This paper outlines challenges encountered in conducting a systematic scoping review of existing literature on the use of map visualizations and other related visual representations in research publications based on a case study of reported cholera outbreaks and epidemics. Unlike traditional reviews, this type of visualisation-based literature review requires the analysis of visuals included, both during article screening and data extraction. Based on a review of the use of visuals in articles on cholera, we describe the methods, search parameters and criteria adopted in the review process and its resulting outcomes. The aim is to highlight the needs of the reviewers during this process, and present the implications of the challenges encountered in relation to the development of more effective visual tools for supporting article screening and map visualization analysis in this type of novel and much-needed literature reviews. While focusing on the case study of map visualizations for cholera research, we would argue that much of the work presented here also applies to literature reviews of any research involving other forms of visualizations and visual material in general.

A surrogate outcome is a substitute measure for a patient-final outcome; a direct measure of how a person feels, functions and/or survives. Intermediate outcomes can be defined as standardised functionality measures. Surrogate outcomes are increasingly used in clinical trials to accelerate drug approvals, yet the reporting of their use as primary endpoints in study protocols remains inconsistent. This is an issue because the primary endpoint is used to determine treatment efficacy and thus is crucial for interpreting results and making regulatory decisions. We considered various supervised learning techniques for detecting surrogate outcome usage using a three-class text classification approach. We evaluated this approach on two nervous system trial protocol datasets (EUCT-NS and NS-HRA). This study serves as a proof of concept for the potential of machine learning to automate the detection of primary surrogate endpoint use in clinical trial protocols, thus addressing gaps in reporting.

Quantitative mass spectrometry based proteomics data is high dimensional and when enriched with biological information for every measured protein, it widens the scope of analysis for end users. In silico analyses where data enrichment aids analysis goals can be further enhanced with tailored visualisations to guide differential pattern activity in proteomic data. In this study, we employed the core analysis tool of a single software platform, Qiagen’s Ingenuity Pathway Analysis (IPA), which is widely used in omic analyses, to explore comparative profiling of multi-source proteomic data. The central aim was to construct added visualisations from the wealth of exportable features available in IPA to provide further visual guides for users to utilise the built in tool metrics and domain based understanding in relation to their data.

In fields such as translation studies and computational linguistics, various tools are used to analyze the content of text corpora, and extract keywords and other entities for analysis. Concordancing – arranging passages of text corpus in alphabetical order of user-defined keywords – is one of most widely used forms of text analysis. This paper describes Multi-Mosaics, a tool for text analysis using multiple implicitly linked Concordance Mosaic visualisations. Multi-Mosaics supports examining linguistic relationships within the context windows surrounding multiple extracted keywords.

Researchers working in areas such as lexicography, translation studies, and computational linguistics, use a combination of automated and semi-automated tools to analyze the content of text corpora. Concordancing - or the arranging of passages of a textual corpus in alphabetical order according to user-defined keywords - is one of the oldest and still most widely used forms of text analysis. Concordance Mosaic is an interactive concordance visualization which emphasises quantitative information such as word frequency. While Concordance Mosaic is in active use by humanities scholars, no quantitative evaluation of the technique exists. In this paper, the Concordance Mosaic is quantitatively evaluated in comparison to a typical concordance browser. The comparison is evaluated using speed and accuracy on identified corpus analysis actions.

A common task in a number of application areas is to create textual documents based on recorded audio data. Visualizations designed to support such tasks require linking temporal audio data with contextual data contained in the resulting documents. In this paper, we present a tool for the visualization of temporal and contextual links between recorded dialogues and their summary documents.

We present a tool for visualization of transcripts of multi-party dialogues, with application to the analysis of communication in medical teamwork. The visualization is based on a "temporal mosaic" metaphor, which provides a temporal overview of dialogues and supports the tasks of transcript browsing and information access, by segmenting the dialogue and laying out the keywords of the different segments on interactive visual "tiles". The tool has been tested on a corpus of transcribed dialogues among the members of a (simulated) critical care team. An analytical evaluation is presented which demonstrates the potential uses of the tool in an educational setting and highlights areas for improvements.

Comparing frequency distributions is a basic task in statistics and in disciplines that rely on statistical analysis, such as corpus linguistics. However, support for comparing word frequencies between different corpora in corpus linguistics tasks such as lexical analysis and corpus-based translation studies, is often limited to fairly basic techniques like tabular word lists. While other visualizations such as word clouds do exist, they are not widely used in linguistic analysis tasks due to their lack of precision, unsuitability to dealing with the high frequencies of common words, and lack of effective mechanisms for direct manipulation. In this paper, we propose a visualization for comparing word frequencies across two corpora using a combination of slope charts and histogram contours. An interactive implementation of this visualization is also presented. The design of visualization, and the development of the prototype, have been guided through the involvement of expert linguist users.

Concordancing, or the arranging of passages of a textual corpus in alphabetical order according to user-defined keywords, is one of the oldest and still most widely used forms of text analysis. It finds applications in areas such as lexicography, computational linguistics, translation studies and computer-assisted machine translation. Yet, the basic form of visualisation employed in the analysis of textual concordances has remained essentially the same since the keyword-in-context technique was introduced, over fifty years ago. This paper presents a generalisation of this technique as an analytical abstraction of concordances represented as undirected graphs, and then characterises keywords in terms of graph eccentricity properties. We illustrate this proposal with two distinct visual renderings: a mosaic (space-filling) display and a bi-directional hierarchical display. These displays can be used in isolation or in conjunction with traditional keyword-in-context components in an overview-plus-detail pattern, or as synchronised views. We discuss scenarios of use for these arrangements in lexicographical corpus analysis, in translation studies and in text comparison tasks.