Open datasets

Download the datasets that power Skolkoll. Files are published as CSV and are intended for analysis, reproducibility and external reuse. Licensed under CC0.

Available datasets

Choose version

The current version downloads as CSV. If you choose an earlier date, the links switch to historical JSON plus metadata for that specific run.

Version

Current version selected. Downloads use the standard CSV files.

School units All school units with organiser, school type, municipality, status and key school indicators. Updated 12 March 2026 21,594 school units ⬇ Download CSV

Municipality indicators Municipality-level school finance, staffing, merit and organiser-mix indicators from Kolada. Updated 8 March 2026 294 municipalities (290 municipalities + regions and national level) ⬇ Download CSV

Historical municipality series Time series for municipality-level school indicators used in Skolkoll's comparisons and trends. Updated 8 March 2026 Multi-year municipality time series ⬇ Download CSV

Upper-secondary admissions Admissions scores where available, combined with current programme supply for the rest. Updated 4 April 2026 Programme and school combinations ⬇ Download CSV

Variable documentation

Key columns in each dataset. Column names match the download files.

School units

Column
`skolenhetskod`
`skolenhetNamn`
`huvudmanNamn`
`huvudmanOrgNr`
`municipalityName`
`municipalityCode`
`schoolTypes`
`status`
`totalPupils`
`meritRating9`
`certifiedTeachersPercent`
`studentsPerTeacher`

Municipality indicators

Column
`kommunkod`
`kommunName`
`costPerPupilGrundskola`
`avgMerit`
`certifiedTeachers`
`pupilsPerTeacher`
`independentShare`
`salsaResidual`
`populationGrowth`

Historical municipality series

Column
`kommunkod`
`kommunName`
`year`
`kpiId`
`value`

Upper-secondary admissions

Column
`skolenhetskod`
`skolnamn`
`kommun`
`programkod`
`program`
`year`
`cutoff`
`platser`

How to import

Excel / Google Sheets

Open Excel or Google Sheets, go to Data → From Text/CSV (or File → Import in Sheets). Select the downloaded CSV file. Set delimiter to comma and encoding to UTF-8.

Python (pandas)

import pandas as pd
df = pd.read_csv("skolor.csv")
The files use UTF-8 encoding and comma delimiters.

library(readr)
schools <- read_csv("skolor.csv")

What the files are for

Who are these files useful for?

Researchers, journalists, municipalities, school organisers and anyone who wants a reproducible dataset behind Skolkoll's views.

Are the files raw?

No. They are processed exports from the same pipeline that powers the site, which means that identifiers, joins and fallbacks have already been normalised.

Where do the numbers come from?

Primarily Skolverket, Kolada, SCB, Bolagsverket and regional admissions sources. See the method and settings pages for source notes.

What licence applies?

The datasets are published under CC0 1.0 Universal — you are free to use them for any purpose without attribution, though we appreciate a link back.

How often are the files updated?

The CSV files are regenerated on every site build. Skolverket data updates daily, Kolada monthly, SCB annually. See the data source table for details.

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