Why Share Experiments on OpenML Instead of Only Publishing Results?

Sharing experiments on OpenML matters because it preserves the full runnable record behind a result—data, algorithm, and run configuration—so others can reproduce, compare, and build on it. A paper's reported number tells you what happened once; a shared experiment lets the community re-run it, test it on new datasets, and see where it breaks. This is most valuable when you want your method to be reusable and comparable, not just citable.

What OpenML Is Built to Share

OpenML describes itself as an open platform for sharing datasets, algorithms, and experiments "to learn how to learn better, together." That framing points to the core difference from a publication: the platform links the three artifacts together rather than reporting a result in isolation.

When these are linked, a result becomes a queryable object: someone can find every experiment using a given dataset or algorithm and compare outcomes directly.

Why This Beats Results-Only Publishing

Reproducibility becomes checkable, not aspirational

A published result usually gives you a number and a prose description of the setup. A shared experiment gives you the actual configuration. If a replication fails, the community can inspect the shared run to see whether the difference is in the data, the algorithm, or the setup—rather than guessing.

Comparison happens on common ground

Because datasets and algorithms have stable identities on the platform, comparisons across papers stop depending on whether two authors preprocessed data the same way. The shared experiment is the common reference point.

Collaboration is built into the record

Others can take your algorithm and run it on datasets you never tried, or take your dataset and apply methods you did not consider. Each new run adds to a connected body of evidence instead of sitting in a separate paper.

The work stays useful after publication

A result in a paper is fixed at publication. A shared experiment can be re-run, extended, and referenced as the platform and community grow.

A Concrete Example

Suppose you develop a classification method and report 87% accuracy on one dataset. If you publish only the result, a reader who gets 84% has no way to tell whether the gap comes from a different data split, a different preprocessing step, or an implementation difference.

If you share the experiment on OpenML instead, the reader can pull the same dataset and algorithm record, re-run it, and compare configurations directly. If they then apply your algorithm to three other datasets, those runs attach to the same algorithm entry—so your method's profile grows beyond the single number in the paper.

When Sharing Experiments Is the Right Choice

Sharing experiments on OpenML fits best when:

• You want your method to be reused and benchmarked by others, not just read.
• Reproducibility is a stated claim of your work and you want it verifiable.
• You are working with datasets others can access and want a shared reference for comparisons.
• You expect follow-on work that applies your method to new data.

It is less central when your contribution is purely theoretical, when the data cannot be shared, or when the result depends on infrastructure others cannot replicate.

Practical Starting Point

To get value from the platform, share the three linked pieces rather than a result alone: upload or reference the dataset, register the algorithm or workflow, and record the experiment that ties them together. The platform's own description emphasizes exactly this combination—datasets, algorithms, and experiments—as the unit of sharing. Start with one well-documented experiment; a single reproducible, linked run is more useful to the community than a dozen reported numbers with no traceable setup.