Abstract: Automatically tagging individual users for identifying expertise or other relevant skills associated with the individual users based on various sources of information used or interacted with by the users is provided. After expertise tags are established for an individual user, the expertise tagging and other information about the user's profile and computing activities may be used for automatically suggesting a user for membership in one or more other project groups or workspaces that may be a good fit for the user's expertise or other relevant skills.

Abstract: An automatic discovery of content to add to a data store for a project is disclosed. A data item may be parsed for data features that are contextually relevant to a given project or task. Discovered interesting data may be extracted and mapped to various search mechanisms. A search may be built and applied to various data sources to discover data items based on the contextually relevant data features. Search results from various search mechanisms may be displayed in a single user interface and may be presented to a user.

Abstract: A space in which a user may organize data and collaborate with other users is provided. Project data and metadata may be saved into a reusable template and stored in a repository, wherein other users may discover and download the template for use as a resource for a related project type. A template may contain various document types that may expose a breadth of applications and application functionalities that may be utilized for a given task or project. Questions and answers, task templates, and terminology may also be provided. Project templates may allow for collaboration on projects, wherein business and/or social groups may collaborate quickly, easily, and more completely.

Abstract: An analysis module, when triggered by a synchronization framework when a new data item is added to a project data store, runs a series of analysis feature extractors on the new content. An analysis may be conducted, and features of interest may be extracted from the data item. The analysis utilizes natural language processing, as well as other technologies, to provide an automatic or semi-automatic extraction of information. The extracted features of interest are saved as metadata within the project data store, and are associated with the data item from which it was extracted. The analysis module may be utilized to discover additional information that may be gleaned from content that is already in the project data store.

Abstract: Project-related data may be aggregated from various data sources, given context, and may be stored in a data repository or organizational knowledge base that may be available to and accessed by others. Documents, emails, contact information, calendar data, social networking data, and any other content that is related to a project may be brought together within a single user interface, irrespective of its data type. A user may organize and understand content, discover relevant information, and act on it without regard to where the information resides or how it was created.