OpenScience can execute Python and R during a research conversation. Use natural-language requests to build an analysis, inspect intermediate outputs, and save code and results as files.
Prepare the environment
Open Customize → Compute and check Python starter and R starter. Follow the available setup or repair action before a substantial analysis.
The Python starter includes NumPy, pandas, SciPy, Matplotlib, Seaborn, and Pillow. The R starter includes tidyverse, ggplot2, and jsonlite. Additional scientific packages have separate setup requirements; use Tools to check them.
Each starter is checked by importing its packages before it is used. A starter that fails that check is removed rather than kept half-built, and the Compute panel shows the interpreter's own error, such as a package that would not load. The starters run from their own Conda layout without activation: on Windows, OpenScience puts the environment's Library\bin, MinGW and Scripts directories on PATH itself and runs R with --vanilla and an empty R_LIBS_USER, so another Anaconda or R installation on the machine cannot shadow the starter's libraries.
Check that the Python analysis environment can import pandas and
Matplotlib. Read the first rows of data/samples.csv and report its columns
before attempting the full analysis.
Analyze incrementally
Variables can persist across executions within the conversation's active environment. This lets you inspect a dataframe and then plot it without reloading it in every step.
Load data/samples.csv in Python. Report missing values by column. Then
plot the distribution of the response, grouped by treatment, without
modifying the input file.
Python calculations can return values and inline Matplotlib figures. Ask to save the plot as a named file when you want to reuse it.
For R:
Use R to inspect data/samples.csv. Summarize the response by treatment and
batch, then save a ggplot figure and the R script in the project.
Make outputs durable
Relative output paths can point to the conversation's scratch area. State that final code and results belong in Project files, and ask for their exact paths.
Save scripts/analyze.py and results/summary.csv in Project files. Save
the figure as results/response.png and confirm that each file opens.
Use Saved Results for selected completed deliverables.
Add dependencies deliberately
Ask for the missing package, why it is needed, and where it will be installed before accepting a setup change. Installing packages can restart an analysis environment and clear its variables.
Keep loading and preprocessing steps in a script so they can be rerun after a restart. A variable existing in the current session does not make the analysis reproducible on another machine.
Stop or recover an execution
If code takes too long, inspect progress and the last output before stopping it. Ask to stop the relevant execution and describe any partial files left behind. A stopped environment may need its variables rebuilt.
For a longer detached workload, use Compute jobs instead of assuming an interactive call should run indefinitely.
Verify the final analysis from a fresh state
Turn the completed analysis into a script that starts from the original
inputs. Record the seed and package versions, run it without relying on
previous variables, and compare the resulting tables and figures with
the reviewed outputs. Report any mismatch.
Choose Statistics, Machine learning, or Figures for method-specific guidance.