Build up big-team science

• Published in: Nature (London) Vol. 601; no. 7894; pp. 505 - 507
• Main Authors: Coles, Nicholas A, Hamlin, J Kiley, Sullivan, Lauren L, Parker, Timothy H, Altschul, Drew
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 27.01.2022
• Subjects: Artificial intelligence; Babies; Collaboration; Consortia; Costs; Donations; Funding; Grasslands; Infants; Infrastructure; Interdisciplinary Research; Machine learning; Monkeys & apes; Participation; Project management; Public policy; Researchers; Science; United States > US; Switzerland; Western Europe; Institutions; Research management
• ISSN: 0028-0836; 1476-4687
• DOI: 10.1038/d41586-022-00150-2

Human infants are perhaps the most powerful learning machines on the planet - and understanding how that learning occurs could inform artificial intelligence, public policy, education and more. In a proof-of-concept study, the ManyBabies Consortium used word of mouth, social media and e-mail lists to amass a team of 69 labs to test whether infants across several world regions prefer 'baby talk': the high-pitched, sing-song speech that adults in many cultures use with babies. In addition to the ManyBabies Consortium, the authors have collectively been involved in creating the Psychological Science Accelerator (involving some 1,200 researchers)5, the Disturbance and Resources Across Global Grasslands network (DRAGNet; around 100 researchers; https://dragnetglobal.weebly.com) and the ManyPrimates project (comprising about 150 researchers6; see 'Examples of big-team science'). The price tag gets much bigger when factoring in labour for project management, which included acquiring more than 150 ethics-approval documents, translating study materials into 23 languages and developing research tools to track progress and validate data from labs all over the world (see go.nature.com/3jcsutx).