DS Wizard Meets DAISY: A Romance Solving Data Protection Requirements in Data Management Planning

This report summarises our activities and achievements in integrating the Data Stewardship Wizard (DSW) and Data Information System (DAISY) tools during the ELIXIR BioHackathon Europe 2021. As a data information system for GDPR compliance, DAISY is focused on a single goal – gathering all information required for GDPR accountability of biomedical research projects. On the other hand, DSW is very flexible and can be used beyond data management planning. We worked on the integration between both tools on two fronts. Firstly, we created a new Knowledge Model in DSW together with a document output template to be able to generate a data protection impact assessment (DPIA). Secondly, we introduced a new integration type between projects in DSW and DAISY that allows the querying of DAISY data upon document generation in DSW. Both of these independent activities brought successful results that were polished and published after the actual BioHackathon. Finally, we provide the related materials as an on-demand training course in the ELIXIR eLearning Platform.

A Reproducible Data Analysis Workflow

In this tutorial, we describe a workflow to ensure long-term reproducibility of R-based data analyses. The workflow leverages established tools and practices from software engineering. It combines the benefits of various open-source software tools including R Markdown, Git, Make, and Docker, whose interplay ensures seamless integration of version management, dynamic report generation conforming to various journal styles, and full cross-platform and long-term computational reproducibility. The workflow ensures meeting the primary goals that 1) the reporting of statistical results is consistent with the actual statistical results (dynamic report generation), 2) the analysis exactly reproduces at a later point in time even if the computing platform or software is changed (computational reproducibility), and 3) changes at any time (during development and post-publication) are tracked, tagged, and documented while earlier versions of both data and code remain accessible. While the research community increasingly recognizes dynamic document generation and version management as tools to ensure reproducibility, we demonstrate with practical examples that these alone are not sufficient to ensure long-term computational reproducibility. Combining containerization, dependence management, version management, and dynamic document generation, the proposed workflow increases scientific productivity by facilitating later reproducibility and reuse of code and data.

SBTM: A joint sentiment and behaviour topic model for online course discussion forums

Large quantities of textual posts are increasingly generated in course discussion forums, and the accumulation of these data greatly increases the cognitive loads on online participants. It is imperative for them to automatically identify the potential semantic information derived from these textual discourse interactions. Moreover, existing topic models can discover the latent topics or sentimental polarities from textual data, but these models typically ignore the interactive ways of discussing topics, thus making it difficult to further construct topics’ semantic space from the perspective of document generation. To solve this issue, we proposed a joint sentiment and behaviour topic model called SBTM, which was an unsupervised approach for automatic analysis of learners’ discussed posts. The results demonstrated that SBTM was quantitatively effective on both model generalisation and topic exploration, and rich topic content was qualitatively characterised. Furthermore, the model can be potentially employed in some practical applications, such as information summarisation and behaviour-oriented personalised recommendation.