Skip to main content

Editor, repository and data catalogue for SCAR Antarctic Digital Database (ADD) discovery metadata

Project description

SCAR Antarctic Digital Database (ADD) Metadata Toolbox

Editor, repository and data catalogue for SCAR Antarctic Digital Database (ADD) discovery metadata.


This project is a mature alpha.

This means the core components needed have now been implemented but are subject to considerable change and refactoring.

Between releases major parts of this project may be replaced whilst the project evolves. As major non-core features are yet to be implemented the shape and scope of this project may change significantly. It is still expected that this project will grow to cover other MAGIC datasets and products in future, and more widely to act as the seed for a new BAS wide data catalogue.

The 0.2.0 release has effectively been a complete rewrite of the project to reorganise and reimplement prototype code in a more structured way. Automated integration tests have been added and the project is now open-sourced.

Some undesirable code from the 0.1.0 release still remains, to workaround issues in other packages until they can be properly addressed. This code has been moved to a 'Hazardous Materials' (scar_add_metadata_toolbox.hazmat) module.

Further information on upcoming changes to this project can be found in the issues and milestones in GitLab (internal).

Note: This project is designed to meet an internal need within the Mapping and Geographic Information Centre (MAGIC) at BAS. It has been open-sourced in case it's of use to others with similar needs.


This project is comprised of several components:

  1. metadata editor - for maintaining metadata records written as JSON files via the BAS Metadata Library
  2. unpublished (working) metadata repository - using an embedded, authenticated PyCSW catalogue
  3. published metadata repository - using an embedded, partially-authenticated PyCSW catalogue
  4. data catalogue - using a static website

These components map to components 2, 4 and 6 in the draft ADD data workflow (#139 (internal)).

Metadata records are persisted within the the metadata repositories, using the ISO 19115-2 (geographic information) metadata standard.

Access to unpublished records, and the ability to publish/retract records is restricted to restricted to relevant ADD project members. Once published, records can viewed through the data catalogue, which presents them as human readable items, (such as visualising geographic extents on a map). Manually curated collections provide a means to group items.

The data catalogue is rendered as a static website for improved performance, reliability and ease of hosting. Currently this content is accessed within the existing BAS data catalogue.


Metadata editor

The metadata editor component of this project is ran on the BAS central workstations using the shared MAGIC user:

$ ssh
$ scar-add-metadata-toolbox [command]

The editor is configured using a settings file: /users/geoweb/.config/scar-add-metadata-toolbox/.env.

Metadata repositories and data catalogue

The unpublished repository, published repository and data catalogue run as a service in the experimental MAGIC Nomad cluster.

Any errors will be automatically reported to Sentry and relevant individuals alerted by email.


Available commands

Command line reference


Flask application using CSW to store Metadata records and display them as Items in Collections rendered as Jinja templates served as a static website within the BAS data catalogue.

CSW catalogues are backed by PostGIS databases, secured using OAuth. Contact forms for feedback and items in the static catalogue use Microsoft Power Automate. Legal policies use templates from the Legal Policies project.


This diagram shows the main concepts in this project and how they relate:

concepts overview

Metadata records

Metadata records are the content and data within project. Records describe resources, which are typically datasets within the ADD, e.g. a record might describe the Antarctic Coastline dataset. Records are based on the ISO 19115 metadata standard (specifically 19115-2:2009), which defines an information model for geographic data.

Records are stored/persisted in a records repository (implemented using CSW) or in files for import and export.

A metadata record includes information to answer questions such as:

  • what is this dataset?
  • what formats is this dataset available in?
  • what projection does this dataset use?
  • what keywords describe the themes, places, etc. related to this dataset?
  • why is this dataset useful?
  • who is this dataset for?
  • who made this dataset?
  • who can I contact with any questions about this dataset?
  • when was this dataset created?
  • when was it last updated?
  • what time period does it cover?
  • where does this dataset cover?
  • how was this dataset created?
  • how can trust the quality of this dataset?
  • how can I download or access this dataset?

This metadata is termed 'discovery metadata' (to separate it from metadata for calibration or analysis for example). It helps users find metadata in catalogues or search engines, and then to help them decide if the data is useful to them.

