* Capitalize Dashboard.
* Article use: "access Workbench," "access the Dashboard."
* Bold references to Workbench UI elements.
* Make example Arvados environment variables more realistic.
You may be asked to log in using a Google account. Arvados uses only your name and email address from Google services for identification, and will never access any personal information. If you are accessing Arvados for the first time, the Workbench may indicate your account status is *New / inactive*. If this is the case, contact the administrator of the Arvados instance to request activation of your account.
-Once your account is active, logging in to the Workbench will present you with the *dashboard*. This gives a summary of your projects and recent activity in the Arvados instance. "You are now ready to run your first pipeline.":{{ site.baseurl }}/user/tutorials/tutorial-pipeline-workbench.html
+Once your account is active, logging in to the Workbench will present you with the Dashboard. This gives a summary of your projects and recent activity in the Arvados instance. "You are now ready to run your first pipeline.":{{ site.baseurl }}/user/tutorials/tutorial-pipeline-workbench.html
!{{ site.baseurl }}/images/workbench-dashboard.png!
Open a shell on the system where you want to use the Arvados client. This may be your local workstation, or an Arvados virtual machine accessed with SSH (instructions for "Unix":{{site.baseurl}}/user/getting_started/ssh-access-unix.html#login or "Windows":{{site.baseurl}}/user/getting_started/ssh-access-windows.html#login).
-Click on the link with your _email address_ in the upper right corner to access the user settings menu, and click on the menu item *Manage account* to go to the account management page. On the *Manage account* page, you will see the *Current Token* panel, which lists your current token and instructions to setup your environment.
+Click on the link with your _email address_ in the upper right corner to access your account menu, then click on the menu item *Manage account* to go to the account management page. On the *Manage account* page, you will see the *Current Token* panel, which lists your current token and instructions to set up your environment.
-h2. Setting the environment
+h2. Setting environment variables
-For your convenience, the "Manage account" page on Workbench provides the "Current Token" panel that includes a command you may copy and paste directly into the shell. It will look something as the following.
+For your convenience, the *Manage account* page on Workbench provides the *Current Token* panel that includes a command you may copy and paste directly into the shell. It will look something as the following.
bc. HISTIGNORE=$HISTIGNORE:'export ARVADOS_API_TOKEN=*'
export ARVADOS_API_TOKEN=2jv9346o396exampledonotuseexampledonotuseexes7j1ld
export ARVADOS_API_HOST={{ site.arvados_api_host }}
-export ARVADOS_API_HOST_INSECURE=true
+unset ARVADOS_API_HOST_INSECURE
* The @export@ command puts a local shell variable into the environment that will be inherited by child processes such as the @arv@ client.
</code></pre>
</notextile>
-This instantiates your pipeline and displays a live feed of its status. The new pipeline instance will also show up on Workbench dashboard.
+This instantiates your pipeline and displays a live feed of its status. The new pipeline instance will also show up on the Workbench Dashboard.
Arvados adds each pipeline component to the job queue as its dependencies are satisfied (or immediately if it has no dependencies) and finishes when all components are completed or failed and there is no more work left to do.
h2. Monitor job progress
-Go to "*Recent jobs*":https://{{site.arvados_workbench_host}}/jobs in the Workbench. Your job should be near the top of the table. This table refreshes automatically. When the job has completed successfully, it will show <span class="label label-success">finished</span> in the *Status* column.
+Go to "*Recent jobs*":https://{{site.arvados_workbench_host}}/jobs in Workbench. Your job should be near the top of the table. This table refreshes automatically. When the job has completed successfully, it will show <span class="label label-success">finished</span> in the *Status* column.
h2. Inspect the job output
-On the "Workbench dashboard":https://{{site.arvados_workbench_host}}, look for the *Output* column of the *Recent jobs* table. Click on the link under *Output* for your job to go to the files page with the job output. The files page lists all the files that were output by the job. Click on the link under the *file* column to view a file, or click on the download icon <span class="glyphicon glyphicon-download-alt"></span> to download the output file.
+On the "Workbench Dashboard":https://{{site.arvados_workbench_host}}, look for the *Output* column of the *Recent jobs* table. Click on the link under *Output* for your job to go to the files page with the job output. The files page lists all the files that were output by the job. Click on the link under the *file* column to view a file, or click on the download button <span class="glyphicon glyphicon-download-alt"></span> to download the output file.
On the command line, you can use @arv job get@ to access a JSON object describing the output:
</code></pre>
</notextile>
-Running locally is convenient for development and debugging, as it permits a fast iterative development cycle. Your job run is also recorded by Arvados, and will show up in the "Recent jobs and pipelines" panel on the "Workbench dashboard":https://{{site.arvados_workbench_host}}. This provides limited provenance, by recording the input parameters, the execution log, and the output. However, running locally does not allow you to scale out to multiple nodes, and does not store the complete system snapshot required to achieve reproducibility; to that you need to "submit a job to the Arvados cluster":{{site.baseurl}}/user/tutorials/tutorial-submit-job.html
+Running locally is convenient for development and debugging, as it permits a fast iterative development cycle. Your job run is also recorded by Arvados, and will appear in the *Recent jobs and pipelines* panel on the "Workbench Dashboard":https://{{site.arvados_workbench_host}}. This provides limited provenance, by recording the input parameters, the execution log, and the output. However, running locally does not allow you to scale out to multiple nodes, and does not store the complete system snapshot required to achieve reproducibility; to do that you need to "submit a job to the Arvados cluster":{{site.baseurl}}/user/tutorials/tutorial-submit-job.html.
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