MasterSheet for submission of simple data models

We have released a master sheet that simplifies the contribution to the smart data models.

It uploads the data model to a new repository called drafted.

The video help is also available

Steps for using the spreadsheet

0.- Make a local copy in your google account and grant permission the first time you use it (it warns you that it is not validated by google, true, but anyhow go to advance and grant the permissions

1.- Include your example in json key values

2.- Include the name of the data model, its title, the subject, and the description together with your email

3.,- Describe the attributes, their NGSI type, their data type, and the written description.

And click on the two buttons


updated common-schema.json

We have noticed an error in the description of the attribute location in the file common-schema.json, a file that is embedded in most of the data models. It does not affect the data types (so the validation of the schemas is not affected) but it affects the qualification of the NGSI type that it is not properly identified,

GeoProperty is written with capital middle P.

The file has been updated and soon it will be launched an update of all the data models affected.

EBSI Verifiable Credentials mapped as data models

There is a new subject – Verifiable Credentials – in the Cross-Sector domain, with 5 data models derived from those at EBSI. Minor changes to be adapted as standard data models. Due to the fact that they are used only in key-values format the examples are only in this format. The @context defined has been removed because they are implicit attributes in NGSI-LD.

Check the derivedFrom metadata in every schema.json file to get to the EBSI source.

ebsi European Blockchain

pysmartdatamodels options in the tools menu

New options for pysmartdatamodels package. There are a new element in he tools menu to access the pysmartdatamodels package, its documentation, or a code example.

New service for customization of @context with official ontologies

Some of the data models available are generated according to some ontologies (like dataModels.OSLO). The @context provided for the subject includes IRIs with the smartdatamodels by default, however, the use of official ontologies can be required.

In order to allow this the file notes_context.jsonld was created and also included in the contribution manual (seection 1.5 slide 23).

Now the service for mapping the external ontologies (Home -> tools -> Mapper @context with external ontologies) can detect that instead of a regular configuration file a notes_context.jsonld is provided and applies the replacement of terms defined there.

It can be also used as an API call, in this link you have an example

The structure of the notes_context.jsonld has to follow the template

  • {

“@context”: {

“Term1”: “uri_term_1”,

“Term2”: “uri_term_2”,



New version of pysmartdatamodels 0.5.41

There is a new version of the python package for smart data models  pysmartdatamodels 0.5.41


– The README include now the basic documentation of each function

  • New function to load in a dictionary all subjects and their data models, with their repository link, and related domains
  • New function to load in a dictionary all the attributes in every data model including  name of the property, data type, NGSI type, description, units, et

The data models available for python developers. pysmartdatamodels 0.5.40 published. Beta version.

Now you can find in the python package pysmartdatamodels with 13 functions for the integrators of the data models (more than 800) in external systems and applications. It is a beta version. There is a function, update_data() that whenever is run, it updates the data models to the last version (including adding the new data models). The code is available at the utils directory.
This python package includes all the data models and several functions (listed below) to use in your developments.

If you want to be updated on this package you can join this mailing list (Announcements are sent only when something relevant happens). We love to get your feedback at

There are several online tools to manage and to create the data models, generate examples or to adapt to existing ontologies. See the tools menu option at the home site.

Functions available include:

1- List all data models. Function list_all_datamodels()
2- List all subjects. Function list_all_subjects()
3- List the data models of a subject. Function datamodels_subject(subject)
4- List description of an attribute. Function description_attribute(subject, datamodel, attribute)
5- List data-type of an attribute. Function datatype_attribute(subject, datamodel, attribute)
6- Give reference model for an attribute. Function model_attribute(subject, datamodel, attribute)
7- Give reference units for an attribute. Function attributes_datamodel(subject, datamodel)
8- List the attributes of a data model. Function attributes_datamodel(subject, datamodel)
9- List the NGSI type (Property, Relationship or Geoproperty) of the attribute. Function ngsi_datatype_attribute(subject, datamodel, attribute)
10- Print a list of data models attributes separated by a separator. Function print_datamodel(subject, datamodel, separator, meta_attributes)
11- Returns the link to the repository of a subject. Function subject_repolink(subject)
12- Returns the links to the repositories of a data model name. Function datamodel_repolink(datamodel)
13- Update the official data model list or the database of attributes from the source. Function update_data()


1.- Create a proper documentation

2.- Function to allow submission of improvements (i.e. missing recommended units or model) and comments to the different data models. Currently, you can do it by searching for your data model here visiting the github repo and making your PR or raising your issues there.
3.- Function to submit a new data model to an incubation repository. Currently, this is done manually incubated repository. By filling this form you are granted to contribute with new data models.
4.- Include new functions like search for the subject of a data model or other that you can suggest to us at

### some example code
from pysmartdatamodels import pysmartdatamodels as sdm
subject = "dataModel.Weather"
dataModel = "WeatherForecast"
attribute = "precipitation"
print(sdm.description_attribute(subject, dataModel, attribute))
print(sdm.datatype_attribute(subject, dataModel, attribute))
print(sdm.model_attribute(subject, dataModel, attribute))
print(sdm.units_attribute(subject, dataModel, attribute))
print(sdm.attributes_datamodel(subject, dataModel))
print(sdm.print_datamodel(subject, dataModel, ",", ["property", "type", "dataModel", "repoName", "description", "typeNGSI", "modelTags", "format", "units", "model"]))

SQL export available for Postgresql

There is a new file ‘schema.sql‘ in all the directories of the data models. It is a SQL script for PostgreSQL.

The script creates the structure of a relational table containing the attributes defined in the data model. It also creates the data types for those attributes with an enumeration of values.

For those attributes being arrays or objects, it creates a JSON attribute (allowed in PostgreSQL).

If you need additional features in this export please report them to

See an example.

Postgresql elephant