{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:U2UVQ32KEQYC5XZEDWMB27W4PZ","short_pith_number":"pith:U2UVQ32K","schema_version":"1.0","canonical_sha256":"a6a9586f4a24302edf241d981d7edc7e7a8382d1358b4eb7ced475885f4132f5","source":{"kind":"arxiv","id":"2508.02271","version":2},"attestation_state":"computed","paper":{"title":"Dynaword: From One-shot to Continuously Developed Datasets","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Andrea Blasi N\\'u\\~nez, Bal\\'azs Szab\\'o, Desmond Elliott, Gianluca Barmina, Jacob Nielsen, Jan Kostkan, Johan Heinsen, Kenneth Enevoldsen, Kirten Vad, Kristian N{\\o}rgaard Jensen, Kristoffer Nielbo, Lukas Galke, M\\'arton Kardos, Per M{\\o}ldrup Dalum, Peter Schneider-Kamp, Peter Vahlstrup, Rasmus Larsen","submitted_at":"2025-08-04T10:30:42Z","abstract_excerpt":"Large-scale datasets are foundational for research and development in natural language processing. However, current approaches face three key challenges: (1) reliance on ambiguously licensed sources restricting use, sharing, and derivative works; (2) static dataset releases that prevent community contributions and diminish longevity; and (3) quality assurance processes restricted to publishing teams rather than leveraging community expertise.\n  To address these limitations, we introduce two contributions: the Dynaword approach and Danish Dynaword. The Dynaword approach is a framework for creat"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2508.02271","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-08-04T10:30:42Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d8f14331ebbf345efdc7425bac209dcea96bb69254af095f2c9406f334a02c32","abstract_canon_sha256":"08b79275b628f1841ca43a087b19ccb98360cc96e3b31610220b37b7464ae050"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:48:28.749950Z","signature_b64":"JHFSOdSDNBBLMA4qeSfQR04wdWVlwqiqGl/T3iTaHcfn90GsjwVd+ovyC36J5iR4ybS3BiVrD866VcmldnKuBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a6a9586f4a24302edf241d981d7edc7e7a8382d1358b4eb7ced475885f4132f5","last_reissued_at":"2026-07-05T11:48:28.749124Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:48:28.749124Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dynaword: From One-shot to Continuously Developed Datasets","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Andrea Blasi N\\'u\\~nez, Bal\\'azs Szab\\'o, Desmond Elliott, Gianluca Barmina, Jacob Nielsen, Jan Kostkan, Johan Heinsen, Kenneth Enevoldsen, Kirten Vad, Kristian N{\\o}rgaard Jensen, Kristoffer Nielbo, Lukas Galke, M\\'arton Kardos, Per M{\\o}ldrup Dalum, Peter Schneider-Kamp, Peter Vahlstrup, Rasmus Larsen","submitted_at":"2025-08-04T10:30:42Z","abstract_excerpt":"Large-scale datasets are foundational for research and development in natural language processing. However, current approaches face three key challenges: (1) reliance on ambiguously licensed sources restricting use, sharing, and derivative works; (2) static dataset releases that prevent community contributions and diminish longevity; and (3) quality assurance processes restricted to publishing teams rather than leveraging community expertise.\n  To address these limitations, we introduce two contributions: the Dynaword approach and Danish Dynaword. The Dynaword approach is a framework for creat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.02271","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2508.02271/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2508.02271","created_at":"2026-07-05T11:48:28.749209+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.02271v2","created_at":"2026-07-05T11:48:28.749209+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.02271","created_at":"2026-07-05T11:48:28.749209+00:00"},{"alias_kind":"pith_short_12","alias_value":"U2UVQ32KEQYC","created_at":"2026-07-05T11:48:28.749209+00:00"},{"alias_kind":"pith_short_16","alias_value":"U2UVQ32KEQYC5XZE","created_at":"2026-07-05T11:48:28.749209+00:00"},{"alias_kind":"pith_short_8","alias_value":"U2UVQ32K","created_at":"2026-07-05T11:48:28.749209+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.06286","citing_title":"LLMs Can Leak Training Data But Do They Want To? A Propensity-Aware Evaluation of Memorization in LLMs","ref_index":20,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/U2UVQ32KEQYC5XZEDWMB27W4PZ","json":"https://pith.science/pith/U2UVQ32KEQYC5XZEDWMB27W4PZ.json","graph_json":"https://pith.science/api/pith-number/U2UVQ32KEQYC5XZEDWMB27W4PZ/graph.json","events_json":"https://pith.science/api/pith-number/U2UVQ32KEQYC5XZEDWMB27W4PZ/events.json","paper":"https://pith.science/paper/U2UVQ32K"},"agent_actions":{"view_html":"https://pith.science/pith/U2UVQ32KEQYC5XZEDWMB27W4PZ","download_json":"https://pith.science/pith/U2UVQ32KEQYC5XZEDWMB27W4PZ.json","view_paper":"https://pith.science/paper/U2UVQ32K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.02271&json=true","fetch_graph":"https://pith.science/api/pith-number/U2UVQ32KEQYC5XZEDWMB27W4PZ/graph.json","fetch_events":"https://pith.science/api/pith-number/U2UVQ32KEQYC5XZEDWMB27W4PZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/U2UVQ32KEQYC5XZEDWMB27W4PZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/U2UVQ32KEQYC5XZEDWMB27W4PZ/action/storage_attestation","attest_author":"https://pith.science/pith/U2UVQ32KEQYC5XZEDWMB27W4PZ/action/author_attestation","sign_citation":"https://pith.science/pith/U2UVQ32KEQYC5XZEDWMB27W4PZ/action/citation_signature","submit_replication":"https://pith.science/pith/U2UVQ32KEQYC5XZEDWMB27W4PZ/action/replication_record"}},"created_at":"2026-07-05T11:48:28.749209+00:00","updated_at":"2026-07-05T11:48:28.749209+00:00"}