Leading big team science as early-career researchers: challenges and solutions

Abstract Big team science networks are large-scale, collaborative, global initiatives that pool intellectual and material resources to address shared scientific questions while increasing transparency, inclusivity and replicability. In animal cognition and behaviour, these networks, such as ManyPrimates, ManyBirds and ManyDogs, emerged in response to the replicability crisis, overcoming limitations such as small sample sizes, taxonomic biases and restricted species or researcher representation. These networks have produced empirical work, commentaries and reviews demonstrating proof-of-concept for comparative, multi-species research. However, leading such networks, particularly as early-career researchers, presents challenges, including: (1) managing a team (communication, relationship-building, community trust), (2) managing others (navigating power dynamics and challenging behaviour), and (3) managing self (confidence, experience and motivation). Challenge impacts may include delayed research progress, risk of reputational harm and increased stress or burnout, especially for researchers navigating job precarity and career progression. We outline solutions and gains, including transparent governance, authorship criteria, communication strategies and the need for mentorship and leadership training. Sustained funding and active, supportive engagement from others are essential. Ultimately, big team science complements traditional single-lab models, promoting collaboration over competition. Our collective experience suggests that big team science leadership, although it can be high risk, is among the most rewarding, transformative career experiences.