{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:DBHPGAZE6Z5X2A2HZRQXRO6B76","short_pith_number":"pith:DBHPGAZE","schema_version":"1.0","canonical_sha256":"184ef30324f67b7d0347cc6178bbc1ff9d55c7573589dc9b4b1fd6f61014328b","source":{"kind":"arxiv","id":"1907.10121","version":1},"attestation_state":"computed","paper":{"title":"SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python","license":"http://creativecommons.org/licenses/by/4.0/","headline":"SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language.","cross_cats":["cs.DS","cs.SE","physics.comp-ph"],"primary_cat":"cs.MS","authors_text":"Andrew R. J. Nelson, Anne M. Archibald, Ant\\^onio H. Ribeiro, Charles R Harris, CJ Carey, David Cournapeau, Denis Laxalde, E.A. Quintero, Eric Jones, Eric Larson, Eric W. Moore, Evgeni Burovski, Fabian Pedregosa, Ian Henriksen, \\.Ilhan Polat, Jake Vanderplas, Jonathan Bright, Josef Perktold, Joshua Wilson, K. Jarrod Millman, Matt Haberland, Matthew Brett, Nikolay Mayorov, Pauli Virtanen, Paul van Mulbregt, Pearu Peterson, Ralf Gommers, Robert Cimrman, Robert Kern, SciPy 1.0 Contributors, St\\'efan J. van der Walt, Travis E. Oliphant, Tyler Reddy, Warren Weckesser, Yu Feng","submitted_at":"2019-07-23T20:31:36Z","abstract_excerpt":"SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave "},"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":true,"formal_links_present":true},"canonical_record":{"source":{"id":"1907.10121","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.MS","submitted_at":"2019-07-23T20:31:36Z","cross_cats_sorted":["cs.DS","cs.SE","physics.comp-ph"],"title_canon_sha256":"fcecfd6110b0c388f348aa810e962b943a959b587774e969a4d742a86f84543f","abstract_canon_sha256":"4c22a7a708ede9d179b958502adfb7f41f76ee25335ed8f29464fa46ea30e589"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:38:14.996579Z","signature_b64":"1yYW512tokaky6VN5Zty+BaR+9Mm9MYJP1Kr0LSSCWorcxPGtYNiwd0Zw005PKf2lN/TnEupGyzCSy8KztMFCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"184ef30324f67b7d0347cc6178bbc1ff9d55c7573589dc9b4b1fd6f61014328b","last_reissued_at":"2026-05-17T23:38:14.995959Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:38:14.995959Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python","license":"http://creativecommons.org/licenses/by/4.0/","headline":"SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language.","cross_cats":["cs.DS","cs.SE","physics.comp-ph"],"primary_cat":"cs.MS","authors_text":"Andrew R. J. Nelson, Anne M. Archibald, Ant\\^onio H. Ribeiro, Charles R Harris, CJ Carey, David Cournapeau, Denis Laxalde, E.A. Quintero, Eric Jones, Eric Larson, Eric W. Moore, Evgeni Burovski, Fabian Pedregosa, Ian Henriksen, \\.Ilhan Polat, Jake Vanderplas, Jonathan Bright, Josef Perktold, Joshua Wilson, K. Jarrod Millman, Matt Haberland, Matthew Brett, Nikolay Mayorov, Pauli Virtanen, Paul van Mulbregt, Pearu Peterson, Ralf Gommers, Robert Cimrman, Robert Kern, SciPy 1.0 Contributors, St\\'efan J. van der Walt, Travis E. Oliphant, Tyler Reddy, Warren Weckesser, Yu Feng","submitted_at":"2019-07-23T20:31:36Z","abstract_excerpt":"SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The assumption that the usage statistics, contributor counts, and described capabilities accurately reflect the library state at the time of writing and that the overview covers the most relevant recent technical developments without significant omissions.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"SciPy 1.0 documents a mature open-source library that has become the de facto standard for scientific algorithms in Python with broad adoption across research projects.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"fbfcf9b17d8cbc5ca03abd7758f8f563146c74747c27953333fbe8fc268a0a06"},"source":{"id":"1907.10121","kind":"arxiv","version":1},"verdict":{"id":"7db035c6-60cd-4754-94b1-51a81fadd3c9","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-17T05:20:09.072861Z","strongest_claim":"SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year.","one_line_summary":"SciPy 1.0 documents a mature open-source library that has become the de facto standard for scientific algorithms in Python with broad adoption across research projects.