Dataset

Dataset identity and designation

The identifier set and human designation of the dataset: the authoritative internal identifier held by the master system, any governed persistent identifier such as a DOI or resolvable IRI, alternate identifiers, and multilingual title/description that name the asset for humans.

1. Which system is the master for this dataset's identifier, and what is the identifier it assigns? identity
2. Does a governed persistent identifier (DOI, Handle, ARK, resolvable IRI) exist, who registered it, and what does it resolve to? authority
3. Does the identifier denote the abstract dataset or one specific version of it? definition
4. Which alternate or legacy identifiers must be kept resolvable to avoid breaking existing references? interoperability
5. What title, description and language variants are authoritative for this dataset? definition

Catalogue record and listing state

The registration facet: which catalogue lists the dataset, when the record was created and modified, the record's own state (draft, listed, hidden, deleted) and the fact that record metadata changes independently of the dataset.

1. In which catalogue or register is this dataset listed, and under what record identifier? identity
2. What is the current listing state of the record, and who may change it? state
3. How do record creation and modification times differ from the dataset's own issued and modified times? temporal
4. Is the same dataset listed in more than one catalogue, and which listing is authoritative? relationship

Thematic classification and keywords

Controlled-vocabulary themes, keywords, resource type and any mandated classification codes that place the dataset in a discovery taxonomy.

1. Which controlled vocabularies are mandatory for themes in the governing profile, and which terms apply? classification
2. What general resource type best describes this asset, and does the target profile constrain the allowed values? classification
3. Which free-text keywords are maintained, in which languages, and who curates them? quality
4. Is the dataset designated under a regulatory category such as an EU high-value dataset thematic category? requirement

Observation unit and granularity

The population or universe covered, the unit of observation or analysis, the variables measured, and the resolution at which records are expressed.

1. What population or universe does the dataset claim to cover, and is coverage a census, a sample or opportunistic? definition
2. What is the unit of observation of one record, and does it differ from the unit of analysis? composition
3. Which variables are measured, and by what measurement technique? measurement
4. What spatial and temporal resolution do the records carry, and what is the smallest reportable granularity? constraint

Temporal coverage and currency

The period the data describes, the reference date of the content, and the currency of the asset expressed through issued, modified and next-expected-update times.

1. What start and end instants does the data content cover, and is the interval open-ended? temporal
2. What is the reference date of the content, as distinct from the date the metadata was recorded? provenance
3. When was the dataset last updated and when is the next update expected? state
4. Which time zone or offset applies to the timestamps inside the data, and is it recorded explicitly? constraint

Spatial coverage and reference system

Geographic extent, named places, coordinate reference system and spatial resolution, applicable when the data has a location dimension.

1. What geographic extent does the dataset cover, expressed as named places, codes or a bounding geometry? spatial
2. Which coordinate reference system do the coordinates use, and is it stated by registry identifier? interoperability
3. What is the spatial resolution or smallest geographic unit released, and does disclosure control coarsen it? constraint
4. If the dataset has no spatial dimension, is that recorded explicitly rather than left empty? exception

Declared schema reference

The reference from the dataset to the schema, data dictionary or data contract it conforms to, with the version of that schema and whether conformance is asserted or verified.

1. Which schema, profile or data contract does the dataset declare conformance to, and at which version? composition
2. Is conformance asserted by the publisher or verified by a validator, and where is the evidence? evidence
3. Where is the field-level data dictionary published, and does it cover code lists and missing-value conventions? interoperability
4. How is divergence between the declared schema and the served data detected and reported? validation

Series and aggregate membership

Whether the dataset is an edition or slice of a continuing series, part of a larger aggregate, or a standalone asset, and the ordering relations among members.

1. Is this dataset a member of a dataset series, and what identifies the series? relationship
2. Which member precedes and follows this one, and on what ordering property? composition
3. Is a new release a new series member or a new version of the same dataset, and which rule decides? decision
4. Does the dataset aggregate identifiable parts or subsets that are themselves catalogued? composition

Distribution manifest and fixity

Each accessible form of the dataset - its media type and format, byte size, download and access URLs, compression or packaging profile, and the checksum that lets a consumer verify the bytes received.

1. Which distributions exist for this dataset, and what media type and format does each use? composition
2. What checksum algorithm and value is published for each distribution, and when was fixity last verified? validation
3. Is the distribution packaged in a container profile such as a data package or research object crate, and where is its descriptor? interoperability
4. Are the distributions semantically equivalent renderings of the same content, or do some carry reduced or transformed content? constraint
5. What byte size and record volume should a consumer plan for, and how volatile is that size? measurement

Access service and protocol

Endpoints that serve the dataset - APIs, query services, streams or bulk download points - with their protocol, declared conformance class, documentation and operational limits.

