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Improved ckanext-scheming with DCAT, DCAT-AP and GeoDCAT-AP/INSPIRE custom schemas and tools.

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ckanext-schemingdcat. LOD/INSPIRE metadata enhancement for ckanext-scheming

Overview • Installation • Configuration • Schemas • Harvesters • DCAT Profiles • Running the Tests • Development

Overview

This CKAN extension provides functions and templates specifically designed to extend ckanext-scheming and ckanext-dcat and includes RDF profiles and Harvest enhancements to adapt CKAN Schema to multiple metadata profiles as: GeoDCAT-AP or DCAT-AP.

Warning

This project requires mjanez/ckanext-dcat (for newer releases) or ckan/ckanext-dcat (older), along with ckan/ckanext-scheming and ckan/ckanext-spatial to work properly.

  • If you want to use custom schemas with multilingual support, you need to use ckanext-fluent. A fixed version is available at mjanez/ckanext-fluent.
  • If you want to use custom harvesters, you need to use ckanext-harvest, an improved, more private version is avalaibe at mjanez/ckanext-harvest.

Tip

It is recommended to use with: ckan-docker deployment or only use ckan-pycsw to deploy a CSW Catalog.

image

Enhancements:

  • Custom schemas for ckanext-scheming in the plugin like CKAN GeoDCAT-AP custom schemas
  • ckanext-dcat profiles for RDF serialization according to profiles such as DCAT, DCAT-AP, GeoDCAT-AP and in the Spanish context, NTI-RISP.
  • Improve metadata management forms to include tabs that make it easier to search metadata categories and simplify metadata editing.
  • Improve the search functionality in CKAN for custom schemas. It uses the fields defined in a scheming file to provide a set of tools to use these fields for scheming, and a way to include icons in their labels when displaying them. More info: ckanext-schemingdcat
  • Add improved harvesters for custom metadata schemas integrated with ckanext-harvest in CKAN using mjanez/ckan-ogc.
  • Add Metadata downloads for Linked Open Data formats (mjanez/ckanext-dcat) and Geospatial Metadata (ISO 19139, Dublin Core, etc. with mjanez/ckan-pycsw)
  • Add custom i18n translations to datasets, groups, organizations in schemas, e.g: GeoDCAT-AP (ES).1
  • Add a set of useful helpers and templates to be used with Metadata Schemas.
  • Update the base theme of CKAN to use with the enhancements of this extension, now using Tabs instead of older stages.
  • Modern UI inspired on datopian/ckanext-datopian.
  • LOD/OGC Endpoints based on avalaible profiles (DCAT) and CSW capabilities with mjanez/ckan-pycsw.
  • Publisher (organization admin) and editor logic to control the visibility of records in a regular metadata monitoring workflow.

Requirements

Compatibility

Compatibility with core CKAN versions:

CKAN version Compatible?
2.8 ❌ No (>= Python 3)
2.9 ✅ Yes (<= v3.2.3)
2.10 ✅ Yes (>= v4.0.0)

Plugins

This plugin needs the following plugins to work properly:

# Install latest stable release of:
## ckan/ckanext-scheming: https://github.com/ckan/ckanext-scheming/tags (e.g. release-3.0.0)
pip install -e git+https://github.com/ckan/ckanext-scheming.git@release-3.0.0#egg=ckanext-scheming

## mjanez/ckanext-dcat: https://github.com/mjanez/ckanext-dcat/tags (e.g. 1.8.0)
pip install -e git+https://github.com/mjanez/ckanext-dcat.git@1.8.0#egg=ckanext-dcat
pip install -r https://raw.githubusercontent.com/mjanez/ckanext-dcat/master/requirements.txt

## ckan/ckanext-spatial: https://github.com/ckan/ckanext-spatial/tags (e.g. v2.1.1)
pip install -e git++https://github.com/ckan/ckanext-spatial.git@v2.1.1/#egg=ckanext-spatial#egg=ckanext-spatial
pip install -r https://raw.githubusercontent.com/ckan/ckanext-spatial/v2.1.1/requirements.txt

## ckan/ckanext-harvest: https://github.com/ckan/ckanext-harvest/tags (e.g. v1.5.6)
pip install -e git++https://github.com/ckan/ckanext-harvest.git@v1.5.6#egg=ckanext-spatial
pip install -r https://raw.githubusercontent.com/ckan/ckanext-harvest/v1.5.6/requirements.txt

## ckan/ckanext-fluent: https://github.com/mjanez/ckanext-fluen/tags (e.g. v1.0.1)
pip install -e git++https://github.com/mjanez/ckanext-fluent.git@v1.0.1#egg=ckanext-fluent

Installation

cd $CKAN_VENV/src/

# Install the scheming_dataset plugin
pip install -e "git+https://github.com/ckan/ckanext-schemingdcat.git#egg=ckanext-schemingdcat"

Configuration

Set the plugin:

# Add the plugin to the list of plugins
ckan.plugins = ... spatial_metadata ... dcat ... schemingdcat

Warning

When using schemingdcat extension,scheming should not appear in the list of plugins loaded in CKAN. But dcat and spatial should.

