# How to repeat your work and compare the results You return to a result a few days later and want to answer a simple question: can I get the same result again? Start with the saved run, check what went into it and make a new run to compare. This guide uses the training example from [Train a model](/studio/train-a-model), but the same approach works for an analysis. ## 1. Recover the original setup Open the reference run in [Studio](https://studio.biosimulant.com). Check **What it ran with**, **Technical details** and **Files**. Keep its input files, code revision, dependencies, settings and warnings together. The current version of your project may have changed since that run. Ask the assistant to recover the recorded setup rather than substituting the latest one. > Read this saved run and identify the exact data, preparation, code, environment and settings it used. Tell me what you can recover and what is missing. Do not run anything yet. If something is missing or inaccessible, record that gap. Recreating a plausible setup is a different task from repeating the original. ## 2. Decide what agreement means Name the output you care about and how you will compare it. A matching score can hide different predictions; a matching endpoint can hide different curves. For an analysis, you might compare included rows, a summary table and the plotted values. For training, check that the same rows were used for training and testing, then compare the learned model and each test prediction against the same sample. In the practice example, we checked the learned line, all six test predictions and both error measures. The [example download](/downloads/reproducibility-teaching-recipe.zip) includes the precise numerical rule. For methods with randomness, ask what range of differences is reasonable and whether several repeats are needed. ## 3. Repeat without changing the recipe Ask for a new recorded run using the same input and saved setup. Review the compute proposal before proceeding. > Repeat the exact saved recipe as a new Biosimulant run. Keep the original unchanged. Compare the retained output files using our agreed criterion, save both run links and report any differences. For training, say that you want to train again from the saved data. Running predictions with an already trained model checks a different part of the work. ## 4. Follow the whole research story in Timeline Return to the session and open **Timeline**. Leave **All types** selected and clear any search. Start with the full history: the saved setup and its changes, the experiment, the runs, and the research note explaining the comparison. [](/images/own-work/dark/timeline-comparison.png) The newest entries appear first. Scroll back to **Model changed** and use **What changed** to inspect a revision. Open the experiment's **Preview** for its recorded purpose. **Open run** takes you to a result, and **Read this version** reopens a saved research note. These entries help you recover both the work and the explanation kept with it. Our example contains four runs: | Run ID shown in Timeline | What it did | | --- | --- | | `cb562fc6` | Original training run: the reference | | `43e40489` | Summary and scatter plot of the input data | | `e2eb1930` | Training repeated with the same recipe | | `becfd505` | Training with one setting changed | Check the description and ID before opening a run. For the same-recipe comparison, we need `cb562fc6` and `e2eb1930`. **Compare with previous** uses the preceding run, which may be a different task; use it only when that is the reference you intend. Once you understand the full history, use the **All types** filter to focus on **Runs**, **Model changes** or **Notes** as needed. Clear the filters to bring the rest of the story back. Several quick runs can show the same minute; agreement still needs a comparison of the saved outputs. ## 5. Inspect both runs Select the completed runs and open **Compare runs**. Review the settings, plots and reported output differences. Open the output files when the comparison needs more detail than the summary shows. Tap or click a screenshot to open it at full size. [](/images/own-work/dark/comparison.jpg) In the recorded teaching example, A is the reference and B repeats the same training recipe. Their learned line, all six test predictions and both error measures were identical. We checked the downloaded files as well as the displayed chart. Ask the assistant to save a short comparison note: what was held fixed, what was compared, the observed differences and any remaining gaps. If only part of the result was checked, keep the conclusion limited to that part. [](/images/own-work/dark/comparison-note.jpg) To find the saved note later, return to **Timeline** and choose **All types** and clear any search. Find **Research note created** and choose **Read this version**. This keeps the comparison connected to the work it describes. ## 6. Change one thing deliberately Once the repeat is understood, try a controlled change. Keep the reference and give the changed run a descriptive name. In our example, C changes one training setting: it makes the fitted line pass through zero. The data and six test rows stay the same. The average prediction error rises from about **0.946 to 1.443**, so that change made the predictions worse on these test rows. For your own work, inspect the result before deciding that a change is an improvement. For your own work, a new preprocessing step, training setting or code revision may be the useful next comparison. Record the reason for changing it alongside the result. ## What you can conclude Write down exactly what agreed and what you checked. Our example was repeated by the same author with the same teaching recipe. A colleague repeating it independently would be a separate check. Agreement also leaves you with the question of whether the method is right for the research problem. You now have a record that helps with the next question: which data and setup produced this result, what happened when you repeated it, and what changed in the next attempt?