{
 "row": "data-provenance-initiative",
 "address": "Data Provenance Initiative",
 "addresses": [
  "Data Provenance Initiative"
 ],
 "extracted": "2026-10-29",
 "extractor": "Claude (Opus 5.5), subagent, one extractor (unaudited, §3.8)",
 "rule": "v0.9 §3.5, source-neutral: opening, defined terms, headline claims, stated limits; quotes verbatim from the saved page texts (whitespace-normalised for the check; multi-part quotes joined by ' … ', each part checked in order), or the Wikipedia response texts; 47/47 verified by script; third-party pages quoted only in short spans",
 "B_note": "The field B (§0.4, §3.1(a)), origins kept apart: 'card', every card on the transcript's rail (B1 the Initiative's home page, B2 the arXiv abstract page, B3 MIT Sloan, B4 Nature Machine Intelligence), all fetched; 'own_page', the census's own-page sources (B5, the repository README, copied from the census's saved copy; the paper, the census's other source, is card B2, the same bytes, carried once as a card) and the Initiative's About page (B7), read under the task's instruction to fetch dataprovenance.org and not cited by the census; 'census_third_party', none (the census read no third-party source for the entity); 'wikipedia', the title, without an article (B6). None unreachable. B1 and B7 are script-rendered: their text is from the site's script bundle, saved.",
 "lineage_note": "Lineages: what it is (B1, B3, B5, B7); its extent now (B1, B7); the term (B1, B2, B4); the founding audit (B2, B3, B4, B5); the Explorer, Collection and Card (B2, B3, B4, B5); the four systems (B1); the later audits (B1, B3); licence findings (B2, B3, B4); attribution requested (B4); the commercial divide and the non-commercial spike (B1, B2, B4); the language skew (B3, B4); the risks (B3); stated limits (B4, B5, B7); reception (B1, B6). Where a census field restates a field source's quote it is one lineage with that source (PE-02, PE-07, PE-08, PE-10, PE-12, PE-15, PE-16, PE-21: same_lineage_as in the archive ledger).",
 "pages": "saved files under datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/ (Bn.html raw, Bn.txt text: for the Initiative's script-rendered pages the served HTML's title and meta description, then the string literals of the route's component in site-main.af19d7fc.js; B5 the census's copy; B6 .wiki, .html, -search.html raw responses with a .txt summary of the HTTP codes and notices); sha256 per file in each source's 'files'",
 "field": [
  {
   "id": "B1",
   "origin": "card",
   "role": "Overview card 1; also the census record's url (https://www.dataprovenance.org/)",
   "card": "Data Provenance Initiative — Data Provenance Initiative",
   "url": "https://www.dataprovenance.org/",
   "kind": "Overview card (own page)",
   "surfaced_by_T": [
    {
     "slug": "data-provenance-initiative-aio-20261029",
     "card": 1
    }
   ],
   "files": {
    "B1.html": {
     "path": "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/B1.html",
     "sha256": "bf609f385257c3a179dc564d68d8ec46b2f096a34f61918cc7d750b83f6d7e2f"
    },
    "B1.txt": {
     "path": "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/B1.txt",
     "sha256": "86ba9a3ecee7633459623ad0b59674ab5ccb5a0035dc912a8bfa1d8d934c8b92"
    },
    "site-main.af19d7fc.js": {
     "path": "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/site-main.af19d7fc.js",
     "sha256": "91de539c0ec5317faec490142648792d586b0ee21e82a88f5d7feba90a0898e9"
    }
   },
   "fetched": {
    "at": "2026-10-29T17:06:17Z",
    "http": 200,
    "url": "https://www.dataprovenance.org/",
    "raw": "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/B1.html",
    "raw_sha256": "bf609f385257c3a179dc564d68d8ec46b2f096a34f61918cc7d750b83f6d7e2f",
    "text": [
     "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/B1.txt"
    ],
    "bundle": {
     "url": "https://www.dataprovenance.org/static/js/main.af19d7fc.js",
     "at": "2026-10-29T17:06:31Z",
     "http": 200,
     "sha256": "91de539c0ec5317faec490142648792d586b0ee21e82a88f5d7feba90a0898e9"
    }
   },
   "card_title": "Data Provenance Initiative",
   "card_snippet": "Platforms: Data Provenance Initiative (maps AI datasets, web policies, model markets, real-world use). Metrics: 4,000+ audited datasets; 14,000 tracked web doma...",
   "card_note": null,
   "names_operator": "yes ('Data Provenance Initiative', 'DPI' in the saved text)",
   "names_archive": "no (none of the Crimson Hexagonal Archive, Alexanarch, Lee Sharks, provenanceerasure, EA-WG-CAPTURES-01 in the saved text, card title and snippet)",
   "note": "The served HTML is the site's script shell (title, meta description, empty root; 1,601 bytes, sha256 bf609f38…, the same shell the data-provenance-explorer row saved at every path). The page text is the string literals of Cr, the / route's component, with the constants it maps, in /static/js/main.af19d7fc.js (fetched verbatim, saved as site-main.af19d7fc.js, sha256 91de539c…, identical to the bundle the data-provenance-explorer row saved), offsets 1862261–1876490, extracted in source order (CSS-like literals and bare years dropped). What dataprovenance.org says (the task's question): four public measurement systems (Data Provenance Explorer, Web Consent Explorer, Open Model Evolution, The AI Observatory) and a metrics strip '4,000+ / datasets audited', '14,000 / web domains tracked', '851k / open models measured', '23,158 / AI conversations annotated' (constant br). The card's 'Metrics: 4,000+ audited datasets; 14,000 tracked web domains' is that strip reworded; its 'Platforms: … (maps AI datasets, web policies, model markets, real-world use)' is the hero line F2."
  },
  {
   "id": "B2",
   "origin": "card",
   "role": "Overview card 2; also the census's own-page source data-provenance-initiative--paper (same URL, same sha256)",
   "card": "arXiv — [2310.16787] The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI",
   "url": "https://arxiv.org/abs/2310.16787",
   "kind": "Overview card (the operator's paper)",
   "surfaced_by_T": [
    {
     "slug": "data-provenance-initiative-aio-20261029",
     "card": 2
    }
   ],
   "files": {
    "B2.html": {
     "path": "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/B2.html",
     "sha256": "d01552052066b391aeed8b50adde61897cb194ea14727553cbf65971af51bb53"
    },
    "B2.txt": {
     "path": "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/B2.txt",
     "sha256": "0063d0167cbd2144f01306ebc2139a2447a859c49a653dbc65c58f2f13c6a6a8"
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   },
   "fetched": {
    "at": "2026-10-29T17:06:19Z",
    "http": 200,
    "url": "https://arxiv.org/abs/2310.16787",
    "raw": "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/B2.html",
    "raw_sha256": "d01552052066b391aeed8b50adde61897cb194ea14727553cbf65971af51bb53",
    "text": [
     "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/B2.txt"
    ],
    "census_copy": {
     "src": "data-provenance-initiative--paper",
     "read": "2026-10-29",
     "sha256": "d01552052066b391aeed8b50adde61897cb194ea14727553cbf65971af51bb53",
     "same_bytes": true
    }
   },
   "card_title": "[2310.16787] The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI",
   "card_snippet": "Audit: 1800+ text datasets traced and audited by legal and machine learning experts to evaluate licensing, lineage, and attribution in AI training. Findings: Co...",
   "card_note": null,
   "names_operator": "yes (title; 'Shayne Longpre and 16 other authors')",
   "names_archive": "no",
   "note": "Fetched as the card; the bytes are identical to the census's saved copy (sha256 d0155205…), so the card and the census's own page are one source, carried once, as a card."
