> For the complete documentation index, see [llms.txt](https://docs.redivis.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.redivis.com/guides/getting-started.md). # Getting started Redivis is a platform that allows researchers to seamlessly discover, access, and analyze 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. {% embed url="" %} ## 1. Create your account Many datasets on Redivis are public, and you can browse them without creating an account. However, you'll need an account in order to analyze data, as well as to apply for access to restricted datasets. Select **Create account** to sign up. You can use your academic institution's login credentials, Google account, or email. Once you create an account, you'll be navigated to your **Workspace,** which is your private area for creating workflows to work with data, uploading your own datasets, and managing your Redivis account. ## 2. Join an organization Most data on Redivis is uploaded and managed by an organization. If you've arrived here because an organization you're working with is using Redivis to host and manage data then you'll want to get started by finding and joining your organization. You can browse **organizations** by selecting the Redivis logo in the top left corner of any Redivis page to navigate to the [Explore](https://redivis.com/explore) page. Once you find an organization, you can **Join** it, which will add this Organization to your workspace and allow you to start applying for any restricted access datasets they have. You can also skip this step and use Redivis to find data directly if you're not affiliated with an organization! ## 3. Find datasets All data on Redivis is stored within a Dataset. If you've joined an organization you can find datasets managed by your Organization by going to your organization's home page and selecting the **Datasets** tab. You can also look for datasets in your broader institution (such as [Stanford](https://redivis.com/stanford) or [Columbia](https://redivis.com/columbia)) or across all of Redivis on the [Explore](https://redivis.com/explore) page.
All searches perform a full-text search across a dataset, its documentation, tables, variables, and rich metadata content. Select any dataset title to go to that **Dataset** page where you can view the data and metadata it contains. The data for this dataset will be available on the **Tables** tab, which you can explore further. While looking for data you may also come across restricted datasets. For these you will need to select **Apply for access** on the **Dataset** page and complete the requirements to gain approval from the dataset's administrators. *Learn more in the* [*Discover & access data*](/guides/discover-and-access-data.md) *guide.* ## 4. Analyze data Once you've found a dataset that you want to work with, you can add it to a **Workflow**. Workflows are the fundamental analysis interface on Redivis, where you can query, merge, reshape, and analyze any dataset that you have access to — all from within your web browser. Add a dataset to a workflow by selecting **Analyze in workflow** on the dataset page.
In a workflow you can create a **Transform** by choosing any table and selecting **+Transform**, which allows you to combine and reshape your data into a final output table that best serves your analysis. These transforms use a SQL engine under the hood, allowing you to query incredibly large tables, even billions of records, in seconds. After creating output tables for analysis, you can create a computational **Notebook** in R, Python, Stata, or SAS to further analyze your data and develop your final figures. Choose any table and select **+Notebook** to get started. The notebook will initialize and pull in the table you've chosen (or a sample if it is a large table). You can also export and query this table from external environments, allowing you to use whatever analytical tools best suit your research question by selecting **Export table** on any table. Here we can download the table in a number of common formats, interface with it programmatically via the API, or export the table to supported integrations. *Learn more in the* [*Analyze data in a workflow*](/guides/analyze-data-in-a-workflow.md) *guide.* ## Next steps #### Upload your own datasets Augment your data analysis in Redivis by uploading your own datasets, with the option to share with your collaborators or the broader research community. *Learn more in the* [*Create & manage datasets*](/guides/create-and-manage-datasets/create-and-populate-a-dataset.md) *guide.* #### **Administer your organization** Organizations allow for research groups and centers to securely distribute data to their research community. Organization administrators can create datasets, manage access, review logs, and create customized reports of their data utilization. *Contact an existing administrator to add you to their organization, or* [*contact us*](https://redivis.com/contact) *to set up a new organization. Learn more in the* [*Administer an organization*](/guides/administer-an-organization.md) *guide.* --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.redivis.com/guides/getting-started.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.