<p>Through the experiences of building several information infrastructures in Australia we have come to wanting to calculate the properties of data licenses in as automated a fashion as possible. To do this, we have trialed decomposing licenses into individual requirements, building on the Creative Commons Rights Expression Language (CC REL) information model (http://creativecommons.org/ns). Such a decomposition, along with the separation of rights management from licenses as per the Open Data Rights Statement vocabulary (ODRS) model (http://schema.theodi.org/odrs/), allows us to model very many different licenses as simple collections of common requirements. In Car & Stenson (2015) we posited requirement resolution actions that systems or people can undertake that satisfy individual requirements and show how systems can b e made to automatically satisfy certain classes of requirements but certainly not all. <p>We are not currently able to automate every aspect of license requirement adherence and we believe that full automation is impossible, however every bit of automation improves the efficiency of data delivery. <p>In this presentation we report on some aspects of our license model, including the modelling of requirements and their resolutions. We also show how such modelling can enhances data access by comparing the status quo and possible future delivery of data via a large multi -agency, Australian data generation project; the Bioregional Assessments Programme.

One of the well-established methods used to ease data sharing between organisations and even teams within organisations is to use standards for data structure, metadata and interfaces. Standards are a form of agreement, as are MoUs, charters, deeds, licences, rules of the road and even the definitions for words. Man y of these other sorts of agreements are also important for data sharing communities too. In this paper we look to improve the efficiency of dealing with different forms of agreement within a data sharing scenario by presenting a prototype agreements ontology which models agreements themselves as ¿things¿ and the relationships between them and between them and data and them and agents. Having an agreements ontology allows us to start automating tasks that require knowledge of them. This may take the form of data repositories that can make intelligent choices about how to deliver or with old data without human intervention. We position this ontology as a 'middle' ontology, that is one which specializes well-known, abstract, upper ontologies and is able to be used fairly widely but is expected to be used in particular contexts in conjunction with detailed, domain-specific, lower ontologies. We have relied on existing agent, data manipulation, and metadata ontologies where possible and as such we specialise the ORG and FOAF ontologies, the PROV ontology and DCAT and ODRS ontologies for those areas respectively. This paper and ontology supports work that we report elsewhere at SciDataCon2016, namely The Role of Social Architecture in Information Infrastructure (Box & Lemon) and Describing and Automating Requirements within Licenses and their Resolutions (Car & Stenson).