Data Availability & Methodology
Data Collection
How is the data collected? Describe the collection methods, tools, and processes.
What data is collected? List the specific variables, fields, and types of information gathered.
Data Analysis & Use
How is the data analyzed? Explain the analytical methods and techniques used.
How is the data used? Describe how the analysis informs decisions, policies, or actions.
Training Data (For Algorithm Projects)
Where does the training data come from?
What's included or excluded?
Who labeled the data? Consider how labeling decisions shape the algorithm.
Data Audit
Document your technical analysis of the dataset. Include:
Data Cleaning & Preprocessing
Describe the steps you took to clean and prepare the data:
- Missing data handling
- Outlier detection and treatment
- Data transformation or normalization
- Variable creation or recoding
Code Snippet (Optional)
# Example: Show key data cleaning steps
# You can include screenshots of your code or embed it here
import pandas as pd
df = pd.read_csv('data.csv')
# ... your analysis code ...Statistical Summary
| Variable | N | Mean/Mode | Std Dev | Min | Max | Missing (%) |
|---|---|---|---|---|---|---|
| Variable 1 | -- | -- | -- | -- | -- | -- |
| Variable 2 | -- | -- | -- | -- | -- | -- |
Data Cleaning Reflection
Reflect critically on the decisions you made during data processing:
- What assumptions did you make? How might these assumptions introduce bias?
- What choices did you face? (e.g., how to handle missing data, outliers, categorization)
- Who might be harmed by these decisions? Consider how your choices might affect different groups.
- What alternatives did you consider? Why did you choose your approach?
Visualization Analysis
Choose one: Either critically examine 3 existing visualizations OR create 2 new visualizations
Visualization 1
Figure 1: [Description of visualization]
What story does it tell?
Analyze what narrative or message this visualization communicates.
What story does it hide?
Critically examine what is obscured, minimized, or excluded from this visualization.
Design Choices
- Color choices and their implications
- Scale and axis decisions
- What's included vs. excluded
- Who is the intended audience?
Visualization 2
Figure 2: [Description of visualization]
What story does it tell?
Analyze what narrative or message this visualization communicates.
What story does it hide?
Critically examine what is obscured, minimized, or excluded from this visualization.
Visualization 3 (if analyzing existing visualizations)
Figure 3: [Description of visualization]
What story does it tell?
Analyze what narrative or message this visualization communicates.
What story does it hide?
Critically examine what is obscured, minimized, or excluded from this visualization.
Algorithmic Implications (Data Initiative Projects)
If this data was fed into an algorithm, who would be harmed?
Potential Harm 1
Describe a specific way this data could cause harm if used algorithmically.
Connection to readings: Link to course concepts (e.g., data feminism principles, power structures, etc.)
Potential Harm 2
Describe another potential harm.
Connection to readings: Link to course concepts.
Potential Harm 3
Describe a third potential harm.
Connection to readings: Link to course concepts.
Algorithm Logic Analysis (Algorithm Projects)
Decision-Making Process
How does the system make decisions? Describe the algorithmic logic and decision rules.
What is it being optimized for? Identify the objective function or goal the algorithm pursues.
Logic Flowchart
Figure: Decision-making flowchart showing how the algorithm processes inputs and generates outputs
Intersectional Harm Documentation
Analyze how this system creates compounded harm across multiple dimensions of identity and power.
Harm Analysis 1
Describe a specific harm and how it compounds across intersecting identities (e.g., race + gender, class + disability, etc.)
Connection to readings: Link to course concepts about intersectionality and data justice.
Harm Analysis 2
Describe another intersectional harm.
Connection to readings: Link to course concepts.
References
- Author, A. (Year). Title of work. Journal Name, volume(issue), pages.
- Author, B. (Year). Title of work. Journal Name, volume(issue), pages.
- [Continue with at least 8 sources total, including those from Part 1]