  },
  {
   "id": "B3",
   "origin": "card",
   "role": "Overview card 3",
   "card": "MIT Sloan — Bringing transparency to the data used to train artificial intelligence | MIT Sloan",
   "url": "https://mitsloan.mit.edu/ideas-made-to-matter/bringing-transparency-to-data-used-to-train-artificial-intelligence",
   "kind": "Overview card (third-party article; Sara Brown, 2025-03-03)",
   "surfaced_by_T": [
    {
     "slug": "data-provenance-initiative-aio-20261029",
     "card": 3
    }
   ],
   "files": {
    "B3.html": {
     "path": "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/B3.html",
     "sha256": "563661c73ddce481dd825a948f5c3afa657f017607a21a2e603427ab6ed1996f"
    },
    "B3.txt": {
     "path": "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/B3.txt",
     "sha256": "da84c05f68986b58e3fc26343b53743db0b5bcea2ad1b2b517aeda710a4fb6ea"
    }
   },
   "fetched": {
    "at": "2026-10-29T17:08:07Z",
    "http": 200,
    "url": "https://mitsloan.mit.edu/node/49214",
    "effective_url": "https://mitsloan.mit.edu/ideas-made-to-matter/bringing-transparency-to-data-used-to-train-artificial-intelligence",
    "raw": "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/B3.html",
    "raw_sha256": "563661c73ddce481dd825a948f5c3afa657f017607a21a2e603427ab6ed1996f",
    "text": [
     "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/B3.txt"
    ],
    "found_by": "WebSearch on the card's title and site (mitsloan.mit.edu/node/49214, which redirects to the article; the page's <title> is the card's title)"
   },
   "card_title": "Bringing transparency to the data used to train artificial intelligence | MIT Sloan",
   "card_snippet": "Problem: Untraced AI training datasets cause legal, copyright, regulatory, bias, and quality risks due to poor documentation and high license error rates (>50%)",
   "card_note": null,
   "names_operator": "yes ('the Data Provenance Initiative'; Pentland, Mahari, Longpre)",
   "names_archive": "no",
   "note": "MIT Sloan, Ideas Made to Matter, by Sara Brown, datePublished 2025-03-03 (the page's JSON-LD). The card's snippet ('Problem: Untraced AI training datasets cause legal, copyright, regulatory, bias, and quality risks due to poor documentation and high license error rates (>50%)') is a summary of ¶2 and the first 'key action', not a sentence of the page."
  },
  {
   "id": "B4",
   "origin": "card",
   "role": "Overview card 4",
   "card": "Nature — A large-scale audit of dataset licensing and attribution in AI | Nature Machine Intelligence",
   "url": "https://www.nature.com/articles/s42256-024-00878-8",
   "kind": "Overview card (the operator's journal paper, open access)",
   "surfaced_by_T": [
    {
     "slug": "data-provenance-initiative-aio-20261029",
     "card": 4
    }
   ],
   "files": {
    "B4.html": {
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     "sha256": "766eb441fb2e78dbad63ca8676edc561bf24da0d7157ce18fda4df0234dc1220"
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    "B4.txt": {
     "path": "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/B4.txt",
     "sha256": "e9b2e293b13212b16dfadf85aff1892f0d264c909576c62cd064d40df1d9e4d1"
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   },
   "fetched": {
    "at": "2026-10-29T17:08:12Z",
    "http": 200,
    "url": "https://www.nature.com/articles/s42256-024-00878-8",
    "effective_url": "https://www.nature.com/articles/s42256-024-00878-8?error=cookies_not_supported&code=fbf2fb11-d6b3-4433-8b88-c13b3c109760",
    "raw": "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/B4.html",
    "raw_sha256": "766eb441fb2e78dbad63ca8676edc561bf24da0d7157ce18fda4df0234dc1220",
    "text": [
     "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/B4.txt"
    ],
    "found_by": "WebSearch on the card's title (DOI 10.1038/s42256-024-00878-8 via OpenAlex); the page's <title> is the card's title"
   },
   "card_title": "A large-scale audit of dataset licensing and attribution in AI | Nature Machine Intelligence",
   "card_snippet": "Audit: 1,858 text/finetuning datasets across 44 collections evaluated for licensing, attribution, and lineage via the Data Provenance Initiative and DPExplorer.",
   "card_note": null,
   "names_operator": "yes (the paper's 'data provenance initiative'; authors Longpre … Hooker)",
   "names_archive": "no",
   "note": "Nature Machine Intelligence 6, 975–987, published 30 August 2024 (received 15 February 2024); full text served."
  },
  {
   "id": "B5",
   "origin": "own_page",
   "role": "the census's own-page source data-provenance-initiative--repo-readme (basis operator); not surfaced by T",
   "card": "Data Provenance Initiative (GitHub) — Data-Provenance-Collection README.md",
   "url": "https://raw.githubusercontent.com/Data-Provenance-Initiative/Data-Provenance-Collection/main/README.md",
   "kind": "own page (repository README, markdown)",
   "surfaced_by_T": "no (not surfaced by T; admitted beside the cards by the operator's ruling of 2026-10-29)",
   "files": {
    "B5.html": {
     "path": "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/B5.html",
     "sha256": "db4c35a5f4f2b17a14680fc1c6854a6f36f82a930c6dfffa4f0297b5ea7dd4f1"
    },
    "B5.txt": {
     "path": "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/B5.txt",
     "sha256": "db4c35a5f4f2b17a14680fc1c6854a6f36f82a930c6dfffa4f0297b5ea7dd4f1"
    }
   },
   "fetched": {
    "census_copy": true,
    "src": "data-provenance-initiative--repo-readme",
    "read": "2026-10-29",
    "http": "200",
    "sha256": "db4c35a5f4f2b17a14680fc1c6854a6f36f82a930c6dfffa4f0297b5ea7dd4f1",
    "raw": "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/B5.html",
    "raw_sha256": "db4c35a5f4f2b17a14680fc1c6854a6f36f82a930c6dfffa4f0297b5ea7dd4f1",
    "text": [
     "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/B5.txt"
    ],
    "note": "copied from /home/claude/fleet/provenance-erasure/data/provenance-census/sources/data-provenance-initiative--repo-readme.html.gz (gunzipped; sha256 matches the census's) and .txt; not refetched (addendum §3)"
   },
   "card_title": null,
   "card_snippet": null,
   "card_note": null,
   "names_operator": "yes",
   "names_archive": "no",
   "note": "The README is markdown; B5.txt is the census's text copy (identical bytes to the raw)."
  },
  {
   "id": "B6",
   "origin": "wikipedia",
   "role": "the entity's Wikipedia title",
   "card": "Wikipedia — Data Provenance Initiative (no article)",
   "url": "https://en.wikipedia.org/wiki/Data_Provenance_Initiative",
   "kind": "Wikipedia (no article)",
   "surfaced_by_T": "no (not surfaced by T; checked by the operator's ruling of 2026-10-29)",
   "files": {
    "B6.wiki": {
     "path": "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/B6.wiki",
     "sha256": "f92e16be9fa948f544a2bc138b25dacc6eef5cb5b320497e1e4103057b07b9b2"
    },
    "B6.html": {
     "path": "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/B6.html",
     "sha256": "64f4d986f0e9aa6273b7bd72742a84affdbd64b51b8df152690684f86ba8050c"
    },
    "B6-search.html": {
     "path": "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/B6-search.html",
     "sha256": "43e59e395580b5d884df4155e6c24b540141e6f1019207775d5e54974dc07853"
    },
    "B6.txt": {
     "path": "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/B6.txt",
     "sha256": "ff30d99dd38cff9d7e80d164ebf0768cfd6d796728641ae5d959318ac842bbd4"
    }
   },
   "fetched": {
    "raw": {
     "url": "https://en.wikipedia.org/w/index.php?title=Data_Provenance_Initiative&action=raw&redirect=no",
     "at": "2026-10-29T17:06:20Z",
     "http": 404,
     "sha256": "f92e16be9fa948f544a2bc138b25dacc6eef5cb5b320497e1e4103057b07b9b2"
    },
    "rendered": {
     "url": "https://en.wikipedia.org/wiki/Data_Provenance_Initiative",
     "at": "2026-10-29T17:06:38Z",
     "http": 404,
     "sha256": "64f4d986f0e9aa6273b7bd72742a84affdbd64b51b8df152690684f86ba8050c",
     "retries": [
      {
       "at": "2026-10-29T17:06:21Z",
       "http": 429
      }
     ]
    },
    "search": {
     "url": "https://en.wikipedia.org/w/index.php?search=%22Data+Provenance+Initiative%22&title=Special:Search&profile=advanced&fulltext=1&ns0=1",
     "at": "2026-10-29T17:06:22Z",
     "http": 200,
     "sha256": "43e59e395580b5d884df4155e6c24b540141e6f1019207775d5e54974dc07853"
    },
    "text": [
     "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/B6.txt"
    ]
   },
   "card_title": null,
   "card_snippet": null,
   "card_note": null,
   "names_operator": "no",
   "names_archive": "no",
   "note": "no article, no redirect; the full-text search for the quoted phrase in article space returns no results (the same state the data-provenance-explorer row recorded for this title earlier the same day)"
  },
  {
   "id": "B7",
   "origin": "own_page",
   "role": "the entity's About page on the census record's url host (dataprovenance.org/about); not cited by the census's sources; read under the task's instruction to fetch dataprovenance.org; not surfaced by T",
   "card": "Data Provenance Initiative — About",
   "url": "https://www.dataprovenance.org/about",
   "kind": "own page (script-rendered route)",
   "surfaced_by_T": "no (not surfaced by T)",
   "files": {
    "B7.html": {
     "path": "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/B7.html",
     "sha256": "bf609f385257c3a179dc564d68d8ec46b2f096a34f61918cc7d750b83f6d7e2f"
    },
    "B7.txt": {
     "path": "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/B7.txt",
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    },
    "site-main.af19d7fc.js": {
     "path": "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/site-main.af19d7fc.js",
     "sha256": "91de539c0ec5317faec490142648792d586b0ee21e82a88f5d7feba90a0898e9"
    }
   },
   "fetched": {
    "at": "2026-10-29T17:10:15Z",
    "http": 200,
    "url": "https://www.dataprovenance.org/about",
    "raw": "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/B7.html",
    "raw_sha256": "bf609f385257c3a179dc564d68d8ec46b2f096a34f61918cc7d750b83f6d7e2f",
    "text": [
     "datasets/negative-of-the-negative/v2/intake/data-provenance-initiative-20261029/B7.txt"
    ],
    "bundle": {
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   },
   "card_title": null,
   "card_snippet": null,
   "card_note": null,
   "names_operator": "yes",
   "names_archive": "no",
   "note": "The served HTML is the same shell as B1; the text is the string literals of xh, the /about route's component, and what follows it to offset 2100100, before the contributors list (offsets 2090236–2100100). The contributors list that follows (not ledgered) names some fifty people, most of them at MIT, the others at universities and companies in some twenty countries."