Scheming DCAT

Set the schemas you want to use with configuration options:

# Each of the plugins is optional depending on your use
ckan.plugins = schemingdcat_datasets schemingdcat_groups schemingdcat_organizations

To use CSW Endpoint in ckanext-schemingdcat:

schemingdcat.geometadata_base_uri = http://localhost:81/csw
ckanext.dcat.base_uri = http://localhost:81/catalog

To use custom schemas in ckanext-scheming:

# module-path:file to schemas being used
scheming.dataset_schemas = ckanext.schemingdcat:schemas/geodcat_ap/es_geodcat_ap_full.yaml
scheming.group_schemas = ckanext.schemingdcat:schemas/geodcat_ap/es_geodcat_ap_group.json
scheming.organization_schemas = ckanext.schemingdcat:schemas/geodcat_ap/es_geodcat_ap_org.json

#   URLs may also be used, e.g:
#
# scheming.dataset_schemas = http://example.com/spatialx_schema.yaml

#   Preset files may be included as well. The default preset setting is:
scheming.presets = ckanext.schemingdcat:schemas/default_presets.json

#   The is_fallback setting may be changed as well. Defaults to false:
scheming.dataset_fallback = false

Harvest

Requirement:

  • If you want to use custom harvesters, you need to use ckanext-harvest, an improved, more private version is avalaibe at mjanez/ckanext-harvest.

Next add the custom Harvesters to the list of plugins as you need:

ckan.plugins = ... spatial_metadata ... dcat ... schemingdcat ... harvest ... schemingdcat_ckan_harvester schemingdcat_csw_harvester ...

Endpoints

You can update the endpoints.yaml file to add your custom OGC/LOD endpoints, only has 2 types of endpoints: lod and ogc, and the profile avalaible in ckanext-dcat Preferably between 4 and 8.

Examples:

  • LOD endpoint: A Linked Open Data endpoint is a DCAT endpoint that provides access to RDF data. More information about the catalogue endpoint, how to use the endpoint, (e.g. https://{ckan-instance-host}/catalog.{format}?[page={page}]&[modified_since={date}]&[profiles={profile1},{profile2}]&[q={query}]&[fq={filter query}], and more at ckanext-dcat

      - name: eu_dcat_ap_2_rdf
    display_name: RDF DCAT-AP
    type: lod
    format: rdf
    image_display_url: /images/icons/endpoints/eu_dcat_ap_2.svg
    description: RDF DCAT-AP Endpoint for european data portals.
    profile: eu_dcat_ap_2
    profile_label: DCAT-AP
    version: null

  • OGC Endpoint: An OGC CSW endpoint provides a standards-based interface to discover, browse, and query metadata about spatial datasets and data services. More info about the endpoint at OGC: Catalogue Servicestandard/cat/

      - name: csw_inspire
    display_name: CSW INSPIRE 2.0.2
    type: ogc
    format: xml
    image_display_url: /images/icons/endpoints/csw_inspire.svg
    description: OGC-INSPIRE Endpoint for spatial metadata.
    profile: es_dcat
    profile_label: INSPIRE
    version: 2.0.2

Facet Scheming

To configure facets, there are no mandatory sets in the config file for this extension. The following sets can be used:

schemingdcat.facet_list: [list of fields]      # List of fields in scheming file to use to faceting. Use ckan defaults if not provided.
schemingdcat.default_facet_operator: [AND|OR]  # OR if not defined

 schemingdcat.icons_dir: (dir)                  # images/icons if not defined

As an example for facet list, we could suggest:

schemingdcat.facet_list = "theme groups theme_es dcat_type owner_org res_format publisher_name publisher_type frequency tags tag_uri conforms_to spatial_uri"

The same custom fields for faceting can be used when browsing organizations and groups data:

schemingdcat.organization_custom_facets = true
schemingdcat.group_custom_facets = true

This two last settings are not mandatory. You can omit one or both (or set them to false), and the default fields for faceting will be used instead.

Facet Scheming integration with Solr

  1. Clear the index in solr:

    ckan -c [route to your .ini ckan config file] search-index clear

  2. Modify the schema file on Solr (schema or managed schema) to add the multivalued fields added in the scheming extension used for faceting. You can add any field defined in the schema file used in the ckanext-scheming extension that you want to use for faceting. You must define each field with these parameters:

    • type: string - to avoid split the text in tokens, each individually "faceted".
    • uninvertible: false - as recomended by solr´s documentation
    • docValues: true - to ease recovering faceted resources
    • indexed: true - to let ckan recover resources under this facet
    • stored: true - to let the value to be recovered by queries
    • multiValued: well... it depends on if it is a multivalued field (several values for one resource) or a regular field (just one value). Use "true" or "false" respectively.

    E.g. ckanext-iepnb extension are ready to use these multivalued fields. You have to add this configuration fragment to solr schema in order to use them:

    <!-- Extra fields -->
  <field name="tag_uri" type="string" uninvertible="false" docValues="true" indexed="true" stored="true" multiValued="true"/>
  <field name="conforms_to" type="string" uninvertible="false" docValues="true" indexed="true" stored="true" multiValued="true"/>
  <field name="lineage_source" type="string" uninvertible="false" docValues="true" indexed="true" stored="true" multiValued="true"/>
  <field name="lineage_process_steps" type="string" uninvertible="false" docValues="true" indexed="true" stored="true" multiValued="true"/>
  <field name="reference" type="string" uninvertible="false" docValues="true" indexed="true" stored="true" multiValued="true"/>
  <field name="theme" type="string" uninvertible="false" docValues="true" indexed="true" stored="true" multiValued="true"/>
  <field name="theme_es" type="string" uninvertible="false" docValues="true" multiValued="true" indexed="true" stored="true"/>
  <field name="metadata_profile" type="string" uninvertible="false" docValues="true" multiValued="true" indexed="true" stored="true"/>
  <field name="resource_relation" type="string" uninvertible="false" docValues="true" indexed="true" stored="true" multiValued="true"/>

    [!NOTE] You can ommit any field you're not going to use for faceting, but the best policy could be to add all values at the beginning.

