Open, machine-readable data with full traceability. All sources documented, all measures defined, everything under CC BY 4.0.
Neutral material for your own editorial work — we provide data and tools, not conclusions. Municipal and independent school operators are presented on equal terms.
Markus Reimer
Phone: +46 79 334 98 35
Email: redaktion@skolkoll.se
Press images and press kit: see Graphics for editorial use below.
Need a specific data extract, a regional cut or a graphic? Fill in the order form below and the newsroom will get back to you.
Order a data breakdownOr email redaktion@skolkoll.se.
Ready-made data cards (CC BY 4.0) — credit Skolkoll. Square and portrait formats are in the press kit.
Scalable vector (CC BY 4.0) for print and large format. A complement to the PNG logo — credit Skolkoll.
| Data point | Where on Skolkoll | Neutral angle |
|---|---|---|
| Teacher qualification per municipality | Municipality data | How does qualification vary between municipalities and operators? |
| Merit value year 9 per municipality | Municipality data | Differences between municipalities |
| Cost per pupil | Municipality data | Resources vs results? |
| Admission scores (upper secondary) | Admissions data | Demand and cut-offs per programme |
| Regional breakdown per county | County CSV (incl. Gotland) | Compare municipalities and operator types within a county |
Skolkoll is independent: no ads, no data sales, all operators on equal terms. Read the separation policy,method and sources and thecorrections policy.
Are you a journalist who needs a data extract, a source reference or a custom cut? Tell us what you need and we'll get back to you.
Design partner, not a customer claim: Journalist Pro is not a finished direct-purchase product and starts through interest registration or quote.
Server-verified source references from open school data, with clear marking when data is not enough to support a conclusion.
No individual data, no personal identity numbers and no student names are included in the pilot workflow.
The target workflow moves from verified news signal to story package in under 10 minutes without new newsroom data infrastructure.
Start with a watchlist for a municipality, county, provider or topic and see which open indicators are moving.
Gather hypothesis, counter-questions and affected schools in a card the newsroom can review before publication.
Link the source row, method note and comparison values without AI-generated conclusion text.
Export aggregate tables with metadata and citation line for charts, fact boxes or internal research.
The pilot package is matched to the newsroom's coverage areas, publishing cadence and evidence-package needs. There is no public checkout for Journalist Pro.
Three steps from question to publication.
Curated collections of visualisations and stories, grouped by topic. Each package gives you ready-made material for reporting.
A starting point for local cases where SALSA highlights schools or municipalities performing clearly above or below expectations.
Refresh: Quarterly: rerun top lists, check the latest SALSA period and flag schools with small pupil cohorts.
Story starterCompare certification, results and regional recruitment strength to find municipalities where teacher shortages become a concrete story.
Refresh: Quarterly: update certification rankings, compare with the previous quarter and highlight municipalities crossing the 60 or 80 percent thresholds.
Story starterTrack organisers with large pupil volumes, growing market share and results that deviate from expectations.
Refresh: Quarterly: check new organiser links, pupil volume and SALSA changes before publication.
Story starterCombine socioeconomic context, school choice and results to find municipalities where school differences are editorially clear.
Refresh: Quarterly: update municipal rankings, compare with history and verify that demographic measure years match the text.
Story starterFind municipalities where cost per pupil does not track outcomes, and frame questions about governance, resource allocation and efficiency.
Refresh: Quarterly: check Kolada years, recalculate cost per pupil and flag municipalities with large jumps.
Story starterBuild local stories about popular programmes, admission scores and whether pupils complete their education.
Refresh: Quarterly: update the admissions file, compare first-choice applicants with admitted pupils and check programme codes.
Story starterKey measures used in the theme packages above.
From question to publication — a practical guide for journalists and analysts.
Five common mistakes — and how to avoid them.
| Pitfall | Warning |
|---|---|
| Comparing raw merit values without SALSA | Raw merit values reflect pupil composition more than school quality. Use SALSA-adjusted values for fair comparisons. |
| Ignoring pupil count | Small schools have large statistical variation. Always check pupil count — a school with 15 pupils can swing 30 points between years. |
| Mixing school types | Compulsory and upper secondary schools are measured with different metrics. Make sure you compare within the same school type. |
| One year as a trend | At least 3 years of data are needed to identify trends. Use historical trends to see changes over time. |
| Using raw certification figures | Regional labour markets affect certification rates. Compare similar municipalities rather than absolute figures. |
Three questions — and how we answered them with data.
Compares merit values, teacher certification and pupil counts between independent and municipal schools. The differences are smaller than the debate suggests.
Read the analysisRanked overview of the largest independent school groups — pupil count, merit value and SALSA deviation side by side.
Read the analysisMaps teacher certification by county and subject. Shows that certain subjects face a chronic shortage nationwide.
Read the data storyAll 11 data stories ·All data-driven analyses
All data on Skolkoll is available under CC BY 4.0. You may freely use, share and adapt the material — as long as you credit the source.
Press enquiries: info@skolkoll.se
When is the data updated and how do you know if something has changed?
| Source | Frequency | Content |
|---|---|---|
| Skolverket | Daily | School units, key metrics, SALSA |
| Kolada | Monthly | Municipal statistics, demographic measures |
| SCB | Monthly | Education level, income data |
| Schools Inspectorate | Quarterly | Inspection decisions, injunctions |
| Companies Registration Office | Weekly | Operators, corporate structure |
How do you know if data has changed? Every CSV export includes a sync timestamp. The download page shows the latest update per dataset.
