Metadata-Version: 2.1
Name: dtoolcore
Version: 3.18.3
Summary: Core API for managing (scientific) data
Author-email: Tjelvar Olsson <tjelvar.olsson@gmail.com>
Project-URL: Documentation, https://dtoolcore.readthedocs.io
Project-URL: Repository, https://github.com/jic-dtool/dtoolcore
Project-URL: Changelog, https://github.com/jic-dtool/dtoolcore/blob/master/CHANGELOG.rst
Description-Content-Type: text/x-rst
License-File: LICENSE.rst
Requires-Dist: setuptools
Provides-Extra: test
Requires-Dist: pytest; extra == "test"
Requires-Dist: pytest-cov; extra == "test"
Requires-Dist: pytz; extra == "test"
Provides-Extra: docs
Requires-Dist: sphinx; extra == "docs"
Requires-Dist: sphinx_rtd_theme; extra == "docs"

Manage scientific data sets
===========================

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   :target: https://ci.appveyor.com/project/tjelvar-olsson/dtoolcore
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   :target: https://codecov.io/github/jic-dtool/dtoolcore?branch=master
   :alt: Code Coverage

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   :alt: Documentation Status

|dtool| |pypi| |build| |test| |test| |codecov| |docs|

- Documentation: http://dtoolcore.readthedocs.io
- GitHub: https://github.com/jic-dtool/dtoolcore
- PyPI: https://pypi.python.org/pypi/dtoolcore
- Free software: MIT License

Features
--------

- Core API for adding different types of metadata to files on disk
- Automatic generation of structural metadata
- Programmatic discovery and access of items in a dataset
- Structural metadata includes hash, size and modification time for
  subsequent integrity checks
- Ability to annotate individual files with arbitrary metadata
- Metadata stored on disk as plain text files, i.e. disk datasets
  generated using this API can be accessed without special tools
- Ability to create plugins for custom storage solutions
- Plugins for iRODS and Microsoft Azure storage backends available
- Cross-platform: Linux, Mac and Windows are all supported
- Works with Python 2.7, 3.5 and 3.6
- No external dependencies

Overview
--------

The dtoolcore project provides a Python API for managing (scientific) data.
It allows researchers to:

- Package data and metadata into a dataset
- Organise and backup datasets easily
- Find datasets of interest
- Verify the contents of datasets
- Discover and work with data programatically