1. Which services serve this dataset, and what is the endpoint URL and description of each? composition
2. What protocol or API standard does each endpoint implement, and which conformance classes does it declare? interoperability
3. What rate limits, pagination, quota or payload limits apply to programmatic access? constraint
4. Is a bulk download path available alongside the API, as required for designated public-sector datasets? requirement

Access conditions and embargo

The access level of the asset, authentication and authorisation requirements, request and approval processes for restricted data, and time-bound embargoes.

1. What is the declared access level of this dataset, using the governing profile's vocabulary? access
2. What authentication and authorisation does each channel require, and who can approve a grant? security
3. If access is restricted, what is the request process, expected decision time and appeal route? process
4. Is an embargo in force, when does it lift, and what becomes available at that moment? temporal
5. When the data itself is restricted, does descriptive metadata remain publicly visible? exception

Licence and rights statement

The licence under which each distribution is offered, expressed by a governed licence identifier, together with the rights holder, attribution requirement and any separate rights statement for metadata versus data.

1. Which licence applies to each distribution, identified by a governed licence identifier rather than free text? authority
2. Who holds the rights being licensed, and on what basis do they hold them? ownership
3. What attribution text or citation must a re-user reproduce? requirement
4. Does the metadata carry a different licence from the data itself? constraint
5. Does a legal obligation constrain the licence choice, such as an open licence mandate for designated datasets? requirement

Use restrictions and machine-readable policy

Permissions, prohibitions and duties that a licence alone does not express: third-party rights, contractual limits, purpose limitations, redistribution and text-and-data-mining conditions.

1. Which actions are permitted, prohibited or subject to a duty, and for which assignee parties? constraint
2. Do third-party rights encumber part of the content, and which part? ownership
3. Are there purpose limitations, such as research-only use or a prohibition on training models? privacy
4. Is the policy expressed in a machine-readable form an agent can evaluate before use? interoperability

Holder, publisher and contact roles

The accountable data holder, the publisher that lists the dataset, creators or contributors, and the contact point through which questions and access requests are handled.

1. Which party is accountable for the content and service level of this dataset? ownership
2. How do the holder, publisher, creator and catalogue operator differ for this asset, and are any the same party? relationship
3. What contact point is published, and is it a durable role address rather than an individual? access
4. Who is the operational steward responsible for updates, quality checks and metadata upkeep? authority

Personal and sensitive data controls

Whether the dataset contains personal, confidential or otherwise sensitive content, what legal basis and protective measures apply, and what disclosure control has been performed.

1. Does the dataset contain personal or otherwise sensitive data, and which components carry it? privacy
2. On what legal basis or consent is the sensitive content processed and published? authority
3. What de-identification or statistical disclosure control has been applied, and with what residual risk? process
4. Could this dataset be combined with others to re-identify individuals, and is that risk documented? security
5. If special-category data is retained solely for bias detection, are the required safeguards documented? requirement

Source and derivation

The upstream datasets, systems or collection activities this dataset was derived from, the agent responsible, and the generation event that produced this version.

1. Which upstream datasets or sources was this dataset derived from, and at which of their versions? provenance
2. Which activity generated this version, when did it start and end, and which agent was responsible? event
3. To whom is the dataset attributed, as distinct from who published it? ownership
4. How far back does asset-level lineage go here before the question is delegated to the lineage model? composition

Production method and processing

How the data was collected or computed: collection mode, instruments, sampling procedure, processing and cleaning steps, and the assumptions those steps embed.

1. By what mode and instrument was the data collected or generated? process
2. Which preparation operations - cleaning, joining, labelling, aggregation - were applied, and in what order? process
3. What assumptions or design choices does the production method embed that a re-user must know? evidence
4. Can the production be re-executed, and what would be needed to reproduce this exact output? validation

Version identity and change record

How versions are identified and chained, what changed between them, whether a change is breaking, and which version is current.

1. What versioning scheme is used, and does a new version get a new persistent identifier? identity
2. Which version precedes this one, which supersedes it, and which is currently authoritative? relationship
3. What changed relative to the previous version, and is the change breaking for consumers? lifecycle
4. Which event triggers a new version rather than an in-place correction? decision

Release cadence and status

The declared update frequency, planned release calendar, punctuality against that plan, and the maturity or availability status of the asset.

