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Orchestrating privacy-protected big data analyses of data from different resources with R and DataSHIELD.

ABSTRACT: Combined analysis of multiple, large datasets is a common objective in the health- and biosciences. Existing methods tend to require researchers to physically bring data together in one place or follow an analysis plan and share results. Developed over the last 10 years, the DataSHIELD platform is a collection of R packages that reduce the challenges of these methods. These include ethico-legal constraints which limit researchers' ability to physically bring data together and the analytical inflexibility associated with conventional approaches to sharing results. The key feature of DataSHIELD is that data from research studies stay on a server at each of the institutions that are responsible for the data. Each institution has control over who can access their data. The platform allows an analyst to pass commands to each server and the analyst receives results that do not disclose the individual-level data of any study participants. DataSHIELD uses Opal which is a data integration system used by epidemiological studies and developed by the OBiBa open source project in the domain of bioinformatics. However, until now the analysis of big data with DataSHIELD has been limited by the storage formats available in Opal and the analysis capabilities available in the DataSHIELD R packages. We present a new architecture ("resources") for DataSHIELD and Opal to allow large, complex datasets to be used at their original location, in their original format and with external computing facilities. We provide some real big data analysis examples in genomics and geospatial projects. For genomic data analyses, we also illustrate how to extend the resources concept to address specific big data infrastructures such as GA4GH or EGA, and make use of shell commands. Our new infrastructure will help researchers to perform data analyses in a privacy-protected way from existing data sharing initiatives or projects. To help researchers use this framework, we describe selected packages and present an online book (https://isglobal-brge.github.io/resource_bookdown).

SUBMITTER: Marcon Y

PROVIDER: S-EPMC8034722 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionCoronavirus disease 2019 (COVID-19) has led to radical changes in social distancing awareness and affected social relationships. Owing to large-scale lockdown, home quarantine and social distancing requirements, it was anticipated that sexual activities would be severely impacted. However, retrospective self-report studies showed that pornography use and autoerotism increased during the pandemic.AimThis study used big-data databases available on the Internet to investigate factors that modulated pornography use during the pandemic.MethodsDaily relative search volume (RSV) data from Google Trends for the period from 24 February 2020 to 13 July 2020 were extracted. Pornhub traffic data were extracted from the Pornhub Insights website, for the period from 24 February 2020 to 13 July 2020. The parameter was defined as 'percent change in traffic compared to an average day in 2019'. The number of daily new cases of COVID-19 was extracted from the database on Our World in Data.Outcome measuresThe normality of the data was examined using the Shapiro-Wilk test. All variables included in this study were non-normally distributed. Therefore, non-parametric tests or parametric tests with bootstrapping were adopted where appropriate.ResultsAccording to Google Trends, the RSV for 'pornography' increased after late March 2020, which is close to the date when the World Health Organization declared COVID-19 a global pandemic. The number of daily new cases of COVID-19 was positively correlated with the traffic of Pornhub, a popular pornography website, and the RSV for 'pornography'. Moderation analysis demonstrated a significant main effect of daily new cases of COVID-19 and the RSV for 'social distancing' in predicting Pornhub traffic/RSV for 'pornography'. Furthermore, the RSV for 'social distancing' significantly moderated the relationship between daily new cases and Pornhub traffic/RSV for 'pornography'. A stronger COVID-pornography use association was observed with increased social distancing awareness.ConclusionIncreased pornography consumption during the pandemic was observed, and it was associated with the severity of the pandemic. Social distancing awareness could be a key factor influencing interest in and use of pornography. Further studies on the changes in sexual desire and birth-rate control are worthwhile because long-term public health may be affected by the changes in sexual behaviour during the pandemic.

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Orchestrating privacy-protected big data analyses of data from different resources with R and DataSHIELD.

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