July 21, 2025
The GIST Platform can make machine learning more transparent and accessible 
Gaby Clark
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Andrew Zinin
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What began as a Ph.D. project has grown into a website with 120,000 unique visitors each year. With the platform OpenML, researcher Jan van Rijn is contributing to open science, aiming to make machine learning more transparent, accessible, and fair.
From climate research to behavioral science: machine learning (ML) is playing an increasingly important role in science. Researchers use it to discover patterns in large datasets, make predictions, or simulate complex processes. But despite this growth, ML results can still be difficult to assess or reproduce.
"There's no standard way to share data, models and results," says Jan van Rijn. "That's a shame, because if we want to be taken seriously as a field, we need to make sure our work is verifiable and reproducible."
What is machine learning?
Machine learning is a way for computers to learn from examples—like an email program that recognizes spam based on thousands of previous messages. The system learns to spot patterns on its own, without every rule being programmed manually. In a sense, it works like human learning, just on a much larger scale. Applications are everywhere: from facial recognition and medical diagnoses to Netflix recommendations.
A shared workspace for machine learning
To make machine learning more transparent, Van Rijn founded OpenML over ten years ago: a shared digital workspace where researchers and students can upload their datasets, algorithms and experiments. Anyone can browse, contribute and learn from others' approaches. The platform fits perfectly with the principles of open science: science that is accessible, verifiable, and reusable.
And there's clearly a need for that. OpenML is now used worldwide and has already contributed to around 1,500 scientific publications. Van Rijn and his fellow researchers recently looked back on ten years of OpenML in a publication in the journa Patterns. They identified three main ways researchers use the platform: to improve algorithms, to gain higher-level insights through so-called meta-learning, and for teaching.
"OpenML is often used in courses on machine learning and reproducible research," he says.
'It's not that researchers don't want to share their code'
Open practices are still far from standard. "In science, there are many different research cultures," Van Rijn explains. "That brings valuable perspectives, but it also means there's a lack of shared standards. Creating and applying a common standard takes a lot of time and effort. It's not that researchers don't want to share their code—it's just more work. Even with a platform like ours."
Still, Van Rijn is sticking to his mission. "The goal is something like Wikipedia for machine learning—but not just with text. Also with data, models and experiments. Everything you need to understand, replicate and build on research."
OpenML is more than just a platform
He sees open science gradually becoming more established. "Our publications are being cited more often, which helps. But there also needs to be structural support—from universities and funders alike. For example, by making it a condition to openly share your code and data."
So OpenML is more than just a platform. It's a step towards a scientific culture built on collaboration, transparency, and reuse. "There are other platforms like ours," says Van Rijn. "Our aim is to break down those silos and connect them. So that sharing research becomes even easier—for everyone."
More information: Bernd Bischl et al, OpenML: Insights from 10 years and more than a thousand papers, Patterns (2025). DOI: 10.1016/j.patter.2025.101317
Journal information: Patterns Provided by Leiden University Citation: Platform can make machine learning more transparent and accessible (2025, July 21) retrieved 21 July 2025 from https://techxplore.com/news/2025-07-platform-machine-transparent-accessible.html This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.
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