ailable and provides full detail on the above findings. Between these two user research phases, we also performed extensive desktop research, thoroughly mapping all existing statutory data returns and other known standard datasets in the sector, and using these to refine our understanding of the current data landscape for LAs, and the possible structures for our new dataset specification. 5.3. Prototype specification We deployed our first draft specification early in the project’s life, indeed before we had completed our map of existing data items. We used the live specification as a primary reference point for continuous engagement with LA colleagues in the partnership and in other LAs. While desktop research continued, we tested approaches to presenting the specification, and identified a production approach which used Python to automate the generation of YAML data files, and associated web-based and PDF visualisations, from a local Excel information store. Once in place, we established a routine refresh of the local working document to the public-facing dataset visualisations, allowing us both to test and refine our production approach, and make our work visible to LAs colleagues as early as possible, thereby generating opportunities for feedback. 10 As our three research strands generated insight, we interpreted this into the draft specification as part of our iterative process. We also expanded the metadata associated with each aspect of the specification – adding new visualisations in response to user feedback, and developing the dataset specification itself to include more information about data definitions, sources, and connections. Code and guidance for the specification is located at the Data to Insight code repository, hosted by GitHub: GitHub - data-to-insig