2024 NNLM Data Visualization Challenge: Mirian Ramirez

Posted by kherzog on May 30th, 2024 Posted in: Blog

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2024 DVC Winner Profile: Mirian Ramirez (MR), Ruth Lilly Medical Library, Indiana University School of Medicine

Complex Visualization (First Place)
“Global Snapshot: Physician Distribution and Density of Physicians per 1000 population- Worldwide 2021”

NEC: Mirian, congratulations on your winning submission! We are so happy you decided to participate in the 2024 NNLM Data Visualization Challenge and will now love to get to know you better.  Please tell us a little about yourself and about your background in data visualization.

MR: I currently work as a Research Metrics Librarian for the Ruth Lilly Medical Library at the Indiana University School of Medicine. My role involves analyzing large datasets related to research outputs, impact metrics, and operational metrics. As part of my job and my work, I use data visualization tools to communicate insights from this data to various stakeholders through effective data visualization.

As for my background in data visualization, I first became interested in it during my work as a knowledge management specialist; while searching for software tools to create interactive visualizations, I was captivated by the emerging tools that allowed for the creation of well-designed, appealing, and visually striking charts and graphs, even without an extensive background in informatics or coding. I have been continuously exploring new tools in data visualization, driven by a desire to effectively communicate data-driven stories and compelling visuals, especially in my presentations and reports.

Data and knowledge visualization is a field that combines creativity, design, aesthetics, and critical thinking but also considers ethical and social responsibility principles. considerations to make a relevant impact on the society or audience and at the same time minimize potential risks or harmful consequences. Responsible design is a field that I’m eager to learn and understand. I plan to continue learning and improving my skills in data visualization and data storytelling by exploring software tools and AI-powered tools to use in data cleaning and preparation and to get recommendations of the most appropriate chart types and visualizations to effectively communicate information.

NEC: Now, walk us through the creative process behind your winning visualization. What were the key steps and decisions that led to its development? Why did you choose to display the data as you did?

MR: The process started with searching the datasets available on and comparing the data available on worldwide statistics of Health Personnel. After downloading the dataset, I needed to understand it to begin the test of what type of chart and visualization worked better to showcase what I needed to highlight. While working on this I looked for inspiration revisiting other visualization using a Pinterest board that I keep where I save comprehensive and advanced data visualization examples from other users. Also, I did some tests in my favorite tools, RawGraphs and Datawrapper, to understand what was feasible to do in a short period of time.

After cleaning and coding the dataset in Excel, I began to run the mapping samples in RawGraphs, which is the tool I selected to help me generate the raw chart. During the process, I did quick research on fonts and layouts that work better for static visualizations, but the real inspiration was from other people’s visualizations. For the best colors to use in my visualization, I prompted an AI chatbot to learn what are the best colors to represent the countries income level groups. I tested different layouts and visual hierarchies and decided to use the circular dendrogram type of chart which is helpful to display hierarchies. I tried out several versions with slightly different arrangements and annotations.

As part of the process, it was crucial to get feedback from a non-data or non-viz expert audience, and I shared visualization with two members of my family who provided valuable feedback to improve the chart and the layout. The most difficult part was the written description of the visualization. Especially for country statistics, there are many nuanced findings and also conditions regarding countries reporting (e.g., frequency) their statistics to multilateral organizations such as the World Bank or the United Nations; there are many factors that can influence this, including political stability in the country. It’s important to acknowledge these factors when deciding what data to use, how to benchmark, and how to communicate visually this information.

NEC: Finally, what advice or words of encouragement would you give to data visualization practitioners who aim to participate and succeed in contests or challenges like ours?

MR:

1. If you don’t have a new idea, start by reusing something that you worked on before, and build upon a previous project, study, or report. Don’t be afraid and try to start small.
2. Seek inspiration in other visualizations as a starting point to understand what things worked well in terms of aesthetics, layout, typography, design, colors, flow, and navigation that successfully conveyed information and helped to tell the story; look for the specific elements that made you feel connected with that visualization.
3. Learn and experiment with new tools that can generate appealing and beautiful visualizations and run tests with sample datasets.
4. Ask for feedback and share with other colleagues to improve the visualization. Learning from other people’s perspectives is always helpful.
5. The most important: Get out of your comfort zone.

NEC: Mirian, it was a pleasure to talk to you. Once again, thank you for participating in the 2024 NNLM Data Visualization Challenge and we look forward to hearing from you and your data visualization projects in the years to come! Take care!