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Copy file name to clipboardExpand all lines: docs/1-metadata-at-the-source.md
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If you are comfortable with Python, consider to try the following experiment.
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:::{.callout-tip}
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Download a mcf sample [pygeometa repository](https://raw.githubusercontent.com/geopython/pygeometa/refs/heads/master/sample.yml). Then open a shell and set up a virtual Python (or COnda) environment, then:
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Download an MCF sample [pygeometa repository](https://raw.githubusercontent.com/geopython/pygeometa/refs/heads/master/sample.yml). Then open a shell and set up a virtual Python (or Conda) environment, then:
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```bash
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pip3 install pygeometa
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MCF documents can be written in a text editor like [Visual Studio Code](https://code.visualstudio.com). Consider to install the [YAML plugin](https://marketplace.visualstudio.com/items?itemName=redhat.vscode-yaml) for instant YAML validation.
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Another option to create and update mcf files is via [MDME](https://github.com/osgeo/mdme). MDME is a web based software package providing a dynamic metadata edit form. An operational package is available at [osgeo.github.io](https://osgeo.github.io/mdme). Notice that if you install the package locally, you can customize the metadata model to your organizational needs.
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Another option to create and update MCF files is via [MDME](https://github.com/osgeo/mdme). MDME is a web based software package providing a dynamic metadata edit form. An operational package is available at [osgeo.github.io](https://osgeo.github.io/mdme). Notice that if you install the package locally, you can customize the metadata model to your organizational needs.
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:::{.callout-tip}
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Imagine a dataset you have recently worked with. Then open [mdme](https://osgeo.github.io/mdme) and populate the form, describing that dataset. Now save the MCF file so we can later place it in a sample data repository.
Copy file name to clipboardExpand all lines: docs/3-catalogue-publication.md
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[Compose](https://docs.docker.com/compose/) is a utility of docker, enabling setup of a set of containers using a composition script. A composition script can automate the manual startup operations of the previous paragraph. We've prepared a composition script for this workshop. The script includes, besides the pycsw container, other containers from next paragraphs.
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Clone the workshop repository to a local folder (You don't have git installed? You can also download the repository as a [zip file](https://github.com/pvgenuchten/training-gitops-sdi/archive/refs/heads/main.zip)).
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Clone the workshop repository to a local folder (You don't have Git installed? You can also download the repository as a [zip file](https://github.com/pvgenuchten/training-gitops-sdi/archive/refs/heads/main.zip)).
Notice at <http://localhost:8000/collections/metadata:main/items> that all records are removed.
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We exported mcf records as iso19139 in the [previous section](./2-interact-with-data-repositories.md).
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We exported MCF records as iso19139 in the [previous section](./2-interact-with-data-repositories.md).
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Copy the ISO XML documents to the `./docker/data/export` folder in the Docker project. This folder will be mounted into the container, so the records can be loaded into the pycsw database.
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Use pycsw-admin.py to load the records into the catalogue database:
- Copy the mcf files, which have been generated in [Catalogue publication](./3-catalogue-publication.md), to a `data` folder in the cloned docker repository.
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- Copy the MCF files, which have been generated in [Catalogue publication](./3-catalogue-publication.md), to a `data` folder in the cloned docker repository.
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- Commit & push the files
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```bash
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## Cross linking catalogue and Git
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While users are browsing the catalogue (or this page), they may find irregularities in the content. They can flag this as an issue in the relevant Git repository. A nice feature is to add a link in the catalogue page which brings them back to the relevant mcf in the git repository. With proper authorizations they can instantly improve the record, or suggest an improvement via an issue or [pull request](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/about-pull-requests).
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While users are browsing the catalogue (or this page), they may find irregularities in the content. They can flag this as an issue in the relevant Git repository. A nice feature is to add a link in the catalogue page which brings them back to the relevant MCF in the Git repository. With proper authorizations they can instantly improve the record, or suggest an improvement via an issue or [pull request](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/about-pull-requests).
Copy file name to clipboardExpand all lines: docs/7-providing-map-services.md
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## Mapfile creation exercise
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- Navigate with shell to a folder with data files.
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- Verify if mcf's are available for the files, if not, create initial metadata with `crawl-metadata --mode=init --dir=.`
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- Verify if MCFs are available for the files, if not, create initial metadata with `crawl-metadata --mode=init --dir=.`
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- Add a index.yml file to the folder. This metadata is introduced in the mapfile to identify the service.
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```yaml
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You can also try the url in QGIS. Add a WMS layer, of service http://localhost/ows/foss4g?request=GetCapabilities&service=WMS.
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Notice that the mcf.yml files now include a link to the mapservice via which the datasets are shared. These links have been added by the crawl-maps method (and use the `pgdc_ms_url` environment variable). Publish the records to the catalogue again, so users will be able to find the service while browsing the catalogue.
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Notice that the MCF files now include a link to the mapservice via which the datasets are shared. These links have been added by the crawl-maps method (and use the `pgdc_ms_url` environment variable). Publish the records to the catalogue again, so users will be able to find the service while browsing the catalogue.
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pyGeoDataCrawler uses default (gray) styling for vector and an average classification for grids. You can finetune the styling of layers through the [robot section in index.yml](https://github.com/pvgenuchten/pyGeoDataCrawler?tab=readme-ov-file#layer-styling) or by providing an [Styled Layer Descriptor](https://www.ogc.org/standards/sld/) (SLD) file for a layer, as `{name}.sld`. Sld files can be created using QGIS (export style as SLD).
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