--- title: "Quickstart" slug: "quick-references" tags: ["Terms and Definitions"] updated: 2026-04-30T03:39:51Z published: 2026-04-30T03:39:51Z canonical: "docs.dataloop.ai/quick-references" --- > ## Documentation Index > Fetch the complete documentation index at: https://docs.dataloop.ai/llms.txt > Use this file to discover all available pages before exploring further. # Quickstart This guide provides a structured overview of key functionalities and step-by-step instructions to help you efficiently navigate and utilize the DDOE platform. --- ## Quick Start References - [Create or Join an Organization](https://docs.dataloop.ai/docs/org-setup#organization-setup) – Set up your workspace by creating a new organization or joining an existing one. - [Add Members to an Organization](https://docs.dataloop.ai/docs/org-setup#createjoin-an-organization-and-add-members) – Invite team members for seamless collaboration. - Access Control - [Project](https://docs.dataloop.ai/docs/project-team#roles-and-permissions) & [Organization](https://docs.dataloop.ai/docs/org-members-groups#organization-role-permissions) Level – Manage roles and permissions for teams. - [Create a Project](https://docs.dataloop.ai/docs/project-dashboard-1#create-a-project) – Manage datasets and tasks within a dedicated project. - [Upload](https://docs.dataloop.ai/docs/upload-items#uploading-data-into-dataloops-storage) or [Sync Your External Data](https://docs.dataloop.ai/docs/sync-cloud-storage-drivers) – Bring in your data via uploads or cloud storage integration. - [Filtering & Searching](https://docs.dataloop.ai/docs/schema-based-search) Data – Locate relevant data quickly with advanced search tools. - [Manage Your Datasets](https://docs.dataloop.ai/docs/manage-datasets) – Organize datasets, set up cloud storage drivers, and integrate cloud services. - [Set Up Ontology (Labels & Attributes)](https://docs.dataloop.ai/docs/ontology-overview) – Define labels and attributes for annotation tasks. - [Create a Labeling Task](https://docs.dataloop.ai/docs/tasks#create-a-labeling-task) – Assign annotation tasks using DDOE’s Annotation Studios. - [Use Labeling Studios](https://docs.dataloop.ai/docs/annotation-studios) – Perform annotations on images, videos, text, and PDFs. - [Create a QA/QC Task](https://docs.dataloop.ai/docs/tasks#create-a-review-qa-task-for-a-labeling-task) – Ensure high-quality labeled data with Quality Assurance and Control tasks. - [ML Subsets & Data Splitting](https://docs.dataloop.ai/docs/organize-your-data#ml-subsets) – Organize datasets into train, validation, and test subsets. - [Install](https://docs.dataloop.ai/docs/dl-hub-models#how-to-install-a-dataloops-model-to-the-model-management) & [Use AI Models](https://docs.dataloop.ai/docs/dl-hub-models#overview) – Integrate ML models and automate workflows. - [Create a Pipeline](https://docs.dataloop.ai/docs/creating-pipelines) – Automate processes by connecting annotation, QA, and ML services. - [Create an Active Learning Pipeline](https://docs.dataloop.ai/docs/active-learning-pipeline#access-active-learning-pipeline) – Use model predictions and confidence scores to prioritize uncertain data for annotation, improving ML model performance iteratively. - [Deploy](https://docs.dataloop.ai/docs/marketplace-applications#how-to-create-an-application) & [Install](https://docs.dataloop.ai/docs/marketplace-applications#how-to-install-an-application) Application Services – Enhance your project with custom or marketplace services. --- ## API & SDK References - [SDK Documentation](https://developers.dataloop.ai/) – Programmatically interact with DDOE. - API Guide – Integrate DDOE with external applications. --- ## Terms and Definitions This table provides simple explanations of key terms used across the **DDOE platform**. It is designed to help users—whether new or experienced—quickly understand the main concepts, roles, and components that power data management, annotation workflows, machine learning integration, and automation in DDOE. | **Term** | **Definition** | | --- | --- | | **Admin** ***(organization role)*** | A user who can manage projects and members, similar to an owner. | | **Annotation** | A label (e.g., bounding boxes, tags, text) that can be queried. | | **Annotation Manager** ***(project role)*** | Manages annotation and QA tasks, redistributes assignments, and reviews annotation quality. | | **Annotator** ***(project role)*** | Works on assigned annotation or QA tasks only, without permissions to view or manage others. | | **Application** | Software built using the Function-as-a-Service (FaaS) model, where functionality