The information in a metadata record is encoded in a different formats at different stages:

  • during editing, records are encoded as JSON, using the BAS Metadata Library's record configuration
  • when stored in a repository, records are encoded as XML using the ISO 19139 standard
  • when viewed in the data catalogue, records are encoded in HTML or as (styled) XML

These different formats are used for different reasons:

  • JSON is easily understood by machines and is concise to understand and edit by humans
  • XML is also machine readable but more suited/mature for complex standards such ISO 19139
  • HTML is designed for presenting information to humans, with very flexible formatting options


Items represent Metadata records but in a form intended for human consumption. They are derived from Records and are specific to the data catalogue, allowing greater flexibility compared to the strict rigidity and formality enforced by metadata records.

For example, a resource's coordinate reference system may be defined as urn:ogc:def:crs:EPSG::3031 in a metadata record but will be shown as WGS 84 / Antarctic Polar Stereographic (EPSG:3031) in items. Both are technically correct but the descriptive version is easier for a human to understand, at the sake of being less precise and harder for machines to parse.

As items are derived from records, they are not persisted themselves, except as rendered pages within the data catalogue static site.


Collections are a simple way to group Items together based on a shared purpose, theme or topic. They are specific to the data catalogue and are not based on metadata records.

Collections are stored/persisted in a collections repository (implemented as a JSON file) or in files for import and export.

They support a limited set of properties compared to records/items:

Property Data Type Required Description
identifier String Yes UUID
title String Yes Descriptive title
topics Array Yes BAS Research Topics associated with collection
topics.* String Yes BAS Research Topic
publishers Array Yes Data catalogue publishers associated with collection
publishers.* String Yes Data catalogue publisher
summary String Yes Descriptive summary, supports markdown formatting
item_identifiers Array Yes Items associated with collection, specified by their Item ID
item_identifiers.* String Yes Item ID

Note: Items in collections will be shown in the order they are listed in the item_identifiers property.

For example:

    "identifier": "1790c9d5-af77-4a03-9a08-6ba8e83ce748",
    "title": "Operation Tabarin",
    "topics": [
        "Living and Working in Antarctica"
    "publishers": [
        "BAS Archives Service"
    "summary": "A secret British Antarctic expedition launched in 1943 during the course of World War II ...",
    "item_identifiers": [


This project uses OAuth to protect access to the Unpublished and Published repositories via the Microsoft (Azure) identity platform.

The Unpublished and Published repositories are registered together as the resource to be protected with different scopes and roles to authorise users to read, write and publish records. The Flask Azure AD OAuth Provider is used to verify access tokens when CSW requests are made and enforce these permissions as needed.

The Metadata editor is registered as a separate application as a client that will interact with protected resource (i.e. to read, write and publish records). The Microsoft Authentication Library (MSAL) for Python library is used to request access tokens using the OAuth device code grant type.

Both applications are registered in the NERC Azure tenancy administered by the UKRI/NERC DDaT team, currently via the old RTS Helpdesk.

The Azure Portal is used to assign permissions to applications and users as needed:


The Unpublished and Published repositories are implemented as embedded PyCSW servers. The embedded mode allowing integration with Flask for authentication and authorisation of requests via OAuth.

The CSW transactional profile is used extensively for clients (such as the Metadata editor and Data catalogue) to insert, update and delete records programmatically.

The CSW version is fixed to 2.0.2 because it's the latest version supported by OWSLib, the CSW client used by the Metadata editor.

Note: The CSW repositories are considered to be APIs, and so ran as services through the BAS API Load Balancer (internal) with documentation in the BAS API Documentation project (internal).

Note: Some elements of both the PyCSW server and the OWSLib client have been extended by this project to incorporate OAuth support. These modifications will be formalised, ideally as upstream contributions, but currently reside within the 'Hazardous Materials' module as a number of unsightly workarounds are currently needed.

Max records limit

Both PyCSW (CSW servers) and OWSLib (CSW clients) have a maximum record of 100 per request.

CSW backing databases

CSW servers are backed using PostGIS (PostgreSQL) databases provided by BAS IT (via the central Postgres database bsldb). As PyCSW uses a single table for all records, all servers share the same database and schema, configured through SQLAlchemy connection strings.

