The State of Sustainable Research Software: Results from the Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE5.1)

This article summarizes motivations, organization, and activities of the Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE5.1) held in Manchester, UK in September 2017. The WSSSPE series promotes sustainable research software by positively impacting principles and best practices, careers, learning, and credit. This article discusses the Code of Conduct, idea papers, position papers, experience papers, demos, and lightning talks presented during the workshop. The main part of the article discusses the speed-blogging groups that formed during the meeting, along with the outputs of those sessions

Predicting whether users view dynamic content on the world wide web

Dynamic micro-content—interactive or updating widgets and features—is now widely used on the Web, but there is little understanding of how people allocate attention to it. In this paper we present the results of an eye tracking investigation examining how the nature of dynamic micro-content influences whether or not the user views it. We propose and validate the Dynamic Update Viewing-likelihood (DUV) model, a Chi-Squared Automatic Interaction Detector (CHAID) model that predicts with around 80 % accuracy whether users view dynamic updates as a function of how they are initiated, their size and their duration. The model is constructed with data from live websites and does not rely on knowledge of the user’s task to make its predictions, giving it a high-level of external validity. We discuss one example of its application: informing how dynamic content should be presented in audio via assistive technology for people with visual impairments

How Researchers Use Diagrams in Communicating Neural Network Systems

Neural networks are a prevalent and effective machine learning component, and their application is leading to significant scientific progress in many domains. As the field of neural network systems is fast growing, it is important to understand how advances are communicated. Diagrams are key to this, appearing in almost all papers describing novel systems. This paper reports on a study into the use of neural network system diagrams, through interviews, card sorting, and qualitative feedback structured around ecologically-derived examples. We find high diversity of usage, perception and preference in both creation and interpretation of diagrams, examining this in the context of existing design, information visualisation, and user experience guidelines. Considering the interview data alongside existing guidance, we propose guidelines aiming to improve the way in which neural network system diagrams are constructed.Comment: 19 pages, 6 tables, 3 figure