Mapping Usability Heuristics to Medical Ethical Principles in Clinical Decision Support Design

Human factors professionals voice concerns about the slow and uneven adoption of human-centered design (HCD) principles in healthcare. With good intentions and best efforts, human factors work in healthcare continues, but to understand and to be understood we must share a language that speaks to one another. One way to facilitate this understanding is by bringing attention to how we talk about decision-making approaches in medical ethics and HCD. Both disciplines suggest efforts should be participatory, guided by a set of principles applied thoughtfully, with critical consideration to context and trade-offs. Considering the connections between usability heuristics for design and medical ethical principles may offer a bridge between disciplines and their associated dialects. In this paper, we provide three representations mapping ethical principles to design principles, including usability heuristics: a word tree visualization, narrative passages, and a table format. We believe this mapping demonstrates both that ethical design is inherently human-centric and that human-centered design is ethical design.

EMPress Enables Tree-Guided, Interactive, and Exploratory Analyses of Multi-omic Data Sets

ABSTRACT Standard workflows for analyzing microbiomes often include the creation and curation of phylogenetic trees. Here we present EMPress, an interactive web tool for visualizing trees in the context of microbiome, metabolome, and other community data scalable to trees with well over 500,000 nodes. EMPress provides novel functionality—including ordination integration and animations—alongside many standard tree visualization features and thus simplifies exploratory analyses of many forms of ‘omic data. IMPORTANCE Phylogenetic trees are integral data structures for the analysis of microbial communities. Recent work has also shown the utility of trees constructed from certain metabolomic data sets, further highlighting their importance in microbiome research. The ever-growing scale of modern microbiome surveys has led to numerous challenges in visualizing these data. In this paper we used five diverse data sets to showcase the versatility and scalability of EMPress, an interactive web visualization tool. EMPress addresses the growing need for exploratory analysis tools that can accommodate large, complex multi-omic data sets.

Subjective Norms and Academic Dishonesty : A Decision Tree Algorithm Analysis

Academic dishonesty becomes an exciting phenomenon to be examined. This research aimed to examine the effect of subjective norms on academic dishonesty. Data were collected from 426 accounting students from public and private universities in Yogyakarta, Indonesia. The data were analysed with the J48 algorithm decision tree. The interest that happened in the low subjective norms node was divided into public universities and private universities. Based on the decision of tree visualization, male students with the more extended length of study in public universities tended to have lower subjective norms but higher academic dishonesty than their counterparts. The results were discussed, and recommendations were also provided to several relevant parties.

A State-of-the-Art Survey of Tasks for Tree Design and Evaluation with a Curated Task Dataset

In the field of information visualization, the concept of ``tasks'' is an essential component of theories and methodologies for how a visualization researcher or a practitioner understands what tasks a user needs to perform and how to approach the creation of a new design. In this paper, we focus on the collection of tasks for tree visualizations, a common visual encoding in many domains ranging from biology to computer science to geography. In spite of their commonality, no prior efforts exist to collect and abstractly define tree visualization tasks. We present a literature review of tree visualization papers and generate a curated dataset of over 200 tasks. To enable effective task abstraction for trees, we also contribute a novel extension of the Multi-Level Task Typology to include more specificity to support tree-specific tasks as well as a systematic procedure to conduct task abstractions for tree visualizations. All tasks in the dataset were abstracted with the novel typology extension and analyzed to gain a better understanding of the state of tree visualizations. These abstracted tasks can benefit visualization researchers and practitioners as they design evaluation studies or compare their analytical tasks with ones previously studied in the literature to make informed decisions about their design. We also reflect on our novel methodology and advocate more broadly for the creation of task-based knowledge repositories for different types of visualizations. The Supplemental Material will be maintained on OSF:~\url{https://osf.io/u5ehs/

