2  Module 0: Introduction (15 minutes)

Facilitator: Mónica Muñoz Torres, Bridge Center

2.1 Learning Objectives

By the end of this module, participants will be able to:

  1. Articulate why large-scale, multi-institutional collaboration has become a requirement — not an option — for contemporary biomedical research.
  2. Identify at least one personal friction point in their current collaborative work that the workshop is designed to address.
  3. Recognize the Research Life Cycle as a framework for understanding how team science supports every stage of research, from ideation to dissemination.

2.2 Module Overview

Science has always been social, but the scale and structure of that sociality has changed dramatically. The lone scientist making a solitary breakthrough — what this module calls “hero science” — is increasingly a myth even in popular imagination and a near-impossibility in practice. Studies of publication patterns show that team-authored papers now dominate every scientific field, and the teams are getting larger. What was once a collaboration between two colleagues down the hall is now a 200-person consortium spanning three countries, four disciplines, and a dozen institutional compliance regimes.

This shift creates a new kind of problem. The skills researchers develop to succeed individually — deep expertise, independent judgment, focused persistence — are not the same skills required to make a large team function. The habits that work for a two-person collaboration (informal hallway conversations, shared assumptions, flexible deadlines) actively break down at consortium scale. This introductory module frames the rest of the workshop as a response to that gap: not a soft-skills seminar, but a practical toolkit for making large-scale collaborative science actually work.

2.3 Participant Background Reading

Participants are encouraged to review the following before the session. Each takes 15–20 minutes.

  • Wuchty, S., Jones, B. F., & Uzzi, B. (2007). “The Increasing Dominance of Teams in Production of Knowledge.” Science, 316(5827), 1036–1039. This landmark study of 19.9 million papers and 2.1 million patents demonstrated empirically that teams now dominate knowledge production and that team-produced work receives more citations. Read this to understand the empirical foundation for the “collaboration imperative” introduced in Module 1. A plain-language summary is widely available if the full paper is not accessible.

  • NIH Common Fund: Bridge2AI Program Overview (available at commonfund.nih.gov/bridge2ai). A short overview of the Bridge2AI initiative that provides the real-world context for this workshop. Participants unfamiliar with Bridge2AI should review this before arrival to understand the scale of coordination the workshop addresses.

2.4 Instructor Notes

2.4.1 Conceptual Background

“Hero science” as a cultural norm. The lone-genius narrative is deeply embedded in how science is taught, funded, and rewarded. Graduate training typically emphasizes individual mastery; promotion criteria weight solo-authored work; Nobel Prizes until recently were awarded to individuals even for collaborative discoveries. Understanding why this norm persists helps instructors respond empathetically when participants resist the “teaming” framing — they have often been rewarded their entire careers for not depending on others.

The Research Life Cycle as a framework. The wheel metaphor introduced in Key Message 5 is deliberately non-linear. It pushes back against the common assumption that team coordination is only relevant at the “collaboration” or “data sharing” stages. In reality, team dynamics shape what questions get asked (ideation), who gets credit (dissemination), and what data gets preserved (data generation). Facilitators should feel comfortable drawing connections from any point on the wheel to any module in the workshop.

Calibrating the room. The live poll in Key Message 2 serves a pedagogical function beyond ice-breaking: it surfaces the specific friction points in this particular cohort and creates implicit permission to bring real problems into the discussion. Facilitators should note the responses and refer back to them throughout the day when a module addresses something participants named.

2.4.2 Key Concepts

  • Team science: A field of study and practice concerned with understanding and optimizing how scientific teams form, function, and produce knowledge. Distinct from simply “working with others.”
  • Hero science: The cultural model of the solitary genius researcher; useful shorthand for the set of norms (individual credit, solo authorship, independent labs) that team science challenges.
  • Consortium: A formally organized group of independent institutions collaborating toward a shared goal, typically under a common funding agreement. Bridge2AI is an example.
  • Research Life Cycle: The iterative sequence of stages through which a research project moves — ideation, funding/planning, data generation, analysis, dissemination — here visualized as a wheel to emphasize that the process is not linear.

