data
Let an agent handle data by type, source, quality, format, licensing and protection, supporting responsible use and privacy.
Research draft, second pass
A second pass drafted this model: the structure a model of this thing needs, and what is known about it in the world. The line under this one says how the second half was obtained - researched against sources, or recalled without web access, in which case nothing here was read anywhere and every claim is a lead to verify. Unreviewed either way.
written by Claude from model knowledge without web access - no source was read, every claim is a lead to verify
Researched by: Claude
Purpose and description
Let an agent handle data by type, source, quality, format, licensing and protection, supporting responsible use and privacy.
Representations of facts, measurements, observations or concepts in a form suitable for storage, processing and communication, such as numbers, text, images and records; personal data is regulated by data protection law.
What it is for: Analysis, decision making, research and services.
It can be describe data sources and formats; assess data quality; check licences and reuse rights; apply data protection principles.
Distinguishing features
Recorded facts or observations
Structured or unstructured
Quality varies
Personal data is protected
What it looks like
Not physical; stored in files, databases and records.
How it is recognised
Datasets, tables and records
Formats such as CSV and JSON
Information is data with meaning
Related models
is a kind of - category
is stored in - storage
is analysed by - method
is protected by - law
In practice
Families and kinds
structured data
unstructured data
personal data
open data
metadata
Standards and regulation
GDPR and other data protection laws
FAIR data principles
Open data licences
Failure modes and hazards
Privacy breaches
Poor quality data leading to wrong decisions
Unlicensed reuse
Where this came from
wikidata · CC0 1.0
Also registered as vr.tr.data
Drafted structure
Bundle to layer to finding to question, as the second pass will find it: 4 bundles · 8 layers · 8 findings · 16 questions.
Source Where data comes from.
Provenance matters.
Origin
Collection.
Origin
Data origin.
- Who collected the data, when and how? provenance
- Is the collection method documented? boundary
Format
Structure.
Format
Format.
- What format and schema does the data use? definition
- Is metadata provided? boundary
Quality Fitness for use.
Quality must be checked.
Assess
Completeness and accuracy.
Assess
Quality assessment.
- How complete, accurate and current is the data? measurement
- What biases might it contain? boundary
Cleaning
Preparation.
Cleaning
Data cleaning.
- What cleaning steps are needed? action
- Are they documented? boundary
Rights Licences and privacy.
Rights constrain use.
Licence
Reuse.
Licence
Licensing.
- Under which licence can the data be reused? provenance
- How must it be attributed? action
Privacy
Personal data.
Privacy
Personal data.
- Does the data include personal data, and on what lawful basis is it processed? boundary
- How can people exercise their data rights? action
Use Analysis and sharing.
Use responsibly.
Analysis
Methods.
Analysis
Analysis.
- Which analysis suits this data? action
- Are conclusions supported? boundary
Sharing
FAIR principles.
Sharing
Data sharing.
- How can the data be shared following FAIR principles? action
- Is anonymisation sufficient? boundary
What the second pass must settle
- Should data types be separate entries?
- How should licences be linked?
- How should metadata standards be linked?