Citrine

Latest version: v3.17.0

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0.3.0

Not secure
This release supports the deletion of data objects and the reading of tables from the data service. Also included in code is experimental support for project member management, but note that it is highly experimental and will very likely change.

What's new
* Support for deleting objects! Deletion is conditional on not orphaning any objects that link to them.
* Support for reading tables by downloading them as csv files.

Coming soon
* Improved project member management
* Delete datasets

0.2.0

Not secure
This release mostly updates the version of taurus to 0.2.0, improving the `repr` method on data objects to include soft links. It also includes additional documentation of the perils of cyclic material histories

What's new
* [taurus 0.2.0](https://github.com/CitrineInformatics/taurus/releases/tag/v0.2.0), which improves the representation of data objects to include soft links.
* An FAQ, which describes how to avoid time-traveling materials.

Coming soon
* Delete data objects to correct mistakes

0.1.0

Not secure
This is the first beta release of `citrine`: the python client for Citrine's next platform. It supports access to the Citrine Data Platform, which stores, validates, and serves the [`taurus` data model](https://citrineinformatics.github.io/taurus-documentation/). This is a beta release that we expect to actively develop over the next several months.

What's new
* Everything! This is the first release of the `citrine` client for the first implementation of the `taurus` data model.
* Create projects and datasets. These let you control who has what kinds of access to your data.
* Upload files and data objects to datasets, supporting both structured and semi-structured data storage with cross-linking.
* Validate writes with templates, returning helpful validation errors and maintaining data quality.
* Retrieve entire material histories that provide a complete picture about the material, how it was formed, what went into it, how they were formed, and so on and so forth.
* Make datasets public (among authenticated site users) and private again if you change your mind.
* Discover data with tags, and annotate your data with tags for improved discover-ability.

Coming Soon
* Delete data objects to correct mistakes

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