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A dataset in Antidote is a versioned collection of files (images, text, or 3D volumes) plus its metadata and full lineage. Every scan, every healing run, and every compliance report ties back to a specific dataset version.

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Creating a dataset

From Datasets → New, pick a source. Each source captures credentials once and remembers them for next time.
SourceAccepts
Upload ZIPA zipped folder tree. Class name is taken from the parent folder.
Upload folderDrag‑drop a directory from your machine. Same class convention.
Paste textPlain‑text corpus, treated as a single text document.
Antidote does Zip Slip protection on extraction, so a malformed archive cannot escape the dataset directory.

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Dataset detail page (/datasets/:id)

The dataset page is your home base for one piece of data.
Tab / sectionWhat it does
Preview gridLazy‑loaded thumbnails for image datasets, zoom on hover, class filter.
File treeThe dataset’s files with size, type, and class. Right‑click for actions (rename, move, exclude).
Action historyEvery upload, scan, healing, branch, and edit, attributed to a user, timestamped, exportable.
BranchesVirtual versions of the dataset, each backed by a manifest CSV.
LineageGraph of parent / child datasets produced by healing, branching, or imports.
Scan historyPast scans against this dataset, with severity and duration.
SchedulerAssign a cron schedule. See Automation.
SettingsRename, move between projects, configure webhooks, change webhook URL, delete.
Click any thumbnail in the preview grid to open a full‑size viewer with class metadata, EXIF, and the list of every scan that has touched that file. Useful while triaging a single weird sample.

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Branches

Branches let you maintain alternative versions of the same dataset without physically copying files. Each branch is a manifest CSV that declares which files are in or out, plus any label overrides. Typical uses:
  • A clean branch after healing, so you can A/B train on cured vs raw data without losing the original.
  • A balanced branch with a downsampled subset for fast iteration.
  • An experimental branch with tweaked labels for an A/B scan.
Switching branches on the dataset detail page instantly changes which files are used in the next scan. Every branch keeps its own scan history.
1

Create a branch

From the Branches tab, click New branch, give it a name, optionally describe how it differs from the main branch.
2

Define what's in it

Either upload a manifest CSV (path,split,label,include) or use the inline editor to flip the include flag on individual files.
3

Run a scan against it

The branch shows as a selector on the New scan screen. Pick it and Antidote scans only the branch’s manifest.
4

Promote or discard

Branches can be promoted to become the dataset’s main view, or deleted without affecting any other branch.

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Lineage

The lineage view is a directed graph showing where a dataset came from and what was produced from it.
  • Healing produces a child dataset with a healed_from edge.
  • Branching produces a child with a branched_from edge.
  • Playground publishes produce a child with a playground_session edge.
  • Imports keep a reference to the original source_uri.
Auditors typically ask “where did this training corpus come from?” and lineage is the single answer. Compliance reports include the same graph automatically.

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What to expect

  • Dataset uploads under ~5 GB usually appear within seconds.
  • Larger uploads stream progress via the WebSocket and resume cleanly if the network drops.
  • Statistics (valid_images, invalid_images, classes, class_count) populate as soon as ingestion finishes; you don’t have to wait for a scan to see them.