Link Roundup April 2020

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Cameron Cook

UW-Madison’s American Family Insurance Data Science Institute has posted a collection of COVID-19 resources that demonstrate the contributions data science can make to better understanding the virus. These resources include projections for the virus’ spread and treatment, visualizations, research datasets and code bases, as well as stories about how data scientists are helping the efforts to combat the virus.

Semantic Scholar has made the COVID-19 Open Research Dataset (CORD-19) accessible online for download and analysis. Access to the data provides researchers with an opportunity to apply the newest methods of analysis to the data to aid in understanding the virus. The dataset was prepared through a partnership between leading researchers and the Allen Institute for AI.

Jennifer Patiño

In their series called Chart Chat, Tableau has shared a discussion of COVID-19 data visualizations. It covers the history of pandemic visualizations, different iterations of what flattening the curve might look like, and how to use data responsibly in visualizations.

Open access publisher, Frontiers, has developed a portal that connects researchers studying the COVID-19 virus to sources of funding. In addition to listing open funding calls, the portal features a dashboard that presents essential information about funding requirements, deadlines, and organizations, all of which streamlines the search for funding for researchers. You can also find resources for COVID-19 research funding, general funding, and tools to help throughout the award lifecycle from UW Madison’s Research and Sponsored Programs.

Kent Emerson

To help during the COVID-19 related campus closure, DoIT shares technology for working remotely and technology for learning remotely.

Look to Open Source Options for GIS Software

Topographic map of a hiking trail made using QGIS

Creating maps as a way to communicate data is becoming increasingly popular across and integrated into a broader set of academic disciplines. In the humanities, mapping is often used to create visual and interactive objects of  scholarship from research in subjects such as history or literature; in public health, maps can be used to show the spread or prevalence of disease. Certain mapping and geospatial data management software is prohibitively expensive, but some equally powerful open-source options have emerged, making the incorporation of mapping into research workflows more easily accessible and available. Continue reading to discover some of the most popular open-source options for geospatial data analysis and visualization. (more…)

Tool: Scalar

What Is Scalar?

Scalar is a free and open-source authoring and publishing platform that allows users to integrate multiple media types into born-digital scholarly works. Built by the Alliance for Networking Visual Culture, Scalar allows users to create publications that would be the length of an essay, article, or even a book. Scalar’s flexible content management structure means that it allows users to adapt its features for their own needs.


Data Visualization: Choosing Tools and Workflows Across the Research Process


Data Visualization can serve as a complement to statistics and as a part of your research process from analysis through publication. Visualization works with the human eye-brain system and can help a viewer see relationships, patterns, and outliers that would not otherwise be readily apparent in the data.

Data visualization as a broad term can refer to anything from a small bar graph with a few values to an elaborate poster-like display or interactive dashboard that integrates multiple graphs, maps, photographs, short annotations, and longer text.

The variety of tools and types of visualizations have varying degrees of interoperability with other data analysis tools. When choosing a tool and a workflow, a model developed by David Dibiase for application in geography can be helpful to connect the purpose of visualization with your design and communication needs. Although the model was developed for mapping, it can apply to other disciplines also as a way to help consider the audience and purpose of visualization, and help inform tool and workflow choices.


This model presents a research process with four stages:

  1. Exploration of data to reveal pertinent questions
  2. Confirmation of apparent relationships in the data in light of a formal hypothesis
  3. Synthesis or generalization of findings
  4. Presentation of the research at professional conferences and in scholarly publications
Dibiase Model

Fig. 1

DiBiase Model: Visual Thinking/Private Realm

The visual thinking, tools, and methods can change as your research stages change. During early stages of your data analysis, visualizations may complement statistical methods and help you explore the data to look for patterns or outliers. You might not show initial visualizations to anyone else, nor will they all result in meaningful insights.

The early stages are typically done privately, as an individual or small team of experts deeply involved with the research subject. At this stage of your research, the characteristics of visualization tools should support you in working efficiently with your time to generate multiple visualizations with repeatable, documentable methods. Different visual design elements, such as colors and graphic symbols, the types of visualizations, and levels of detail, are best suited to reveal different features of the data. In the early stages, the audience is an individual researcher or small team who is familiar with the data; the visualizations aren’t intended for a broader audience.

DiBiase Model: Visual Communication/Public Realm

As the research progresses, you begin to communicate ideas and results to colleagues and peers, and eventually to a broader public.

As your audience widens, the visualizations change to serve as a tool for communicating beyond the research team and possibly to an audience with less expertise in the field. Visualizations that are clear to experts might not be understood by a broader audience without the depth of knowledge or interest in the subject.

Graphic design elements become more important to help you use your visualizations to communicate your research results to an external audience. Choices of chart type, level of detail, color, symbols, typography, labels and annotation can make a difference in the clarity of communications.

A Simple Example

This simple example can help illustrate a distinction between an exploratory graph and a communication graph, potential tools, and one example workflow.

Exploratory Graph:

Exploratory Graph

Fig 2

This graph was produced in R, a language and environment that offers several statistical and graphical capabilities. R is a freely available software that featuring a programming language for handling and analyzing data, and allowing users to define and implement new functions. R is extensible through packages that can provide specialized functions applicable to a variety of domains. One strength of R for visualization in a research process is its ability to generate individual or multiple graphs through scripts that then serve as a documentation of the data handling and visualization process.

When a research project has progressed  beyond the researcher(s) closely familiar with the data, the communication value of the graphs could be enhanced by making use of R’s more advanced packages that offer additional graphing functions.

Communication Graph:


Fig. 3

It’s also possible to export a graph produced directly from the data into software that offers flexibility in design and pre-publication details. The example shown in Figure 3 was created by importing the scatterplot created in R into Adobe Illustrator, a vector-graphics software, and editing it for design elements. A strength of illustration software is that it affords flexibility to fine tune graphic design through wide choices in type, colors, shapes, annotations, and the ability to alter design element placement. A disadvantage is that a hand-editing process is prone to human errors. Because the graphic is separated from the data management environment, the editing tasks cannot be automatically replicated or easily traced.

The tools and methods that are effective for data exploration and analysis might not be the same as those for fine-tuning the visualizations for a public audience. As you work through a research process, considering the purpose and audience for your visualizations may help inform your choices in tools, methods, and efforts spent in polishing the graphic presentation.

DiBiase, David. 1990. Visualization in the Earth Sciences. Department of Geography, The Pennsylvania State University.

The R Project for Statistical Computing.

Adobe Illustrator.

WordCAKE Tool Workshop Friday 4/25


Please join us tomorrow, Friday, April 25, 1:00 in Memorial Library Commons (4th floor) for a workshop covering the collaborative development and features/use of the WordCAKE tool (link for download will be distributed to participants).  WordCAKE offers an interactive 3D experience for exploring how word frequencies change over sequential texts.

We encourage participants to bring laptops and try out the tool following the introductory presentation.

WordCAKE is a plugin for a free 3D modeling application, SketchUp, so please begin by downloading the latest version here:

The WordCAKE plugin can be downloaded here:

In SketchUp, click “Preferences” then select “Extensions” click the button to “Install Extensions” and select the WordCAKE plugin

A link to the manual with more detailed install instructions:

A sample files folder (note file naming conventions):

Word selection tool overview video: