Everybody Share: The Challenge of Data-Sharing Systems I ndividuals, businesses, and government agencies share vast quantities of data via a wide range of technologies, including Web services, data ware- houses, portals, RSS feeds, and peer-to-peer (P2P) file sharing. Scientific data sharing has produced a new subdiscipline, data-driven science, in which researchers explore large data stores such as genomic databases and digital sky surveys to generate and test new hypotheses. Environments for sharing data have expanded dramatically���for example, a crawl of the Gnutella P2P network three years after its inception discovered 100,000 nodes with 20,000,000 files.1 On the heels of these successes, the demand for data sharing continues to grow: The US Government���s 9/11 commission cited inad- equate information sharing as a key impediment to preventing acts of terrorism. Many governments are now enacting measures to improve counterterrorism data sharing. A key challenge in combating a pandemic disease outbreak is enabling a wide variety of government agencies, hospitals, and other organizations to rap- idly share ���biosurveillance��� data. At the same time, data sharing is a source of consid- erable controversy. P2P file-sharing users continue to clash with the entertainment industry, while government efforts to share terrorism information have raised strong concerns from privacy advocates. ��� ��� Data sharing: Governments require it, vendors prom- ise it, the media discuss it, and researchers in many fields study it. But what exactly is ���it���? Among a diverse set of advertised ���data-sharing solutions��� are network routers, ontologies, federated databases, search engines, and por- tal software. While many studies have addressed techni- cal aspects of data sharing, what the term means varies considerably. Despite the increasing societal importance of data sharing, we are not aware of a published framework that defines data sharing, identifies its key dimensions and challenges, and explains how representative systems relate to it. Previous researchers have approached the problem from the perspective of a single technology, such as Web services, or have emphasized one aspect, such as semantic integration or digital rights management. However, enterprise architects and technology planners require a general framework that applies across a broad range of implementation technologies. We present such a framework and illustrate its conceptual features with examples from biomedical research, defense, and law enforcement. Motivating ExaMplE: nEuroMorpho.org A growing area of science involves simulating the behavior of interconnected neurons to better understand the brain���s operation. Modelers who do this research require detailed representations of actual neurons, but, until recently, these have been hard to find, access, and interpret. A second group of scientists study neurons��� Data sharing is increasingly important in modern society, yet researchers typically focus on a single technology, such as Web services, or explore only one aspect, such as semantic integration. the authors propose a technology-neutral framework for characterizing data sharing problems and solutions and discuss open research challenges. Ken Smith, Len Seligman, and Vipin Swarup The Mitre Corporation R E S E A R C H F E A T U R E 54 Computer Published by the IEEE Computer Society 0018-9162/08/$25.00 �� 2008 IEEE