StoryFacets: A design study on storytelling with visualizations for collaborative data analysis

Tracking the sensemaking process is a well-established practice in many data analysis tools, and many visualization tools facilitate overview and recall during and after exploration. However, the resulting communication materials such as presentations or infographics often omit provenance information for the sake of simplicity. This unfortunately limits later viewers from engaging in further collaborative sensemaking or discussion about the analysis. We present a design study where we introduced visual provenance and analytics to urban transportation planning. Maintaining the provenance of all analyses was critical to support collaborative sensemaking among the many and diverse stakeholders. Our system, STORYFACETS, exposes several different views of the same analysis session, each view designed for a specific audience: (1) the trail view provides a data flow canvas that supports in-depth exploration + provenance (expert analysts); (2) the dashboard view organizes visualizations and other content into a space-filling layout to support high-level analysis (managers); and (3) the slideshow view supports linear storytelling via interactive step-by-step presentations (laypersons). Views are linked so that when one is changed, provenance is maintained. Visual provenance is available on demand to support iterative sensemaking for any team member.

Ecological insights from three decades of animal movement tracking across a changing Arctic

The Arctic is entering a new ecological state, with alarming consequences for humanity. Animal-borne sensors offer a window into these changes. Although substantial animal tracking data from the Arctic and subarctic exist, most are difficult to discover and access. Here, we present the new Arctic Animal Movement Archive (AAMA), a growing collection of more than 200 standardized terrestrial and marine animal tracking studies from 1991 to the present. The AAMA supports public data discovery, preserves fundamental baseline data for the future, and facilitates efficient, collaborative data analysis. With AAMA-based case studies, we document climatic influences on the migration phenology of eagles, geographic differences in the adaptive response of caribou reproductive phenology to climate change, and species-specific changes in terrestrial mammal movement rates in response to increasing temperature.

The ENIGMA Sports Injury Working Group: an International Collaboration to Further our Understanding of Sports-Related Brain Injury

Sports-related brain injury is very common, and the potential long-term effects include a widerange of neurological and psychiatric symptoms, and potentially neurodegeneration. Aroundthe globe, researchers are conducting neuroimaging studies on primarily homogenoussamples of athletes. However, neuroimaging studies are expensive and time consuming, andthus current findings from studies of sports-related brain injury are often limited by smallsample size. Further, current studies apply a variety of neuroimaging techniques and analysistools which limit comparability among studies. The ENIGMA Sports Injury working group aimsto provide a platform for data sharing and collaborative data analysis thereby leveragingexisting data and expertise. By harmonizing data from a large number of studies from aroundthe globe, we will work towards reproducibility of previously published findings and towardsaddressing important research questions with regard to diagnosis, prognosis, and efficacy oftreatment for sport-related brain injury. Moreover, the ENIGMA Sports Injury working group iscommitted to providing recommendations for future prospective data acquisition to enhancefurther, both, data quality and scientific rigor.

Secure and Scalable Document Similarity on Distributed Databases: Differential Privacy to the Rescue

Privacy-preserving collaborative data analysis enables richer models than what each party can learn with their own data. Secure Multi-Party Computation (MPC) offers a robust cryptographic approach to this problem, and in fact several protocols have been proposed for various data analysis and machine learning tasks. In this work, we focus on secure similarity computation between text documents, and the application to k-nearest neighbors (k-NN) classification. Due to its non-parametric nature, k-NN presents scalability challenges in the MPC setting. Previous work addresses these by introducing non-standard assumptions about the abilities of an attacker, for example by relying on non-colluding servers. In this work, we tackle the scalability challenge from a different angle, and instead introduce a secure preprocessing phase that reveals differentially private (DP) statistics about the data. This allows us to exploit the inherent sparsity of text data and significantly speed up all subsequent classifications.

Drawing as transcription: how do graphical techniques inform interaction analysis?

Drawing as a form of analytical inscription can provide researchers with highly flexible methods for exploring embodied interaction. Graphical techniques can combine spatial layouts, trajectories of action and anatomical detail, as well as rich descriptions of movement and temporal effects. This paper introduces some of the possibilities and challenges of adapting graphical techniques from life drawing and still life for interaction research. We demonstrate how many of these techniques are used in interaction research by illustrating the postural configurations and movements of participants in a ballet class. We then discuss a prototype software tool that is being developed to support interaction analysis specifically in the context of a collaborative data analysis session.

Drawing as transcription: how do graphical techniques inform interaction analysis?

Drawing as a form of analytical inscription can provide researchers with highly flexible methods for exploring embodied interaction. Graphical techniques can combine spatial layouts, trajectories of action and anatomical detail, as well as rich descriptions of movement and temporal effects. This paper introduces some of the possibilities and challenges of adapting graphical techniques from life drawing and still life for interaction research. We demonstrate how many of these techniques are used in interaction research by illustrating the postural configurations and movements of participants in a ballet class. We then discuss a prototype software tool that is being developed to support interaction analysis specifically in the context of a collaborative data analysis session.