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A database builder for digital preservation information.

Project description

Preservation Status Database Builder

Returns the preservation status of a Crossref DOI matched against mainstream digital preservation platforms.

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Django Git GitHub Linux Python

This application allows you to build a database of digital preservation sources and then to match a DOI against common digital preservation systems.

Installation

The easiest install is via pip:

pip install preservation-database

Then add "preservationdatabase" (no hyphen) to your list of INSTALLED_APPS.

Usage

export DJANGO_SETTINGS_MODULE=import_settings.settings

Usage: python -m preservationdatabase.cli [OPTIONS] COMMAND [ARGS]...

Options:
  --help  Show this message and exit.

Commands:
  import-all         Download and import all data (excluding HathiTrust)
  import-cariniana   Download and import data from Cariniana
  import-clockss     Download and import data from CLOCKSS
  import-hathi       Import data from HathiTrust (requires local file download)
  import-lockss      Download and import data from LOCKSS
  import-pkp         Download and import data from PKP's private LOCKSS network
  import-portico     Download and import data from Portico
  show-preservation  Determine whether a DOI is preserved

Features

  • Cariniana import.
  • CLOCKSS import.
  • HathiTrust import.
  • LOCKSS import.
  • PKP PLN import.
  • Portico import.
  • Crossref DOI lookup.

First-Run Setup

First, copy example_settings.py to settings.py and check settings.py to ensure that the database you want to use is set correctly. The default is db.sqlite. You should carefully read and check all of settings.py.

DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.sqlite3',
        'NAME': BASE_DIR / 'db.sqlite3',
    }
}

Next, run the database build commands:

python3 manage.py makemigrations
python3 manage.py makemigrations preservation-database
python3 manage.py migrate 

You should then have a working database into which you can import new preservation data.

Credits

© Crossref 2023

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