In the new testing process, 4th option in the tools menu, now it is available a new test that checks if the example-normalized.json is a valid NGSIv2 file.
This process helps contributors to debug their data models before submit them officially (where there will be new tests before final approval)
The source code for the test is available at the repo.
Remember that if you want to improve / create a new test, just create a PR on the repo.
When you want to contribute a new data model (or an improvement in an existing one) you need to pass a test.
The current process (3rd option in tools menu) keeps on working as it was.
But we have drafted a new method because
– We need to be more explicit about the tests passed and the errors
– We need to improve the performance
So you can check the new method in the 4th option of the Tools menu
Besides this, the tests are very modular so if you are a python programmer you can use them in your own system because the code is being released or indeed you can write new tests that would be included in the official site. Make a PR on the data-models repo and we will add it eventually. Check this post.
Current test process for new and extended data models
In order to approve a new data model a test needs to be passed. It cold be accessed in the 3rd option in the tools menu at the front page:
Pro: it is currently working
Con: It is mostly created in a single file for testing and error messages are not very explicit about the errors detected
The new process
1) Every test is an independent file:
2) To test the new data model it copies to local the files and then run the tests, which is quicker.
What can you do with basic knowledge of python (or with a good AI service)
Here you can see the current files available in the github repository data-models subdirectory test_data_model
Instructions
There is a new data model InteroperableAssets in the new subject Data Space.
The data model aims at make feasible the operation between data spaces. It is create as a derivative work of the DSSC project.
Eating your own food is for SDM a demonstration that agile standardization works
We have created a new subject, SmartDataModels, where the structure of the assets of SDM will be released. We have started with the data models of Attribute according to the global data base of attributes (more than 157.000 currently, >100 MB os it takes a while to download)
The already existing U data quality assessment data model has now some metrology parameters.
– completeness
– timeliness
– accuracy
– precision
Thanks to the Waterverse project and Grigoris Stathopoulos, the contributor.
In the subject of the CROSS Sector domain, CPSV-AP, there is a new data model ‘Address’ which is coming from the new version 3.1 Vocabulary Application Profile (CPSV-AP).
NOTE: We did yesterday 17-9 the changes. Unfortunately we made a mistake and now we have to revert all these changes, do it again properly and push. this Friday will be ready if not earlier.
NOTE2: It is already updated. Its Wednesday 15:30. Hopefully this time we made no errors.
The single-source-of-truth of the data models is the json schema (file schema.json). This json schema has a tag ‘$schema’ indicating the meta schema the schema is compliant with.
Now all data models have been updated to the last one “https://json-schema.org/draft/2020-12/schema”
Therefore some errors provided by validators due to the obsolete previous value have been removed.
Thanks to the user Elliopardad in GitHub for its contribution and to the community of json schema for its support.
As we announce earlier we are one of the project listed in its global landscape of projects.
The Subject of Satellite Imagery, inside the CrossSector domain, has a new data model EOSatelliteImagery:
EOSatelliteImagery. This schema defines a unified entity within the SatelliteImagery domain, consolidating attributes from all existing entities related to satellite imagery, designed to offer a holistic view in the EO proccess
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The directory /code/ (see image with one example)Â in every data model has now a new draft export the pydantic export.
Pydantic is a Python library that provides data validation and settings management using Python type annotations, allowing you to define data models that enforce type constraints and validate data automatically.
Now in most (if not all) data models you have such export to use it freely. Mind that is a first version and errors could happen (It is welcomed if you find any error or just make a suggestion)