1. What update frequency is declared, using which controlled vocabulary? temporal
2. How does actual release timing compare with the announced calendar? measurement
3. What is the current status of the asset - draft, in development, stable, deprecated - and who sets it? state
4. Is any part served in real time or near real time, and what latency is guaranteed? requirement

Deprecation and withdrawal

How a dataset is marked superseded or removed from service, what replaces it, what consumers are told, and what remains resolvable after removal.

1. Is the dataset deprecated or withdrawn, when did that take effect and on whose decision? lifecycle
2. What supersedes the withdrawn dataset, and how are consumers redirected? relationship
3. What does the tombstone record state, and does it keep the citation and identifier resolvable? evidence
4. How is removal signalled to downstream catalogues that previously harvested this record? interoperability

Retention, preservation and deletion

The retention rule governing how long the dataset and its distributions are kept, the preservation commitment, and how deletion is executed and evidenced without destroying the descriptive record.

1. Which retention rule applies, what is the retention period and what triggers its start? retention
2. What preservation commitment has the holder made, and for which distributions? requirement
3. When data is deleted, what is deleted, what metadata is retained, and where is the deletion evidenced? process
4. How are conflicts resolved between an erasure obligation and an archival or legal-hold obligation? exception

Quality measurements

Individual measurements of a quality metric computed on this dataset or distribution, each with its value, metric reference, dimension, method and measurement time, plus certificates or user feedback where they exist.

1. Which metric was computed, in which quality dimension, and what value resulted? measurement
2. What exactly was measured - the whole dataset, one distribution, or a subset? composition
3. When was the measurement computed, and against which dataset version? temporal
4. What threshold makes the result acceptable, and who set it? constraint
5. Do certificates or user feedback annotations exist alongside computed measurements? evidence

Fitness for use and known limitations

Statements of intended and discouraged uses, known caveats, coverage gaps, comparability breaks, and who made the fitness judgement for which purpose.

1. For which stated purpose has fitness been assessed, and what was the verdict? decision
2. What known limitations, caveats or coverage gaps must a re-user account for? quality
3. Which uses are explicitly discouraged or unsupported by the holder? constraint
4. Are there breaks in series or definitional changes that prevent comparison across time or units? temporal

ML readiness and training use

The durable documentation an ML consumer needs: record structure and field typing, label provenance and annotation process, representativeness and bias examination, and any restriction on using the dataset to train models.

1. Is the dataset described in a machine-readable ML format that resolves files to record sets and typed fields? interoperability
2. How were labels or annotations produced, by whom, and with what agreement or review? provenance
3. What is documented about representativeness relative to the target population and about examined biases? evidence
4. Is training a model on this dataset permitted, restricted or prohibited, and by which instrument? authority
5. Are canonical splits or benchmark subsets defined at asset level, or left to the consuming training run? composition

Application profile conformance and validation

Which application profiles the catalogue entry claims to satisfy, the obligations those profiles impose, the validation performed against them, and the mapping losses when projecting into each profile.

1. Which application profiles does this entry declare conformance to, at which versions? interoperability
2. Which properties are mandatory under each declared profile, and are all of them populated? requirement
3. What validation was run, against which rule set, and what were the violations? validation
4. What information is lost or coerced when projecting this entry into a given profile? constraint
5. Where two declared profiles disagree on a property's obligation or vocabulary, which prevails? exception

Harvesting and federation

How the entry is exchanged with other catalogues: source of truth, harvest datestamps and increments, deleted-record signalling, duplicate detection and the rules for locally overriding harvested values.

1. Was this entry authored locally or harvested, and from which source catalogue? provenance
2. Which datestamp drives incremental harvesting, and at what granularity is it exposed? temporal
3. What deleted-record support does the source declare, and how are removals propagated? lifecycle
4. May local edits override harvested values, and how is the divergence recorded and reconciled? decision
5. How are duplicate entries for the same dataset across federated catalogues detected and merged? identity

Citation and related identifiers

The citation form of the dataset and its typed relations to publications, software, source datasets, successors and other resources.

1. What is the recommended citation for this dataset, and which version does it pin? requirement
2. Which typed relations to other resources are asserted, using which relation-type vocabulary? relationship
3. Are related references qualified enough for a machine to know the direction and nature of each link? interoperability
4. Who must be credited in the citation, and how are contributor roles distinguished from creators? ownership