{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:77GTC4AMXZ7E7TBO2EIRNLCDBR","short_pith_number":"pith:77GTC4AM","canonical_record":{"source":{"id":"1812.06492","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2018-12-16T15:55:08Z","cross_cats_sorted":[],"title_canon_sha256":"027f5060f8b87304f732009e8ec0f43bdc90049a1eba9e90c43de250867ac5ac","abstract_canon_sha256":"192d6eba22c331ba9a85ba87bbcfbf2bf1379eb0b75827a0fcc19cf164d32dbd"},"schema_version":"1.0"},"canonical_sha256":"ffcd31700cbe7e4fcc2ed11116ac430c51110abbee7a064e49e2bc82307aee84","source":{"kind":"arxiv","id":"1812.06492","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.06492","created_at":"2026-05-17T23:49:33Z"},{"alias_kind":"arxiv_version","alias_value":"1812.06492v2","created_at":"2026-05-17T23:49:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.06492","created_at":"2026-05-17T23:49:33Z"},{"alias_kind":"pith_short_12","alias_value":"77GTC4AMXZ7E","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"77GTC4AMXZ7E7TBO","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"77GTC4AM","created_at":"2026-05-18T12:32:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:77GTC4AMXZ7E7TBO2EIRNLCDBR","target":"record","payload":{"canonical_record":{"source":{"id":"1812.06492","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2018-12-16T15:55:08Z","cross_cats_sorted":[],"title_canon_sha256":"027f5060f8b87304f732009e8ec0f43bdc90049a1eba9e90c43de250867ac5ac","abstract_canon_sha256":"192d6eba22c331ba9a85ba87bbcfbf2bf1379eb0b75827a0fcc19cf164d32dbd"},"schema_version":"1.0"},"canonical_sha256":"ffcd31700cbe7e4fcc2ed11116ac430c51110abbee7a064e49e2bc82307aee84","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:33.039237Z","signature_b64":"3eTbuDValcwt4neny+cUxT5C6FAkO3A9JcSsVuTi+oP7HbXplq6SOBWjgbrRFqG/8nDwOpsWDwdKo/U1fX2oAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ffcd31700cbe7e4fcc2ed11116ac430c51110abbee7a064e49e2bc82307aee84","last_reissued_at":"2026-05-17T23:49:33.038748Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:33.038748Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1812.06492","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:49:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FfHsX6ocBhlrrpJjD+J3yUjPuIWLDsJwFz3elwLSd8WDzZijPdUmZxFQxY1aqn9MewLsojPGHxKiay1zBOw5Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T01:12:36.083281Z"},"content_sha256":"ec22d216333b0ebe3fc979f241c334d2f38bf9ca8c7c44684323a16e92e458ea","schema_version":"1.0","event_id":"sha256:ec22d216333b0ebe3fc979f241c334d2f38bf9ca8c7c44684323a16e92e458ea"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:77GTC4AMXZ7E7TBO2EIRNLCDBR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Performance Evaluation of Big Data Processing Strategies for Neuroimaging","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.DC","authors_text":"Shawn T Brown, Tristan Glatard, Val\\'erie Hayot-Sasson","submitted_at":"2018-12-16T15:55:08Z","abstract_excerpt":"Neuroimaging datasets are rapidly growing in size as a result of advancements in image acquisition methods, open-science and data sharing. However, the adoption of Big Data processing strategies by neuroimaging processing engines remains limited. Here, we evaluate three Big Data processing strategies (in-memory computing, data locality and lazy evaluation) on typical neuroimaging use cases, represented by the BigBrain dataset. We contrast these various strategies using Apache Spark and Nipype as our representative Big Data and neuroimaging processing engines, on Dell EMC's Top-500 cluster. Big"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.06492","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":""},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:49:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CRV3hvD1ODuIXRAh+RSuQhRKct7dV7HRhrFjFg+61do8pkq1Gvh+AOqJ6nvzCv/+IgwnaZHhlRILyNYtN68mBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T01:12:36.083647Z"},"content_sha256":"014ecfff7020da000381c013e1e40efc14f5ec77543e4051b249ab6ad39d93b9","schema_version":"1.0","event_id":"sha256:014ecfff7020da000381c013e1e40efc14f5ec77543e4051b249ab6ad39d93b9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/77GTC4AMXZ7E7TBO2EIRNLCDBR/bundle.json