Separate databases are used for each environment (development, staging and production). Credentials are stored in the MAGIC 1Password shared vault. In local development, a local PostGIS database configured in docker-compose.yml can be used:


To test against real data in a non-production environment, use the staging environment database, which is synced from the production database automatically by BAS IT every Tuesday at 02:00.

Jinja templates

A series of Jinja2 templates are used for rendering pages in the Data catalogue.

Templates use the BAS Style Kit Jinja Templates and styled using the BAS Style Kit.

S3 static website

Rendered pages and other assets are hosted through an AWS S3 bucket with static website hosting enabled.

Reverse proxy rules are used to expose content from this static site within the existing/legacy BAS Data Catalogue, DMS.

Feedback and contact forms

A Microsoft Power Automate Flow is used to process feedback and contact form submissions. Messages support Markdown formatting, converted to HTML prior to submission. On submitted, Power Automate creates an issue for the message in a relevant GitLab project.

Sentry error tracking

Errors in this service are tracked with Sentry:

Error tracking will be enabled or disabled depending on the environment. It can be manually controlled by setting the APP_ENABLE_SENTRY Configuration option.

Application logging

Logs for this service are written to stdout/stderr as appropriate.


Application configuration options are set in per-environment classes extending a base Config class in scar_add_metadata_toolbox/ The active environment is set using the FLASK_ENV environment variable.

Configuration options are defined, and documented, using class properties. Some configuration options may optionally be set at runtime using environment variables. If not set, default values will be used.

Configuration Option Description Allowed Values Example Value
APP_ENABLE_SENTRY Feature flag to enable/disable Sentry error tracking true/false true
APP_LOGGING_LEVEL Minimum logging level to include in application logs debug/info/warning/error/critical warning
APP_AUTH_SESSION_FILE_PATH Path to file used for authentication information valid file path /home/user/.config/scar_add_metadata_toolbox/auth.json
APP_COLLECTIONS_PATH Path to file used for data catalogue collections valid file path /home/user/.config/scar_add_metadata_toolbox/collections.json
APP_SITE_PATH Path to directory used for rendered static site content valid directory path /home/user/.config/scar_add_metadata_toolbox/_site
CSW_ENDPOINT_UNPUBLISHED CSW endpoint for accessing unpublished catalogue valid URL
CSW_ENDPOINT_PUBLISHED CSW endpoint for accessing published catalogue valid URL
CSW_SERVER_CONFIG_UNPUBLISHED_ENDPOINT Endpoint at which to run unpublished CSW catalogue valid URL
CSW_SERVER_CONFIG_PUBLISHED_ENDPOINT Endpoint at which to run published CSW catalogue Valid URL
CSW_SERVER_CONFIG_UNPUBLISHED_DATABASE_CONNECTION Connection string for unpublished CSW catalogue backing database Valid SQLAlchemy connection string postgresql://
CSW_SERVER_CONFIG_PUBLISHED_DATABASE_CONNECTION Connection string for published CSW catalogue backing database Valid SQLAlchemy connection string postgresql://
APP_S3_BUCKET AWS S3 bucket name used for hosting static website content Valid AWS S3 bucket name

These options are typically set when running this application as a client (metadata editor and data catalogue):


These options are typically set when running this application as a server (metadata repositories):



Continuous deployment will configure this application to run on the BAS central workstations as a Podman container, using an automatically generated launch script and environment variables.

Continuous deployment will configure this application to run in the experimental MAGIC Nomad cluster, using an automatically generated job definition.

See the Usage section for how to use the application.

PyCSW backing database setup

Backing databases for PyCSW servers require initialisation using the csw setup application CLI command for both the published and unpublished repositories.

Note: Backing databases must use the PostgreSQL engine with the PostGIS extension enabled.

Normally this command will create the required database table, geometry column and relevant indexes. As catalogues only require a single table, multiple can be stored in the same database/schema. However, two of the indexes used (fts_gin_idx [full text search] and wkb_geometry_idx [binary geometry]) are named non-uniquely, preventing multiple catalogues being co-located in the same schema.

