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Next generation sequencing applied in pigs have recently produced re-sequenced pig genome data from different individuals belonging to a large variety of breeds. The availability of these large datasets is opening new opportunities to mine public nucleotide archives and identify mutations that could putatively affect economic relevant traits. Moreover, resequencing data from pooled pig DNA could provide cost-effective whole genome information from a large number of animals. In this study, we mined 110 individual pig genomes retrieved from the European Nucleotide Archive and from proprietary datasets generated from pigs of 28 different breeds. This dataset was integrated from 8 pooled whole genome resequencing datasets generated from 35 individuals each from 8 distinct commercial or autochthonous breeds (Italian Large White, Italian Duroc, Italian Landrace, Apulo Calabrese, Cinta Senese, Casertana, Mora Romagnola, Nero Siciliano), respectively. Individual and pooled pig genome datasets were searched for polymorphisms in 135 annotated candidate genes, including 25 genes involved in androsterone and skatole biochemical related pathways. Short reads from these genomes were aligned using bowtie to a customized reference sequence generated from the reference pig genome, including sequence of selected genes (with depth ranging from 4 to 40X for each genome). A total of 100k variants were identified (2.3% in coding regions with about 500 missense mutations and a few other potential functional mutations). About 15% of these numbers refers to genes encoding enzymes involved in the androsterone and skatole biochemical pathways. This study provided an overview of the variability in targeted gene regions potentially involved in determining boar taint in pigs. Partially funded by European Union's H2020 RIA program (grant agreement no. 634476). Abstract reflects the authors' view. European Union Agency is not responsible for any use that may be made of the information it contains.

Barack Obama's 2012 re-election campaign relied heavily on applying social science findings and methods, drawing on large, linked data sets to target, persuade, and turn out voters. As the Canadian Election Study (CES) data sets include detailed data on respondents' geographic location, they are well suited for research that integrates statistical and GIS analysis. Using CES data, Canadian census data, election results, and geospatial data, we will demonstrate how multiple, diverse data sets can be combined and analyzed with a mix of statistical and GIS methods. The CES and other data, and the methods, should be of interest to political scientists, sociologists, and other social scientists. Our illustrated examples are also structured to guide others in using similar combined methods on different data, or in helping users take full advantage of comparably rich data for better research and more vivid presentation of results.

This article describes datasets for the FPwatch Project, a comprehensive facility‐based family planning survey conducted by Population Services International in five countries in Africa and Asia from 2015 to 2017. Contents cover research design and background, methodology, sample selection, data collection, an overview of FPwatch indicators, and quality assurance measures taken. These datasets from the Democratic Republic of Congo, Ethiopia, India, Myanmar, and Nigeria complement other facility‐based family planning surveys and are unique in their large‐scale, standardized methodology, and comprehensive sampling approach. In addition, all datasets but Myanmar (private only) include both private and public facilities, a feature that gives a more complete picture of the family planning supply environment. Because of these factors, the data is well suited to inform global family planning efforts.

In the current framework of human rights, data privacy is finding its home as an independent human right, separate from its historical home under the umbrella of general privacy. However, there is no consistent system or standard for defining this right, and different regions require substantially different levels of protection. This inconsistency has allowed for corporations, by way of their executive officers, to avoid or completely ignore the requirements imposed by many countries. Moreover, the penalties in many regions are not severe enough to incentivize corporations to change their behavior. The lack of a truly global system and standard for enforcing this right, and the specific lack of pressure on the officers that direct corporate policy, has allowed data privacy violations to go severely under-checked. This Note seeks to provide a novel solution for tackling corporate holdup in complying with data privacy laws. This Note examines the historical roots of data privacy as a human right, discusses its similarity to resources that have been considered public utilities, and provides examples of instances where the right to data privacy has been ignored by corporate officers. By modifying the United Nations' existing sanction procedure and jurisdiction, this Note proposes that the United Nations would be able to target corporate officers individually for their roles in data privacy violations. By leveraging personal liability for noncompliance, the United Nations could generate a global sense of accountability to the modern, human right to privacy in one's personal data.

This article examines the role of open government data in capital accumulation. Open government data is a relatively new phenomenon that involves the pro-active and regular release of government data, in the form of downloadable records, for use and re-use by anyone. Private capitalist enterprises are among those who make use of such data. Publicly produced data is transferred into the hands of private capital through a non-rivalrous form of enclosure. Capital then uses government datasets to create new commodities by the application of skilled labor to the data, in processes including data mashup and data visualization. Inherent in all commodity production is the extraction of surplus value from labor by capital. This, together with the process of enclosure, provides opportunities for capital accumulation from open government data.

Prof. Sebastian Stier, Scientific Director of Computational Social Science at GESIS and Professor of CSS at the University of Mannheim, discusses how web tracking data can inform social science questions. We discuss the data structure of web browsing data, how it is collected, and the types of incentives used to recruit participants. Prof. Stier also...
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