    The extra fields depend on your schema

    Be sure to restart Solr after modify the schema.

  3. Restart CKAN.

  4. Reindex solr index:

    ckan -c [route to your .ini ckan config file] search-index rebuild-fast

    Sometimes solr can issue an error while reindexing. In that case I'd try to restart solr, delete index ("search-index clear"), restart solr, rebuild index, and restart solr again.

    Ckan needs to "fix" multivalued fields to be able to recover values correctly for faceting, so this step must be done in order to use faceting with multivalued fields.

Icons

Icons for each field option in the scheming file can be set in multiple ways:

  • Set a root directory path for icons for each field using the icons_dir key in the scheming file.
  • If icons_dir is not defined, the directory path is guessed starting from the value provided for the schemingdcat.icons_dir parameter in the CKAN config file, adding the name of the field as an additional step to the path (public/images/icons/{field_name).
  • For each option, use the icon setting to provide the last steps of the icon path from the field's root path defined before. This value may be just a file name or include a path to add to the icon's root directory.
  • If icon is not used, a directory and file name are guessed from the option's value.
  • Icons files are tested for existence when using schemingdcat_schema_icon function to get them. If the file doesn't exist, the function returns None. Icons can be provided by any CKAN extension in its public directory.
  • Set a default icon for a field using the default_icon setting in the scheming file. You can get it using schemingdcat_schema_get_default_icon function, and it is your duty to decide when and where to get and use it in a template.

New theme

Update the base theme of CKAN to use with the enhancements of this extension.

Improved home statistics: image

Catalog endpoints /endpoints: image

Search: screenshot 1695622478

Dataset - Metadata info /dataset/{sample-dataset}: screenshot 1695622650 image

Dataset - Form tabs: image

Organizations /organization/{sample-org}: screenshot 1695622687

Groups /group/{sample-group}: screenshot 1695622719

Schemas

With this plugin, you can customize the group, organization, and dataset entities in CKAN. Adding and enabling a schema will modify the forms used to update and create each entity, indicated by the respective type property at the root level. Such as group_type, organization_type, and dataset_type. Non-default types are supported properly as is indicated throughout the examples.

Are available to use with this extension a number of custom schema, more info: schemas/README.md

Schema Enhancements: We've made several improvements to our schema to provide a better metadata and metadata group management. Here are some of the key changes:

  • Form Groups: We've introduced the use of form_groups and improve form_pages in our schemas. This allows us to group related fields into the same form, making it easier to navigate and manage metadata.
  • Metadata Management Improvements: We've improved how we manage metadata in our schema. It's now easier to add, remove, and modify metadata, allowing us to keep our data more organized and accessible.
  • Metadata Group Updates: We've made changes to how we handle metadata groups (form_groups). It's now easier to group related metadata, helping us keep our data more organized and making it easier to find specific information.

For more details on these enhancements check Form Groups documentation, please refer to the schema files in ckanext/schemingdcat/schemas.

GeoDCAT-AP (ES)

schemas/geodcat_ap/es_geodcat_ap_full with specific extensions for spatial data and GeoDCAT-AP/INSPIRE metadata profiles.

Note

RDF to CKAN dataset mapping: GeoDCAT-AP (ES) to CKAN

DCAT

schemas/dcat based on: DCAT.

Note

RDF to CKAN dataset mapping: DCAT to CKAN

DCAT-AP (EU)

schemas/dcat_ap/eu_dcat_ap based on: DCAT-AP for the european context.

Note

RDF to CKAN dataset mapping: DCAT-AP (EU) to CKAN

GeoDCAT-AP (EU)

schemas/geodcat_ap/eu_geodcat_ap based on: GeoDCAT-AP for the european context.

Note

RDF to CKAN dataset mapping: GeoDCAT-AP (EU) to CKAN

Form Groups

Form groups are a way to group related fields together in the same form. This makes it easier to navigate and manage metadata. A form group is defined with the following elements:

  • form_group_id: A unique identifier for the form group. For example, contact.
  • label: A human-readable label for the form group. This can be provided in multiple languages. For example: 
    label: 
  en: Contact information 
  es: Información de contacto
  • fa_icon: An optional Font Awesome icon that can be used to visually represent the form group. For example, fa-address-book.

Here is an example of a form group definition:

form_group_id: contact 
label: 
  en: Contact information 
  es: Información de contacto 
fa_icon: fa-address-book

Adding Fields to Form Groups

Fields can be added to a form group by specifying the form_group_id in the field definition. The form_group_id should match the form_group_id of the form group that the field should be part of.

Here is an example of a field that is part of the general_info form group:

field_name: owner_org
label:
  en: Organization
  es: Organización
required: True
help_text:
  en: Entity (organisation) responsible for making the Dataset available.
  es: Entidad (organización) responsable de publicar el conjunto de datos.
preset: dataset_organization
form_group_id: general_info

In this example, the owner_org field will be part of the general_info form group.

Harvesters

Basic using

In production, when gather and consumer processes are running, the following command are used to start and stop the background processes:

  • ckan harvester run: Starts any harvest jobs that have been created by putting them onto the gather queue. Also checks running jobs - if finished it changes their status to Finished.