This appears to be an oversight, as all other indexes are made unique by prefixing them with the name of the records table, and doing this manually for these indexes appears to work without issue. To workaround this issue, you will need to manually modify the indexes of catalogue tables once they've been setup.

Assuming the Unpublished catalogue is setup first, perform these steps before setting up the Published catalogue:

  1. verify that the records_unpublished table was created successfully (contains fts_gin_idx and wkb_geometry_idx indexes)
  2. alter the affected indexes in the records_unpublished table [1]
  3. setup the Published catalogue flask csw setup published
  4. alter the affected indexes in the second table [2]


ALTER INDEX fts_gin_idx RENAME TO ix_records_unpublished_fts_gin_indx;
ALTER INDEX wkb_geometry_idx RENAME TO ix_unpublished_wkb_geometry_idx;


ALTER INDEX fts_gin_idx RENAME TO ix_records_published_fts_gin_indx;
ALTER INDEX wkb_geometry_idx RENAME TO ix_published_wkb_geometry_idx;

Azure permissions

Terraform will create and configure the relevant Azure application registrations required for using OAuth to protect the CSW catalogues. However manual approval by a Tenancy Administrator is needed to grant the registration representing the client role of the application access to the registration for the server role.

This has been approved by NERC RTS in #3.


Terraform is used for:

  • resources required for hosting the Data catalogue component as a static website
  • resources required for protecting and accessing the unpublished repository, published repository components
  • a templated job file for Nomad during Continuous deployment
  • a templated launch script for Podman during Continuous deployment

Access to the BAS AWS account, Terraform remote state and NERC Azure tenancy are required to provision these resources.

Note: The templated Podman and Nomad runtime files are not included in Terraform state.

$ cd provisioning/terraform
$ docker compose run terraform

$ az login --allow-no-subscriptions

$ terraform init
$ terraform validate
$ terraform fmt
$ terraform apply

$ exit
$ docker compose down

Once provisioned the following steps need to be taken manually:

  1. set branding icons (if desired)
  2. set Azure permissions
  3. assign roles to users and/or groups
  4. set accessTokenAcceptedVersion: 2 in both application registration manifests

Note: Assignments are 1:1 between users/groups and roles but there can be multiple assignments. I.e. roles Foo and Bar can be assigned to the same user/group by creating two role assignments.

Terraform remote state

State information for this project is stored remotely using a Backend.

Specifically the AWS S3 backend as part of the BAS Terraform Remote State project.

Remote state storage will be automatically initialised when running terraform init. Any changes to remote state will be automatically saved to the remote backend, there is no need to push or pull changes.

Remote state authentication

Permission to read and/or write remote state information for this project is restricted to authorised users. Contact the BAS Web & Applications Team to request access.

See the BAS Terraform Remote State project for how these permissions to remote state are enforced.

Docker image tag expiration policy

The Docker image for this project uses a Tag expiration policy which needs to be configured manually in GitLab.

  • Expiration policy: enabled
  • Expiration interval: 90 days
  • Expiration schedule: Every week
  • Number of tags to retain: 10 tag per image name
  • Tags with names matching this regex pattern will expire: (review.+|build.+)
  • Tags with names matching this regex pattern will be preserved: release.+


Manually request a new PostGIS database for the CSW catalogue backing databases from the BAS IT ServiceDesk.

Manually request a new application to be deployed from the BAS IT ServiceDesk using the request template.

See #44 for an example.

BAS API Load Balancer

Manually add a new service and related documentation.

See #60 for an example.


$ git clone
$ cd add-metadata-toolbox

Development environment

A flexible development environment is available for developing this application locally. It can be used in a variety of ways depending on what is being developed:

  • all components, the Flask application, CSW database and static website can be ran locally
    • useful for end-to-end testing
    • useful for testing changes to how data is loaded into the CSW catalogues
  • the Flask application can be ran directly, without needing to convert it into a static site
    • useful for iterating on changes to the data catalogue website
  • the Flask application can use the production CSW database
    • useful for testing with real-world data

The local development environment is defined using Docker Compose in ./docker-compose.yml. It consists of:

  • an app service for running the Flask application as a web application
  • an app-cli service for running application commands
  • a csw-db service for storing data added to local CSW catalogues (if used)
  • a web service for serving a local version of the data catalogue static site (if used)

To create a local development environment:

  1. pull docker images: docker compose pull [1]
  2. run the Docker Compose stack: docker compose up
  3. run application Commands [2]

To destroy a local development environment:

  1. run docker compose down

[1] This requires access to the BAS Docker Registry (part of

$ docker login

Note: You will need to sign-in using your GitLab credentials (your password is set through your GitLab profile) the first time this is used.

