Suchergebnisse

Official data produced by the National Statistical Institutes (NSIs) have an essential place in the governmental economic and social decision-making process. Addressing official data with data mining methods rather than traditional statistical approaches is crucial to extract new information and hidden patterns. However, the usefulness of data mining methods for official statistics remains unexplored. In the present study, SILC (Survey of Income and Living Conditions) data for the year 2015 conducted by the Turkish Statistical Institute (TurkStat) are examined with data mining methods. Cross-sectional data of 36036 individuals were handled, and the variables affecting the individual income were determined, also the welfare status of the individuals was examined. To determine the socio-economic profiles of individuals, latent class analysis (LCA) and k-modes clustering analysis were used. The socio-economic status of individuals was classified using clustering and random forest (RF) algorithm models. In the LCA model with ten classes, it was obtained which probability of a newly selected individual would belong to which class. The latent class profile definitions of the individuals were obtained according to the variable values obtained from the latent classes with the highest probability. Ten clusters obtained as a result of k-modes were defined according to cluster modes, and cluster profile definitions of individuals were obtained, and also their results were compared with LCA results. In this study, in which categorical variables were considered, it was seen that LCA method provided more consistent results than k-modes method. In the RF model, where individual income is selected as a function of all nine input variables, the importance of the variables was determined. It is observed that education, occupation, and age variables were more important and made the most contribution to the RF model, respectively. In the SILC data, which is an extensive and detailed data, methods such as LCA and RF seem to be appropriate for the application of data mining and obtaining meaningful results from the data. Similar data mining processes can be used to obtain meaningful results for different official data.

Abstract
Independent data stewardship remains a core component of good data governance practice. Yet, there is a need for more robust independent data stewardship models that are able to oversee data-driven, multi-party data sharing, usage and re-usage, which can better incorporate citizen representation, especially in relation to personal data. We propose that data foundations—inspired by Channel Islands' foundations laws—provide a workable model for good data governance not only in the Channel Islands, but also elsewhere. A key advantage of this model—in addition to leveraging existing legislation and building on established precedent—is the statutory role of the guardian that is a unique requirement in the Channel Islands, and when interpreted in a data governance model provides the independent data steward. The principal purpose for this paper, therefore, is to demonstrate why data foundations are well suited to the needs of data sharing initiatives. We further examine how data foundations could be established in practice—and provide key design principles that should be used to guide the design and development of any data foundation.

Beurteilung des Datenschutzes in Unternehmen durch Datenschutzbeauftragte in Betrieben.

Data sharing is key for replication and re-use in empirical research. Scientific journals can play a central role by establishing data policies and providing technologies. In this study factors of influence for data sharing are analyzed by investigating journal data policies and author behavior in sociology. The websites of 140 journals from sociology were consulted to check their data policy. The results are compared with similar studies from political science and economics. For five selected journals with a broad variety all articles from two years are examined to see if authors really cite and share their data, and which factors are related to this.

As datafication proceeds rapidly, a large, unwieldy amount of data is available online. In this article, we ask: How valuable is this data, how is it made valuable? To answer this question, we study how online data is endowed with worth in virtual collaboration workshops. Our workshops challenged participants to assert and question the worth of online data – a challenge that participants addressed by using a set of techniques of which we describe collage, hierarchy building, and calculation. Data, we show, gains value through attachment. Thinking with attachment, we foreground affect, materiality, and the situatedness of valuing online data. As ethnographers, we study how data, as haphazard as it comes, is attached to the circumstances and stakes at hand, to ourselves and to other data. Our study contributes a conceptual perspective that attends to the shifting boundaries of the personal and the public, tensions between locality and generality, the role of contiguity, and the limits of combinatorial connectivity.

This article explores the process by which intelligent transport system technologies have further advanced a data-driven culture in public transport and traffic control. Based on 12 interviews with transport engineers and fieldwork visits to three control rooms, it follows the implementation of Real-Time Passenger Information in Dublin and the various technologies on which it is dependent. It uses the concept of 'data ratcheting' to describe how a new data-driven rational order supplants a gradualist, conservative ethos, creating technological dependencies that pressure organisations to take control of their own data and curate accessibility to outside organisations. It is argued that the implementation of Real-Time Passenger Information forms part of a changing landscape of urban technologies as cities move from a phase of opening data silos and expanded communication across departments and with citizens towards one in which new streams of digital data are recognised for their value in stabilising novel forms of city administration.