To testing harvesters in development, you can use the following command:

  • ckan harvester run-test {source-id/name}: This does all the stages of the harvest (creates job, gather, fetch, import) without involving the web UI or the queue backends. This is useful for testing a harvester without having to fire up gather/fetch_consumer processes, as is done in production.

    [!WARNING] After running the run-test command, you should stop all background processes for gather and consumer to avoid conflicts.

Scheming DCAT CKAN Harvester: CKAN Harvester for custom schemas

The plugin includes a harvester for remote CKAN instances using the custom schemas provided by schemingdcat and ckanext-scheming. This harvester is a subclass of the CKAN Harvester provided by ckanext-harvest and is designed to work with the schemingdcat plugin to provide a more versatile and customizable harvester for CKAN instances.

To use it, you need to add the schemingdcat_ckan_harvester plugin to your options file:

  ckan.plugins = harvest schemingdcat schemingdcat_datasets ... schemingdcat_ckan_harvester

The Scheming DCAT CKAN Harvester supports the same configuration options as the CKAN Harvester, plus the following additional options:

  • dataset_field_mapping/distribution_field_mapping (Optional): Mapping field names from local to remote instance, all info at: CKAN Harvester Field mapping structure
  • field_mapping_schema_version (Mandatory if exists dataset_field_mapping/distribution_field_mapping): Schema version of the field_mapping to ensure compatibility with older schemas. The default is 2.
  • schema (Optional): The name of the schema to use for the harvested datasets. This is the schema_name as defined in the scheming file. The remote and local instances must have the same dataset schema. If not provided, the dataset_field_mapping/distribution_field_mapping is needed to mapping fields.
  • allow_harvest_datasets (Optional): If true, the harvester will create new records even if the package type is from the harvest source. If false, the harvester will only create records that originate from the instance. Default is false.
  • remote_orgs (Optional): [WIP]. Only only_local.
  • remote_groups (Optional): [WIP]. Only only_local.
  • clean_tags: By default, tags are stripped of accent characters, spaces and capital letters for display. Setting this option to False will keep the original tag names. Default is True.

And example configuration might look like this:

    {
    "api_version": 2,
    "clean_tags": false,
    "default_tags": [{"name": "inspire"}, {"name": "geodcatap"}],
    "default_groups": ["transportation", "hb"],
    "default_extras": {"encoding":"utf8", "harvest_description":"Harvesting from Sample Catalog", "harvest_url": "{harvest_source_url}/dataset/{dataset_id}"},
    "organizations_filter_include": ["remote-organization"],
    "groups_filter_include":[],
    "override_extras":false,
    "user":"harverster-user",
    "api_key":"<REMOTE_API_KEY>",
    "read_only": true,
    "remote_groups": "only_local",
    "remote_orgs": "only_local",
    "schema": "geodcatap",
    "allow_harvest_datasets":false,
    "field_mapping_schema_version":2,
    "dataset_field_mapping": {
      "title": {
          "field_name": "my_title"
        },
      "title_translated": {
          "languages": {
              "en": {
                  "field_name": "my_title-en"
              },
              "es": {
                  "field_name": "my_title"
              }
          }
      },
      "private": {
          "field_name": "private"
      },
      "tag_string": {
          "field_name": ["theme_a", "theme_b", "theme_c"]
      },
      "theme_es": {
          "field_value": "http://datos.gob.es/kos/sector-publico/sector/medio-ambiente"
      },
      "tag_uri": {
          "field_name": "keyword_uri",
          // "field_value" extends the original list of values retrieved from the remote file for all records.
          "field_value": ["https://www.example.org/codelist/a","https://www.example.org/codelist/b", "https://www.example.org/codelist/c"] 
      },
      "my_custom_field": {
          // If you need to map a field in a remote dict to the "extras" dict, use the "extras_" prefix to indicate that the field is there.
          "field_name": "extras_remote_custom_field"
      },
    },
    }

Field mapping structure

The dataset_field_mapping/distribution_field_mapping is structured as follows (multilingual version):

{
  ...
  "field_mapping_schema_version": 2,
  "<dataset_field_mapping>/<distribution_field_mapping>": {
    "<schema_field_name>": {
      "languages": {
        "<language>":  {
          <"field_value": "<fixed_value>/<fixed_value_list>">,/<"field_name": "<excel_field_name>/<excel_field_name_list>">
        },
        ...
      },
      ...
    },
    ...
  }
}
  • <schema_field_name>: The name of the field in the CKAN schema.
    • <language>: (Optional) The language code for multilingual fields. This should be a valid ISO 639-1 language code. This is now nested under the languages key.
  • <fixed_value>/<fixed_value_list>: (Optional) A fixed value or a list of fixed values that will be assigned to the field for all records.
  • Field labels: Field name:
    • <field_name>/<field_name_list>: (Optional) The name of the field in the remote file or a list of field names.

For fields that are not multilingual, you can directly use field_name without the languages key. For example:

{
  ...
  "field_mapping_schema_version": 2,
  "<dataset_field_mapping>/<distribution_field_mapping>": {
    "<schema_field_name>": {
      <"field_value": "<fixed_value>/<fixed_value_list>">,/<"field_name": "<excel_field_name>/<excel_field_name_list>">
    },
    ...
  }
}

Important

The field mapping can be done either at the dataset level using dataset_field_mapping or at the resource level using distribution_field_mapping. The structure and options are the same for both. The field_mapping_schema_version is 2 by default, but needs to be set to avoid errors.

