Data visualization in science transforms complex data sets into visual representations, such as graphs, maps, and tables. Effective data visualization reveals meaningful insights and identifies patterns, increasing accountability and interpretability. The use of data visualization facilitates communication among researchers, scientists, and stakeholders. Data visualization enables efficient analysis, decision-making, and knowledge dissemination within and beyond the scientific community.

Badge details

Earning Criteria

Recipients must complete the earning criteria to earn this badge

A Digital Badge in Science Data Visualization is earned through studies incorporating the following key topics and corresponding competencies:

Iterative Process

Learners will be able to:

1. Identify and apply the iterative process of data visualization: research, goal and audience identification, concept development, sketching, and design refinement.
2. Apply the iterative process to a data visualization project, such as a chart or a map.
3. Analyze the benefits of the iterative process in improving the clarity, efficiency, and effectiveness of data visualizations.
4. Evaluate the effectiveness of different iterations of a data visualization, identifying areas of improvement and refinement.
5. Reflect on the iterative process and identify strategies for incorporating it into future data visualization projects.

Data Visualization Principles

Learners will be able to:

1. Describe the key principles of data visualization, including maximizing content, minimizing chart junk, and utilizing different graphing approaches.
2. Analyze the impact of maximizing content in data visualizations, such as using small multiples to maximize content variation and minimize style variation.
3. Apply data visualization principles to create visually engaging and informative examples of data visualization.
4. Evaluate existing data visualizations to identify instances of chart junk and suggest improvements to enhance clarity and efficiency.
5. Compare and contrast different graphing approaches and select the most appropriate approach for visualizing specific types of data.

Software Proficiency

Learners will be able to:

1. Demonstrate proficiency in multiple softwares, such as Excel, InDesign, Illustrator, R, and ArcPro/ArcOnline, to create effective and engaging data visualizations.
2. Use the features and functionalities of each software to organize, analyze, and visualize data accurately and efficiently.
3. Select the most appropriate software tool based on the specific requirements and characteristics of the data and visualization goals.

Data Visualization Examples

Learners will be able to:

1. Analyze the effectiveness of different data visualization examples in uncovering trends, realizing insights, exploring sources, and telling stories.
2. Apply principles of analytical design to create effective data visualizations using examples as inspiration.
3. Evaluate the suitability of different data visualization examples for specific datasets and target audiences.
4. Create original data visualizations using examples as a reference, incorporating effective design elements and storytelling techniques.

Maps

Learners will be able to:

1. Explain the role of maps in data visualization.
2. Analyze how maps visually describe data and uncover spatial trends and patterns.
3. Apply principles of cartography and color selection to create visually appealing and informative maps for specific geographic datasets.
4. Evaluate the effectiveness of maps in communicating spatial information and guiding decision-making processes.
5. Incorporate interactive elements into maps to enhance user engagement and exploration of the data.

Evidence of Mastery is submission of a final portfolio, including the following skills, to be signed-off by the Unit Director:

Software Proficiency

• Demonstrates proficiency in using multiple software programs.
• Effectively utilizes software features and functionalities.
• Selects appropriate software tools based on data and visualization requirements.

Visualization Techniques

• Applies a variety of visualization techniques and design principles.
• Uses suitable chart types to represent different data effectively.
• Incorporates visual storytelling techniques to engage the audience.

Design and Aesthetics

• Creates visually appealing visualizations with a cohesive design.
• Utilizes principles of design, such as layout, color, typography, and hierarchy.
• Enhances clarity, readability, and comprehension through appropriate graphic elements.

Data Analysis and Interpretation

• Demonstrates accurate data analysis and interpretation.
• Applies relevant statistical techniques and data processing methods.
• Evaluates the accuracy, reliability, and validity of the data used.

Portfolio Development

• Develops a comprehensive portfolio showcasing a range of skills and techniques.
• Creates a chart that is appropriate for that dataset and uses good design principles.
• Creates a table that is appropriate for that dataset and uses good design principles.
• Creates a map that is appropriate for that dataset and uses good design principles.
• Organizes the portfolio logically with clear descriptions and explanations.
• Reflects on the effectiveness of the visualizations and identifies areas for imp