2.5 Content Block (15 minutes)

2.5.1 Key Message 1: Welcome & Context (5 minutes)

Facilitator Note: Start by grounding the workshop in reality. We aren’t here just to talk about getting along; we are here because modern science demands scale.

  • Who We Are: Name title, your role in navigating complex scientific landscapes.
    • Pamela Foster, Colleen Cuddy, Yulia Levites Strekalova, Jamie Toghranegar, Christine Velez, Grace Gonzalez, Jake Chen, Janani Ravi, Mónica Muñoz Torres (in person)
    • Sara Singer, Sean Davis (online)
  • The Bridge2AI Context: Introducing the Bridge2AI initiative. It is not just a grant; it’s a massive experiment in generating AI-Ready data. This requires a level of coordination that traditional “hero science” (the lone genius) simply cannot support.
  • Objective: We are here today because the problems we want to solve—in health, genomics, and informatics — are now too complex for any single discipline to solve alone. This workshop is a toolkit for thriving in that multidisciplinary reality.

2.5.2 Key Message 2: Calibrating the Room (Expectations & Ice-Breaker) (3 minutes)

Facilitator Note: Shift the energy from listening to participating immediately.

  • The Ask: Before we show you our roadmap, we want to know where you are trying to go.
  • Prompt: Go around the room using a live poll. Ask: What is one friction point in your current collaborations that you hope to solve today? (e.g., miscommunication, unclear roles, slow data sharing).
  • The Ice-Breaker: Validate their struggles. If you’re frustrated by email chains or confused about authorship, you are in the right place.

2.5.3 Key Message 3: Defining the Spectrum of Collaboration (2 minutes)

Facilitator Note: Move from the personal to the structural. Help them see that collaboration isn’t one thing; it’s a spectrum.

  • Micro: Acknowledge the traditional model: 2–3 colleagues from the same department working on a paper. The communication cost is low; the shared language is high.
  • Macro (Consortium): Contrast this with the reality of Bridge2AI. This is Team Science at scale—hundreds of researchers across different institutions, time zones, and vocabularies (e.g., clinicians vs. coders).
  • Gap: The habits that work for the ‘Micro’ (informal chats in the hallway) will cause the ‘Macro’ to collapse. Today is about building the infrastructure for the Macro.

2.5.4 Key Message 4: Core Philosophy: Innovation Requires Infrastructure (2 minutes)

Facilitator Note: This is the Why. Connect their desire for scientific success directly to the concept of Teaming.

The Logic Chain: Walk them through this deduction: - Goal: We conduct research to achieve Innovation and Significance. - Requirement: Innovation at the biomedical frontier requires All Expertise (genomics + ethics + CS + stats). Pillars: Data and Ethics - Mechanism: Accessing that expertise requires Teaming. Pillar: People - Conclusion: We don’t build teams to be social; we build them to be successful. An environment that supports Teaming is an environment that supports Discovery.

2.5.5 Key Message 5: Framework: The Research Life Cycle (Wheel) (2 minutes)

Facilitator Note: Introduce the visual anchor for the day. This Wheel demonstrates that Team Science isn’t a separate activity; it powers every stage of the research.

Visualizing the Cycle: Present the Research Life Cycle as a wheel, not a line. - Ideation: Where diverse perspectives spark the initial question. - Funding/Planning: Where governance and roles are defined. - Data Generation: Where protocols and standardization (FAIR, CARE, ELSI, etc.) matter. - Analysis: Where interdisciplinary translation happens. - Dissemination: Where authorship and credit are navigated.

Research Life Cycle

2.5.6 Key Message 6: Takeaway (1 minute)

As we move through the modules today, we are effectively moving around this wheel. Team Science is the grease that keeps this wheel turning. If the team breaks, the cycle stops.

Module Materials