https://github.com/datalad/datalad-catalog
Create a user-friendly data catalog from structured metadata
https://github.com/datalad/datalad-catalog
closember datalad metadata open-data
Last synced: 5 months ago
JSON representation
Create a user-friendly data catalog from structured metadata
- Host: GitHub
- URL: https://github.com/datalad/datalad-catalog
- Owner: datalad
- License: mit
- Created: 2021-05-26T13:18:59.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2024-11-28T20:35:52.000Z (over 1 year ago)
- Last Synced: 2025-08-19T20:08:56.834Z (10 months ago)
- Topics: closember, datalad, metadata, open-data
- Language: Python
- Homepage: https://datalad-catalog.netlify.app
- Size: 8.55 MB
- Stars: 18
- Watchers: 5
- Forks: 9
- Open Issues: 44
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
- awesome-opendata-software - DataCat - DataLad Catalog is a free and open source command line tool, with a Python API, that assists with the automatic generation of user-friendly, browser-based data catalogs from structured metadata. (Data catalogs / Research data repositories)
README
[](http://docs.datalad.org/projects/catalog/en/latest/?badge=latest)
[](https://GitHub.com/datalad/datalad-catalog/releases/)
[](https://pypi.python.org/pypi/datalad-catalog/)
[](https://github.com/datalad/datalad-catalog/actions/workflows/docbuild.yml)
[](https://ci.appveyor.com/project/mih/datalad-catalog/branch/main)
[](https://codecov.io/github/datalad/datalad-catalog?branch=main)
[](https://github.com/datalad/datalad-catalog/actions/workflows/test_crippledfs.yml)
[](https://app.netlify.com/sites/datalad-catalog/deploys)
[](https://zenodo.org/badge/latestdoi/371040885)
[](#contributors-)
---
### *:loudspeaker: A new catalog is coming...*
We're working on a newer, leaner, more modular, and more interoperable solution to the same challenge that the current `datalad-catalog` aims to address. This new development is taking place within the broader context of making DataLad datasets interoperable with linked and semantic (meta)data. For more background, see [this issue](https://github.com/psychoinformatics-de/datalad-concepts/issues/115). To keep up to date, follow progress at [`psychoinformatics-de/datalad-concepts`](https://github.com/psychoinformatics-de/datalad-concepts), [`psychoinformatics-de/shacl-vue`](https://github.com/psychoinformatics-de/shacl-vue), and in the [new development branch](https://github.com/datalad/datalad-catalog/tree/revolution). Because of this redirected focus, `datalad-catalog` itself will be downscaled by focusing on maintenance and assessing the priority of new features on a case-by-case basis.
---
DataLad Catalog is a free and open source command line tool, with a Python API, that assists with the automatic generation of user-friendly, browser-based data catalogs from structured metadata. It is an extension to [DataLad](https://datalad.org) and forms part of the broader ecosystem of DataLad's distributed metadata handling and (meta)data publishing tools.
### Acknowledgements
This software was developed with support from:
- the German Federal Ministry of Education and Research (BMBF 01GQ1905)
- the US National Science Foundation
(NSF 1912266)
- the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under grant SFB 1451 ([431549029](https://gepris.dfg.de/gepris/projekt/431549029), INF project).
- the [MKW-NRW: Ministerium fΓΌr Kultur und Wissenschaft des Landes Nordrhein-Westfalen](https://www.mkw.nrw/) under the Kooperationsplattformen 2022 program, grant number: KP22-106A
## 1. Online demo
Navigate to [https://datalad-catalog.netlify.app/](https://datalad-catalog.netlify.app/) to view a live demo of a catalog generated with DataLad Catalog.
This demo site is hosted via Netlify and it builds from the `datalad_catalog/catalog` directory of the `main` branch of this repository.
