This project will (Aim 1) build, test, and implement a cost-effective mobile/wearable cross-platform continuous time interaction (CTI) data acquisition system operated via (Aim 2) a web-based administration service. This combination of a mobile app and administration service software will allow future social and behavioral health researchers to administer their studys behavioral and social tracking protocols and manage the collected CTI data remotely, across significantly larger social research scales, and to do so either locally or in the cloud. The new set of tools will combine ultra-fine-grained social network tracking with cutting-edge instantaneous time sampling to allow for thicker description of captured interactions. To complement the app and admin service, the project will also implement (Aim 3) the first extensible suite of continuous interaction data analysis tools in the open-source cross-platform analytical environment R, which public health researchers can use to analyze the CTI data collected in the course of their studies. The final product, including all of these elements, will constitute an Open Dynamic Interaction Network (ODIN) platform, which will be widely available for public use within 3 years of the start of the project. To validate the outcomes of the development and implementation process of Aims 1 & 2 for a broad range of possible uses, the project will test the ODIN App and web-based ODIN Admin Service via test-bed implementation using a mobile technology lab at the University of Nebraska-Lincoln. These tests will allow the research team to validate the integration of software and hardware systems, test their robustness, usability, and flexibility, towards finalizing the deployment specifications of the platform as a whole. To test the ODIN Analysis suite, synthetic CTI data generated by the MABUSE simulation platform (previously developed by the PIs (via RC1 DA028476-01) will be fed into the analysis software in order to validate its capabilities, and quantify and optimize its robustness to noisy input. Taken together the above battery of tests ensures that we can validate ODINs CTI data collection capabilities and the ability of the ODIN analytic software to reliably discover and model diffusion and selection processes in social and bio-ecological data. As part of our commitment to see this technology used widely in the understanding and promotion of public health, in the final year, the system will be made deployable in the cloud (over Amazon Web Services, Google Play, CRAN, and GitHub), under open source licenses. Increasing the accuracy and fidelity of dynamic network data collection is of growing importance as basic health science research looks to human interaction and social network analysis to explain health dynamics. Of the currently supported R01-level program announcements at the National Institutes of Health, 17 explicitly identify social networks as important foci of research. As such, ODIN represents a significant contribution to health science more generally, with a potential impact across a wide range of project implementations.