offered the opportunity to 'benefit' from sharing algorithms at various times through the working day. Plaxo and Linkedin, Friends Reunited and Facebook are examples. This is algorithmic value in the corporate and personal data domain. All are examples of extended social metadata tagging where the tagged object is us. This is generally only acceptable if we have elected to accept the process. It also suggests a need to establish that we are who we say we are. There is an implicit need for social disambiguation. 4 http://eclipse.gsfc.nasa.gov/SEmono/TSE2006/TSE2006.html. Information-Centric Software Disambiguating Social Mobile Metadata 10.9.1 Voice Recognition and the Contribution of Wideband Codecs and Improved Background Noise Cancellation We have said that spatial and temporal metadata are implicitly unambiguous. Social metadata is potentially more ambiguous. How can we be sure the user of the phone/camera phone is the owner of the camera phone? Voice recognition helps in that it can uniquely identify the user. Wideband codecs and improved background noise cancellation will significantly improve present voice-recognition performance. Note that these capabilities inherently depend on a user's willingness to be identified that in turn is dependent on the user's perception of value from the function. Higher-quality codecs and better background noise control will also improve general audio capture quality, making voice tags and audio tag additions to images (manual metadata input) and user-captured ring tones (a form of mobile metadata) more functionally attractive. 10.9.2 Text-Tagging Functionality The same principal applies to speech recognition in that accuracy should steadily improve over time making speech-driven text tagging more functionally effective (manual metadata input). 10.10 The Requirement for Standardised Metadata Descriptors A combination of factors are therefore improving the functionality of automatic metadata capture and manual metadata input over time. Automatic spatial metadata capture improv