## 2. How it works
DataLad Catalog can receive commands to `create` a new catalog, `add` and `remove` metadata entries to/from an existing catalog, `serve` an existing catalog locally, and more. Metadata can be provided to DataLad Catalog from any number of arbitrary metadata sources, as an aggregated set or as individual metadata items. DataLad Catalog has a dedicated [schema](https://datalad-catalog.netlify.app/display_schema) (using the [JSON Schema](https://json-schema.org/) vocabulary) against which incoming metadata items are validated. This schema allows for standard metadata fields as one would expect for datasets of any kind (such as `name`, `doi`, `url`, `description`, `license`, `authors`, and more), as well as fields that support identification, versioning, dataset context and linkage, and file tree specification.
The process of generating a catalog, after metadata entry validation, involves:
1. aggregation of the provided metadata into the catalog filetree, and
2. generating the assets required to render the user interface in a browser.
The output is a set of structured metadata files, as well as a [Vue.js](https://vuejs.org/)-based browser interface that understands how to render this metadata in the browser. What is left for the user is to host this content on their platform of choice and to serve it for the world to see.
## 3. Install `datalad-catalog`
### Step 1 - Setup and activate virtual environment
With your virtual environment manager of choice, create a virtual environment and ensure
you have a recent version of Python installed. Then activate the environment. E.g. with `venv`:
```
python -m venv my_catalog_env
source my_catalog_env/bin/activate
```
### Step 2 - Install the package from PyPI
Run the following from your command line:
```
pip install datalad-catalog
```
If you are a developer and would like to contribute to the code, instead clone the code base from GitHub and install with `pip` local changes :
```
git clone https://github.com/datalad/datalad-catalog.git
cd datalad-catalog
pip install -e .
```
Congratulations! You have now installed `datalad-catalog`.
#### Note on dependencies:
Because this is an extension to `datalad` and builds on metadata handling functionality, the installation process also installs [`datalad`](https://github.com/datalad/datalad) and [`datalad-metalad`](https://github.com/datalad/datalad-metalad) as dependencies, although these do not have to be used as the only sources of metadata for a catalog.
While the catalog generation process does not expect data to be structured as DataLad datasets, it can still be very useful to do so when building a full (meta)data management pipeline from raw data to catalog publishing. For complete instructions on how to install `datalad` and `git-annex`, please refer to the [DataLad Handbook](https://handbook.datalad.org/en/latest/intro/installation.html).
Similarly, the metadata input to `datalad-catalog` can come from any source as long as it conforms to the catalog schema. While the catalog does not expect metadata originating only from `datalad-metalad`'s extractors, this tool has advanced metadata handling capabilities that will integrate seamlessly with DataLad datasets and the catalog generation process.
## 4. Generating a catalog
The overall catalog generation process actually starts several steps before the involvement of `datalad-catalog`. Steps include:
1. curating data into datasets (a group of files in an hierarchical tree)
2. adding metadata to datasets and files (the process for this and the resulting metadata formats and content vary widely depending on domain, file types, data availability, and more)
3. extracting the metadata using an automated tool to output metadata items into a standardized and queryable set
4. in the current context: translating the metadata into the [catalog schema](https://datalad-catalog.netlify.app/display_schema)
5. in the current context: using `datalad-catalog` to generate a catalog from the [schema-conforming metadata](https://datalad-catalog.netlify.app/display_schema)
The first four steps in this list can follow any arbitrarily specified procedures and can use any arbitrarily specified tools to get the job done. If these steps are completed, correctly formatted data can be input, together with some configuration details, to `datalad-catalog`. This tool then provides several basic commands for catalog generation and customization. *For example:*
```bash
# CREATE a new catalog from scratch:
datalad catalog-create -c /tmp/my-cat
#ADD metadata to an existing catalog:
datalad catalog-add -c /tmp/my-cat -m path/to/metadata.jsonl
# SET a property of an existing catalog, such as the home page of an existing catalog - i.e. the first dataset displayed when navigating to the root URL of the catalog:
datalad catalog-set -c /tmp/my-cat -i abcd -v 1234 home