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The assumption that the usage statistics, contributor counts, and described capabilities accurately reflect the library state at the time of writing and that the overview covers the most relevant recent technical developments without significant omissions.","pith_extraction_headline":"SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language."},"references":{"count":160,"sample":[{"doi":"","year":2006,"title":"Oliphant, T. E. Guide to NumPy (Trelgol Publishing USA, 2006), 1st edn. URL https://oez.es/Guide%20to% 20NumPy.pdf","work_id":"663a58d9-df00-4ae8-ab63-30b9902fe901","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1109/mcse.2011.37","year":2011,"title":"C., & Varoquaux, G","work_id":"6ac013b7-e1e0-4982-995d-48f2126aee46","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2011,"title":"Pedregosa, F. et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011). URL http://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf","work_id":"d6eaac20-1065-4516-9653-2a2bc9838676","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2014,"title":"van der Walt, S. et al. scikit-image: image processing in Python. PeerJ 2, e453 (2014). URL https://doi.org/10. 7717/peerj.453","work_id":"caa9142c-ab3f-4cf3-9e9f-495256f7c7bc","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.5281/zenodo","year":2018,"title":"Nitz, A. et al. gwastro/pycbc: PyCBC v1.13.2 release (2018). URL https://doi.org/10.5281/zenodo. 1596771","work_id":"a2be0426-0f15-4d3d-a62d-9b21d5ec8960","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":160,"snapshot_sha256":"17557ece5772b30b2452d51a8038ddb7f72fa2b2500d78a256d621fce59b5797","internal_anchors":2},"formal_canon":{"evidence_count":2,"snapshot_sha256":"0467eff9416e299bed90e7e126fbbc51ef376f2fdffb566fd5bc00f2f15363c5"},"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":"1907.10121","created_at":"2026-05-17T23:38:14.996050+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.10121v1","created_at":"2026-05-17T23:38:14.996050+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.10121","created_at":"2026-05-17T23:38:14.996050+00:00"},{"alias_kind":"pith_short_12","alias_value":"DBHPGAZE6Z5X","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_16","alias_value":"DBHPGAZE6Z5X2A2H","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_8","alias_value":"DBHPGAZE","created_at":"2026-05-18T12:33:15.570797+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":32,"internal_anchor_count":32,"sample":[{"citing_arxiv_id":"2209.07472","citing_title":"A $(D_\\tau,D_x)$-manifold with $N$-correlators of $N_t$-objects","ref_index":57,"is_internal_anchor":true},{"citing_arxiv_id":"2501.14864","citing_title":"Super-Kamiokande Strongly Constrains Leptophilic Dark Matter Capture in the Sun","ref_index":106,"is_internal_anchor":true},{"citing_arxiv_id":"2509.03458","citing_title":"Comparison of Halo Model and Simulation Predictions for Projected-Field Kinematic Sunyaev-Zel'dovich Cross-Correlations","ref_index":46,"is_internal_anchor":true},{"citing_arxiv_id":"2605.21276","citing_title":"Benchmarking a machine-learning differential equations solver on a neutral-atom logical processor","ref_index":76,"is_internal_anchor":true},{"citing_arxiv_id":"2605.21294","citing_title":"Optical Super-orbital Modulation of SMC X-1: Disk Precession and a Revised Pulsar Mass","ref_index":60,"is_internal_anchor":true},{"citing_arxiv_id":"2605.16540","citing_title":"ULYSSES the Third: An Odyssey Towards a Unified Python Toolkit for Leptogenesis","ref_index":54,"is_internal_anchor":true},{"citing_arxiv_id":"2605.17318","citing_title":"RooAgent: An LLM Agent for Root-Based High Energy Physics Analysis","ref_index":36,"is_internal_anchor":true},{"citing_arxiv_id":"2605.18615","citing_title":"Primordial power spectrum reconstructions from BOSS + eBOSS","ref_index":104,"is_internal_anchor":true},{"citing_arxiv_id":"2605.19326","citing_title":"Constraints on the Crystallinity of Water Ice in Planet-forming Disks from Infrared Scattered-Light