[2] In a new terminal:

$ docker compose run app-cli flask [task]

Development container

A development container image, defined by ./Dockerfile, is built manually, tagged as :latest and hosted in the private BAS Docker Registry (part of

It is separate to the deployment container and installs both runtime and development dependencies (deployment containers only install runtime dependencies).

If you don't have access to the BAS Docker Register, you can build this image locally using docker compose build app.

Python version

When upgrading to a new version of Python, ensure the following are also checked and updated where needed:

  • Dockerfile:
    • base stage image (e.g. FROM python:3.X-alpine as base to FROM python:3.Y-alpine as base)
    • pre-compiled wheels (e.g. https://.../linux_x86_64/cp3Xm/lxml-4.5.0-cp3X-cp3X-linux_x86_64.whl to http://.../linux_x86_64/cp3Ym/lxml-4.5.0-cp3Y-cp3Y-linux_x86_64.whl)
  • support/docker-packaging/Dockerfile:
    • base stage image (e.g. FROM python:3.X-alpine as base to FROM python:3.Y-alpine as base)
    • pre-compiled wheels (e.g. http://.../linux_x86_64/cp3Xm/lxml-4.5.0-cp3X-cp3X-linux_x86_64.whl to http://.../linux_x86_64/cp3Ym/lxml-4.5.0-cp3Y-cp3Y-linux_x86_64.whl)
  • pyproject.toml
    • [tool.poetry.dependencies]
      • python (e.g. python = "^3.X" to python = "^3.Y")
    • []
      • target-version (e.g. target-version = ['py3X'] to target-version = ['py3Y'])

Package structure

All code for this project should be defined in the scar_add_metadata_toolbox package, with the exception of tests.

In brief, this package is comprised of these modules:

  • scar_add_metadata_toolbox - contains Flask application
  • scar_add_metadata_toolbox.classes - contains classes for concepts (Repositories, Records, Items, Collections)
  • scar_add_metadata_toolbox.commands - contains Flask blueprints used for CLI commands
  • scar_add_metadata_toolbox.config - contains Flask configuration
  • scar_add_metadata_toolbox.csw - contains classes for CSW servers and clients
  • scar_add_metadata_toolbox.hazmat - contains 'Hazardous Material' code
  • scar_add_metadata_toolbox.static - contains static site assets (CSS, JS, etc.)
  • scar_add_metadata_toolbox.templates - contains Application templates
  • scar_add_metadata_toolbox.utils - contains various utility/helper methods and classes

Hazardous Materials module

Whilst this application has been developed, extended/modified versions of class from 3rd party packages have been created to address unresolved bugs or add new required functionality. As this code often requires workarounds and hacks it is ugly, non-standard and against established best practices (such as not to use mocks outside of tests).

In time, these changes and additions are expected to be either incorporated into upstream packages, or if not possible, into forked packages that we maintain. Until then, this code is kept in a 'Hazardous Materials' (Hazmat) module, scar_add_metadata_toolbox.hazmat, to indicate that it shouldn't be treated like other modules in this project.

Ideally no additional code will be added to this module, however if other changes/extensions need to be made in a non-clean way then they should be added here, rather than 'polluting' the main package.


Python dependencies for this project are managed with Poetry in pyproject.toml.

Non-code files, such as static files, can also be included in the Python package using the include key in pyproject.toml.

Adding new dependencies

To add a new (development) dependency:

$ docker compose run app ash
$ poetry add [dependency] (--dev)

Then rebuild the Development container and push to GitLab (GitLab will rebuild other images automatically as needed):

$ docker compose build app
$ docker compose push app

Updating dependencies

$ docker compose run app ash
$ poetry update

Then rebuild the Development container and push to GitLab (GitLab will rebuild other images automatically as needed):

$ docker compose build app
$ docker compose push app

Static security scanning

To ensure the security of this API, source code is checked against Bandit for issues such as not sanitising user inputs or using weak cryptography. Bandit is configured in .bandit.

