Scholarly interest in network analysis has increased dramatically in the social sciences and beyond. The explosion of social media tools such as Facebook, Flickr, and Twitter, along with new developments in machine learning and data mining have produced new types of behavioral data for scholars to analyze (Mislove et al., 2007). Significant advances in mathematics, statistics, and computer science have also produced unprecedented opportunities to analyze ?Big? social network data. Social network analysis sits at the cutting edge of social science and also links the social, natural, and computational sciences. Understanding the multi-faceted nature of social networks and their effects on human behavior is one of the grand challenges faced as this project seeks to maximize our investments in scientific research (NSF-ACCI, 2011). However, the community has not seen comparable advances in the management, archiving, and sharing of social network data. This presents a fundamental obstacle to advancing network science across the social, natural, and computational sciences. This proposal seeks to begin to remedy this problem. The data management needs social network scholars are complex. Network data often come from unstructured environments that require researchers to define and describe a set of units or actors (called nodes) and the connections (called edges) between them. Networks might be static or dynamic, include one type of node or multiple node types, and include edges that are uni-directional or bi-directional and weighted or unweighted. In addition, as relationships spread within the network and/or a network grows, the associated data management, data storage, and analytical memory requirements can grow exponentially. This proposal brings together the social network analysis, information science, computer science, and data archive communities to develop a data infrastructure to support advanced analysis and research on social networks as well as to facilitate data sharing and archiving within this community. The group will address key questions concerning data storage architecture and lifecycle requirements, develop design specifications for creating a sustainable data infrastructure that will be discoverable, searchable, accessible, and usable to the entire research and education community, and initialize a prototype solution based on that plan. Intellectual Merit: The proposed project will bring together the social network analysis community to work with information technology professionals to design a robust data management infrastructure to promote the sharing and interoperability of social network data. Effective data management for social network data amplifies the impact of research by revealing data quality issues early in the data collection process, ensuring that required data is retained and usable throughout the life of a research project. It also facilitates data sharing and reuse. A key to responsible data stewardship is the application and auditing of quality data management policies ? something included in this proposal. Providing a robust infrastructure to store, analyze, curate, share, and manage important social network data will increase researchers? production and provide an unprecedented view of the social world only visible through social network data. Broader Impacts: The project will facilitate data sharing and increase social network data availability while assuring researchers that data management policies are followed. It will help formalize a community of network data experts that will begin developing best practices for the community. Availability of data particularly benefits early-stage researchers, and researchers at diverse institutions. Widespread availability of data facilitates citizen science and the integration of science and teaching at all levels of education. Managed data sharing is critical to the multi disciplinary research to answer the critical challenges facing society today. It would be difficult to overstate the importance of social network analysis to better understand human networks and social behavior.
