> For the complete documentation index, see [llms.txt](https://docs.rhinofcp.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.rhinofcp.com/projects/what-is-a-project.md).

# What is a Project?

A **project** in the Rhino Federated Computing Platform (FCP) is the main way to organize a data analysis or research effort. It gives you one secure place to manage work, people, and resources.

A project acts as a container for the key parts of your work, including Datasets, Code Objects, Code Runs, Collaborators, and Data Schemas. This structure helps teams work together while protecting data privacy and security.

## Key Components of a Project

Here are the key components of a project.

* **Name:** The name of the project.
* **Description:** The description of the project.
* **UUID:** The unique identifier for a specific project within the Rhino FCP.
* **Permissions Policy:** A set of project permissions and privacy settings that can be applied to actions at the project and site levels. 
* **Data** **Schema(s):** Data schemas play a crucial role in projects where specific data models need to be enforced. This feature allows users to define the structure and relationships within the data for standardized analysis. 
* **Dataset(s):** Datasets that participate in the project. A project on the FCP can include Datasets from multiple collaborating sites. Further information about datasets is covered later in the [Datasets section](/datasets/what-is-a-dataset.md) of the User Guides.
* **Code:** Code objects are the computational units within your project. A Code Object can be as simple as a single line of Python code or as complex as a multi-file pipeline for federated learning.
* **Code Runs:** Code Runs provides you with access to runtime logs and additional information about the executions of your code. 
* **Collaborator(s):** Collaborators are key contributors to your project. You can invite external partners to participate, fostering a collaborative environment where insights and expertise can be shared securely. 

## Typical Project Workflow

After logging in, you will arrive at the FCP Dashboard, which serves as the central location for project management. A typical project follows the steps outlined below. Depending on the scope and pace of your work, this process may take days, weeks, or months.

{% stepper %}
{% step %}

### Project Creation

From the dashboard, you can create a new project. Provide a clear name and description that define the project’s purpose and scope. You must also configure the project and site permissions.
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{% step %}

### Define Data Schemas (Optional)

If your project requires standardized data models, configure data schemas to enforce structured data relationships and formats.
{% endstep %}

{% step %}

### Add Datasets

Depending on your project's scope, import the relevant datasets. These datasets will be the foundation for your analyses and initiatives.
{% endstep %}

{% step %}

### Incorporate Code

Integrate your code objects — machine learning models, preprocessing code, or analysis algorithms — to drive the core functions of your project.
{% endstep %}

{% step %}

### Manage Code Runs

Monitor and assess the outcomes of your Code Runs within the designated Code Runs section. This is where you can access logs and insights from the computational executions.
{% endstep %}

{% step %}

### Invite Collaborators

You can invite collaborators to participate in the project at any time. This supports knowledge sharing and allows contributors to work together securely. Depending on the project permissions policy, collaborators may import their own datasets and contribute their own code objects.
{% endstep %}

{% step %}

### Managing and Monitoring Projects

Once a project has been created, the FCP Dashboard provides a range of tools and features to support ongoing project management. These include cohort analytics, workflow orchestration, and federated learning management, which help teams manage work efficiently and collaborate effectively.
{% endstep %}
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