Warning: Bandit is a static analysis tool and can't check for issues that are only be detectable when running the application. As with all security tools, Bandit is an aid for spotting common mistakes, not a guarantee of secure code.

To run checks manually:

$ docker compose run app bandit -r .

Checks are ran automatically in Continuous Integration.

Code Style

PEP-8 style and formatting guidelines must be used for this project, with the exception of the 80 character line limit.

Black is used to ensure compliance, configured in pyproject.toml.

Black can be integrated with a range of editors, such as PyCharm, to perform formatting automatically.

To apply formatting manually:

$ docker compose run app black scar_add_metadata_toolbox/

To check compliance manually:

$ docker compose run app black --check scar_add_metadata_toolbox/

Checks are ran automatically in Continuous Integration.

Flask application

The Flask application representing this project is defined in the scar_add_metadata_toolbox package. The application uses the application factory pattern.

Flask Blueprints are used to logically organise application commands, currently all within the scar_add_metadata_toolbox.commands module. Until this is refactored, additional commands should be registered in the same module.

Flask configuration

The Flask application's configuration (app.config) is populated from an environment specific class in the scar_add_metadata_toolbox.config module.

New configuration options should be added to the base config class as properties, overridden as needed in environment sub-classes. Where a configuration should be configurable at runtime it should be read as an environment variable and documented in the Configuration section.


Use the Flask application's logger, for example:

from flask import current_app'Log message')

File paths

Use Python's pathlib library for file paths.

Where displaying a file path to the user, use the absolute form to aid in debugging:

from pathlib import Path

foo_path = Path("foo.txt")
print(f"foo_path is: {str(foo_path.absolute())}")


Application templates use the Flask application's Jinja environment configured to use general templates from the BAS Style Kit Jinja Templates package (for layouts, etc.) and application specific templates from the scar_add_metadata_toolbox.templates module.

Styles, components and patterns from the BAS Style Kit should be used where possible. Configuration options for Style Kit Jinja Templates are set in the scar_add_metadata_toolbox.config module, including loading local styles and scripts defined in scar_add_metadata_toolbox/static.

Application views should inherit from the application layout, app.j2, and using includes and macros to breakdown and reuse content within views is strongly encouraged.

Editor support


Multiple run/debug configurations are included in the project for debugging and testing:

  • App runs the Flask application and is useful for debug the server role of this application (e.g. CSW requests)
  • App CLI runs the Flask application and is useful for debug the client role of this application (e.g. static site commands requests) - change the parameters option to set which command to run


All code in the scar_add_metadata_toolbox package must be covered by tests, defined in tests/. This project uses PyTest which should be ran in a random order using pytest-random-order.

To run tests manually from the command line:

$ docker compose run app -e FLASK_ENV=testing app pytest --random-order

To run/debug tests using PyCharm, use the included App (Tests) run/debug configuration.

Tests are ran automatically in Continuous Integration.

Test coverage

pytest-cov is used to measure test coverage.

A .coveragerc file is used to omit code from the scar_add_metadata_toolbox.hazmat module.

To measure coverage manually:

$ docker compose run -e FLASK_ENV=testing app pytest --cov=scar_add_metadata_toolbox --cov-fail-under=100 --cov-report=html .

Continuous Integration will check coverage automatically and fail if less than 100%.

Continuous Integration

All commits will trigger a Continuous Integration process using GitLab's CI/CD platform, configured in .gitlab-ci.yml.

Review apps

To review changes to functionality, commits made in branches will trigger review apps to be created using GitLab's CI/CD platform, configured in .gitlab-ci.yml.

Review apps run as Nomad services only, not as Command line applications.

