MARKET RESEARCH

Hugging Face dataset-card monitor to Notion research tracker

Watches the Hugging Face Hub on a schedule for newly published or updated dataset cards matching your research domain.

CategoryMarket Research
Enginesim
Difficultybeginner
Triggerschedule
Steps5
Setup~5 min

How it runs

The automated pipeline, trigger to output.

  • TriggerDaily schedule fires
  • ActionList recent datasets by domain tagsHugging FaceHugging Face
  • LogicDrop already-tracked and incomplete cards
  • ActionRead dataset card for license, size, modalityHugging FaceHugging Face
  • OutputCreate Notion tracker rowNotionNotion

What it does

Keeps a living Notion table of every new dataset card on the Hugging Face Hub that fits a research domain you define (for example "clinical NLP" or "satellite imagery"). Each run pulls fresh listings, filters by your keyword and modality rules, and writes a clean row per dataset so your team has one canonical, searchable backlog instead of scattered Hub bookmarks.

When to use it

Use it when a research or data team needs to stay current on relevant open datasets without anyone manually browsing the Hub. Ideal for literature-review prep, benchmark sourcing, or maintaining a curated dataset inventory.

How it works

  1. 1A daily schedule fires the workflow.
  2. 2The Hugging Face step lists datasets sorted by last-modified, filtered to your domain tags and search terms.
  3. 3A logic step drops anything already seen (matched against existing Notion rows) and skips cards missing a license or with zero downloads.
  4. 4For each new match, an action reads the dataset card to extract size, modality, and license.
  5. 5The final output creates a Notion database row with name, link, license, modality, and first-seen date.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect Hugging FaceModels, datasets, spaces — the open-source hub.
  2. 2
    Connect NotionPages, databases, comments.
  3. 3
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  4. 4
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  5. 5
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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