TaxOnTree: a tool that generates trees annotated with taxonomic information

Phylogenetic analysis is a widely used approach for analyzing and illustrating gene/protein/species evolution and is being benefited by the increasing number of species with their DNA/genome sequenced. Generating a phylogenetic tree with sequences from hundreds of species can be considered a routine task. However, tree visualization has been challenged to organize and bring by accessible means relevant information, e.g. taxonomy, about the sampled genes/proteins. Here we present TaxOnTree, a computational tool that incorporates and allows a quick accession of the taxonomic information of samples in a phylogenetic tree. TaxOnTree takes as input a single phylogenetic tree in Newick format containing gene/protein identifiers from NCBI or Uniprot databases in their leaves but TaxOnTree also allows users to have as input a protein identifier, a single protein in FASTA, a list of protein accessions, or a(n) (un)aligned multi-FASTA file. Non-tree inputs are submitted to a phylogenetic reconstruction pipeline implemented into TaxOnTree. The tree provided by the user or generated by the pipeline is converted to Nexus format and then automatically annotated with the taxonomic information of each sample comprising the tree. The taxonomic information is retrieved by web requests from NCBI or Uniprot servers or from a local MySQL database and annotated as tags in the tree nodes. The final tree archive is in Nexus format and should be opened with FigTree software which allows visual inspection, by branch coloring or tip/node labeling, of the taxonomic information incorporated in the tree. TaxOnTree provides prompt inspection of the taxonomic distribution of orthologs and paralogs. It can be used for manual curation of taxonomic/phylogenetic scenarios and coupled to any tool that links homologous sequences to a seed sequence. TaxOnTree provides computational support to help users to inspect phylogenetic trees with a taxonomic view, even without being taxonomy experts. TaxOnTree is available at http://bioinfo.icb.ufmg.br/taxontree.

EMPress enables tree-guided, interactive, and exploratory analyses of multi-omic datasets

Standard workflows for analyzing microbiomes often include the creation and curation of phylogenetic trees. Here we present EMPress, an interactive tool for visualizing trees in the context of microbiome, metabolome, etc. community data scalable beyond modern large datasets like the Earth Microbiome Project. EMPress provides novel functionality—including ordination integration and animations—alongside many standard tree visualization features, and thus simplifies exploratory analyses of many forms of ‘omic data.

Towards Identification and Mitigation of Task-Based Challenges in Comparative Visualization Studies

The effectiveness of a visualization technique is dependent on how well it supports the tasks or goals of an end-user. To measure the effectiveness of a visualization technique, researchers often use a comparative study design. In a comparative study, two or more visualization techniques are compared over a set of tasks and commonly measure human performance in terms of task accuracy and completion time. Despite the critical role of tasks in comparative studies, the current lack of guidance in existing literature on best practices for task selection and communication of research results in evaluation studies is problematic. In this work, we systematically identify and curate the task-based challenges of comparative studies by reviewing existing visualization literature on the topic. Furthermore, for each of the presented challenges we discuss the potential threats to validity for a comparative study. The challenges discussed in this paper are further backed by evidence identified in a detailed survey of comparative tree visualization studies. Finally, we recommend best practices from personal experience and the surveyed tree visualization studies to provide guidelines for other researchers to mitigate the challenges. The survey data and a free copy of the paper is available at https://osf.io/g3btk/

Khmer Treebank Construction via Interactive Tree Visualization

Despite the fact that there are a number of researches working on Khmer Language in the field of Natural Language Processing along with some resources regarding words segmentation and POS Tagging, we still lack of high-level resources regarding syntax, Treebanks and grammars, for example. This paper illustrates the semi-automatic framework of constructing Khmer Treebank and the extraction of the Khmer grammar rules from a set of sentences taken from the Khmer grammar books. Initially, these sentences will be manually annotated and processed to generate a number of grammar rules with their probabilities once the Treebank is obtained. In our experiments, the annotated trees and the extracted grammar rules are analyzed in both quantitative and qualitative way. Finally, the results will be evaluated in three evaluation processes including Self-Consistency, 5-Fold Cross-Validation, Leave-One-Out Cross-Validation along with the three validation methods such as Precision, Recall, F1-Measure. According to the result of the three validations, Self-Consistency has shown the best result with more than 92%, followed by the Leave-One-Out Cross-Validation and 5-Fold Cross Validation with the average of 88% and 75% respectively. On the other hand, the crossing bracket data shows that Leave-One-Out Cross Validation holds the highest average with 96% while the other two are 85% and 89%, respectively.