Inspect before transforming
Start by asking for the data structure, units, missingness, and suspicious values.
Inspect data/measurements.csv. Describe the columns and units, count
missing and duplicate rows, and flag values that need review.
Keep the original file unchanged.
Provide a data dictionary or protocol when available. Ask about ambiguous units, group labels, or missing-value codes before fitting a model.
Agree on the method
Use /plan to compare methods and assumptions. State the question you want the analysis to answer.
/plan Compare treatment and control while accounting for repeated
measurements from the same subject. Explain the assumptions, uncertainty
estimate, and sensitivity checks. Ask before excluding observations.
Specify the outcome and evaluation criteria before examining many alternatives. Tell OpenScience which decisions are fixed by the study design.
Run a small analysis
Open Customize → Compute if Python or R setup needs attention. Start with a small sample when the dataset or analysis is expensive.
Run the agreed analysis. Save the script in scripts/analyze.py, the
summary table in results/comparison.csv, and the methods in
results/methods.md. Record package versions and the random seed.
OpenScience can use Python and R for analysis. Availability depends on the tools installed or configured on your machine; a bundled skill does not install every library it describes.
Check the result
Ask for checks that could reveal a wrong answer, such as inconsistent units, leakage between training and test data, sensitivity to exclusions, or an implausible baseline.
Read the generated code and inspect intermediate tables. A successful command means the program ran; it does not establish that the method is appropriate.
For biological analyses, state the organism, reference assembly, accession versions, and expected identifier types. For molecular data, include structure conventions and units.
Save reproducible outputs
Keep the original data, analysis code, figures, and methods together. Ask for a command that reruns the analysis from those inputs.
For an analysis with stochastic steps, request repeated runs and an uncertainty estimate appropriate to the study. The right number of runs depends on the question and budget.
See Figures and writing to prepare outputs for collaborators and Files and storage for organizing them.