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This chapter details a blind date between two researchers who have very different notions about the nature of data and the ethos and practices of science. One is an electronic engineer, while the other is an anthropologist. The anthropologist studies how digital technology is built and used, examining the politics and praxes of some start-up companies who were developing new apps. Meanwhile, the electronic engineer works on a sound-sensing network for monitoring and modelling background noise across the city. The chapter then looks at their debate on data creation and collection. The anthropologist makes a point about scientific practice, arguing that the electronic engineer is practising mechanical objectivity — trying to minimize biases, errors, calibration issues, and so on — but it is still set up in their vision, based on their education and experience, and compromising for circumstance. Thus, they are still making choices that influence the outcome.

This chapter details a conversation between open data advocates and a civil servant in charge of the process, which reveals the challenges of getting government data made open. Without an injection of funds, an open data initiative called the Regional Data Lab, was in danger of winding down. Government had very little interest in making their data available, and even less enthusiasm for spending money during austerity. And open data was not free data; somebody had to pay for the labour of preparing data for release and building the necessary data infrastructure. What the Regional Data Lab does is take what data are already openly available and make them useable for those that lack the skills to build their own tools so they can use them in formulating policy. Rather than negotiating separate contracts every time, it would make more sense to simply centrally fund the Regional Data Lab to provide a suite of core data services. However, the advocates are more interested in the development of a national open data repository and access to more data, and a coordinated approach to providing data analytics for the public sector.

This chapter discusses issues of data quality and veracity in open datasets, using a variety of examples from the Irish data system. These examples include the Residential Property Price Register (RPPR), the Dublin Dashboard project, the TRIPS database, and Irish crime data. There are a number of issues with Irish crime data, such as crimes being recorded in relation to the police stations that handle them, rather than the location they are committed. There are also issues in the standardization of crime categorization, with some police officers recording the same crimes in slightly different ways, and also in timeliness of recording. Moreover, there are difficulties of retrieving data from the crime management software system. In addition to errors, every dataset has issues of representativeness — that is, the extent to which the data faithfully represents that which it seeks to measure. In generating data, processes of extraction, abstraction, generalization and sampling can introduce measurement error, noise, imprecision and bias. Yet internationally, there has been much work expended on formulating data-quality guidelines and standards, trying to get those generating and sharing data to adhere to them, and promoting the importance of reporting this information to users.

This chapter assesses the transitory nature of data and its deletion, by either design or accident. In the data-driven world we live in, while it sometimes seems that data lasts forever, they all pass through a lifecycle. For the majority of data points, this cycle passes very quickly as the data are transitory — created, processed, and deleted in micro-seconds. Value is extracted, or they are never examined or processed, and then they are discarded because they have little further utility and there is no point wasting resources storing them. In some cases, useful data might be deleted accidentally, or without forethought. Given the massive amounts of data presently being produced, often only a sample are retained and stored rather than a full set. Indeed, the data we store in archives and repositories is often derived data. In the case of data brokers, producing derived data is also a means to bypass the fair-information-practice principle of data minimization which states that data should only be used for the purpose for which it was intended. Similarly, metadata — factual information about data — is often retained rather than the data themselves.

This chapter focuses on the role of finance and the politics of collaboration, charting the development of the Digital Repository of Ireland (DRI). DRI have been beset with institutional politics concerning its framing, development, and operation. The future funding issue was just the latest example in a long list of fraught exchanges that could be traced back to its original conception and funding mechanism. The DRI was born out of a funding opportunity, but seemed destined to die due to a funding failure. Without a political solution, the data life cycle would turn full circle much more quickly than initially anticipated. Unless there is a means of covering the costs for labour, equipment and other essential inputs, data are not generated or stored, and thus cannot be used or shared. Even in open data projects, the data might be free to use but they were not free to create, or to process and host.