Objectives/Goals: To design a flexible, comprehensive framework for Data Science Units to cultivate sustainable, long-term relationships with Clinical and Translational Science Research Units. Best practices for managing Data Science collaborations are presented to improve the quality and efficiency of research conducted throughout academic health centers. Methods/Study Population: Leaders of Data Science Units across six institutions formed a workgroup to develop guidance and best practices for Data Science Units to establish long-term, sustainable collaborations with Clinical and Translational Science Research Units. This guidance is based on tools and protocols developed and employed by the participating units, which range from larger groups with over 20 partnerships to a unit with three partnerships that is actively working to expand. Importantly, partnerships are highly variable, with some partnerships at one institution representing engagement with over 500 faculty, whereas some partnerships at another institution involve the lab of a single investigator. Results/Anticipated Results: We offer guidance in three domains: (1) Identifying the needs for a new partnership, including assessing required effort and data science expertise, setting partnership priorities, developing formal agreements, and identifying goals and metrics; (2) managing data science teams by implementing regular meetings, creating project intake and prioritization processes, and effort monitoring; and (3) evaluating the successes and failures/gaps of the collaboration by measuring the metrics mapped to the goals. For each domain, we provide specific suggestions on which parties should be involved and how frequently the processes should occur. This guidance is applicable both to larger collaborative partnerships and to smaller, single faculty or staff partnerships, whether they are new or well-established. Discussion/Significance of Impact: Effective collaboration between data scientists and clinical and translational investigators is key to advancing data-driven research. The guidance and resources are presented to support Data Science Units in successfully managing long-term collaborations through goal-development, evaluation, and adapting to evolving research needs.

The idea that handedness indicates something about a person’s cognitive ability and personality is a perennial issue. A variety of models have been put forward to explain this relationship and predict a range of outcomes from higher levels of cognitive ability in left-handers or moderate right-handers to lower levels of achievement in left- or mixed-handers. We tested these models using a sample (n = 895) drawn from the BRAINnet database (www.brainnet.net). Participants completed a general cognitive ability (GCA) scale and a test of hand preference/performance. Moderate right-handers, as indexed by their performance measures, had higher GCA scores compared with strong left- or right-handers. The performance measure also showed lower levels of GCA for left-handers compared with right-handers. The hand preference data showed little or no association with cognitive ability—perhaps because this measure clusters individuals toward the extremes of the handedness distribution. While adding support to Annett’s heterozygous advantage model, which predicts a cognitive disadvantage for strong left- or right-handers, the data also confirm recent research showing a GCA disadvantage for left-handers. Although this study demonstrates that handedness is related to cognitive ability, the effects are subtle and might only be identified in large-scale studies with sensitive measures of hand performance. (JINS, 2010, 16, 585–592.)