This paper explores the embedding of data producing technologies in people's everyday lives and practices. It traces how repeated encounters with digital data operate to naturalise these entities, while often blindsiding their agentive properties and the ways they get implicated in processes of exploitation and governance. I propose and develop the notion of 'data doxa' to conceptualise the way in which digital data – and the devices and platforms that stage data – have come to be perceived in Western societies as normal, necessary and enabling. The 'data doxa' concept also accentuates the enculturation of many individuals into a data sharing habitus which frames digital technologies in simplistic terms as (a) panaceas for the problems associated with contemporary life, (b) figures of progress and convenience, and (c) mediums of knowledge, pleasure and identity. I suggest that three types of data-based relations contribute to the formation of this doxic sensibility: fetishisation, habit and enchantment. Each of these relations come to mediate public understandings of digital devices and the data they generate, obscuring the multifaceted nature and hidden depths of data and their propensity to double up as technologies of exposure and discipline. As a result of this situation, imaginative educational programs and revamped regulatory frameworks are urgently needed to inform individuals about the contribution of data to the leveraging of value and power in today's digital economies, but also to protect them from experiencing data-based harms.

This article documents the "context cultures" underpinning efforts to develop regulations for collecting and reporting data in a United States public database known as Open Payments. Open Payments is a dataset published annually by the US Center for Medicare and Medicaid Services that documents the transfers of value from pharmaceutical and medical device manufacturers to physicians, prescribing non-physicians, and teaching hospitals. In the article, I show context became a manifold concern as differentially-situated actors engaged in modes of public advocacy and social action around not only what data meant, but also what it meant to make data meaningful. I show how "context" took on multiple meanings as it was brought into relationship with certain concepts (such as "light," "transparency," and "interpretation") and as stakeholders developed arguments for where they believed meaning should originate. In presenting this case, I call for further ethnographic attention to the ways in which meaning-making is enacted in relation to datasets—particularly those datasets intended to hold institutions accountable. I conclude the article meditating on the political significance of attending to various "context cultures" when putting data signification in context, along with the implications for how critical data studies scholars historicize big data epistemologies and rhetoric.

Call for Papers for a Conference in Bangalore, India, and Graz, Austria, on September 4–6, 2024. Deadline: January 19, 2024

The need to develop a system of collecting & circulating data concerning state politics is articulated. Although data regarding state politics have become more available during the late 20th century, it is argued that methods for collocating & disseminating such data remain inadequate. Three problems that have arisen from this failure to properly categorize state politics data are identified, eg, using informal networks for gathering information may reduce the empirical quality of state politics data. Consequently, it is announced that the journal State Politics & Policy Quarterly has initiated measures to periodically inform scholars about existing state politics data, especially from sources available via the Internet. Short synopses of the content of several Web sites that have state politics data readily available are also provided. 3 References. J. W. Parker

AbstractIndigenous Data Sovereignty, in its proclamation of the right of Indigenous peoples to govern the collection, ownership, and application of data, recognises data as a cultural and economic asset. The impact of data is magnified by the emergence of Big Data and the associated impetus to open publicly held data (Open Data). Aboriginal and Torres Strait Islander peoples, families and communities, heavily overrepresented in social disadvantage–related data will also be overrepresented in the application of these new technologies, but in a data landscape, Indigenous peoples remain largely alienated from the use of data and its utilization within the channels of policy power. Existing data infrastructure, and the emerging Open Data infrastructure, neither recognise Indigenous agency and worldviews nor consider Indigenous data needs. This is demonstrated in the absence of any consideration of Indigenous data issues in Open Data discussions and publication. Thus, while the potential benefits of this data revolution are trumpeted, our marginalised social, cultural and political location suggests we will not share equally in these benefits. This paper discusses the unforeseen (and likely unseen) consequences of the influence of Open Data and Big Data and discusses how Indigenous Data Sovereignty can mediate risks while providing pathways to collective benefits.

Technological development has created new forms of information, altered expectations of privacy, and given law enforcement more tools to examine that information and intrude on that privacy. One crucial facet of these changes involves internet service providers (ISPs): as people expose more of their lives to their ISPs—all the websites they visit, people they communicate with, emails they send, files they store, and more—law enforcement efforts to access that data become more and more common. But scholars and policymakers alike recognize that the existing statutory frameworks governing those efforts are based on obsolete technology and strike balances that are difficult to justify and that are both over- and underprotective of privacy.This Article proposes a new approach to regulating government investigations of data that has been shared with ISPs—one that is inspired by a legal tool designed to achieve the very balance between public benefits and private burdens that has thus far proven elusive. This tool is the Takings Clause. Under the Takings Clause, the government can acquire private property, including intangible and intellectual property, but this wide-ranging power is disciplined by the requirement that the government pursue a public purpose and pay just compensation for the property it takes. This Article argues that adapting these features of the takings framework to govern the investigation of ISP-held data would be feasible, theoretically and doctrinally sound, and normatively desirable.In making this argument, this Article addresses one of the primary problems with the various existing mechanisms by which government conducts investigations online, which is that the costs of diminished privacy fall on the civilian targets of those investigations. The result is that law enforcement does not adequately consider these costs when making investigation decisions. Acquiring information under a takings-inspired regime, by contrast, would trigger a requirement to compensate the person whose privacy has been diminished and thus impose a direct cost on the government entity conducting the investigation. This obligation to pay would force the investigating entity to be more thoughtful about which investigations are the highest priorities, most likely to yield valuable information, and most tailored to achieve their purposes.