Urban planners are increasingly working with ideas around datafied cities, such as platform urbanism, to understand urban life and changes with technology. This article seeks to assist urban planners in these efforts by analysing and mapping the qualities of platform urbanism. Drawing on a dataset of approximately 100 examples that detail urban data practices, we trace some of the current tendencies that are shaping the nature and dynamics of platform urbanism. While we identify no unifying narrative or overarching pattern to our data, we interpret this as supporting Barns’ (2019) notion of a pivot towards platforms. We argue this through exploring the interoperability between data sources and domains (vertical and horizontal integration), identifying elements of how platforms intermediate urban life through their growth in different sectors and the use of geolocation, and note the different artefacts that contribute to platform urbanism. We also note a concerning dynamic where city administration becomes ‘locked in’ to specific corporate products and interests, and thereby ‘locked out’ from alternatives. We discuss this in the context of social inclusion and what this means for urban planners, including the fragility of corporate platforms and what platforms urbanism means for social relationships in the city.

The FAIR (findable, accessible, interoperable, and re-usable) principles and practice recommendations provide high level guidance and recommendations that are not research-domain specific in nature. There remains a gap in practice at the data provider and domain scientist level demonstrating how the FAIR principles can be applied beyond a set of generalist guidelines to meet the needs of a specific domain community.We present our insights developing FAIR thresholds in a domain specific context for self-governance by a community (agricultural research). 'Minimum thresholds' for FAIR data are required to align expectations for data delivered from providers' distributed data stores through a community-governed federation (the Agricultural Research Federation, AgReFed). Data providers were supported to make data holdings more FAIR. There was a range of different FAIR starting points, organisational goals, and end user needs, solutions, and capabilities. This informed the distilling of a set of FAIR criteria ranging from 'Minimum thresholds' to 'Stretch targets'. These were operationalised through consensus into a framework for governance and implementation by the agricultural research domain community. 2Wong et al.

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This study delineates the relative importance of organisational, research discipline and application domain factors in influencing researchers' data sharing practices in Australia's national scientific and industrial research agency. We surveyed 354 researchers and found that the number of data deposits made by researchers were related to the openness of the data culture and the contractual inhibitors experienced by researchers. Multi-level modelling revealed that organisational unit membership explained 10%, disciplinary membership explained 6%, and domain membership explained 4% of the variance in researchers' intentions to share research data. However, only the organisational measure of openness to data sharing explained significant unique variance in data sharing. Thus, whereas previous research has tended to focus on disciplinary influences on data sharing, this study suggests that factors operating within the organisation have the most powerful influence on researchers' data sharing practices. The research received approval from the organisation's Human Research Ethics Committee (no. 014/18).