The codebook — the metadata engine behind every dashboard

Define variables, value labels, chart types, the navigation, net rows and filters once — in the codebook. It is the quiet engine between import, analysis and dashboard: it keeps trackers consistent across every wave and makes any dataset instantly chartable.

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Codebook view in DataLion showing variables, value labels, chart types and net rows in a table

The codebook is DataLion’s central metadata engine. It defines variables, value labels, chart types, the navigation hierarchy, net rows and formulas, missing values and per-variable filters — once. DataLion builds it automatically from labelled datasets; it is editable online with an Excel round-trip. That is what makes trackers consistent across waves.

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Without central metadata, every tracker falls apart

  • Labels, chart types and filters live scattered across individual charts — every new wave is patched by hand.
  • Net rows like "Top-2-Box" and derived variables are rebuilt in every report, and every deviation is a risk.
  • A dashboard’s menu structure is dragged together by hand instead of coming from one source — never truly comparable across studies.

Every piece of metadata defined in one place

The codebook is the single place where DataLion knows everything about your data. Here you set variables, value labels, the right chart type, net rows with formulas, missing values and per-variable filters — once, in one place.

Charts, tables, reports and exports all draw from this one definition. Change a label or a net formula and it changes everywhere — no patching across dozens of charts.

  • Variables and value labels as a single source of truth
  • Chart type and missing values stored per variable
  • Net rows and their formulas maintained centrally
  • Per-variable filters — consistent across the whole project
Codebook in DataLion with variables, value labels, net rows and chart-type columns

Generated automatically, editable online, Excel round-trip

You never start from scratch: from a labelled dataset — such as SPSS, Excel or CSV — DataLion builds the codebook automatically. For open numeric and text variables it inserts placeholders like <num> and <label> for you to fill in.

You refine it in the online codebook editor — or via the Excel round-trip: export the codebook, adjust it in Excel, import it back. That lets even large teams work on the same metadata foundation.

  • Automatic generation from labelled datasets
  • Placeholders <num> / <label> for open variables
  • Online editor in the browser
  • Excel round-trip: export, adjust, import
DataLion builds a codebook automatically from an imported, labelled dataset

Codebook scripting: derived questions and merges

The codebook doesn’t just describe your data — it shapes it. With codebook scripting you create derived questions and merge variables without touching the raw dataset.

Use "type":"union" to merge several variables into one shared question; use "multistack" to stack variables for comparative analysis. Ideal when waves or splits should grow into one continuous question.

  • Define derived questions right in the codebook
  • Merge variables with "type":"union"
  • Comparative stacks with "multistack"
  • Raw data stays untouched
DataLion dashboard whose questions were merged from several variables via codebook scripting

Calculated variables and alternative bases via SQL

For demanding analysis you compute right in the codebook: in the Value column you place SQL expressions to create calculated variables or alternative bases — for instance a net row on a different population.

That keeps the logic where the metadata lives: central, versioned through the codebook and consistent in every chart that uses it.

  • SQL expressions directly in the Value column
  • Calculated variables without touching the raw data
  • Alternative bases for net rows and special analyses

See DataLion with your own data

Start a free trial or book a personal demo — from raw data to a finished dashboard.

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We now work much more efficiently, giving us more time to take care of the derivations and insights from the data for the customers.
Jens Falkenau, Vice President of Market Research · Nielsen Sports
Read the case study →

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Common questions about the codebook

What is the codebook in DataLion?
The codebook is the central metadata engine. It defines variables, value labels, chart types, the navigation hierarchy, net rows and formulas, missing values and per-variable filters — once, for the whole project. Charts, reports and exports all draw from this one source.
Do I have to build the codebook by hand?
No. DataLion generates the codebook automatically from labelled datasets (e.g. SPSS, Excel, CSV). For open numeric and text variables it inserts placeholders like and
Can I edit the codebook in Excel?
Yes. Besides the online editor there is an Excel round-trip: export the codebook, adjust it in Excel and import it back. That lets even large teams work on the same metadata foundation.
How do I create derived questions or merge variables?
Through codebook scripting. Use "type":"union" to merge several variables into one shared question, and "multistack" to stack variables for comparative analysis — without altering the raw dataset.
Can I compute in the codebook, for example define alternative bases?
Yes. In the Value column you place SQL expressions to create calculated variables or alternative bases — for example a net row on a different population.

Build your first codebook

Try DataLion free and let a codebook be built automatically from your labelled dataset — or see in a demo how the metadata engine keeps trackers consistent.

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