Containers for review apps are built using the deployment Docker image but tagged as review:[slug], where [slug] is a reference to the merge request the review app is related to. Images are hosted in the private BAS Docker Registry (part of


  • the URL for review apps point to the Nomad job via it's UI, rather than the managed application, as the port number for the application is set dynamically and not stored in the Terraform state for the Nomad job
  • the application will currently use the production CSW database, therefore records MUST NOT be changed by review apps, this is currently unenforced but will be when ServiceDesk ticket #42232 is resolved


Python package

A project Python package is built by Continuous Delivery, hosted through the private BAS Repo Server:

Docker image

A deployment container image, defined by ./support/docker-packaging/Dockerfile, is built by Continuous Delivery for releases (Git tags). Images are tagged as /release:[tag], where [tag] is the name of the Git tag a release is related to. Images are hosted in the private BAS Docker Registry (part of

Note: All container builds (including those from Review apps) are also tagged as /build:[commit], where [commit] is a reference to the Git commit that triggered the image to be built.

Docker image expiration policy

An image expiration policy is used to limit the number of non-release container images that are kept. This policy is set within, and enforced automatically by, GitLab. See the Setup section for how this is configured.

Nomad service

The deployment Docker image is deployed as a service job in the experimental MAGIC Nomad cluster (internal).

BAS IT service

The deployment Python package is deployed as a WSGI application via BAS IT using an Ansible playbook: /playbooks/magic/add-metadata-toolbox.yml (internal)

Variables for this application are set in: /group_vars/magic/add-metadata-toolbox.yml (internal)

Environment variables used by this application are set in: /playbooks/magic/add-metadata-toolbox.yml (internal)

This application is deployed to a development, staging and production environment. Hosts for each environment are listed in the relevant Ansible inventory in: /inventory/magic/ (internal)

Note: The process to run/update this playbook/variables is still under development (see #44 (internal) for background). Currently either needs to be requested through the IT ServiceDesk.

Key paths

Key files/directories within this deployed application are:

  • /etc/httpd/sites/10-add-metadata-toolbox.conf: Apache virtual host
  • /var/opt/wsgi/.virtualenvs/add-metadata-toolbox: Python virtual environment
  • /var/www/add-metadata-toolbox/ Application entrypoint script
  • /var/log/httpd/access_log.add_metadata_toolbox: Apache virtual host access log
  • /var/log/httpd/error_log.add_metadata_toolbox: Apache/Application error/log file

SSH access

Environment SSH Access Sudo Access
Development Yes Yes felnne
Staging Yes (for logs) No felnne
Production Yes (for logs) No felnne

Currently access to the servers for each environment is bespoke but should be standardised in future, see #100 for more information.

Flask CLI

To use the Flask CLI:

$ ssh [server]
$ sudo su
$ . [path to virtual environment]/bin/activate
$ export FLASK_APP=scar_add_metadata_toolbox
$ export FLASK_ENV=production
$ flask [command]
$ deactivate
$ exit
$ exit

API Service

The CSW Catalogues are deployed as a service within the BAS API Load Balancer, backed by the production BAS IT service.

API Documentation

Usage documentation for this API service is held in docs/api/ and currently manually published using these service paths:

  • s3://bas-api-docs-content-testing/services/data/metadata/add/csw/
  • s3://bas-api-docs-content/services/data/metadata/add/csw/

Command line application

The deployment Docker image is made available as a command line application on the BAS central workstations using Podman. A wrapper shell script is used to mask the podman run run command for ease of use.

Continuous Deployment

All commits will trigger a Continuous Deployment process using GitLab's CI/CD platform, configured in .gitlab-ci.yml.

Release procedure

For all releases:

  1. create a release branch
  2. close release in
  3. push changes, merge the release branch into master and tag with version
  4. create a ServiceDesk request to deploy the new package version (and change/add environment variables if needed)
  5. re-deploy API documentation if needed


The maintainer of this project is the BAS Mapping and Geographic Information Centre (MAGIC), they can be contacted at:

Issue tracking

This project uses issue tracking, see the Issue tracker for more information.

Note: Read & write access to this issue tracker is restricted. Contact the project maintainer to request access.


© UK Research and Innovation (UKRI), 2020 - 2021, British Antarctic Survey.

You may use and re-use this software and associated documentation files free of charge in any format or medium, under the terms of the Open Government Licence v3.0.

You may obtain a copy of the Open Government Licence at

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

scar-add-metadata-toolbox-0.3.0.tar.gz (151.2 kB view hashes)

Uploaded source

Built Distribution

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page