Visualize Experimental Study Data
Budget / Salary$30–250
TypeFreelance project
LocationRemote
Posted3 hours ago
I already have a complete data set of numeric results from controlled experiments and I need those numbers turned into publication-ready visuals. Your task is to explore the file, choose the most appropriate descriptive or inferential techniques, and then produce clear, easy-to-read charts and tables that I can drop straight into the methods and results sections of my paper.
You’ll receive the raw data in CSV along with a brief outline of the variables and hypotheses. From there, please:
• Clean and structure the data where necessary.
• Select and apply the right tests or summary measures (e.g., means, SDs, ANOVA, regression—whatever best communicates the story in the numbers).
• Deliver the final output as editable tables (Word or Excel) and high-resolution vector graphics (PNG/SVG) labelled according to journal guidelines.
I’m comfortable if you work in R, Python (pandas, seaborn, matplotlib), SPSS, or similar—just include the script or notebook so I can reproduce every figure.
Acceptance criteria: each figure is correctly labelled, axes are clear, statistical annotations are included where relevant, and every table matches the values in the data file without rounding errors. Once everything checks out, the job is done.
You’ll receive the raw data in CSV along with a brief outline of the variables and hypotheses. From there, please:
• Clean and structure the data where necessary.
• Select and apply the right tests or summary measures (e.g., means, SDs, ANOVA, regression—whatever best communicates the story in the numbers).
• Deliver the final output as editable tables (Word or Excel) and high-resolution vector graphics (PNG/SVG) labelled according to journal guidelines.
I’m comfortable if you work in R, Python (pandas, seaborn, matplotlib), SPSS, or similar—just include the script or notebook so I can reproduce every figure.
Acceptance criteria: each figure is correctly labelled, axes are clear, statistical annotations are included where relevant, and every table matches the values in the data file without rounding errors. Once everything checks out, the job is done.
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