About Metrka
Metrka turns public source files into versioned, inspectable data publications. Each publication connects the original source, preparation history, quality evidence and downloadable tables.
Why Metrka exists
I built Metrka because I got tired of public data I couldn’t trust and sometimes couldn’t even understand.
Not because anyone is lying. Because the data keeps moving: a column gets renamed, a table splits in two, a monthly file quietly turns quarterly, categories change. Because the exceptions (the rows that don’t add up, the “county” that isn’t a county, the footnote that changes the meaning of everything above it) are either not explained at all or explained in a 40-page PDF. And because nobody tells you where the data falls short, you discover its limitations the hard way.
So Metrka does the boring part out loud. For every dataset, it shows where the data came from and the contract it was held to: every table, column and definition, written down. It shows each step it went through on the way to the table you download, which quality rules it passed (and which it didn’t) and what changed since the last release. If something looks off, you can trace it back instead of guessing.
How to inspect a publication
Start with a dataset version, review its source and limitations, then inspect its quality results, recorded changes and Data Journey.
Source attribution
The source organization named on each dataset page remains the authority for the original material. Metrka records where the data came from and how it was prepared; it does not claim to be the original publisher.
Version pages include a source link and a suggested citation tied to the exact Metrka publication.
Reuse and licensing
Reuse terms for the underlying data are set by the original publisher. Follow the source link on the dataset version before redistributing or using the data.
If no license is named, do not assume that the data is in the public domain. Metrka's preparation does not replace or change the source's terms.
What verification means
Quality results show whether the files met configured structural and processing rules. They do not certify the factual accuracy, completeness or official status of the source data.
The Data Journey and transformation evidence show what the platform observed and changed during preparation.
Contact
Questions, ideas or spotted something off in the data? Email us: . If it’s about the data, include the dataset, version, table and what looks wrong.
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This information explains how the catalog works and is not legal advice.