Suchergebnisse

Data localization laws are emerging as a pernicious form of non-tariff barrier which significantly harms the growth of trade in a digitally powered world. An International Political Economy approach provides a more comprehensive analysis of the policy rationale behind such laws, as compared to a purely economic approach, which only focuses on economic losses resulting from protectionism. On a closer analysis, it is found that different countries may have different policy rationales for implementing data localization laws – while some promote their domestic ICT industry through forced localization measures, others have concerns regarding national security, privacy, and ensuring sovereign control in the highly privatized world of internet governance. It is not always possible to demarcate the "protectionist" rationale from that of rational "data protection". To address data localization effectively and facilitate digital trade, it is not sufficient to negotiate for free flow of data in trade agreements without Governments and companies being open and transparent about the related issues of privacy, national security and consumer protection. Particularly, the role of US Government as well as leading US-based technology companies will be instrumental in this regard. At the same time, it may be necessary to develop policy initiatives both to encourage transparent and clear international standards on data security, as well as to enable higher levels of digital innovation in developing countries such that they can harness the benefits of evolving internet technologies.

Enth. u.a.: "Ich habe doch nichts zu verbergen"/ E. Morozov. - Politikfeld Big Data/ C. Stöcker. - Von Big zu Smart: zu Sustainable?/ R. Kreibich. - Dr. Algorithmus? Big Data in der Medizin/ P. Langkafel. - Big Data und die Macht des Marktes/ Y. Hofstetter

An editorial on the recent data and electronic resources changes made by the federal government.

Personal data is increasingly positioned as a valuable asset. While individuals generate and expose ever-expanding volumes of personal information online, certain tech companies have built their business models on the personal data they gather. In this context, lawmakers are revising data protection regulations in order to provide individuals with enhanced rights and set new rules regarding the way corporations collect, manage, and share personal information. We argue that recent data protection regulatory frameworks such as the European Union's General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA) are fundamentally about data management. Yet, there have been no attempts to analyze the regulations in terms of their implications on the data life cycle. In this paper, we systematically analyze the GDPR and the CCPA, and identify their implications on the data life cycle. To synthesize our findings, we propose a semi-formal notation of the resulting changes on the personal data life cycle, in the form of a process and data model governed by business rules, consolidated in a reference personal data life cycle model for data protection. To the best of our knowledge, this study represents one of the first attempts to provide a data-centric view on data protection regulatory requirements.

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.

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.

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.

Beurteilung des Datenschutzes in Unternehmen durch Datenschutzbeauftragte in Betrieben.

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

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.

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