is implemented as independent functions. | | **Assignment** | A subset of items from the labeling task that are distributed to a specified annotator. | | **Attribute** | Metadata about annotations (e.g., Yes/No answers, sliders, free text), providing more context. | | **Bot** | A system-generated user used to run services. Bots log in automatically and make API calls with tokens. | | **Cycle** | A pipeline cycle representing the full sequence of node executions during a single pipeline run, typically tied to a specific item. | | **Dataset** | A collection of items (files) with metadata and annotations. Acts as the main unit of data storage and management. | | **Developer** ***(project role)*** | Can manage datasets, create tasks, set recipes, and export data. Typically handles technical aspects of a project. | | **DQL (DDOE Query Language)** | A query language used to filter, sort, and search data items and annotations across datasets. | | **Execution** | Functions are the basic units of an application. Defined in a class, they can be executed once the service is deployed. | | **Filter** | A tool to narrow down datasets, items, or tasks by specific criteria. Alongside basic filtering, use **Smart Search** for intuitive queries, or **DQL** for advanced exploration. | | **Function** | A single operation inside a module that can be executed when a service is deployed. | | **Group** | Created within an organization, groups are reusable teams of users that can be assigned roles collectively in workflows and projects. | | **Integration/Secrets** | Secure connections that store encrypted credentials (tokens, keys, passwords) for accessing cloud resources like GCS, S3, STS, and registries. | | **Item** | A single file (image, video, text, audio, etc.) in a dataset. Each item can be annotated, labeled, or processed. | | **Item Status** | A label showing progress or outcome of an item (e.g., Completed, Approved, Discarded). | | **Labels** | Categories or classes used to create annotations (e.g., “cat,” “car,” “tree”). | | **Master Dataset** | The original dataset that stores the actual files. | | **Member** ***(organization role)*** | A user with limited permissions: can create projects and view members but cannot manage others or delete the organization. | | **Modality** | A link between items, used to show overlays (e.g., multi-sensor images), replacements, or previews. | | **Model** | A trained machine learning algorithm used for predictions or data labeling. | | **Node** | A single step in a pipeline, such as dataset management, function execution, or ML training. | | **Ontology** | The structure of labels and attributes in a project, forming the building blocks for training models. | | **Organization** | A workspace in DDOE, made up of users who collaborate on projects, share data, and manage resources together. | | **Owner** ***(organization role)*** | The creator of an organization with full control (create projects, manage members, delete organization). Cannot be removed. | | **Package** | A collection of static code (modules, functions, schema) used to build models or deploy services, processed using application technology. | | **Pipeline** | An automated workflow of connected nodes that process data using humans, code, and ML models. | | **Project** | A high-level workspace within an organization, focused on a specific goal. It manages datasets, tasks, and annotations. | | **Project Owner** ***(project role)*** | Has full control of a project: can manage datasets, assign contributors, export data, set workflows, and delete the project. | | **Recipe** | A set of labeling rules and instructions linked with an ontology, defining how data should be processed or annotated. | | **Service** | A serverless computing service for running code without managing infrastructure. Conceptually, a deployed package serving the code. | | **Storage Driver** | Bucket-specific connectors attaching external storage (e.g., GCS, S3, databases) to DDOE, created from a storage integration. | | **Task** | A unit of work (annotation, QA, review) assigned by project managers and containing assignments. | | **Trigger** | A rule that starts an action automatically when conditions are met, either cron-based or event-based. | | **UI Slot** | A button in the DDOE UI that allows users to run functions directly within Annotation Studio, Dataset Browser, and other pages. | | **Worker** ***(organization role)*** | A user role for annotators or workforce teams. They can work on tasks but have no access to secrets, projects, or organization settings. This role overrides project-level access. |