Arvest Resources

Documentation, API and use cases

Official Resources

Importing Images

Local Images

  1. Create a workspace
  2. Click Import → Upload files
  3. Supported formats: JPEG, PNG, TIFF, WebP

IIIF Manifests

  1. Find a Manifest URL (Europeana, Nakala, Omeka S, Gallica, etc.)
  2. Click Import → IIIF Manifest URL
  3. Paste the URL → Arvest imports all canvases

W3C Web Annotation Standard

Arvest uses the W3C Web Annotation Data Model for all annotations, ensuring interoperability with other tools.

Key concepts:

  • Annotation — the annotation itself (with creator, date, motivation)
  • Target — what is being annotated (a canvas region, specified as a URI fragment)
  • Body — the content of the annotation (text, tag, linked data URI)

API Access

The Arvest REST API allows programmatic access to your workspace data:

# Get all manifests in a workspace
GET https://api.arvest.app/v1/workspaces/{id}/manifests

# Get all annotations
GET https://api.arvest.app/v1/workspaces/{id}/annotations

# Get annotations for a specific canvas
GET https://api.arvest.app/v1/manifests/{id}/annotations

Consult the full API documentation at docs.arvest.app/api.

Video Tutorials

Official tutorial series produced by the From Stage to Data research programme (Université Rennes 2 / Huma-Num), which initiated the development of Arvest.

The full playlist is available on YouTube: 🔗 Arvest Tutorial Playlist

Quickstart Guide (13 min)

A compact end-to-end walkthrough covering everything needed to start using Arvest: creating an account, setting up a first workspace, importing images, and making a basic annotation. The fastest way to get up and running before diving into the full tutorial series.

Tutorial 1/6 — Introduction & Overview (15 min)

Opens the six-part series with a broad overview of what Arvest is, what it is designed for, and the key concepts that run through all the tutorials: workspaces, media, manifests, projects, and annotations. A useful orientation before engaging with the more detailed episodes.

Tutorial 2/6 — Media (22 min)

Covers the two main ways to bring images into Arvest: uploading local files directly from your computer, and linking to remote media already hosted elsewhere. Explains how Arvest handles different file types, how uploaded files are stored, and how to manage your media library within a workspace.

Tutorial 3/6 — Manifests (25 min)

The longest tutorial in the series, dedicated to IIIF Manifests — the core data structure in Arvest. Explains how to create a Manifest from uploaded media, how to import an existing Manifest from an external IIIF source (Europeana, Nakala, Gallica, Omeka S…), and how Canvases and sequences are organised within a Manifest.

Tutorial 4/6 — Projects (33 min)

The most comprehensive episode. Covers the full project lifecycle in Arvest: creating a project, configuring its settings and access levels, inviting collaborators, assigning roles (viewer, annotator, editor, admin), and sharing a finished project with the outside world. Essential for anyone using Arvest for collaborative or team-based research.

Tutorial 5/6 — Annotations (22 min)

A complete tour of Arvest’s annotation capabilities. Demonstrates all available annotation types — rectangular and polygonal regions, points, and full-canvas notes — as well as how to add textual bodies, tags, and linked data URIs. Also covers annotation layers, filtering, and exporting annotations in W3C Web Annotation format.

Note

Tutorial 6/6 (covering additional advanced features) has not yet been published. The series continues with two further playlists also available on the same channel: one dedicated to the Arvest API (3 videos) and one on machine learning workflows with Python (4 videos) — both particularly relevant for researchers looking to integrate Arvest into computational pipelines.

Use Cases in the Humanities

  • Iconographic analysis: annotate depicted figures, symbols, scenes
  • Historical geography: tag depicted places with GeoNames URIs
  • Text transcription: annotate visible text passages
  • Comparative analysis: compare the same motif across multiple images in a corpus
  • ML dataset preparation: create annotated training sets for computer vision models