Work with custom data

Custom Data Commons provides a simple mechanism to import your own data, but it requires that the data be provided in a specific format and file structure.

  • All data must be in CSV format, using the schema described below.
  • You must also provide a JSON configuration file, named config.json, to map the CSV contents to the Data Commons schema knowledge graph. The contents of the JSON file are described below.
  • All CSV files and the JSON file must be in the same directory

Examples are provided in custom_dc/sample and custom_dc/examples directories.

Prepare the CSV files

Custom Data Commons provides a simplified data model, which allows your data to be mapped to the Data Commons knowledge graph schema. Data in the CSV files should conform to a variable per column scheme. This requires minimal manual configuration; the Data Commons importer can create observations and statistical variables if they don’t already exist, and it resolves all columns to DCIDs.

With the variable-per-column scheme, data is provided in this format, in this exact sequence:


There are two properties, the ENTITY and the OBSERVATION_DATE, that specify the place and time of the observation; all other properties must be expressed as statistical variables. To illustrate what this means, consider this example: let’s say you have a dataset that provides the number of public schools in U.S. cities, broken down by elementary, middle, secondary and postsecondary. Your data might have the following structure, which we identify as variable per row (numbers are not real, but are just made up for the sake of example):

San Francisco,2023,elementary,300
San Francisco,2023,middle,300
San Francisco,2023,secondary,200
San Francisco,2023,postsecondary,50
San Jose,2023,elementary,400
San Jose,2023,middle,400
San Jose,2023,secondary,300
San Jose,2023,postsecondary,50

For custom Data Commons, you need to format it so that every property corresponds to a separate statistical variable, like this:

San Francisco,2023,300,300,200,50
San Jose,2023,400,400,300,0

The ENTITY is an existing property in the Data Commons knowledge graph that is used to describe an entity, most commonly a place. The best way to think of the entity type is as a key that could be used to join to other data sets. The column heading can be expressed as any existing place-related property; see Place types for a full list. It may also be any of the special DCID prefixes listed in Special place names. The DATE is the date of the observation and should be in the format YYYY, YYYY-MM, or YYYY-MM-DD. The heading can be anything, although as a best practice, we recommend using a corresponding identifier, such as year, month or date.

The VARIABLE should contain a metric observation at a particular time. We recommend that you try to reuse existing statistical variables where feasible; use the main Data Commons Statistical Variable Explorer to find them. If there is no existing statistical variable you can use, name the heading with an illustrative name and the importer will create a new variable for you.

The variable values must be numeric. Zeros and null values are accepted: zeros will be recorded and null values ignored.

All headers must be in camelCase.

Special place names

In addition to the place names listed in Place types, you can also use the following special names:

  • dcid — An already resolved DC ID. Examples:country/USA, geoId/06
  • country3AlphaCode — Three-character country codes. Examples: USA, CHN
  • geoId — Place geo IDs. Examples: 06, 023
  • lat#lng — Latitude and longitude of the place using the format lat#long. Example: 38.7#-119.4
  • wikidataId — Wikidata place identifiers. Example: Q12345

You can also simply use the heading name or place and the importer will resolve it automatically.

The following are all valid examples of headers:


Write the data config file

The config.json file specifies how the CSV contents should be mapped and resolved to the Data Commons schema. See the example in the sample/config.json file provided, which describes the data in the sample/average_annual_wage.csv and sample/gender_wage_gap.csv files.

Here is the general spec for the JSON file:

  "inputFiles": {  
    "FILE_NAME1": {  
      "entityType": "ENTITY_PROPERTY",  
      "ignoreColumns": ["COLUMN1", "COLUMN2", ...],  
      "provenance": "NAME"  
    "FILE_NAME2": {  
  "variables": {  
    "VARIABLE1": {"group": "GROUP_NAME1"},  
    "VARIABLE2": {"group": "GROUP_NAME1"},  
    "VARIABLE3": {  
      "name": "DISPLAY_NAME",  
      "description": "DESCRIPTION",  
      "searchDescriptions": ["SENTENCE1", "SENTENCE2", ...],  
      "group": "GROUP_NAME2",  
      "properties": {  
  "sources": {  
    "SOURCE_NAME1": {  
      "url": "URL",  
      "provenances": {  
        "PROVENANCE_NAME1": "URL",  
        "PROVENANCE_NAME2": "URL",  

Each section contains some required and optional fields, which are described in detail below.

Input files

The top-level inputFiles field should encode a map from the input file name to parameters specific to that file. Keys can be individual file names or wildcard patterns if the same config applies to multiple files.

