Collections

Collections are named, ordered groups of datasets. Unlike a saved search (which is a query that re-runs against the catalog), a collection has explicit membership — you decide what’s in it and the order. Collections show up in their own list in the sidebar and render a landing page that lists each member dataset, with a search bar scoped to the collection.

What collections are

A collection is a small piece of metadata pointing at a list of dataset IDs. There’s no copy of the data — adding a dataset to a collection doesn’t duplicate it; removing a dataset from a collection doesn’t delete the dataset. The collection is purely a curation surface.

Typical use cases:

Project deliverables — group all the datasets you produced for a specific project so a stakeholder can browse them in one place.
Thematic catalogs — “Population & demographics”, “Hydrology”, “Land cover 2024” — small curated sub-catalogs organized by theme.
Quarterly reviews — collect the datasets in scope for a specific review and share the URL with reviewers.
Onboarding — give a new team member a “Start here” collection of the most-used datasets.

A dataset can belong to any number of collections; collections can overlap. Collections themselves can’t be nested (no collections-of- collections), but tags work as a coarser grouping above the collection level if you need a hierarchy.

Browsing collections

The Collections entry in the sidebar opens the collection list — every collection visible to you, sorted by recently-updated.

Each collection card shows:

Collection name and description
Dataset count (e.g., “12 datasets”)
Owner and last-updated timestamp

Click a card to open the collection’s detail page.

Collection detail

The collection detail page lists every member dataset — each row links to the dataset, with its geometry type and feature count — plus collection-scoped search. The search bar at the top of the collection page runs against the collection’s members only, not the full catalog.

This makes collections useful as small catalogs in their own right — a team member exploring “Hydrology” can keyword-search and filter just within that scope.

Creating a collection

Creating a collection requires the editor role (you can read collections without being an editor; you just can’t make new ones). If you don’t see a New collection button on the Collections page, you don’t have the editor role on this instance — see User management & RBAC for how roles are assigned.

To create a collection:

Open Collections in the sidebar.
Click New collection.
Fill in the create dialog:
- Name (required) — short, human-readable name.
- Description — markdown-supported longer description.
Click Create. The collection is saved and you land on its detail page, where you can add member datasets.

Who can see a collection

A collection has no visibility setting of its own — there’s no public/private/restricted toggle on a collection. Every collection is listed to anyone who can browse collections. What governs access is the visibility of each member dataset: when a viewer opens a collection, GeoLens filters the member list down to the datasets that viewer is allowed to see (RBAC). A private member dataset is simply hidden from viewers who lack access, while the collection itself remains listed.

If you need access control around a set of datasets, set it on the datasets themselves — see the Access tab on each dataset’s detail page.

Managing membership

Once a collection exists, the detail page has an Edit menu (visible to editors) with several options:

Add datasets — opens the same catalog picker as the create dialog. Select one or many datasets and click Add to push them into the collection.
Remove datasets — multi-select datasets in the collection and click Remove to take them out.
Reorder — drag handles on each dataset card let you reorder; the collection landing renders in the order you set.
Edit metadata — update name and description.
Delete — remove the collection. Members are not affected.

You can also add a dataset to a collection from the dataset itself — the Add to collection action on any dataset detail page opens a picker showing collections you can edit; click any to add the dataset there.

Bulk membership

For larger reorganizations, the catalog picker supports filter-based bulk add: search by tag, geometry type, or bbox in the picker, then select all matches and add them in one operation. Useful when you’ve curated a saved search and now want to materialize it as a collection.

Saved searches vs collections

It’s worth being explicit about the distinction, because the two features look similar at first glance:

A saved search is a stored query. Its membership changes as the catalog changes — new datasets matching the query auto-appear, deleted datasets disappear. Use it when the criteria are what matter (“every hydrology dataset in California”).
A collection is an explicit list of dataset IDs. Membership only changes when you add or remove a dataset. Use it when the specific datasets are what matter (“the seven datasets we used for the Q3 review”).

If you need both behaviors — a query result that’s also a stable shareable list — start with a saved search, then materialize the result into a collection. The collection captures a snapshot; the saved search keeps tracking the live query.

See Search & Discovery → Saved searches for the saved-search side.

Collection URLs are stable: https://your-instance/collections/<id>. Anyone who can browse collections can open the URL; the collection page renders the same for editors and viewers — viewers just don’t see the Edit menu. Which member datasets each viewer sees still depends on the per-dataset visibility (RBAC), as described above.

To watch or mirror a collection programmatically, fetch its member list from the API — GET /api/catalog/collections/{id}/datasets/ returns the member datasets the caller is allowed to see.

Tips

Naming — short collection names render better in the sidebar. Aim for 2-4 words.
Description as a landing page — the description supports markdown, including images, links, and headings. Treat it as a brief landing page for the collection, not just a summary.
Order with intent — the order datasets render in is the order visitors encounter them. Lead with the most-important or most-recent datasets.
Lean on descriptive titles. The collection landing page lists each member dataset by title with its geometry type and feature count, so clear, descriptive dataset titles make a collection easy to scan.