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Examining the production and dissemination of data across organizations reveals how people and processes respond to new technologies. This comparative study traces how data created by AR, VR, and wearable devices impacted organizational culture and professional practice when implemented in industry settings.

A Self-powered Reversibly Deformable Cord Sensor for Natural Human Input

Dimensionality reduction (DR) is often used for exploring the evolution patterns of a collection of dynamic ego-networks. However, DR often lacks flexibility: as analysts' questions evolve during data exploration, the low-dimensional projection remains static, limiting the depth of exploration. To address the inflexibility of DR, we designed a data transformation pipeline which enables analysts to transform dynamic ego-networks into event sequences for steering MDS to create different scatterplots of dynamic ego-networks.