You can use the * wildcard; matches are applied in the order in which they are specified in the config. For example, in the following:

 "inputFiles": {
    "foo.csv": {...},
    "bar*.csv": {...},
    "*.csv": {...}

The first set of parameters only applies to foo.csv. The second set of parameters applies to bar.csv, bar1.csv, bar2.csv, etc. The third set of parameters applies to all CSVs except the previously specified ones, namely foo.csv and bar*.csv.

Input file parameters


Required: All entities in a given file must be of a specific type. This type should be specified as the value of the entityType field. The importer tries to resolve entities to DCIDs of that type. In most cases, the entityType will be a supported place type; see Place types for a list.


Optional: The list of column names to be ignored by the importer, if any.


Required: The provenance (name) of this input file. Provenances typically map to a dataset from a source. For example, WorldDevelopmentIndicators provenance (or dataset) is from the WorldBank source.

You must specify the provenance details under sources.provenances; this field associates one of the provenances defined there to this file.


The variables section is optional. You can use it to override names and associate additional properties with the statistical variables in the files, using the parameters described below. All parameters are optional.

Variable parameters


The display name of the variable, which will show up in the site’s exploration tools. If not specified, the column name is used as the display name.
The name should be concise and precise; that is, the shortest possible name that allow humans to uniquely identify a given variable. The name is used to generate NL embeddings.


A long-form description of the variable.


Additional Data Commons properties associated with this variable. These are Data Commons property entities. See Representing statistics in Data Commons for more details.

Each property is specified as a key:value pair. Here are some examples:

  "populationType": "schema:Person",
  "measuredProperty": "age",
  "statType": "medianValue",
  "gender": "Female"

You can arrange variables in groups, so that they appear together in the Statistical Variables Explorer and other exploration tools. The group name is used as the heading of the group. For example, in the sample data, the group name OECD is used to group together the two variables from the two CSV files:


You can have a multi-level group hierarchy by using / as a separator between each group.


An array of descriptions to be used for creating more NL embeddings for the variable. This is only needed if the variable name is not sufficient for generating embeddings.


The sources section is optional. It encodes the sources and provenances associated with the input dataset. Each named source is a mapping of provenances to URLs.

Source parameters

Required: The URL of the named source. For example, for named source U.S. Social Security Administration, it would be
Required: A set of name:URL pairs. Here are some examples:
  "USA Top Baby Names 2022": "",
  "USA Top Baby Names 1923-2022": ""

Load local custom data

To load custom data uploaded to Google Cloud, see instead Pointing the local Data Commons site to the Cloud data for procedures.

Start the Docker container with local custom data

Once you have your CSV files and config.json set up, use the following command to restart the Docker container, mapping your custom data directory to the Docker userdata directory.

docker run -it \
-p 8080:8080 \
-e DEBUG=true \
--env-file $PWD/custom_dc/sqlite_env.list \
-v $PWD/custom_dc/CUSTOM_DATA_DIRECTORY:/userdata \
[-v $PWD/custom_dc/CUSTOM_DATA_DIRECTORY/datacommons:/sqlite]

The optional -v flag preserves the SQLite data so it loads automatically when you restart the Docker container.

Every time you make changes to the CSV or JSON files, you should reload the data, as described below.

Load custom data in SQLite

As you are iterating on changes to the source CSV and JSON files, you will need to reload the data. Custom Data Commons allows you to reload data on the fly, while the website is running, so even multiple users can reload data with a shared Docker instance.

You can load the new/updated data from SQLite using the /admin page on the site:

  1. Optionally, in the sqlite_env.list file, set the ADMIN_SECRET environment variable to a string that authorizes users to load data.
  2. Start the Docker container as usual, being sure to map the path to the directory containing the custom data (see command above).
  3. With the services running, navigate to the /admin page. If a secret is required, enter it in the text field, and click Load. This runs a script inside the Docker container, that converts the CSV data into SQL tables, and generates embeddings in the container as well. The database is created as custom_dc/CUSTOM_DATA_DIRECTORY/datacommons/datacommons.db and embeddings are generated in custom_dc/CUSTOM_DATA_DIRECTORY/datacommons/nl/.

Inspect the SQLite database

If you need to troubleshoot custom data, it is helpful to inspect the contents of the generated SQLite database.

To do so, from a terminal window, open the database:

sqlite3 website/custom_dc/CUSTOM_DATA_DIRECTORY/datacommons/datacommons.db

This starts the interactive SQLite shell. To view a list of tables, at the prompt type .tables. The relevant table is observations.

At the prompt, enter SQL queries. For example, for the sample OECD data, this query:

sqlite> select * from observations limit 10;

returns output like this: