{ "model": { "rank": 6792, "code": "thing-q2374463", "model_id": "vr.tr.data-science", "name": "data science", "purpose": "Let an agent explain data science and its components, relay methods, tools and workflows from technical and academic sources, describe subfields and critical perspectives, and distinguish data science from statistics, machine learning, data engineering and business intelligence.", "family": "Thing Registry", "category": "Information and virtual systems", "status": "research-draft", "kind": "thing", "plane": "INF", "domain": "INF.KNW", "industry": "", "version": "", "url": "/models/thing/q2374463/", "tier": 2, "score": 58, "payload": { "layer": "wikidata", "aliases": [ "data engineering", "data feminism", "data visualisation and computational design", "geospatial data science", "spatial data science", "media intelligence", "data ethics", "data tribology", "responsible data science" ], "aliasCount": 9, "merged": 9, "knownIn": 58, "facets": null, "markers": [], "lexicalClass": "", "senseRank": null, "alsoRegisteredAs": null, "source": { "dataset": "wikidata", "item": "Q2374463", "url": "https://www.wikidata.org/wiki/Q2374463", "license": "CC0 1.0" } }, "research": { "vercy": "1.0-draft", "publication": { "status": "research-draft", "adjudicationStatus": "unreviewed", "publishableCanonical": false, "generatedAt": "2026-09-13T06:18:08Z", "providers": [ "Claude" ], "breadth": "written by Claude from model knowledge without web access - no source was read, every claim is a lead to verify", "pass": 2, "wave": 3, "engine": "claude" }, "metaModel": { "id": "THING-Q2374463", "registryId": "vr.tr.data-science", "name": "data science", "version": "0.2.0-wave.3", "entryKind": "thing", "family": "Thing Registry", "domain": [ "INF.KNW" ], "status": "research-draft" }, "canonicalUrl": "https://ver.cy/models/thing/q2374463/", "model": { "registry_id": "vr.tr.data-science", "name": "data science", "purpose": "Let an agent explain data science and its components, relay methods, tools and workflows from technical and academic sources, describe subfields and critical perspectives, and distinguish data science from statistics, machine learning, data engineering and business intelligence.", "definition": "An interdisciplinary field that uses statistics, computing, machine learning and domain knowledge to extract insight from data, encompassing data engineering that builds pipelines and infrastructure, analysis and modelling, data visualisation and computational design, spatial and geospatial data science applied to location data, media intelligence applied to news and social media, and critical approaches such as data feminism that examine power and bias; data science underpins decision-making in business, science and government and raises questions of privacy, fairness and reproducibility.", "what_it_is_for": "Extracting insight from data.", "affordances": [ "explain components and workflow", "relay methods and tools", "describe subfields and critiques", "distinguish related fields" ], "distinguishing_features": [ "Interdisciplinary", "Data-driven", "Computational scale", "Ethical questions" ], "appearance": "Not a visible object; code, models and visualisations.", "visual_identification": [ "Insight from data through statistics and computing", "Data engineering, visualisation and computational design, geospatial and spatial data science, media intelligence, data feminism", "Statistics is a foundation; machine learning is a method family; business intelligence is reporting-focused" ], "physical_properties": [ { "quantity": "term popularised", "typical_range": "2000s", "unit": "period", "note": "" }, { "quantity": "common languages", "typical_range": "Python, R, SQL", "unit": "list", "note": "" }, { "quantity": "workflow stages", "typical_range": "collection, cleaning, analysis, modelling, communication", "unit": "list", "note": "" } ], "families_and_kinds": [ "data engineering and pipelines", "exploratory analysis and statistics", "machine learning and predictive modelling", "data visualisation and computational design", "spatial and geospatial data science", "media intelligence and text analytics", "critical data studies including data feminism" ], "related_models": [ { "relation": "is a kind of", "target": "science", "why": "in registry terms" }, { "relation": "draws on", "target": "statistics", "why": "and computer science" }, { "relation": "uses", "target": "machine learning", "why": "for modelling" }, { "relation": "is contrasted with", "target": "business intelligence", "why": "reporting-focused analysis" } ], "identifiers": [], "standards_and_regulation": [ "Data protection laws such as the GDPR", "Research reproducibility and ethics guidelines", "Emerging AI and algorithmic accountability regulations" ], "failure_modes_and_hazards": [ "Biased data and models", "Privacy violations", "Overclaiming from correlations", "Confusing data science with its component fields" ], "in_scope": [], "out_of_scope": [], "characteristics": [] }, "sources": [], "structure": { "bundles": [ { "id": "understand", "name": "Understand", "description": "What data science is.", "rationale": "Definition.", "layers": [ { "id": "definition", "name": "Definition", "description": "Definition.", "findings": [ { "id": "definition-finding", "name": "Definition", "description": "Definition.", "questions": [ { "text": "What is data science, and how does it differ from statistics, machine learning, data engineering and business intelligence?", "kind": "definition" }, { "text": "Is the question about data science in general, a subfield, a tool or a specific project?", "kind": "boundary" } ] } ] }, { "id": "subfields", "name": "Subfields", "description": "Subfields.", "findings": [ { "id": "subfields-finding", "name": "Subfields", "description": "Subfields.", "questions": [ { "text": "What are data engineering, visualisation and computational design, geospatial data science, media intelligence and data feminism?", "kind": "definition" }, { "text": "Which entry fits the specific subfield?", "kind": "action" } ] } ] } ] }, { "id": "practice", "name": "Practice", "description": "Practice.", "rationale": "Practice.", "layers": [ { "id": "workflow", "name": "Workflow", "description": "Workflow.", "findings": [ { "id": "workflow-finding", "name": "Workflow", "description": "Workflow.", "questions": [ { "text": "How does a data science project run from data collection and cleaning to modelling and communication?", "kind": "action" }, { "text": "Which references are standard?", "kind": "provenance" } ] } ] }, { "id": "tools", "name": "Tools", "description": "Tools.", "findings": [ { "id": "tools-finding", "name": "Tools", "description": "Tools.", "questions": [ { "text": "What languages, libraries and platforms are used, and how are they chosen?", "kind": "provenance" }, { "text": "Which sources are cited?", "kind": "provenance" } ] } ] } ] }, { "id": "ethics", "name": "Ethics", "description": "Ethics and critique.", "rationale": "Attribution.", "layers": [ { "id": "ethics", "name": "Ethics", "description": "Privacy and fairness.", "findings": [ { "id": "ethics-finding", "name": "Ethics", "description": "Ethics.", "questions": [ { "text": "What privacy, fairness and accountability issues arise, and how are they addressed, with positions attributed?", "kind": "provenance" }, { "text": "Is the presentation neutral?", "kind": "boundary" } ] } ] }, { "id": "critique", "name": "Critique", "description": "Critical perspectives.", "findings": [ { "id": "critique-finding", "name": "Critique", "description": "Critique.", "questions": [ { "text": "What do data feminism and critical data studies argue about power in data, with positions attributed?", "kind": "provenance" }, { "text": "Which entry fits critical data studies?", "kind": "action" } ] } ] } ] }, { "id": "context", "name": "Context", "description": "Careers and history.", "rationale": "Context.", "layers": [ { "id": "careers", "name": "Careers", "description": "Careers.", "findings": [ { "id": "careers-finding", "name": "Careers", "description": "Careers.", "questions": [ { "text": "What roles exist in data science, and how do people train for them?", "kind": "provenance" }, { "text": "Which entry fits data scientist?", "kind": "action" } ] } ] }, { "id": "history", "name": "History", "description": "History.", "findings": [ { "id": "history-finding", "name": "History", "description": "History.", "questions": [ { "text": "How did data science emerge from statistics, databases and machine learning?", "kind": "provenance" }, { "text": "Which entry fits the history of data science?", "kind": "action" } ] } ] } ] } ] }, "openQuestions": [ "Should data engineering and geospatial data science be separate primary entries?", "How should technical and academic sources be linked?", "The registry entry has merged aliases naming subfields and a critical movement; should they be split off?" ], "statistics": { "bundles": 4, "layers": 8, "findings": 8, "questions": 16 } }, "draft": { "generator": "vr.draft.v3", "status": "draft-generated", "researched": false, "archetype": "discipline or field of knowledge", "method": "Written from the archetype playbook - what this kind of thing needs beyond identity and provenance - and from the structure that recurred across 6,333 models already researched by two engines. Applied to this entry by rule. No source was read for this thing and no claim here is researched. This entry carries no facets of its own, so they were inferred from its domain - a guess about a whole domain applied to one thing.", "facetsInferred": true, "nextPass": "A researcher replaces this draft with a sourced specification. Treat every sentence below as a proposal to argue with.", "purpose": "Give an agent a durable, checkable way to recognise a data science, record what state it is in, and decide what may be done with it.", "whatItIs": "Let an agent explain data science and its components, relay methods, tools and workflows from technical and academic sources, describe subfields and critical perspectives, and distinguish data science from statistics, machine learning, data engineering and business intelligence.", "characteristics": { "substance": "information", "origin": "conceptual", "agency": "inert" }, "whatYouCanDoWithIt": [ "read and interpreted" ], "distinguishingFeatures": [ "Names folded into this entry, which a task may need to split apart again: data engineering, data feminism, data visualisation and computational design, geospatial data science, spatial data science, media intelligence, data ethics, data tribology, responsible data science.", "9 finer distinctions are held as aliases rather than separate entries, because telling them apart needs a task that asks for it.", "Described in 58 Wikipedia languages, which is a measure of how widely the thing is known, not of how important it is." ], "openQuestionsForResearch": [ "Which of the bundles below does a real task actually need, and which are ceremony?", "What does this thing have that the facets do not capture at all?", "Which neighbouring kind is most often confused with a data science, and on what evidence are they told apart?" ], "whatItIsMadeOf": "content that has to be carried by something else", "physicalCharacter": [ "Does nothing on its own; everything it does, something else did to it.", "These come from the domain this entry sits in rather than from the entry itself, so treat them as a first guess about the whole domain applied to one thing." ], "whatCanBeDoneWithIt": [ "read it and act on what it says" ], "howItIsRecognised": [], "relatedModels": [ { "relation": "covers", "note": "Finer kinds folded into this entry because telling them apart needs a task that asks for it. Each is a model waiting to be split out when one does.", "targets": [ "data engineering", "data feminism", "data visualisation and computational design", "geospatial data science", "spatial data science", "media intelligence", "data ethics", "data tribology", "responsible data science" ] } ], "standing": "Described in 58 Wikipedia languages, which measures how widely it is written about rather than how important or how common it is. 9 finer distinctions are held inside this entry as names rather than as separate models.", "structure": { "bundles": [ { "id": "identity-and-classification", "name": "Identity, naming and classification", "description": "How an agent tells one data science from another, and a data science from things that resemble it.", "rationale": "Recognition comes before every other claim. Without stable identity nothing else in the model can be trusted to be about the same thing twice.", "layers": [ { "id": "naming-and-identifiers", "name": "Names and identifiers", "description": "The names this thing goes by and the identifiers that survive translation and time.", "findings": [ { "id": "preferred-name-and-aliases", "name": "Preferred name, aliases and local names", "description": "Which name to use, which names mean the same thing, and which merely sound similar.", "questions": [ { "id": "preferred-name-and-aliases-q01", "text": "What identifies and describes the name of a data science, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "preferred-name-and-aliases-q02", "text": "Who or what asserted this about the name of a data science, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "preferred-name-and-aliases-q03", "text": "What may an agent decide or do once the name of a data science is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] }, { "id": "stable-identifiers", "name": "Stable identifiers and external keys", "description": "Identifiers that keep pointing at this kind of thing across systems and languages.", "questions": [ { "id": "stable-identifiers-q01", "text": "What identifies and describes an identifier for a data science, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "stable-identifiers-q02", "text": "Who or what asserted this about an identifier for a data science, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "stable-identifiers-q03", "text": "What may an agent decide or do once an identifier for a data science is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] }, { "id": "classification-and-granularity", "name": "Classification and granularity", "description": "Where a data science sits among kinds, and how finely a task needs to cut it.", "findings": [ { "id": "kind-and-parents", "name": "Kind, parents and neighbouring kinds", "description": "The classes this thing belongs to and the ones it is next to.", "questions": [ { "id": "kind-and-parents-q01", "text": "What identifies and describes the kind of a data science, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "kind-and-parents-q02", "text": "Who or what asserted this about the kind of a data science, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "kind-and-parents-q03", "text": "What may an agent decide or do once the kind of a data science is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] }, { "id": "distinguishing-features", "name": "Distinguishing features", "description": "What separates a data science from the things most often confused with it.", "questions": [ { "id": "distinguishing-features-q01", "text": "What identifies and describes what distinguishes a data science, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "distinguishing-features-q02", "text": "Who or what asserted this about what distinguishes a data science, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "distinguishing-features-q03", "text": "What may an agent decide or do once what distinguishes a data science is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] } ] }, { "id": "state-and-lifecycle", "name": "State and lifecycle", "description": "The states a data science passes through and the events that move it between them.", "rationale": "Most decisions about a thing depend on what state it is in now, which is a claim with a time on it, not a property.", "layers": [ { "id": "lifecycle-stages", "name": "Lifecycle stages", "description": "From coming into existence to ceasing to be one of these.", "findings": [ { "id": "stages-and-transitions", "name": "Stages and transitions", "description": "The stages worth naming and what moves a data science between them.", "questions": [ { "id": "stages-and-transitions-q01", "text": "What identifies and describes the lifecycle of a data science, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "stages-and-transitions-q02", "text": "Who or what asserted this about the lifecycle of a data science, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "stages-and-transitions-q03", "text": "What may an agent decide or do once the lifecycle of a data science is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] }, { "id": "observations-and-status", "name": "Observations and current status", "description": "What is observed about a data science, how often and by whom.", "findings": [ { "id": "observation-record", "name": "Observation record", "description": "How an observation of a data science is recorded so that it can be superseded rather than overwritten.", "questions": [ { "id": "observation-record-q01", "text": "What identifies and describes an observation of a data science, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "observation-record-q02", "text": "Who or what asserted this about an observation of a data science, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "observation-record-q03", "text": "What may an agent decide or do once an observation of a data science is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] } ] }, { "id": "content-and-access", "name": "Content, encoding and access", "description": "What a data science says, how it is encoded and who may read it.", "rationale": "An informational thing carries content that can be copied, versioned and withheld, none of which its physical carrier explains.", "layers": [ { "id": "content-and-encoding", "name": "Content and encoding", "description": "The content itself, its format and its language.", "findings": [ { "id": "content-and-format", "name": "Content, format and language", "description": "What a data science contains and in what form it is held.", "questions": [ { "id": "content-and-format-q01", "text": "What identifies and describes the content of a data science, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "content-and-format-q02", "text": "Who or what asserted this about the content of a data science, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "content-and-format-q03", "text": "What may an agent decide or do once the content of a data science is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] }, { "id": "access-and-rights", "name": "Access, rights and retention", "description": "Who may read, copy or change it, and for how long it is kept.", "findings": [ { "id": "access-rules", "name": "Access rules and retention", "description": "The permissions attached to a data science and the period it survives.", "questions": [ { "id": "access-rules-q01", "text": "What identifies and describes access to a data science, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "access-rules-q02", "text": "Who or what asserted this about access to a data science, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "access-rules-q03", "text": "What may an agent decide or do once access to a data science is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] } ] }, { "id": "subject-and-method", "name": "Subject matter and method", "description": "What data science studies and how it establishes anything.", "rationale": "A field is identified by its questions and its methods, and those are what distinguish it from the fields next to it.", "layers": [ { "id": "subject", "name": "Subject matter", "description": "The questions it takes as its own.", "findings": [ { "id": "subject-record", "name": "Questions and objects of study", "description": "What it is about, stated so a neighbouring field can be told apart.", "questions": [ { "id": "subject-record-q01", "text": "What questions and objects does data science take as its own?", "kind": "definition" }, { "id": "subject-record-q02", "text": "Which questions does it share with a neighbouring field, and who claims them?", "kind": "boundary" } ] } ] }, { "id": "method", "name": "Method and evidence", "description": "How claims are established and what counts as evidence.", "findings": [ { "id": "method-record", "name": "Methods and standards of evidence", "description": "The methods used and what they are taken to establish.", "questions": [ { "id": "method-record-q01", "text": "By what methods does data science establish claims, and what counts as sufficient evidence?", "kind": "provenance" }, { "id": "method-record-q02", "text": "What kind of claim can this field not settle, and where should an agent look instead?", "kind": "action" } ] } ] } ] }, { "id": "institutions-and-classification", "name": "Institutions and classification", "description": "Where data science is practised and how catalogues place it.", "rationale": "The institutional footprint is the evidence that a field exists as a field rather than as a topic.", "layers": [ { "id": "institutions", "name": "Institutional markers", "description": "Societies, journals, degrees and departments.", "findings": [ { "id": "institution-record", "name": "Bodies and venues", "description": "Where the field organises itself.", "questions": [ { "id": "institution-record-q01", "text": "Which societies, journals or degrees mark data science as an established field?", "kind": "provenance" }, { "id": "institution-record-q02", "text": "What would show that it is emerging or dissolving rather than established?", "kind": "boundary" } ] } ] }, { "id": "classification", "name": "Classification schemes", "description": "Where library and research classifications put it.", "findings": [ { "id": "classification-record", "name": "Codes and their disagreements", "description": "The classification codes that place it, and where they differ.", "questions": [ { "id": "classification-record-q01", "text": "Which classification schemes place data science, under what codes?", "kind": "definition" }, { "id": "classification-record-q02", "text": "Where do those schemes disagree about its scope?", "kind": "boundary" } ] } ] } ] }, { "id": "provenance-and-evidence", "name": "Provenance, evidence and time", "description": "Where every claim about a data science came from and when it held.", "rationale": "A claim without a source and a time cannot be superseded, only overwritten, and an agent that overwrites loses the ability to explain itself.", "layers": [ { "id": "source-and-authority", "name": "Source and authority", "description": "Who said it, on what evidence, and how strongly.", "findings": [ { "id": "claim-provenance", "name": "Claim provenance and confidence", "description": "The authority behind each claim about a data science and how confident it is.", "questions": [ { "id": "claim-provenance-q01", "text": "What identifies and describes a claim about a data science, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "claim-provenance-q02", "text": "Who or what asserted this about a claim about a data science, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "claim-provenance-q03", "text": "What may an agent decide or do once a claim about a data science is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] }, { "id": "time-and-versions", "name": "Time, versions and supersession", "description": "When a claim was true, when it was learnt, and what replaced it.", "findings": [ { "id": "validity-and-supersession", "name": "Validity period and supersession", "description": "How an old claim about a data science is retired without being erased.", "questions": [ { "id": "validity-and-supersession-q01", "text": "What identifies and describes the validity of a claim about a data science, and in what units or vocabulary?", "kind": "definition", "answer_data": [ "identifiers", "types and classes", "values with units", "explicit unknowns" ] }, { "id": "validity-and-supersession-q02", "text": "Who or what asserted this about the validity of a claim about a data science, by which method, and when was it true?", "kind": "provenance", "answer_data": [ "authority", "method", "evidence", "event time", "knowledge time" ] }, { "id": "validity-and-supersession-q03", "text": "What may an agent decide or do once the validity of a claim about a data science is known, and what must it refuse?", "kind": "action", "answer_data": [ "permitted actions", "preconditions", "refusals", "escalation" ] } ] } ] } ] } ] }, "statistics": { "bundles": 6, "layers": 12, "findings": 14, "questions": 38 } } } }