AI Systems · Software and Coding
Data, Datasets, and Algorithms
Data is represented material that a computing system can store, communicate, or process. A dataset is an identified collection of data. An algorithm is a defined procedure for transforming input into output. These roles can work together, but they are not interchangeable.
Sources & review
Evidence and review
How Atlas supports this record.
Atlas uses original Kinesema wording. These public sources support narrow parts of the record; they are not endorsements or proof of claims outside the roles shown below.
- Review
- Human reviewed
- AI assistance
- Yes—human reviewed
- Last reviewed
- 2026-08-23
Data — Glossary
National Institute of Standards and Technology, Computer Security Resource Center
- Used here for
- Data can be represented for communication, interpretation, processing, storage, or transmission; the exact term remains scoped to its identified source context.
- Source use
- Paraphrased from source
- Source type
- official terminology index pointing to named source publications
Algorithm, in Dictionary of Algorithms and Data Structures
National Institute of Standards and Technology
- Used here for
- An algorithm can be described as a computable set of steps intended to achieve a result, separately from one concrete source-code file or implementation.
- Source use
- Paraphrased from source
- Source type
- official technical dictionary entry; entry modified 2020
Data Catalog Vocabulary (DCAT) — Version 3
World Wide Web Consortium
- Used here for
- A dataset is a conceptual collection of data that can be distinguished from its distributions and data services and described with identifiers, versions, provenance, and access metadata.
- Source use
- Paraphrased from source
- Source type
- W3C Recommendation
This is a concise first-release record, not a complete course. Examples, terminology, and reviewed connections can grow through later public projections.
Follow the structure
Connected concepts
These are reviewed Atlas relationships, not suggestions inferred from page order or visual proximity. Open a boundary only when you want the extra detail.
Connected from
Computing and AI Systems
AI systems use computing components alongside data and algorithms. These elements can participate in one system, but none of them alone defines the complete system.
Connection boundary
This relationship does not claim that every computing system uses AI or that every AI system uses the same data or algorithmic design.
Connects to
Model Training and Inference
Training can use data, an objective, and a learning procedure to develop or adjust a model. Inference then uses a model and input in a different role.
Connection boundary
This relationship does not assess whether a dataset is suitable, representative, lawful, private, or sufficient for a particular model.
Connects to
Retrieval and Evaluation
Retrieval looks through an identified collection, while evaluation can use recorded cases, expectations, and observations to check bounded behavior.
Connection boundary
This relationship does not prove that a collection is complete, that retrieved material is relevant, or that an evaluation method fits every use.
Connected from
Parsers and Structured Transformation
Parsing uses algorithms to recognize or organize input, while schemas and data structures can define the forms that the parser accepts and produces.
Connection boundary
This relationship does not make every algorithm a parser or establish that accepted input is complete, safe, meaningful, or correct.