No abstract

Promoting affiliation between scientists is relatively easy, but creating larger organizational structures is much more difficult, due to traditions of scientific independence, difficulties of sharing implicit knowledge, and formal organizational barriers. The Science of Collaboratories (SOC) project conducted a broad five‐year review to take stock of the diverse ecosystem of projects that fit our definition of a collaboratory and to distill lessons learned in the process. This article describes one of the main products of that review, a seven‐category taxonomy of collaboratory types. The types are: Distributed Research Centers, Shared Instruments, Community Data Systems, Open Community Contribution Systems, Virtual Communities of Practice, Virtual Learning Communities, and Community Infrastructure Projects. Each of the types is defined and illustrated with one example, and key technical and organizational issues are identified.

Reuse of the works of others has become common practice on the Internet and has formed the basis for collaboration in some online communities. However, some works are reused much more frequently than others. In this article we build a quantitative model that explains which factors are most salient in determining the likelihood that an author's work will be reused. Controlling for common factors, such as the work's popularity, we show that the probability of reuse depends on (a) the degree of derivativity of the work in question, (b) the specific ways in which it derives meaning from other works (intertextuality), (c) the audience's preferential attachment to authors of high fecundity, and (d) the author's social embeddedness in networks of reuse. We use trace data from an online community that was built for the purpose of demonstrating the ability of open sharing and reuse to spur collaboration and innovation in music. Although our model is designed for broad applicability, we explain that the size and direction of the effects reported in this paper may vary, when reuse is performed with other media or for different purposes.

We present insights from the design and development process of Singapore Heritage Trails (SGTrails), a location-aware mobile application that supports users' exploration of sites of heritage and cultural value.Our findings are based on (a) a survey to understand users' perceptions about the role of technology, (b) a field study to compare a prototype version with a paper guide, and (c) analysis of implementation, initial adoption, and usage to uncover the multilayered meanings of heritage locations. Drawing on Activity Theory, we examine the ecology of actors and tools necessary to facilitate the co-curating and experiencing of sites of cultural heritage.

In this article, we present a method for predicting the view count of a YouTube video using a small feature set collected from a synchronous sharing tool. We hypothesize that videos which have a high YouTube view count will exhibit a unique sharing pattern when shared in synchronous environments. Using a one-day sample of 2,188 dyadic sessions from the Yahoo! Zync synchronous sharing tool, we demonstrate how to predict the video's view count on YouTube, specifically if a video has over 10 million views. The prediction model is 95.8% accurate and done with a relatively small training set; only 15% of the videos had more than one session viewing; in effect, the classifier had a precision of 76.4% and a recall of 81%. We describe a prediction model that relies on using implicit social shared viewing behavior such as how many times a video was paused, rewound, or fast-forwarded as well as the duration of the session. Finally, we present some new directions for future virality research and for the design of future social media tools.