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title: Welcome to Arvados!
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Copyright (C) The Arvados Authors. All rights reserved.

SPDX-License-Identifier: CC-BY-SA-3.0
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This guide provides an introduction to using Arvados to solve big data bioinformatics problems.

h2. What is Arvados?

Arvados is a free and open source bioinformatics platform for genomic and biomedical data.

We address the needs of IT directors, lab principals, and bioinformaticians.

h2. Why use Arvados?

Arvados enables you to quickly begin using cloud computing resources in your bioinformatics work. It allows you to track your methods and datasets, share them securely, and easily re-run analyses.

h3. Take a look (Screenshots gallery) 

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        [START] After logging in, you will see Workbench's dashboard.
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        Pipelines describe a set of computational tasks (jobs).
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        The output of all jobs is logged and stored automatically.
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        Pipelines can also be viewed in auto-generated graph form.
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        Pipelines can easily be re-run exactly as before, or...
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        ...you can change parameters or pick new datasets.
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        With web upload, data can be uploaded right in Workbench.
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        Collections allow sharing datasets and job outputs easily. 'Create sharing link' with one click.
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        Data provenance is tracked automatically. [END]
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Note: Workbench is the web interface to Arvados.
Tip: You may need to make your browser window bigger to see full-size images in the gallery above.

h3. Key Features

<ul>
<li><strong>Track your methods</strong><br/>
We log every compute job: software versions, machine images, input and output data hashes. Rely on a computer, not your memory and your note-taking skills.<br/><br/></li>
<li><strong>Share your methods</strong><br/>
Show other people what you did. Let them use your workflow on their own data. Publish a permalink to your methods and data, so others can reproduce and build on them easily.<br/><br/></li>
<li><strong>Track data origin</strong><br/>
Did you really only use fully consented public data in this analysis?<br/><br/></li>
<li><strong>Get results sooner</strong><br/>
Run your compute jobs faster by using multi-nodes and multi-cores, even if your programs are single-threaded.<br/><br/></li>
</ul>