","state_url":"https://pith.science/pith/77GTC4AMXZ7E7TBO2EIRNLCDBR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/77GTC4AMXZ7E7TBO2EIRNLCDBR/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-04T01:12:36Z","links":{"resolver":"https://pith.science/pith/77GTC4AMXZ7E7TBO2EIRNLCDBR","bundle":"https://pith.science/pith/77GTC4AMXZ7E7TBO2EIRNLCDBR/bundle.json","state":"https://pith.science/pith/77GTC4AMXZ7E7TBO2EIRNLCDBR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/77GTC4AMXZ7E7TBO2EIRNLCDBR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:77GTC4AMXZ7E7TBO2EIRNLCDBR","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"192d6eba22c331ba9a85ba87bbcfbf2bf1379eb0b75827a0fcc19cf164d32dbd","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2018-12-16T15:55:08Z","title_canon_sha256":"027f5060f8b87304f732009e8ec0f43bdc90049a1eba9e90c43de250867ac5ac"},"schema_version":"1.0","source":{"id":"1812.06492","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.06492","created_at":"2026-05-17T23:49:33Z"},{"alias_kind":"arxiv_version","alias_value":"1812.06492v2","created_at":"2026-05-17T23:49:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.06492","created_at":"2026-05-17T23:49:33Z"},{"alias_kind":"pith_short_12","alias_value":"77GTC4AMXZ7E","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"77GTC4AMXZ7E7TBO","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"77GTC4AM","created_at":"2026-05-18T12:32:11Z"}],"graph_snapshots":[{"event_id":"sha256:014ecfff7020da000381c013e1e40efc14f5ec77543e4051b249ab6ad39d93b9","target":"graph","created_at":"2026-05-17T23:49:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Neuroimaging datasets are rapidly growing in size as a result of advancements in image acquisition methods, open-science and data sharing. However, the adoption of Big Data processing strategies by neuroimaging processing engines remains limited. Here, we evaluate three Big Data processing strategies (in-memory computing, data locality and lazy evaluation) on typical neuroimaging use cases, represented by the BigBrain dataset. We contrast these various strategies using Apache Spark and Nipype as our representative Big Data and neuroimaging processing engines, on Dell EMC's Top-500 cluster. Big","authors_text":"Shawn T Brown, Tristan Glatard, Val\\'erie Hayot-Sasson","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2018-12-16T15:55:08Z","title":"Performance Evaluation of Big Data Processing Strategies for Neuroimaging"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.06492","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:ec22d216333b0ebe3fc979f241c334d2f38bf9ca8c7c44684323a16e92e458ea","target":"record","created_at":"2026-05-17T23:49:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"192d6eba22c331ba9a85ba87bbcfbf2bf1379eb0b75827a0fcc19cf164d32dbd","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2018-12-16T15:55:08Z","title_canon_sha256":"027f5060f8b87304f732009e8ec0f43bdc90049a1eba9e90c43de250867ac5ac"},"schema_version":"1.0","source":{"id":"1812.06492","kind":"arxiv","version":2}},"canonical_sha256":"ffcd31700cbe7e4fcc2ed11116ac430c51110abbee7a064e49e2bc82307aee84","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ffcd31700cbe7e4fcc2ed11116ac430c51110abbee7a064e49e2bc82307aee84","first_computed_at":"2026-05-17T23:49:33.038748Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:49:33.038748Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3eTbuDValcwt4neny+cUxT5C6FAkO3A9JcSsVuTi+oP7HbXplq6SOBWjgbrRFqG/8nDwOpsWDwdKo/U1fX2oAQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:49:33.039237Z","signed_message":"canonical_sha256_bytes"},"source_id":"1812.06492","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ec22d216333b0ebe3fc979f241c334d2f38bf9ca8c7c44684323a16e92e458ea","sha256:014ecfff7020da000381c013e1e40efc14f5ec77543e4051b249ab6ad39d93b9"],"state_sha256":"e2e2e87ff8770fa9416ff75f78e317283f79542c4ee99868ccb66d49a1858066"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BalF+BLyQFAP9BWgZWmmSwJ7B17ft+mbXS55yjQK6QMphh7qoKaoC4P+6gmuPd5uyv7JHPfK7VLqwkaVPG5ZBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T01:12:36.085647Z","bundle_sha256":"165b69aa15e308ced71f2ec8fa9c2385153b124e60e431f8e3138f67ef1a21c1"}}