How Adept structures data

Collections

Adept organises your data in a way that makes it easy to navigate and manage. Similar to traditional computer file systems, where files are stored in folders that form a tree-like structure, Adept uses collections to organise your data.

Here’s an example of a typical file system structure found on most operating systems:

.
├─ folder
│  ├─ sub_folder
│  │  └─ file_1.docx
│  ├─ file_2.docx
│  ├─ file_3.xlsx
└─ file_4.pdf

In Adept, collections function like folders, allowing you to logically group your data. Additionally, there is a special type of collection called a root collection. If you want to import data into Adept, you must always first create a root collection.

.
├─ root_collection_1    # root collection with two collections
│  ├─ collection_1
│  │  └─ dataset_1
│  ├─ collection_2
│  │  └─ dataset_2
└─ root_collection_2    # root collection without collection

Why do we need root collections?

Adept was designed for the biomedical research sector, where researchers often handle sensitive data that requires specific access permissions. To address this, Adept enables case-by-case access control. For example, a researcher may request access to a dataset through a system like Daisy that ensures compliance with GDPR. Once access is granted, the researcher receives an Institutional Dataset ID in their Adept account, which they use to create a root collection. This root collection is directly linked to the ID, allowing Adept to track data access and ensure proper management.

It is important to note that the scope of data that a user can upload into a root collection is not restricted. The ID functions primarily as a logging mechanism, keeping records of access rather than enforcing import restrictions. This means that, once users have the necessary permissions, they have the freedom to create root collections and add any data they choose, beyond what they initially accessed through Daisy.

Datasets

In Adept, imported files are stored as datasets which can reside in either a root collection or a collection. Datasets are structured like tables, made up of rows and fields (or columns).

During the data import process, each field is assigned a specific type, ensuring proper data handling.

Dashboards

Dashboards allow you to visually present your data using widgets. Each dashboard is associated with a specific dataset, but a single dataset can have multiple dashboards.

.
├─ root_collection_1
│  ├─ collection_1
│  │  └─ dataset_1      # dataset with two dashboards
│  │     └─ dashboard_1
│  │     └─ dashboard_2
│  │  └─ dataset_2      # dataset with a single dashboard
│  │     └─ dashboard_1
└─ ...

Widgets

A widget is a user interface component used to display specific data within a dashboard. Four different widgets are currently implemented in Adept:

• Table Widget: Displays the imported dataset, with options to filter the dataset directly on the dashboard.

• Text Widget: Displays customisable text with full markdown support.

• Metric Widget: Shows metrics such as average, min or max.

• Chart Widget: Visualises data using different chart types, including pie, bar, line charts, and scatter plots.

Examples