diplomatic phrasing
- What seems like “just asking questions” in one field may feel like “challenging authority” in another
Practical Example: EBRAINS Multi-Scale Modeling Project
Imagine a team combining:
- Neuroscientists studying synaptic plasticity
- Computer scientists building simulation infrastructure
- Clinicians interested in disease applications
Terms that need clarification might include:
| Term | Neuroscientist View | Computer Scientist View | Clinician View |
|---|---|---|---|
| Model | Biological mechanism represented mathematically | Software implementation of an algorithm | Simplified representation for prediction |
| Validation | Matches experimental data from specific preparations | Passes test suite, reproduces known results | Predicts clinical outcomes accurately |
| Scale | Molecular → Network → Systems | Computational complexity | Individual → Population level |
Agreed definition for project: Document what each term means in your specific collaboration context, noting when the different interpretations matter.
Tools and Resources
EBRAINS-Specific Resources
- EBRAINS Abbreviations
- EBRAINS Notes
- EBRAINS GitLabdocumentation
External Resources
Quick Reference: Glossary Building Checklist
- Identify 5–10 key terms from each discipline
- Identify boundary objects (terms used by multiple fields)
- Write ELI5 definitions for each term
- Test understanding with team members
- Document discipline-specific interpretations
- Create concept map showing relationships
- Build central glossary document
- Reference in meetings and documentation
- Include in onboarding materials
- Schedule regular reviews and updates