Field Types

There are two types of fields that can be defined in the configuration:

  1. Regular fields: These fields have a field label to define the mapping or a fixed value for all its records.
    • Properties: A field can have one of these three properties:
      • Fixed value fields (field_value): These fields have a fixed value that is assigned to all records. This is defined using the field_value property. If field_value is a list, field_name could be set at the same time, and the field_value extends the list obtained from the remote field.
      • Field labels: Field name:
        • Name based fields (field_name): These fields are defined by their name in the Excel file. This is defined using the field_name property, or if you need to map a field in a remote dict to the extras dict, use the extras_ prefix to indicate that the field is there.
  2. Multilingual Fields (languages): These fields have different values for different languages. Each language is represented as a separate object within the field object (es, en, ...). The language object can have field_value and field_name properties, just like a normal field.

Example Here are some examples of configuration files:

  • Field names: With field_name to define the mapping based on names of attributes in the remote sheet (my_title, org_identifier, keywords).
{
  "api_version": 2,
  "clean_tags": false,

  ...
  # other properties
  ...

  "field_mapping_schema_version": 2,
  "dataset_field_mapping": {
    "title": {
        "field_name": "my_title"
      },
    "title_translated": {
        "languages": {
            "en": {
                "field_name": "my_title-en"
            },
            "de": {
                "field_value": ""
            },
            "es": {
                "field_name": "my_title"
            }
        }
    },
    "private": {
        "field_name": "private"
    },
    "theme": {
        "field_name": ["theme", "theme_eu"]
    },
    "tag_custom": {
        "field_name": "keywords"
    },
    "tag_string": {
        "field_name": ["theme_a", "theme_b", "theme_c"]
    },
    "theme_es": {
        "field_value": "http://datos.gob.es/kos/sector-publico/sector/medio-ambiente"
    },
    "tag_uri": {
        "field_name": "keyword_uri",
        // "field_value" extends the original list of values retrieved from the remote file for all records.
        "field_value": ["https://www.example.org/codelist/a","https://www.example.org/codelist/b", "https://www.example.org/codelist/c"] 
    },
    "my_custom_field": {
        // If you need to map a field in a remote dict to the "extras" dict, use the "extras_" prefix to indicate that the field is there.
        "field_name": "extras_remote_custom_field"
    }
  }
}

###TODO: Scheming DCAT CSW INSPIRE Harvester A harvester for remote CSW catalogues using the INSPIRE ISO 19139 metadata profile. This harvester is a subclass of the CSW Harvester provided by ckanext-spatial and is designed to work with the schemingdcat plugin to provide a more versatile and customizable harvester for CSW endpoints and GeoDCAT-AP CKAN instances.

To use it, you need to add the schemingdcat_csw_harvester plugin to your options file:

  ckan.plugins = harvest schemingdcat schemingdcat_datasets ... schemingdcat_csw_harvester

Remote Google Sheet/Onedrive Excel metadata upload Harvester

A harvester for remote Google spreadsheets and Onedrive Excel files with Metadata records. This harvester is a subclass of the Scheming DCAT Base Harvester provided by ckanext-schemingdcat to provide a more versatile and customizable harvester for Excel files that have metadata records in them.

To use it, you need to add the schemingdcat_xls_harvester plugin to your options file:

ckan.plugins = harvest schemingdcat schemingdcat_datasets ... schemingdcat_xls_harvester

Remote Google Sheet/Onedrive Excel metadata upload Harvester supports the following options:

  • storage_type - Mandatory: The type of storage to use for the harvested datasets as onedrive or gspread. Default is onedrive.
  • dataset_sheet - Mandatory: The name of the sheet in the Excel file that contains the dataset records.
  • field_mapping_schema_version: Schema version of the field_mapping to ensure compatibility with older schemas. The default is 2.
  • dataset_field_mapping/distribution_field_mapping: Mapping field names from local to remote instance, all info at: Field mapping structure
  • credentials: The credentials parameter should be used to provide the authentication credentials. The credentials depends on the storage_type used.
    • For onedrive: The credentials parameter should be a dictionary with the following keys: username: A string representing the username. password: A string representing the password.
    • For gspread or gdrive: The credentials parameter should be a string containing the credentials in JSON format. You can obtain the credentials by following the instructions provided in the Google Workspace documentation.
  • distribution_sheet: The name of the sheet in the Excel file that contains the distribution records. If not provided, the harvester will only create records for the dataset sheet.
  • datadictionary_sheet: The name of the sheet in the Excel file that contains the data dictionary records. If not provided, the harvester will only create records for the dataset sheet.
  • api_version: You can force the harvester to use either version 1 or 2 of the CKAN API. Default is 2.
  • default_tags: A list of tags that will be added to all harvested datasets. Tags don't need to previously exist. This field takes a list of tag dicts which allows you to optionally specify a vocabulary. Default is [].
  • default_groups: A list of group IDs or names to which the harvested datasets will be added to. The groups must exist in the local instance. Default is [].
  • default_extras: A dictionary of key value pairs that will be added to extras of the harvested datasets. You can use the following replacement strings, that will be replaced before creating or updating the datasets:
    • {dataset_id}
    • {harvest_source_id}
    • {harvest_source_url} Will be stripped of trailing forward slashes (/)
    • {harvest_source_title}
    • {harvest_job_id}
    • {harvest_object_id}
  • override_extras: Assign default extras even if they already exist in the remote dataset. Default is False (only non existing extras are added).
  • user: User who will run the harvesting process. Please note that this user needs to have permission for creating packages, and if default groups were defined, the user must have permission to assign packages to these groups.
  • read_only: Create harvested packages in read-only mode. Only the user who performed the harvest (the one defined in the previous setting or the 'harvest' sysadmin) will be able to edit and administer the packages created from this harvesting source. Logged in users and visitors will be only able to read them.
  • force_all: By default, after the first harvesting, the harvester will gather only the modified packages from the remote site since the last harvesting Setting this property to true will force the harvester to gather all remote packages regardless of the modification date. Default is False.
  • clean_tags: By default, tags are stripped of accent characters, spaces and capital letters for display. Setting this option to False will keep the original tag names. Default is True.
  • source_date_format: By default the harvester uses dateutil to parse the date, but if the date format of the strings is particularly different you can use this parameter to specify the format, e.g. %d/%m/%Y. Accepted formats are: COMMON_DATE_FORMATS

