The challenge
Scientific research depends on data—from lab experiments to weather measurements, medical studies, and even space missions. Yet, collecting scientific data is often expensive, time-consuming, and sometime impossible to reproduce. You can’t, for instance, rerun the passage of an interstellar object through our solar system.

That’s what makes data so valuable. When shared and reused, a single dataset can fuel new discoveries across different disciplines—even decades after it was first collected.

The FAIR principles (Findable, Accessible, Interoperable, Reusable) provide a framework for effective data sharing. But in practice, applying them is challenging, and researchers rearly receive recognition for doing so. So the question is: how can we make FAIR data sharing easier and more worthwhile?

Our answer: The MeteoIO Web Service
We created the MeteoIO Web Service to simplify and improve data management for automatically measured timeseries (such as weather station data).

It tackles two key challenges:

1. Making data FAIR: reducing the effort and cost for researchers to publish and maintain high-quality, reusable datasets.
2. Adding direct value: ensuring that researchers themselves gain tangible benefits from sharing their data.

Supporting day to day scientific data acquisition

Collecting long-term data is demanding: instruments require constant maintenance, file formats evolve, and metadata is often incomplete. Without proper documentation, valuable datasets risk becoming unusable. Problems such as faulty sensors may remain hidden for years, and by the time someone tries to reuse the data, the necessary context is often missing.

Even when the data is available, researchers usually face a laborious process of cleaning, standardizing, and documenting it before it can be analysed. These tasks are not only tedious but also divert time and resources away from actual research.

An illustration of how the MeteoIO processes data

When entering MeteO you can upload and process your datasets (left). A configuration file automatically records every step, making the process reproducible (right: What a data owner sees about data configuration). 

How Researchers Benefit

• A simple web interface to upload data or connect repositories, configure processing pipelines, and monitor data quality.
• Automatic metadata enrichment that ensures datasets are interoperable and reusable.
• Easy access for others to download public datasets or regenerate them with custom settings.

In short: less work for data owners, higher quality for data users.

 Using the service, you can explore datasets that are reliable.

An example of the file for download after data processing (left). An example of scheduling data processing for data that is captured automatically (right).  

Want to learn more?

If you have any questions, feedback, or would simply like to learn more about MeteoIO, please contact Mathias Bavay at bavay@slf.ch