DataTerminology.comNEW
A collection of related data, published or curated, organized for access, analysis, or model training.
DESCRIPTION
A dataset groups related data with shared context, structure, and documentation. It may be tabular, image, text, graph, or time-series. Metadata describes provenance, schema, quality, and licensing, enabling discovery and reuse. In analytics and ML, datasets are split into training, validation, and test sets. Versioning tracks changes and supports reproducible experiments. Publishing often follows standards like DCAT for interoperability. Access may be open, restricted, or governed by privacy policies. Datasets can be distributed as files, APIs, or queryable tables. They are core units of data products and catalogs.
ALTERNATIVE DEFINITIONS
Wikipedia
A data set is a collection of data.
Concise, general definition applicable across domains.
W3C
A dataset is a collection of data, published or curated by a single agent, and available for use.
Stresses publication/curation and availability, suited to open data ecosystems.
U.S. Government (resources.data.gov)
Dataset: A collection of data published or maintained by an agency, often presented in tabular form.
Public-sector framing linking datasets to agency stewardship and tabular distribution.
RELATED TERMS
Data Discovery Platform Metadata Open Dataset Training Data Test Set Data Versioning DCAT Data Product Governance Data Quality
COMPETING TERMS
RELATIONS
- Narrower: C4 Dataset COCO Dataset MATH Dataset Open Dataset Proprietary Dataset
- Referenced by: BIG-bench
KNOWLEDGE AREAS
Data Management Data Science Data Products Metadata Management Data Governance
TARGET AUDIENCE
Data Steward Data Scientist Data Analyst Data Engineer Data Governance Manager
EXTERNAL SOURCES
- Wikipedia - Data set
- W3C - DCAT standard for dataset metadata
- GitHub - Versioning large datasets with Git LFS
- Kaggle - Public datasets for ML practice
TOP QUESTIONS
What makes a Dataset high quality?
Clear schema, complete and accurate records, documented provenance, and known limitations. Validation metrics and data dictionaries help users trust the dataset.
Category: Requirements
How should Datasets be versioned?
Use semantic or timestamped versions, immutable storage, and changelogs. Record exact sources, transforms, and hashes to ensure reproducibility.
Category: Management
What metadata should accompany a Dataset?
Include title, description, owner, license, schema, update cadence, lineage, quality metrics, and access constraints.
Category: Requirements
How are Datasets split for machine learning?
Common splits are train, validation, and test, often stratified to preserve class balance and avoid leakage.
Category: Example
What are Dataset licensing considerations?
Choose licenses aligned with intended use (e.g., CC BY, ODbL). Ensure compliance with privacy and contractual obligations.
Category: Risk
Why use DCAT for Datasets?
DCAT standardizes metadata for discovery and interoperability across catalogs, improving findability and reuse.
Category: Relevancy
How do I handle sensitive data in a Dataset?
Apply minimization, anonymization or pseudonymization, access controls, and retention policies, documenting risks and approvals.
Category: Risk
What is a data dictionary for a Dataset?
It’s a reference listing fields, data types, definitions, and valid values, reducing ambiguity and misuse.
Category: Definition
How do I publish a Dataset for discoverability?
Host in a catalog with DCAT metadata, DOI assignment, and clear licensing. Provide APIs and example notebooks.
Category: GettingStarted