Field mapping structure (Sheets harvester)

The dataset_field_mapping/distribution_field_mapping is structured as follows (multilingual version):

{
  ...
  "field_mapping_schema_version": 2,
  "<dataset_field_mapping>/<distribution_field_mapping>": {
    "<schema_field_name>": {
      "languages": {
        "<language>":  {
          <"field_value": "<fixed_value>/<fixed_value_list>">,/<"field_name": "<excel_field_name>/<excel_field_name_list>">/< "field_position": "<excel_column>/<excel_column_list>">
        },
        ...
      },
      ...
    },
    ...
  }
}
  • <schema_field_name>: The name of the field in the CKAN schema.
    • <language>: (Optional) The language code for multilingual fields. This should be a valid ISO 639-1 language code. This is now nested under the languages key.
  • <fixed_value>/<fixed_value_list>: (Optional) A fixed value or a list of fixed values that will be assigned to the field for all records.
  • Field labels: Field position or field name:
    • <field_position>/<field_position_list>: (Optional) The position of the field in the remote file, represented as a letter or a list of letters (e.g., "A", "B", "C").
    • <field_name>/<field_name_list>: (Optional) The name of the field in the remote file or a list of field names.

For fields that are not multilingual, you can directly use field_name or field_position without the languages key. For example:

{
  ...
  "field_mapping_schema_version": 2,
  "<dataset_field_mapping>/<distribution_field_mapping>": {
    "<schema_field_name>": {
      <"field_value": "<fixed_value>/<fixed_value_list>">,/<"field_name": "<excel_field_name>/<excel_field_name_list>">/< "field_position": "<excel_column>/<excel_column_list>">
    },
    ...
  }
}

Important

The field mapping can be done either at the dataset level using dataset_field_mapping or at the resource level using distribution_field_mapping. The structure and options are the same for both. The field_mapping_schema_version is 2 by default, but needs to be set to avoid errors.

Field Types

There are two types of fields that can be defined in the configuration:

  1. Regular fields: These fields have a field label/position to define the mapping or a fixed value for all its records.
    • Properties: A field can have one of these three properties:
      • Fixed value fields (field_value): These fields have a fixed value that is assigned to all records. This is defined using the field_value property. If field_value is a list, field_name or field_position could be set at the same time, and the field_value extends the list obtained from the remote field.
      • Field labels: Field position or field name:
        • Position based fields (field_position): These fields are defined by their position in the Excel file. This is defined using the field_position property.
        • Name based fields (field_name): These fields are defined by their name in the Excel file. This is defined using the field_name property.
  2. Multilingual Fields (languages): These fields have different values for different languages. Each language is represented as a separate object within the field object (es, en, ...). The language object can have field_value, field_position and field_name properties, just like a normal field.

Example Here are some examples of configuration files:

  • Field positions: With field_position to define the mapping based on positions of attributes in the remote sheet (A, B, AA, etc.).

    {
  "storage_type": "gspread",
  "dataset_sheet": "Dataset",
  "distribution_sheet": "Distribution",

  ...
  # other properties
  ...

  "field_mapping_schema_version": 2,
  "dataset_field_mapping": {
    "title": {
        "field_position": "A"
      },
    "title_translated": {
        "languages": {
            "en": {
                "field_position": "AC"
            },
            "de": {
                "field_value": ""
            },
            "es": {
                "field_position": "A"
            }
        }
    },
    "private": {
        "field_position": "F"
    },
    "theme": {
        "field_position": ["G", "AA"],
    },
    "tag_custom": {
        "field_position": "B"
    },
    "tag_string": {
        "field_position": ["A", "B", "AC"]
    },
    "theme_es": {
        "field_value": "http://datos.gob.es/kos/sector-publico/sector/medio-ambiente"
    },
    "tag_uri": {
        "field_position": "Z",
        // "field_value" extends the original list of values retrieved from the remote file for all records.
        "field_value": ["https://www.example.org/codelist/a","https://www.example.org/codelist/b", "https://www.example.org/codelist/c"] 
    },
  }
}
    • Field names: With field_name to define the mapping based on names of attributes in the remote sheet (my_title, org_identifier, keywords).
    {
  "storage_type": "gspread",
  "dataset_sheet": "Dataset",
  "distribution_sheet": "Distribution",

  ...
  # other properties
  ...

