The Image and Spatial Data Analysis division conducts research and development in general purpose data cyberinfrastructure, addressing specifically the growing need to make use of large collections of non-universally accessible, or individually-managed, data and software (i.e. executable data). We attempt to address these needs through the development of a common suite of internally and externally created open source tools/platforms that provide means of auto and assisted curation for data/software collections. To acquire some of the needed high level metadata not provided with un-curated data we make heavy use of techniques founded in artificial intelligence, machine learning, computer vision, and natural language processing. To close the gap between the state of the art of these fields and current needs, while also providing a sense of oversight many of our domain users desire, we attempt to keep the human in the loop wherever possible by incorporating elements of social curation, crowd sourcing, and error analysis. Given the ever growing urgency to gain benefit from the deluge of un-curated data we push for the adoption of solutions derived from these relatively young fields, highlighting the value of having tools to deal with this data where there would be nothing otherwise. Attempting to follow in the footsteps of the great software cyberinfrastructure successes of NCSA (i.e. mosaic, httpd, and telnet) we attempt to address these scientific and industrial needs in a manner that is also applicable to the general public. By catering toward broad appeal rather than focusing on a niche within the total possible users we aim at stimulating uptake and providing a life for our software solutions beyond funded project deliverables.