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 ],
 "field_claims": {
  "F1": {
   "source": "B1",
   "origin": "card",
   "locus": "served HTML: meta description",
   "quote": "Public measurement infrastructure for AI data, model ecosystems, and real-world AI use.",
   "claim": "the Initiative's self-description: public measurement infrastructure for AI data, model ecosystems and real-world AI use",
   "modality": "self-description",
   "kind": "definition",
   "kernel": true,
   "const": false,
   "lineage": "its extent now: public measurement infrastructure for AI data, models and use",
   "date": "2026-10-29 (page undated; as fetched)",
   "quote_ok": true,
   "checked_against": "saved text B1.txt (sha256 86ba9a3ecee76334…), whitespace-normalised; parts joined by ' … ' each checked, in order"
  },
  "F2": {
   "source": "B1",
   "origin": "card",
   "locus": "home (Cr): hero",
   "quote": "Public evidence for accountable AI … DPI maps the datasets, web policies, model markets, and real-world AI uses that shape how AI systems are built and governed.",
   "claim": "what it does: maps the datasets, web policies, model markets and real-world uses that shape how AI is built and governed",
   "modality": "stipulation",
   "kind": "definition",
   "kernel": true,
   "const": true,
   "lineage": "its extent now: public measurement infrastructure for AI data, models and use",
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   "quote": "Published: 30 August 2024 … Nature Machine Intelligence … volume 6, pages 975–987 (2024)",
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   "quote": "In this work, we term the combination of these indicators, including a dataset’s sourcing, creation and licensing heritage, as well as its characteristics, the ‘data provenance’.",
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   "quote": "Notably, we find that more than 70% of licences for popular datasets on GitHub and Hugging Face are ‘unspecified’ … our rigorous re-annotation of licences finds that 66% of analysed Hugging Face licences were in a different use category, often labelled as more permissive than the author’s original licence.",
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   "quote": "The Data Provenance Explorer (DPExplorer) (www.dataprovenance.org), an open-source repository for downloading, filtering and exploring data provenance and characteristics. Our tools auto-generate data provenance cards for scalable symbolic attribution and future documentation best practices.",
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   "quote": "Our work began with training data provenance: tracing datasets from origin to creation, cataloging sources, licenses, creators, and documentation practices. It now also covers web consent, open model markets, concentration of power, and how people use AI systems in practice.",
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   "locus": "/about: note",
   "quote": "We collect self-reported licenses from the papers and repositories that released these datasets and categorize them according to our best efforts as a volunteer research and transparency initiative. Information provided by our work and outputs of the Data Provenance Initiative does not constitute legal advice",
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  }
 },
 "field_sources": {
  "B1": "Data Provenance Initiative — Data Provenance Initiative (home)",
  "B2": "arXiv — [2310.16787] The Data Provenance Initiative: A Large Scale Audit of Dataset Licensing & Attribution in AI",
  "B3": "MIT Sloan — Bringing transparency to the data used to train artificial intelligence",
  "B4": "Nature Machine Intelligence — A large-scale audit of dataset licensing and attribution in AI",
  "B5": "Data Provenance Initiative — Data-Provenance-Collection README",
  "B6": "Wikipedia — Data Provenance Initiative (no article)",
  "B7": "Data Provenance Initiative — About"
 }
}