  "field_mapping_schema_version": 2,
  "dataset_field_mapping": {
    "title": {
        "field_name": "my_title"
      },
    "title_translated": {
        "languages": {
            "en": {
                "field_name": "my_title-en"
            },
            "de": {
                "field_value": ""
            },
            "es": {
                "field_name": "my_title"
            }
        }
    },
    "private": {
        "field_name": "private"
    },
    "theme": {
        "field_name": ["theme", "theme_eu"]
    },
    "tag_custom": {
        "field_name": "keywords"
    },
    "tag_string": {
        "field_name": ["theme_a", "theme_b", "theme_c"]
    },
    "theme_es": {
        "field_value": "http://datos.gob.es/kos/sector-publico/sector/medio-ambiente"
    },
    "tag_uri": {
        "field_name": "keyword_uri",
        // "field_value" extends the original list of values retrieved from the remote file for all records.
        "field_value": ["https://www.example.org/codelist/a","https://www.example.org/codelist/b", "https://www.example.org/codelist/c"] 
    },
  }
}

Important

All *_translated fields need their fallback non-suffix field as simple field, e.g:

...
   "title": {
        "field_position": "A"
     },
   "title_translated": {
       "languages": {
           "en": {
               "field_value": ""
           },
           "es": {
               "field_position": "A"
           }
      }
   },
...

##TODO: CLI The ckan schemingdcat command offers utilites:

ckan schemingdcat create-inspire-tags -l es

ckan schemingdcat download-rdf-eu-vocabs

SQL Harvester

The plugin includes a harvester for local databases using the custom schemas provided by schemingdcat and ckanext-scheming.

To use it, you need to add the schemingdcat_postgres_harvester plugin to your options file:

  ckan.plugins = harvest schemingdcat schemingdcat_datasets ... schemingdcat_ckan_harvester schemingdcat_postgres_harvester

The SQL Harvester supports the following options:

Schema Generation Guide

This guide will help you generate a schema that is compatible with our system. The schema is a JSON object that defines the mapping of fields in your database to the fields in our system.

Field Mapping Structure

The dataset_field_mapping/distribution_field_mapping is structured as follows (multilingual version):

{
  ...
  "field_mapping_schema_version": 1,
  "<dataset_field_mapping>/<distribution_field_mapping>": {
    "<schema_field_name>": {
      "languages": {
        "<language>":  {
          <"field_value": "<fixed_value>/<fixed_value_list>">,/<"field_name": "<db_field_name>/<db_field_name_list>">
        },
        ...
      },
      ...
    },
    ...
  }
}
  • <schema_field_name>: The name of the field in the CKAN schema.
    • <language>: (Optional) The language code for multilingual fields. Must be a valid ISO 639-1 language code. Now nested under the languages key.
  • <fixed_value>/<fixed_value_list>: (Optional) A fixed value or a list of fixed values to be assigned to the field for all records.
  • Field Labels: Field position or field name:
    • <field_name>/<field_name_list>: (Optional) The name of the field in your database. Must be in the format {schema}.{table}.{field}.

For fields that are not multilingual, you can use field_name directly without the languages key. For example:

{
  ...
  "field_mapping_schema_version": 2,
  "<dataset_field_mapping>/<distribution_field_mapping>": {
    "<schema_field_name>": {
      <"field_value": "<fixed_value>/<fixed_value_list>">,/<"field_name": "<db_field_name>/<db_field_name_list>">
    },
    ...
  }
}
{
   "database_type":"postgres",
   "credentials":{
      "user":"u_fototeca",
      "password":"u_fototeca",
      "host":"localhost",
      "port":5432,
      "db":"fototeca"
   },
   "field_mapping_schema_version":1,
   "dataset_field_mapping":{
      "alternate_identifier": {
          "field_name": "fototeca.vista_ckan.cod_vuelo",
          "is_p_key": true,
          "index": true,
          "f_key_references": [
              "fototeca.vuelos.cod_vuelo"
          ]
      },
      "flight_color": {
          "field_name": "fototeca.vista_ckan.color",
          "f_key_references": [
              "fototeca.l_color.color"
          ]
      },
      },
      "encoding": {
          "field_value": "UTF-8"
      },
      "title_translated":{
         "languages": {
            "es":{
              "field_name": "fototeca.vuelos.nom_vuelo",
            }
         }
      }
   },
   "defaults_groups":[

   ],
   "defaults_tags":[

   ],
   "default_group_dicts":[

   ]
}
  • database_type: The type of your database. Currently, only postgres is supported.
  • credentials: The credentials to connect to your database. Must include username, password, host, port, and database name.
  • field_mapping_schema_version: The version of the field mapping schema. Currently, only version 1 is supported.
  • dataset_field_mapping: The mapping of fields in your database to the fields in our system. Each field must be in the format {schema}.{table}.{field}.
  • Other properties of ckanext-harvest/ckanext-schemingdcat.

Field Types

There are two types of fields that can be defined in the configuration:

  1. Regular Fields: These fields have a field label to define the mapping or a fixed value for all their records.

    • Properties: A field can have one of these three properties:
      • Fixed Value Fields (field_value): These fields have a fixed value that is assigned to all records. This is defined using the field_value property. If field_value is a list, field_name could be set at the same time, and the field_value extends the list obtained from the remote field.
      • Field Labels: Field name in the database:

        • Name-Based Fields (field_name): These fields are defined by their name in the DB table. This is defined using the field_name property. To facilitate data retrieval from the database, especially regarding the identification of primary keys (p_key) and foreign keys (f_key), the following properties can be added to the field_mapping schema:

          1. Primary Key Field (is_p_key) [Optional]: This property will identify if the field is a primary key (p_key) or not if not indicated. This will facilitate join operations and references between tables.
          2. Table References (f_key_references) [Optional (list)]: For fields that are foreign keys, this property would specify which schemas, tables, and fields the foreign key refers to. For example, ["public.vuelo.id", "public.camara.id"]. This is useful for automating joins between tables.
          3. Index (index) [Optional]: A boolean property to indicate if the field should be indexed to improve query efficiency. Although not specific to primary or foreign keys, it is relevant for query optimization. By default, its value is false.