# SERVE the content of the catalog via a local HTTP server at http://localhost:8001:
datalad catalog-serve -c /tmp/my-cat -p 8001
# VALIDATE metadata against a catalog schema without adding it to the catalog::
datalad catalog-validate -c /tmp/my-cat/-m path/to/metadata.jsonl
# GET a property of an existing catalog, such as the catalog configuration:
datalad catalog-get -c /tmp/my-cat/ config
# REMOVE a specific metadata record from an existing catalog:
datalad catalog-remove -c /tmp/my-cat -i efgh -v 5678
# TRANSLATE a metalad-extracted metadata item from a particular source structure into the catalog schema. A dedicated translator should be provided and exposed as an entry point (e.g. via a DataLad extension) as part of the 'datalad.metadata.translators' group:
datalad catalog-translate -c /tmp/my-cat -m path/to/metadata.jsonl
# RUN A WORKFLOW for recursive metadata extraction (using datalad-metalad), translating metadata to the catalog schema, and adding the translated metadata to a new catalog:
datalad catalog-workflow -t new -c /tmp/my-cat -d path/to/superdataset -e metalad_core
# RUN A WORKFLOW for updating a catalog after registering a subdataset to the superdataset which the catalog represents. This workflow includes extraction (using datalad-metalad), translating metadata to the catalog schema, and adding the translated metadata to the existing catalog:
datalad catalog-workflow -t new -c /tmp/my-cat -d path/to/superdataset -s path/to/subdataset -e metalad_core
```
## 5. Tutorial
To explore the basic functionality of `datalad-catalog`, please refer to [the tutorial in the DataLad Handbook](https://handbook.datalad.org/en/latest/beyond_basics/101-182-catalog.html).
## 6. An example workflow
The DataLad ecosystem provides a complete set of free and open source tools that, together, provide full control over dataset/file access and distribution, version control, provenance tracking, metadata addition/extraction/aggregation, and catalog generation.
DataLad itself can be used for decentralised management of data as lightweight, portable and extensible representations. DataLad MetaLad can extract structured high- and low-level metadata and associate it with these datasets or with individual files. And at the end of the workflow, DataLad Catalog can turn the structured metadata into a user-friendly data browser.
Importantly, DataLad Catalog can operate independently as well. Since it provides its own [schema](https://datalad-catalog.netlify.app/display_schema) in a standard vocabulary, any metadata that conforms to this schema can be submitted to the tool in order to generate a catalog. Metadata items do not necessarily have to be derived from DataLad datasets, and the metadata extraction does not have to be conducted via DataLad MetaLad.
Even so, the provided set of tools can be particularly powerful when used together in a distributed (meta)data management pipeline.
## 7. Contributing
### Feedback / comments
Please [create a new issue](https://github.com/jsheunis/data-browser-from-metadata/issues/new) if you have any feedback, comments, or requests.
### Developer requirements
If you'd like to contribute as a developer, you need to install a number of extra dependencies:
```
cd datalad-catalog
pip install -r requirements-devel.txt
```
This installs `sphinx` and related packages for documentation building, `coverage` for code coverage,
`black` for linting, and `pytest` for testing.
### Contribution process
To make a contribution to the code or documentation, please:
- create an issue describing the bug/feature
- fork the project repository,
- create a branch from `main`,
- commit your changes,
- check that linting tests succeed: from the project root directory, run `black .`
- check that tests succeed: from the project root directory, run `python -m pytest`
- push your commits to your fork
- create a pull request with a clear description of the changes
- check that all continuous integration tests succeed on the pull request
## Contributors β¨
Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):
Stephan Heunis
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Alex Waite
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Julian Kosciessa
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Adina Wagner
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Yaroslav Halchenko
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Michael Hanke
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Benjamin Poldrack
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Christian MΓΆnch
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MichaΕ Szczepanik
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Laura Waite
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Leonardo Muller-Rodriguez
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tmheunis
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This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!