Spectra","ref_index":65,"is_internal_anchor":true},{"citing_arxiv_id":"2506.22363","citing_title":"Binary black holes in the heat of merger","ref_index":61,"is_internal_anchor":true},{"citing_arxiv_id":"2404.03001","citing_title":"DESI 2024 IV: Baryon Acoustic Oscillations from the Lyman Alpha Forest","ref_index":64,"is_internal_anchor":true},{"citing_arxiv_id":"2507.13432","citing_title":"INTEGRAL, eROSITA and Voyager Constraints on Light Bosonic Dark Matter: ALPs, Dark Photons, Scalars, $B-L$ and $L_{i}-L_{j}$ Vectors","ref_index":202,"is_internal_anchor":true},{"citing_arxiv_id":"2507.23663","citing_title":"Disentangling spinning and nonspinning binary black hole populations with spin sorting","ref_index":82,"is_internal_anchor":true},{"citing_arxiv_id":"2508.10987","citing_title":"A massive and evolved slow-rotating galaxy in the early Universe","ref_index":69,"is_internal_anchor":true},{"citing_arxiv_id":"2508.21216","citing_title":"Persistence of post-Newtonian amplitude structure in binary black hole mergers","ref_index":70,"is_internal_anchor":true},{"citing_arxiv_id":"2601.21432","citing_title":"Cosmological analysis of the DESI DR1 Lyman alpha 1D power spectrum","ref_index":103,"is_internal_anchor":true},{"citing_arxiv_id":"2311.12098","citing_title":"Union Through UNITY: Cosmology with 2,000 SNe Using a Unified Bayesian Framework","ref_index":129,"is_internal_anchor":true},{"citing_arxiv_id":"2010.14529","citing_title":"Tests of General Relativity with Binary Black Holes from the second LIGO-Virgo Gravitational-Wave Transient Catalog","ref_index":277,"is_internal_anchor":true},{"citing_arxiv_id":"2605.11703","citing_title":"GW240925 and GW250207: Astrophysical Calibration of Gravitational-wave Detectors","ref_index":180,"is_internal_anchor":true},{"citing_arxiv_id":"2605.03947","citing_title":"Effects of magnetically driven shocks on nucleosynthesis and kilonovae from neutron star mergers","ref_index":93,"is_internal_anchor":true},{"citing_arxiv_id":"2605.10768","citing_title":"Unitaria: Quantum Linear Algebra via Block Encodings","ref_index":39,"is_internal_anchor":true},{"citing_arxiv_id":"2112.06861","citing_title":"Tests of General Relativity with GWTC-3","ref_index":282,"is_internal_anchor":true},{"citing_arxiv_id":"2605.03205","citing_title":"From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry","ref_index":171,"is_internal_anchor":true},{"citing_arxiv_id":"2604.21010","citing_title":"Gravity Echoes from Supermassive Black Hole Binaries","ref_index":78,"is_internal_anchor":true},{"citing_arxiv_id":"2604.19922","citing_title":"Measuring neutrino mass and asymmetry through galaxy pairwise peculiar velocity","ref_index":53,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":2,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DBHPGAZE6Z5X2A2HZRQXRO6B76","json":"https://pith.science/pith/DBHPGAZE6Z5X2A2HZRQXRO6B76.json","graph_json":"https://pith.science/api/pith-number/DBHPGAZE6Z5X2A2HZRQXRO6B76/graph.json","events_json":"https://pith.science/api/pith-number/DBHPGAZE6Z5X2A2HZRQXRO6B76/events.json","paper":"https://pith.science/paper/DBHPGAZE"},"agent_actions":{"view_html":"https://pith.science/pith/DBHPGAZE6Z5X2A2HZRQXRO6B76","download_json":"https://pith.science/pith/DBHPGAZE6Z5X2A2HZRQXRO6B76.json","view_paper":"https://pith.science/paper/DBHPGAZE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.10121&json=true","fetch_graph":"https://pith.science/api/pith-number/DBHPGAZE6Z5X2A2HZRQXRO6B76/graph.json","fetch_events":"https://pith.science/api/pith-number/DBHPGAZE6Z5X2A2HZRQXRO6B76/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DBHPGAZE6Z5X2A2HZRQXRO6B76/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DBHPGAZE6Z5X2A2HZRQXRO6B76/action/storage_attestation","attest_author":"https://pith.science/pith/DBHPGAZE6Z5X2A2HZRQXRO6B76/action/author_attestation","sign_citation":"https://pith.science/pith/DBHPGAZE6Z5X2A2HZRQXRO6B76/action/citation_signature","submit_replication":"https://pith.science/pith/DBHPGAZE6Z5X2A2HZRQXRO6B76/action/replication_record"}},"created_at":"2026-05-17T23:38:14.996050+00:00","updated_at":"2026-05-17T23:38:14.996050+00:00"}