{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:JKUCEWZNPSDQYRVS5JC6EMLXDS","short_pith_number":"pith:JKUCEWZN","schema_version":"1.0","canonical_sha256":"4aa8225b2d7c870c46b2ea45e231771c9ddaef5be7a55c781cdc43f9cf39ceac","source":{"kind":"arxiv","id":"1912.09536","version":1},"attestation_state":"computed","paper":{"title":"Data Science through the looking glass and what we found there","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.DC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Avrilia Floratou, Bojan Karlas, Carlo Curino, Ce Zhang, Fotis Psallidas, Konstantinos Karanasos, Markus Weimer, Matteo Interlandi, Subru Krishnan, Wentao Wu, Yiwen Zhu","submitted_at":"2019-12-19T20:29:44Z","abstract_excerpt":"The recent success of machine learning (ML) has led to an explosive growth both in terms of new systems and algorithms built in industry and academia, and new applications built by an ever-growing community of data science (DS) practitioners. This quickly shifting panorama of technologies and applications is challenging for builders and practitioners alike to follow. In this paper, we set out to capture this panorama through a wide-angle lens, by performing the largest analysis of DS projects to date, focusing on questions that can help determine investments on either side. Specifically, we do"},"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":"1912.09536","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-12-19T20:29:44Z","cross_cats_sorted":["cs.DC","stat.ML"],"title_canon_sha256":"eaac6ece4f4e6554a02993cea79b15a6ba06e7bb33637402d310cca9db8295bc","abstract_canon_sha256":"361b285aa88ac00195591f7f37dba0917e599875b75f96bbdf9989049196cbb0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:27:35.279707Z","signature_b64":"EDegO/i9Bfpk8bI+KU3K01bZ9tkvT7ePOLkyRb7DaD0GVuKozrH74N+FUPyAvE7e4PtSPDOmJQiahinqpTGKDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4aa8225b2d7c870c46b2ea45e231771c9ddaef5be7a55c781cdc43f9cf39ceac","last_reissued_at":"2026-07-05T00:27:35.279241Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:27:35.279241Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Data Science through the looking glass and what we found there","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.DC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Avrilia Floratou, Bojan Karlas, Carlo Curino, Ce Zhang, Fotis Psallidas, Konstantinos Karanasos, Markus Weimer, Matteo Interlandi, Subru Krishnan, Wentao Wu, Yiwen Zhu","submitted_at":"2019-12-19T20:29:44Z","abstract_excerpt":"The recent success of machine learning (ML) has led to an explosive growth both in terms of new systems and algorithms built in industry and academia, and new applications built by an ever-growing community of data science (DS) practitioners. This quickly shifting panorama of technologies and applications is challenging for builders and practitioners alike to follow. In this paper, we set out to capture this panorama through a wide-angle lens, by performing the largest analysis of DS projects to date, focusing on questions that can help determine investments on either side. Specifically, we do"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.09536","kind":"arxiv","version":1},"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/1912.09536/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":"1912.09536","created_at":"2026-07-05T00:27:35.279298+00:00"},{"alias_kind":"arxiv_version","alias_value":"1912.09536v1","created_at":"2026-07-05T00:27:35.279298+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.09536","created_at":"2026-07-05T00:27:35.279298+00:00"},{"alias_kind":"pith_short_12","alias_value":"JKUCEWZNPSDQ","created_at":"2026-07-05T00:27:35.279298+00:00"},{"alias_kind":"pith_short_16","alias_value":"JKUCEWZNPSDQYRVS","created_at":"2026-07-05T00:27:35.279298+00:00"},{"alias_kind":"pith_short_8","alias_value":"JKUCEWZN","created_at":"2026-07-05T00:27:35.279298+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JKUCEWZNPSDQYRVS5JC6EMLXDS","json":"https://pith.science/pith/JKUCEWZNPSDQYRVS5JC6EMLXDS.json","graph_json":"https://pith.science/api/pith-number/JKUCEWZNPSDQYRVS5JC6EMLXDS/graph.json","events_json":"https://pith.science/api/pith-number/JKUCEWZNPSDQYRVS5JC6EMLXDS/events.json","paper":"https://pith.science/paper/JKUCEWZN"},"agent_actions":{"view_html":"https://pith.science/pith/JKUCEWZNPSDQYRVS5JC6EMLXDS","download_json":"https://pith.science/pith/JKUCEWZNPSDQYRVS5JC6EMLXDS.json","view_paper":"https://pith.science/paper/JKUCEWZN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1912.09536&json=true","fetch_graph":"https://pith.science/api/pith-number/JKUCEWZNPSDQYRVS5JC6EMLXDS/graph.json","fetch_events":"https://pith.science/api/pith-number/JKUCEWZNPSDQYRVS5JC6EMLXDS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JKUCEWZNPSDQYRVS5JC6EMLXDS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JKUCEWZNPSDQYRVS5JC6EMLXDS/action/storage_attestation","attest_author":"https://pith.science/pith/JKUCEWZNPSDQYRVS5JC6EMLXDS/action/author_attestation","sign_citation":"https://pith.science/pith/JKUCEWZNPSDQYRVS5JC6EMLXDS/action/citation_signature","submit_replication":"https://pith.science/pith/JKUCEWZNPSDQYRVS5JC6EMLXDS/action/replication_record"}},"created_at":"2026-07-05T00:27:35.279298+00:00","updated_at":"2026-07-05T00:27:35.279298+00:00"}