Nils Gehlenborg | bioRxiv Science

bioRxiv2016

From Visual Exploration to Storytelling and Back Again

A major challenge of data-driven biomedical research lies in the collection and representation of data provenance information to ensure reproducibility of findings. In order to communicate and reproduce multi-step analysis workflows executed on datasets that contain data for dozens or hundreds of samples, it is crucial to be able to visualize the provenance graph at different levels of aggregation. Most existing approaches are based on node-link diagrams, which do not scale to the complexity of typical data provenance graphs. In our proposed approach we reduce the complexity of the graph using hierarchical and motif-based aggregation. Based on user action and graph attributes a modular degree-of-interest (DoI) function is applied to expand parts of the graph that are relevant to the user. This interest-driven adaptive provenance visualization approach allows users to review and communicate complex multi-step analyses, which can be based on hundreds of files that are processed by numerous workflows. We integrate our approach into an analysis platform that captures extensive data provenance information and demonstrate its effectiveness by means of a biomedical usage scenario.

Bioinformatics↗

bioRxiv2016

SATORI: A System for Ontology-Guided Visual Exploration of Biomedical Data Repositories

The ever-increasing number of biomedical data sets provides tremendous opportunities for re-use but current data repositories provide limited means of exploration apart from text-based search. Ontological metadata annotations provide context by semantically relating data sets. Visualizing this rich network of relationships can improve the explorability of large data repositories and help researchers find data sets of interest. We developed SATORI--an integrative search and visual exploration interface for the exploration of biomedical data repositories. The design is informed by a requirements analysis through a series of semi-structured interviews. We evaluated the implementation of SATORI in a field study on a real-world data collection.SATORI enables researchers to seamlessly search, browse, and semantically query data repositories via two visualizations that are highly interconnected with a powerful search interface. SATORI is an open-source web application,which is freely available at http://satori.refinery-platform.org and integrated into the Refinery Platform.

Bioinformatics↗

bioRxiv2016

AVOCADO: Visualization of Workflow-Derived Data Provenance for Reproducible Biomedical Research

A major challenge of data-driven biomedical research lies in the collection and representation of data provenance information to ensure reproducibility of findings. In order to communicate and reproduce multi-step analysis workflows executed on datasets that contain data for dozens or hundreds of samples, it is crucial to be able to visualize the provenance graph at different levels of aggregation. Most existing approaches are based on node-link diagrams, which do not scale to the complexity of typical data provenance graphs. In our proposed approach we reduce the complexity of the graph using hierarchical and motif-based aggregation. Based on user action and graph attributes a modular degree-of-interest (DoI) function is applied to expand parts of the graph that are relevant to the user. This interest-driven adaptive provenance visualization approach allows users to review and communicate complex multi-step analyses, which can be based on hundreds of files that are processed by numerous workflows. We integrate our approach into an analysis platform that captures extensive data provenance information and demonstrate its effectiveness by means of a biomedical usage scenario.

Bioinformatics↗