Adapted with permission from Northwestern University, Office of the Provost. (2025). Data Visualization Award – Judging Guide. Northwestern University.
What Counts as Data Visualization?
Data visualization is the graphical representation of data intended to help people understand information quickly and effectively.
Eligible Visualization Formats
Visualizations can be submitted in a variety of forms:
Medium
Submission Requirement
Note: please submit the final visualization.
Examples of valid data visualizations include (but are not limited to):
- Bar Chart Line
- Chart Pie Chart
- Scatter Plot
- Gantt Chart
- Heat Map
- Box and Whisker Plot
- Sankey (flow) Chart
- Area Chart
- Word Cloud
- Pictogram
- Timeline
- Map
- Network Diagram
Note on Infographics: Infographics are only eligible if they contain at least one valid data visualization (e.g., a chart, map, or diagram). Infographics that are purely decorative or text-heavy without visualized data will be excluded.
Data Use
You are not required to use all the data provided for the competition. You may represent only a part of the dataset or the entire set, depending on your narrative and design choice.
Note: more data is not necessarily better. A small, powerful subset of the data that clearly conveys a compelling insight is judged to be just as effective as a visualization using the entire dataset. Focus on quality and insight over quantity.
Submission Requirements & Judging
Review complete Submission Instructions before submitting your visualization.
Dataset requirement: your visualization must use the dataset provided by The Loop (link will download the .xlsx file).
Your submission must include a brief narrative, between 100 and 200 words, addressing the following three key areas:
Story & Goal
Design Choice
Design Process
Submit your final data visualization via the submission form (submission opens on January 12, 2026). Note: You are welcome to also submit any associated code (e.g., Python, R, JavaScript files, etc.). However, please be aware that the judging criteria will focus exclusively on the final visualization product and its effectiveness, not on the underlying code.
Accuracy, Ethics, and Misleading Visualizations
A data visualization must be accurate and ethical. Submissions with misleading visualizations or inconsistencies will receive the lowest possible score in all rubric categories.
Examples of misleading visualizations:
- Truncated Y-axis: Especially problematic in bar charts—always start the axis at 0.
- Correlation ≠ Causation: Avoid implying causality unless it's supported by the data.
- Poor choice of chart type: Charts must match the data and message.
- Violation of visual conventions: Example: timelines that don’t go left to right, or pie charts with too many slices.
Submissions with misleading visualizations or inconsistencies should receive the lowest possible score in all rubric categories.
AI Policy
This competition's AI policy is consistent with GW's AI policy. All AI tools used in the creation of your visualization must be properly cited. Please refer to GW Guidelines for Using Generative Artificial Intelligence for more information on these guidelines.
Judging Rubric
Judges will score each submission from 1 to 10 in the following five categories:
| Category | Criteria | 10 Points (Exceptional) | 7 Points (Effective) | 5 Points (Adequate) | 1 Point (Poor) |
|---|---|---|---|---|---|
| Accessibility | Inclusivity & Readability: Does the visualization adhere to best practices for accessibility? (e.g., color contrast, alternative text, font readability, proper labeling) | Full adherence to accessibility best practices (e.g., high color contrast, all images/charts have descriptive alt text, fonts are readable). Accessible to all audiences. | Accessibility features are present and effective (e.g., good contrast, most visuals have alt text). Minor omissions or inconsistencies. | Basic accessibility features are present but inconsistent or incomplete. Readability suffers due to minor contrast or font issues. | Lacks key accessibility features (e.g., no alt text, poor contrast, unreadable fonts). Inaccessible to large portions of the audience. |
| Visual Design & Presentation | Appropriateness: Alignment with the story. Clarity & Usability: Ease of understanding and navigation. | Exceptional visual clarity and engagement. Publication-quality figures. Excellent use of color, spacing, and appropriate chart types. | Effective visuals. Minor design inconsistencies or clutter. Color and spacing mostly support clarity. | Adequate appeal, but inconsistent design, small font sizes, or missing captions. Visual clarity suffers. | Poorly designed or distracting visuals. Lacks labels, captions, or professional appearance. |
| Clarity of Communication | Clarity of Narrative: Clear story/question explored. Context & Insights: Sufficient context and insights provided. Data Integrity: Accurate, unbiased data; all sources cited. | The message is clear and self-contained. Visualization tells a compelling story. All data sources are fully and accurately cited. | The message is understandable but requires some explanation from other submission elements. Data sources are mostly cited. | Visualization is unclear, lacks appropriate chart type, or requires too much interpretation. Data sources may be missing or incomplete. | Misleading or missing visualizations. Unclear or confusing message. Data integrity issues are present. |
| Creativity & Innovation | Visual Appeal: Use of effective visual elements to enhance the story. | Highly original and insightful. Sparks discussion or new ideas. Design is unique and pushes the boundaries of visualization. | Some creative elements are present. Design is interesting but not unique or ground-breaking. | Standard or overused visualization type. Execution lacks innovation. | Unoriginal, formulaic, or copied. Visualization feels generic or uninspired. |
| Audience Understanding & Insight | Insights & Findings: Reveals meaningful insights. Engagement: Effectively engages the viewer and guides the story. | Clear, insightful, and encourages deeper reflection. Guides the audience effectively and encourages audience inference. | Generally understandable. Minor interpretation effort required. | Viewers must work hard to interpret the message. May violate conventions or use inappropriate formats. | Confusing or overwhelming. Visualization hinders understanding. |
Eligibility for the Open Source Program Office Award
To qualify for the Open Source Award, a data visualization must meet the following requirements:
Software
Visualization
A list of approved Open Source Licenses is available to consult on the OSI Website.
Winning participants will receive additional prizes and will automatically be accepted to give a 5 minute lightning talk at the 2026 GW Open Source Conference (GW OSCON).
