As team-based, transdisciplinary science becomes increasingly central to discovery, questions of governance, equity, and credit allocation have moved from peripheral concerns to core determinants of success. While Open and FAIR practices are often framed as technical challenges, and scientific excellence depends on integrating diverse expertise across methods, institutions, and disciplines, the most complex barriers to effective collaboration are human: how contributions are recognized, how accountability is distributed, and how incentives are structured across career stages, organizational contexts, and cultural norms.

Drawing on lessons from COGITATE, a large international adversarial collaboration in cognitive neuroscience, this presentation examines the organizational challenges that emerged when implementing open and FAIR science practices within a complex, distributed research setting. In particular, we focus on how traditional authorship conventions and publication-centric reward systems fail to capture the variable and specialized labour required in modern collaborative research.

Credit and contribution tracking is not merely an administrative exercise; it is fundamentally a matter of equity. Self-reported contributions are influenced by cultural norms, gender socialization, personality traits, and power dynamics within teams. Some individuals consistently understate their work, while others may be more comfortable asserting their contributions. In the absence of clearly defined decision frameworks, these differences can compound over time, disproportionately disadvantaging early-career researchers, members of underrepresented groups, and those performing essential but less visible foundational work. When recognition is uneven, the consequences are not symbolic. They translate into tangible downstream effects on hiring, promotion, funding competitiveness, and long-term academic trajectories.

To address these challenges, we implemented standardized contribution taxonomies (e.g., CRediT), persistent identifier (PID) graph approaches linking individuals to diverse research outputs, and machine-readable metadata systems that capture contributions across the full research lifecycle. These tools increase transparency, distribute accountability, and make non-traditional scholarly work visible. Importantly, they also provide structured guardrails that reduce reliance on informal negotiation or self-advocacy alone.

Beyond tooling, we developed team-level decision protocols designed to promote equity in contribution reporting. These include early agreement on contribution categories and decision rules; periodic structured contribution reviews rather than end-stage recollection; written documentation of roles; explicit discussion and shared documentation of authorship criteria; and transparent dashboards to support collective verification. These practices help counteract bias introduced by hierarchy, personality, or cultural norms.

This case study contributes to the Science of Team Science by demonstrating how sociotechnical infrastructure and organizational design can jointly support equitable collaboration. By shifting from publication-centric metrics toward contribution-centric recognition, teams can better align incentives with collaborative values, support career development across roles, and strengthen accountability.

As research teams continue to scale, equitable governance must be embedded into collaboration architecture from the outset. Building fair team science necessitates that we use, and continue to build shared systems for recognition, responsibility, and reward.