Today, data volumes have increased so significantly that we need to carefully consider how we interact with, share, and analyze data to avoid bottlenecks. In contexts such as eScience and scientific computing, a large emphasis is placed on collaboration, resulting in many well-known challenges in ensuring that data is in the right place at the right time and accessible by the right users. Yet these simple requirements create substantial challenges for the distribution, analysis, storage, and replication of potentially "large" datasets. Additional complexity is also added through constraints such as budget, data locality, usage, and available local storage. In this paper, we propose a "socially driven" approach to address some of the challenges within (academic) research contexts by defining a Social Data Cloud and underpinning Content Delivery Network: a Social CDN (S-CDN). Our approach leverages digitally encoded social constructs via social network platforms that we use to represent (virtual) research communities. Ultimately, they build upon the intrinsic incentives of members of a given scientific community to address their data challenges collaboratively and in proven trusted settings. We define the design and architecture of a S-CDN and demonstrate its feasibility via a DBLP-based case study as first steps to illustrate its usefulness.