# Studies & Assays

## Overview

[Studies](/introduction/key-concepts.md#studies) and [Assays](/introduction/key-concepts.md#assays-and-assay-types) are the primary units of work in Lab Atlas and make up the bulk of record keeping, but how you choose to define and utilize them is entirely up to you ([we do provide some guidance](/using-lab-atlas/frequently-asked-questions.md#how-should-i-define-studies-and-when-should-i-add-new-ones)). You generally want to think of assays as well-defined activities that have a standard protocol, which are expected to be completed within a short amount of time be only one or two people. Studies are a bit more open-ended and may cover a longer period of time & include more people, but should still have a clearly-defined goal. 

When you are ready to perform some wet- or dry-lab work on a new study or assay, Lab Atlas should be the first place you go before getting started. The reason for this is that Lab Atlas will take care of some of the repetitive first steps in preparing the systems you are required to work within by creating a few things for you:

* A unique [Code](/introduction/key-concepts.md#study-codes) that can be used to identify your study in the lab or in other systems.
* A folder and summary notebook entry in your linked electronic laboratory notebook system (eg. Benchling).
* A folder in your linked cloud storage service (eg. SharePoint or Amazon S3) for capturing documents & results.
* An optional source code repository in a connected Git server (eg. GitLab) for managing data science notebooks.

You can reference the below diagram for an example workflow for when and how to use Lab Atlas:

<figure><img src="/files/ncJBPWTxf70GCntKVe26" alt=""><figcaption><p>Example study workflow</p></figcaption></figure>


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