.. include:: globals.txt

.. _overview:

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Overview
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The Research Workspace (RW) is science collaboration platform that fosters reproducible science and expands research impact. Use the Research Workspace to manage scientific data, write metadata, and publish research data. The Research Workspace supports researchers and scientific program managers throughout the entire research lifecycle, from data collection to making data public.

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Scientific Data Management
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Scientific data management is a process of managing research data from its creation to its reuse. With the Research Workspace, researchers and research programs can collect data, store data, analyze data, preserve data, and publish data.

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Science Collaboration
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- share your data with secure permissions
- make data public with the click of a button

Files you upload to the RW are organized into projects that can be easily shared with individuals, small research groups, large research campaigns, or entire organizations. An integrated and easy-to-use metadata editor allows researchers to clearly document their data using ISO 19110 and 19115-2 standards. Finalized datasets with complete metadata can then be published directly to federal data archives.

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Analyze Data
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Run `Python <https://www.python.org/>`_ or `R <https://www.r-project.org/>`_ in Jupyter notebooks, using your own data, right in the browser. With Research Workspace notebooks, review research team's findings, run scripts using research data, and generate new data products. Use any data you have access to in the Research Workspace, including a large library of public data.

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Share  Research Data
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With the Research Workspace you can easily make your data and metadata public to catalogs and portals. For best practice in data preservation, archive data to national repositories from the Research Workspace with the click of a button. Receiving a digital object identifier (DOI) for your well-documented data has never been easier.

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Preserve Data
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Use the Research Workspace to obtain a digital object identifier (DOI) and submit data to data repositories for long-term storage and preservation.

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Getting Started
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Or, jump to these links for quick tips on getting around:

* :ref:`Create a new project <create-a-new-project>`
* :ref:`Upload files <upload-files>`
* :ref:`Share and organize files <managing-files>`
* See or access data from a :ref:`campaign<managing-campaigns>`
* See or access data from an :ref:`organization<managing-organizations>`


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Related Reading
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This documentation focuses on the technical details of using the RW. For more information on how to use the features of the RW to help you organize your projects and data, as well as suggestions on how to create high-quality metadata to describe your datasets, please see the links below.

* `Data Management Best Practices <http://www.axiomdatascience.com/best-practices/DataManagementBestPractices.html>`_
* `Metadata Management Best Practices <http://www.axiomdatascience.com/best-practices/MetadataBestPractices.html>`_