The global food system puts enormous pressure on the environment. Managing these pressures requires understanding not only where they occur (i.e., where food is produced), but also who drives them (i.e., where food is consumed). However, the size and complexity of global supply chains make it difficult to trace food production to consumption. Here, we provide the most comprehensive dataset of bilateral trade flows of environmental pressures stemming from food production from producing to consuming nations. The dataset provides environmental pressures for greenhouse gas emissions, water use, nitrogen and phosphorus pollution, and the area of land/water occupancy of food production for crops and animals from land, freshwater, and ocean systems. To produce these data, we improved upon reported food trade and production data to identify producing and consuming nations for each food item, allowing us to match food flows with appropriate environmental pressure data. These data provide a resource for research on sustainable global food consumption and the drivers of environmental impact.

Galaxy Zoo is an online project to classify morphological features in extra-galactic imaging surveys with public voting. In this paper, we compare the classifications made for two different surveys, the Dark Energy Spectroscopic Instrument (DESI) imaging survey and a part of the Kilo-Degree Survey (KiDS), in the equatorial fields of the Galaxy And Mass Assembly (GAMA) survey. Our aim is to cross-validate and compare the classifications based on different imaging quality and depth. We find that generally the voting agrees globally but with substantial scatter, that is, substantial differences for individual galaxies. There is a notable higher voting fraction in favour of ‘smooth’ galaxies in the DESI+zoobot classifications, most likely due to the difference between imaging depth. DESI imaging is shallower and slightly lower resolution than KiDS and the Galaxy Zoo images do not reveal details such as disc features and thus are missed in the zoobot training sample. We check against expert visual classifications and find good agreement with KiDS-based Galaxy Zoo voting. We reproduce the results from Porter-Temple+ (2022), on the dependence of stellar mass, star formation, and specific star formation on the number of spiral arms. This shows that once corrected for redshift, the DESI Galaxy Zoo and KiDS Galaxy Zoo classifications agree well on population properties. The zoobot cross-validation increases confidence in its ability to compliment Galaxy Zoo classifications and its ability for transfer learning across surveys.

From early on, infants show a preference for infant-directed speech (IDS) over adult-directed speech (ADS), and exposure to IDS has been correlated with language outcome measures such as vocabulary. The present multi-laboratory study explores this issue by investigating whether there is a link between early preference for IDS and later vocabulary size. Infants’ preference for IDS was tested as part of the ManyBabies 1 project, and follow-up CDI data were collected from a subsample of this dataset at 18 and 24 months. A total of 341 (18 months) and 327 (24 months) infants were tested across 21 laboratories. In neither preregistered analyses with North American and UK English, nor exploratory analyses with a larger sample did we find evidence for a relation between IDS preference and later vocabulary. We discuss implications of this finding in light of recent work suggesting that IDS preference measured in the laboratory has low test-retest reliability.

The expensive-tissue hypothesis (ETH) posited a brain–gut trade-off to explain how humans evolved large, costly brains. Versions of the ETH interrogating gut or other body tissues have been tested in non-human animals, but not humans. We collected brain and body composition data in 70 South Asian women and used structural equation modelling with instrumental variables, an approach that handles threats to causal inference including measurement error, unmeasured confounding and reverse causality. We tested a negative, causal effect of the latent construct ‘nutritional investment in brain tissues’ (MRI-derived brain volumes) on the construct ‘nutritional investment in lean body tissues’ (organ volume and skeletal muscle). We also predicted a negative causal effect of the brain latent on fat mass. We found negative causal estimates for both brain and lean tissue (−0.41, 95% CI, −1.13, 0.23) and brain and fat (−0.56, 95% CI, −2.46, 2.28). These results, although inconclusive, are consistent with theory and prior evidence of the brain trading off with lean and fat tissues, and they are an important step in assessing empirical evidence for the ETH in humans. Analyses using larger datasets, genetic data and causal modelling are required to build on these findings and expand the evidence base.

OBJECTIVES/GOALS: To introduce the new Team Science Community Toolkit, co-created by community and academic partners, and showcase its potential to empower Community Organizations (COs) in achieving equity in community-engaged research (CER). METHODS/STUDY POPULATION: In response to the challenges faced by COs in CER collaborations, qualitative interviews were conducted with CO staff from historically marginalized communities. These interviews informed the development of the Team Science Community Toolkit, a collaborative effort involving a Community Advisory Board (CAB) and Team Science experts from Northwestern University. The toolkit, designed using a community-based participatory research approach, incorporates the Science of Team Science and User-Centered Design principles. Integrated into the NIH-sponsored COALESCE website, it includes templates, checklists, and interactive tools, along with a real-world simulation, to support COs in all stages of the research process. RESULTS/ANTICIPATED RESULTS: Focus groups and usability testing involving external community experts validated the toolkit’s content and usability. Participants expressed enthusiasm and a sense of empowerment, indicating that the toolkit allows them to actively shape research processes and infuse their specific voices and needs into their partnerships. The toolkit is designed to support breaking down barriers like jargon and cultural adaptability to improve accessibility and open conversation. The impact of this Team Science focused toolkit is under evaluation. This presentation will showcase the toolkit, detail its collaborative development, and explore potential applications, ultimately offering a path to more equitable and valuable community-based research. DISCUSSION/SIGNIFICANCE: By providing COs with the resources and knowledge to participate as equal partners in research collaborations, it enhances self-advocacy, transparency, and equity. The toolkit has the potential to utilize Team Science to foster productive communication in community-academic research partnerships.