          The modified schema would allow for more efficient data retrieval and simplify the construction of the DataFrame, especially in complex scenarios with multiple tables and relationships. Here is an example of how the modified schema would look for a field that is a foreign key:

          "dataset_field_mapping": {
  "alternate_identifier": {
      "field_name": "fototeca.vista_ckan.cod_vuelo",
      "is_p_key": true,
      "index": true,
      "f_key_references": [
          "fototeca.vuelos.cod_vuelo"
      ]
  }
}

  2. Multilingual Fields (languages): These fields have different values for different languages. Each language is represented as a separate object within the field object (es, en, ...). The language object can have field_value, and field_name properties, just like a regular field.

DCAT Profiles

This plugin also contains a custom ckanext-dcat profiles to serialize a CKAN dataset to a:

European context:

Spanish context:

To define which profiles to use you can:

  1. Set the ckanext.dcat.rdf.profiles configuration option on your CKAN configuration file:

    ckanext.dcat.rdf.profiles = eu_dcat_ap_2 es_dcat eu_geodcat_ap_2

  2. When initializing a parser or serializer class, pass the profiles to be used as a parameter, eg:

   parser = RDFParser(profiles=['eu_dcat_ap_2', 'es_dcat', 'eu_geodcat_ap_2'])

   serializer = RDFSerializer(profiles=['eu_dcat_ap_2', 'es_dcat', 'eu_geodcat_ap_2'])

Note that in both cases the order in which you define them is important, as it will be the one that the profiles will be run on.

Multilingual RDF support

Requirement:

  • If you want to use custom schemas with multilingual support, you need to use ckanext-fluent. A fixed version is available at mjanez/ckanext-fluent.

To add multilingual values from CKAN to RDF, the [SchemingDCATRDFProfile method _object_value](./ckanext/schemingdcat/profiles/base.py) can be called with optional parameter multilang=true (defaults to false)). If _object_value is called with the multilang=true-parameter, but no language-attribute is found, the value will be added as Literal with the default language (en).

Tip

The custom ckanext-dcat profiles have multi-language compatibility, see the ckanext-dcat documentation for more information on writing custom profiles.

Example RDF:

<dct:title xml:lang="en">Dataset Title (EN)</dct:title>
<dct:title xml:lang="de">Dataset Title (DE)</dct:title>
<dct:title xml:lang="fr">Dataset Title (FR)</dct:title>
{
    "title":
        {
            "en": "Dataset Title (EN)",
            "de": "Dataset Title (DE)",
            "fr": "Dataset Title (FR)"
        }
}

Example with missing language in RDF:

<dct:title>Dataset Title</dct:title>
{
    "title":
        {
            "en": "Dataset Title"
        }
}

Running the Tests

To run the tests:

pytest --ckan-ini=test.ini ckanext/schemingdcat/tests

Run tests quickly with Docker Compose

This repository includes a Docker Compose configuration to simplify running tests. The CKAN image is built using the Dockerfile located in the docker/ directory.

To test against the CKAN version you want to use, proceed as follows

  1. Build the necessary Docker images. This step ensures that all dependencies and configurations are correctly set up.

    docker compose build

  2. After building the images, you can run the tests. The Docker Compose configuration mounts the root of the repository into the CKAN container as a volume. This means that any changes you make to the code will be reflected inside the container without needing to rebuild the image, unless you modify the extension's dependencies.

    docker compose up

Development

Update Internationalization (i18n) Files

To compile a .po file to a .mo file in the terminal, you can use the msgfmt tool, which is part of the gettext package. Here are the steps to do it:

Steps to Compile .po File to .mo File.

  1. Install gettext (if not already installed):

    • On most Linux distributions, you can install gettext using your system's package manager. For example, in Debian/Ubuntu:
    sudo apt-get install gettext

    In Fedora:

    sudo dnf install gettext
```

  2. Compile the .po file to .mo:

    • Use the msgfmt command to compile the .po file to .mo. Make sure you are in the directory where your .po file is located or provide the full path to the file.
    msgfmt -o ckanext-schemingdcat.mo ckanext-schemingdcat.po
    • This command will generate a ckanext-schemingdcat.mo file in the same directory as the .po file.

Complete Example

Let's assume your .po file is located in the i18n/en/LC_MESSAGES/ directory inside your project. Here are the complete commands:

    1. Navigate to the Project Directory:
    cd /path/to/your/project

  2. Navigate to the Directory Containing the .po File:

    cd i18n/en/LC_MESSAGES/
```

  3. Compile the .po file to .mo:

    msgfmt -o ckanext-schemingdcat.mo ckanext-schemingdcat.po
```

Verification

  1. **Verify that the .mo file has been generated in the browser.

Footnotes

  1. An improvement to [ckanext-fluent] (https://github.com/ckan/ckanext-fluent) to allow more versatility in multilingual schema creation and metadata validation. ↩

About

Improved ckanext-scheming with DCAT, DCAT-AP and GeoDCAT-AP/INSPIRE custom schemas and tools.

github.com/mjanez/ckan-docker

Topics

metadata inspire faceted-search ckan dcat dcat-ap geodcat-ap ckanext-dcat ckanext-scheming ckan-schema

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AGPL-3.0 license
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