Get started

The following sections guide you through getting started with URL Shares:

• OpenVPN setup
• Using a Jupyter notebook
• Using Pandas DataFrame
• Next steps

OpenVPN setup

Until January 7, 2025, you can access URL Shares through a Virtual Private Network (VPN). This section shows you how to install and configure the OpenVPN client and connect to our VPN server. Once connected, you will be able to access URL Shares and perform queries.

Note that while you are connected to our VPN server, all of your internet traffic will be routed through it, so be sure to disconnect when you are finished.

Step 1: Install OpenVPN

Download the OpenVPN client. Doubleclick the downloaded file to launch the setup wizard.

When the setup wizard completes, OpenVPN Connect launches and you are required to accept the OpenVPN Inc. Data Collection, Use and Retention policy to continue.

Step 2: Prepare to set up your profile

In the Import Profile window, select the UPLOAD FILE tab.

Step 3: Download the OpenVPN configuration file

Clicking this link downloads the OpenVPN configuration file (fortVpnCredentials.ovpn) to your computer (check your downloads folder). Once downloaded, drag and drop the file into the Import Profile window.

Step 4: Connect to the VPN

In the Imported Profile window, click CONNECT.

Once successfully connected, you will see this window:

Note that while you are connected to our VPN server, all of your internet traffic is routed through it, so be sure to disconnect from the VPN server when you are finished.

Troubleshooting VPN issues

It is possible for other certificate-based VPNs to interfere with certificate authentication. If you have trouble connecting, try disabling any other VPNs you have in use.

If you're having any other issues connecting to the VPN, please contact the support staff by creating a JIRA Service Management case. See Get help for information on how to open a case.

Using a Jupyter notebook

This section steps you through creating a new Jupyter notebook and using it to run queries. The examples below query the public environment.

Step 1: Log in

While connected to the VPN, visit URL Shares by going to the webpage of the environment you want to access.

Environment URLs:

• Public Environment
• Private Environment

If you'd like to use the latest Amazon WorkSpaces Secure Browser version of Researcher Platform, you can follow these URLs without being connected to the VPN:

• Public Environment
• Private Environment

Log into the site using your Facebook account.

The Researcher Platform offers a GPU server as an alternative to a CPU server. See GPU server for information about this feature.

Once you have logged in and selected your server type, Jupyter spins up an instance of the Jupyter notebook server for your use.

Step 2: Create a notebook

Click the blue + button and select Python 3. This creates a new Jupyter notebook in a new browser tab. You can optionally rename the notebook by right-clicking on the notebook in the left pane and selecting Rename.

Step 3: Run a Python-based SQL query

To use our URL Shares SQL module, click in an empty notebook cell and enter the following code:

from fbri.public.sql.query import execute

database = "fbri_prod_public"
table = "erc_condor_url_attributes_dp_final_public_v3"

sql = f"""
SELECT *
FROM {database}.{table}
LIMIT 20
"""

result = execute(sql)

Some rows in the raw results (now in memory) contain negative values within the numeric columns. This is due to the Gaussian noise that we have added to the original values in the data for the purpose of preserving privacy. Specifically, it's an implementation of user-level zero-Concentrated Differential Privacy (zCDP).

Using Pandas DataFrame

While the raw result is still in memory, you might find it easier to manipulate the data if you load the file as a Pandas DataFrame. It can also be saved to a .tsv file by parameterization in the `execute()` function by using the Pandas Library.

Create a tab-separated save file (.tsv)

Import the Pandas DataFrame module by clicking in an empty notebook cell and running the following code:

import pandas as pd

database = "fbri_prod_public"
table = "erc_condor_url_attributes_dp_final_public_v3"

sql = f"""
SELECT *
FROM {database}.{table}
LIMIT 20
"""

result = execute(sql, "attributes.tsv")
df = pd.read_csv('attributes.tsv', delimiter = '\t')
df

Next steps

See the public and private environment guides for more examples and forms of analysis:

• Public environment guides
• Private environment guides