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Best Practices for Scientific Data Management
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Introduction
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This guide describes Axiom Data Science's best practices for scientific data management. The intent of these practices is to improve the accessibility and usability of your data. These practices may be followed at any time during the preparation of your dataset, but are most useful when considered at the onset of project planning and implemented during data collection.

.. note::
	While these practices have been developed with the `Research Workspace <http://www.researchworkspace.com/>`_ in mind as the data management platform, the principles involved apply to data management using any software platform.

The guide is divided into two main sections: data management and metadata. The data management section provides general guidance on data management planning, such as how to organize files and folders and the preferred data formats for long-term preservation. The metadata section provides in-depth guidance on how to describe your dataset make it more discoverable.

Follow the links below to nagivate to specific sections of this guide, or use the links in the sidebar to the left. If you can't find the answer you're looking for, please contact us at metadata@axiomdatascience.com. Your feedback will help improve this documentation.

.. toctree::
   :maxdepth: 1
   :caption: Data Management

   DataManagementIntro
   DataManagementCheatSheet
   DataManagementBestPractices

.. toctree::
   :maxdepth: 1
   :caption: Metadata

   MetadataIntro
   MetadataCheatSheet
   MetadataBestPractices
   MetadataForModelsBP
   CodeListDefinitions

.. toctree::
   :maxdepth: 1
   :caption: Submitting Data to Axiom

   SubmitDataIntro
   SubmitDataSensors
   SubmitDataModelsGrids
   SubmitDataSpatial
   SubmitDataBiodiversity
   SubmitDataAnimalTelemetry