Subset: Composition
The questions in this section are intended to provide dataset consumers with the information they need to make informed decisions about using the dataset for their chosen tasks. Some of the questions are designed to elicit information about compliance with the EU’s General Data Protection Regulation (GDPR) or comparable regulations in other jurisdictions.
URI: Composition
Identifier and Mapping Information
Schema Source
- from schema: https://w3id.org/bridge2ai/data-sheets-schema
Classes in subset
| Class | Description |
|---|---|
| Confidentiality | Does the dataset contain data that might be considered confidential (e |
| ContentWarning | Does the dataset contain data that, if viewed directly, might be offensive, i... |
| DataAnomaly | Are there any errors, sources of noise, or redundancies in the dataset? If so... |
| Deidentification | Is it possible to identify individuals (i |
| ExternalResource | Is the dataset self-contained, or does it link to or otherwise rely on extern... |
| Instance | What do the instances that comprise the dataset represent (e |
| MissingInfo | Is any information missing from individual instances? If so, please provide a... |
| Relationships | Are relationships between individual instances made explicit (e |
| SamplingStrategy | Does the dataset contain all possible instances or is it a sample (not necess... |
| SensitiveElement | Does the dataset contain data that might be considered sensitive in any way (... |
| Splits | Are there recommended data splits (e |
| Subpopulation | Does the dataset identify any subpopulations (e |
Confidentiality
Does the dataset contain data that might be considered confidential (e.g., data that is protected by legal privilege or by doctor patient confidentiality, data that includes the content of individuals’ non-public communications)?
ContentWarning
Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If so, please describe why.
DataAnomaly
Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description.
Deidentification
Is it possible to identify individuals (i.e., one or more natural persons), either directly or indirectly (i.e., in combination with other data) from the dataset?
ExternalResource
Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)? If it links to or relies on external resources, a) are there guarantees that they will exist, and remain constant, over time; b) are there official archival versions of the complete dataset (i.e., including the external resources as they existed at the time the dataset was created); c) are there any restrictions (e.g., licenses, fees) associated with any of the external resources that might apply to a dataset consumer? Please provide descriptions of all external resources and any restrictions associated with them, as well as links or other access points, as appropriate.
Instance
What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)?
MissingInfo
Is any information missing from individual instances? If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text.
Relationships
Are relationships between individual instances made explicit (e.g., users’ movie ratings, social network links)? If so, please describe how these relationships are made explicit.
SamplingStrategy
Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not (e.g., to cover a more diverse range of instances, because instances were withheld or unavailable).
SensitiveElement
Does the dataset contain data that might be considered sensitive in any way (e.g., data that reveals race or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history)?
Splits
Are there recommended data splits (e.g., training, development/validation, testing)? If so, please provide a description of these splits, explaining the rationale behind them.
Subpopulation
Does the dataset identify any subpopulations (e.g., by age, gender)? If so, please describe how these subpopulations are identified and provide a description of their respective distributions within the dataset.