Getting started

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Getting started
Overview
Redivis is a platform that allows researchers to seamlessly discover, access, and manipulate data. This brief guide will walk you through the basics to get up and running and provide a launching point for exploring other resources in this documentation.
Prefer a video tour? Check out the Redivis demo video!
1. Create your account
Many datasets on Redivis are public, and you are free to explore them without ever creating an account. However, you will need an account in order to apply for access and save any query results. 
Click the Create account button in the top right of any page to sign up. You can use your institutional login credentials or a Google account to sign up. Learn more about your account here.
2. Discover data
Redivis is home to numerous organizations hosting rich and diverse datasets. These datasets are made up of documentation, metadata, and tables containing raw tabular data. Some datasets may be fully public, while others may have access controls imposed by the data owner.
In order to find your organization(s), navigate to the "Organizations" section of your workspace. Note that some organizations may be hidden from public view, in which case you will need to contact someone at the organization to be added as a member.
On an organization's home page, you can click on the Datasets tab to browse all datasets and filter by their metadata. All searches perform a full-text search across a dataset, its documentation, tables, variables, and rich metadata content.
As you discover datasets that might be relevant to your research, you can click on them to get further information and interact with the actual data. Some of this content may be limited, depending on the datasets' access configuration.
Organizations are the primary home for thousands of datasets on Redivis
3. Apply for access
Many datasets on Redivis are public, while others have requirements for certain levels of access enforced by the data owner. If the dataset is public you can skip to querying the data, otherwise you'll need to gain access before you can fully utilize the data.
To learn more about gaining access, see our applying for access guide.
4. Manipulate data
You can learn a lot about a dataset on the dataset page — documentation, metadata, summary statistics and a full tabular view are available; advanced users can even run custom SQL queries. For smaller, less restricted datasets, you can download them directly.
However, once you've identified a dataset as worthy of further exploration, you'll generally want to add it to a project. The Redivis project interface allows you to develop scalable, reproducible, and self-documenting data pipelines as you filter, transform, and merge data from across Redivis.
This demo project is publicly available here. Its source dataset can be found here.
To begin, click the Add to project button on any dataset page, or create a new project from your workspace and click the + Dataset button. 
Add a dataset to a project from the dataset page
This will bring the dataset — in this example, CMS Public Medicare Data — into the Redivis project interface. 
Datasets, and their tables, are available in your project
You can click on any table in the dataset to inspect it further, and view that table's cells and summary statistics.
To manipulate the data, click the Transform button underneath that table to create a new transform on the inpatient charges data.
You can then build a transform to select the variables and rows that you want to propagate to the next table. Here, we're keeping all records for California, and computing the average medicare payments, aggregated per provider.
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When you're ready to generate an output table, click the "Run" button at top right. This transform shown below selects all providers in California whose medicare payments are higher than the average, for a particular diagnosis group. This project shown here is public for you to explore and create your own copy.
After your run job is complete, you can click on the output table to explore your results and make sure the output is valid, and update your transform accordingly.
On the output table you can:
View variables 
View summary statistics
View cells
Run queries
Export the table to your computer, data analysis tool, or other computational environment
5. Analyze data
Once you have a table with all the information you need in it, you can select any table and click the + Notebook button to create a notebook referencing this table. You can programmatically add any other table references to this notebook, as long as the tables are within this project.
Use this sandbox to install packages and work with the languages you're familiar with to get to your final results!
6. Next steps
Dive into the project tool
The project tool can be used for far more than just simple data manipulation. You can utilize saved value lists, run analytical queries, and even compute simple multivariate statistics — even across billions of records.
Learn more in the Creating a project guide as well as the Projects documentation.
Share and collaborate
Redivis is more than just your own workspace — it is a growing community of researchers who are tackling novel, data-driven questions. 
​Share your project to work with collaborators in real time, and make it public so that others can fork off of and build upon your work.
Upload your own datasets
Augment your data analysis in Redivis by uploading your own datasets, with the option to share with your collaborators (or even the broader research community).  
Learn more in the Creating a dataset guide as well as the Datasets documentation.
Export data
If you'd like to export data to a different system, you can download it in various file formats, reference in Python / R, or visualize in tools such as Google Data Studio.
Learn more in the Export and integrations documentation.
Create a data portal for your organization
Drive research at your organization by deploying a Redivis data portal. Learn more about setting up an organization and how Redivis enables data managers to easily and securely distribute rich datasets to their research community.