mtg / metadb

A simple database containing metadata linked to musicbrainz ids

metadb

Installation

Docker

Setup:

Build the docker containers:

docker-compose build

Start all containers:

docker-compose up

Once the containers are up, you can initialise the database:

docker-compose run --rm web python manage.py init_db
docker-compose run --rm web python manage.py fixtures

To make a web token to submit/retrieve using the webservice

docker-compose run --rm web python manage.py addtoken --admin

metadb's Issues

Advanced Processing Lab - Big Data Assignment Task Selection

I would like to work with the musicbrainz (tags, artist/release metadata) annotation list for the data selection task.

Student: Kosmas Kritsis

AMP LAb - Task: Data Collection

For the AMP Lab I would like to work with the Discogs API.
The title of the lab is "Data Collection" so I interpret this in 2/3 different ways:

1. collect all the information related to genre and style present in the website we select?
   I take a look to the Discogs API documentation and it seems not so difficult to get the data from the website.
2. retrieve the data, parse the json or XML and format the informations in a useful way to reuse in the AcusticBraiz project!
3. build a Python client that uses the API, that can be adapted to the scope of future projects?

This is the way I interpreted the task, I wait for your suggestion on how to start working on that.
Sincerely
:Dan

Look up allmusic based on MB links

Many releases and releasegroups in musicbrainz have a link to allmusic. If this exists, we should use it.

Rate Your Music

Hi,

I will be using http://rateyourmusic.com to fetch as much info as i can based on query tags.

Do random verification of data

Our data is automatically collected - this means that bugs in our script or strange search results from external services could result in incorrect data.
We should do some kind of verification of a random subset of the data for each source to see if our data is good, mostly good, or bad