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Dataset Contents:  100 unique natural movie clipsDuration: 2.5 seconds per clipFrame rate: 60 Hz (150 frames per clip)Spatial resolution: 72×72 pixelsTotal stimulus duration: 250 secondsFormat: MATLAB (.mat) files  Each stimulus file contains:  clip_frames: Three-dimensional array (5184, 150, clip_index)  First dimension: Flattened spatial information (72×72 = 5,184 pixels)Second dimension: Temporal frames (150 frames at 60 Hz)Third dimension: Clip identifierData type: uint8 (0-255 grayscale intensity values)  time: One-dimensional array (1, 150)  Temporal timestamps for each frameData type: float64Units: seconds (0.0 to 2.5)  Reconstructing 2D Frames:To convert the flattened spatial format back to 2D images:  MATLAB: frame_2D = reshape(clip_frames(:, frame_idx, clip_idx), 72, 72);Python: frame_2D = clip_frames[:, frame_idx, clip_idx].reshape(72, 72)"],"publicationDate":"2026-01-07","publisher":"Zenodo","embargoEndDate":null,"sources":null,"formats":null,"contributors":["Zoi, Stefania"],"coverages":null,"bestAccessRight":{"code":"c_abf2","label":"OPEN","scheme":"http://vocabularies.coar-repositories.org/documentation/access_rights/"},"container":null,"documentationUrls":null,"codeRepositoryUrl":null,"programmingLanguage":null,"contactPeople":null,"contactGroups":null,"tools":null,"size":null,"version":null,"geoLocations":null,"id":"doi_dedup___::1710a8a2d401cd3a56ee70342e8976e6","originalIds":["50|datacite____::1710a8a2d401cd3a56ee70342e8976e6","10.5281/zenodo.18174694","50|od______2659::1710a8a2d401cd3a56ee70342e8976e6","oai:zenodo.org:18174694","50|datacite____::fde1308e60d70d3179e2956e148fb6f9","10.5281/zenodo.18174693"],"pids":[{"scheme":"doi","value":"10.5281/zenodo.18174694"},{"scheme":"doi","value":"10.5281/zenodo.18174693"}],"dateOfCollection":null,"lastUpdateTimeStamp":null,"indicators":{"citationImpact":{"citationCount":0.0,"influence":2.2251732E-9,"popularity":2.7165745E-9,"impulse":0.0,"citationClass":"C5","influenceClass":"C5","impulseClass":"C5","popularityClass":"C5"}},"projects":[{"id":"corda_____he::68d692c07dc1e53ed7c4a186e87651e6","code":"101119924","acronym":"BE-LIGHT","title":"BE-LIGHT Improving BiomEdical diagnosis through LIGHT-based technologies and machine learning","funder":"European Commission","pids":[{"scheme":"doi","value":"10.3030/101119924"}]}],"organizations":[{"legalName":"Sorbonne Université","acronym":"Sorbonne Université","id":"ror_________::f96a072a73f381c44ad394ad0a589878","pids":[{"scheme":"ROR","value":"https://ror.org/02en5vm52"},{"scheme":"fundref","value":"501100005737"},{"scheme":"fundref","value":"501100019125"},{"scheme":"fundref","value":"100012946"},{"scheme":"GRID","value":"grid.462844.8"},{"scheme":"ISNI","value":"0000000123081657"},{"scheme":"wikidata","value":"Q41497113"},{"scheme":"wikidata","value":"Q3491150"},{"scheme":"wikidata","value":"Q546118"},{"scheme":"wikidata","value":"Q1144549"}]}],"communities":[{"code":"eosc","label":"EOSC","provenance":null}],"collectedFrom":[{"key":"openaire____::9e3be59865b2c1c335d32dae2fe7b254","value":"Datacite"},{"key":"opendoar____::358aee4cc897452c00244351e4d91f69","value":"ZENODO"}],"instances":[{"pids":[{"scheme":"doi","value":"10.5281/zenodo.18174694"}],"license":"CC BY","type":"Dataset","urls":["https://dx.doi.org/10.5281/zenodo.18174694"],"publicationDate":"2026-01-07","refereed":"nonPeerReviewed","hostedBy":{"key":"opendoar____::358aee4cc897452c00244351e4d91f69","value":"ZENODO"},"collectedFrom":{"key":"openaire____::9e3be59865b2c1c335d32dae2fe7b254","value":"Datacite"}},{"pids":[{"scheme":"doi","value":"10.5281/zenodo.18174694"}],"alternateIdentifiers":[{"scheme":"oai","value":"oai:zenodo.org:18174694"}],"license":"CC BY","accessRight":{"code":"c_abf2","label":"OPEN","scheme":"http://vocabularies.coar-repositories.org/documentation/access_rights/","openAccessRoute":null},"type":"Dataset","urls":["https://zenodo.org/records/18174694","http://dx.doi.org/10.5281/zenodo.18174694"],"publicationDate":"2026-01-07","refereed":"nonPeerReviewed","hostedBy":{"key":"opendoar____::358aee4cc897452c00244351e4d91f69","value":"ZENODO"},"collectedFrom":{"key":"opendoar____::358aee4cc897452c00244351e4d91f69","value":"ZENODO"}},{"pids":[{"scheme":"doi","value":"10.5281/zenodo.18174693"}],"license":"CC BY","type":"Dataset","urls":["https://dx.doi.org/10.5281/zenodo.18174693"],"publicationDate":"2026-01-07","refereed":"nonPeerReviewed","hostedBy":{"key":"opendoar____::358aee4cc897452c00244351e4d91f69","value":"ZENODO"},"collectedFrom":{"key":"openaire____::9e3be59865b2c1c335d32dae2fe7b254","value":"Datacite"}}],"links":[{"header":{"relationType":"resultProject","relationClass":"isProducedBy","relatedIdentifier":"corda_____he::68d692c07dc1e53ed7c4a186e87651e6","relatedRecordType":"project","relationProvenance":"sysimport:crosswalk:repository","trust":"0.9"},"collectedfrom":[{"dsId":"openaire____::3f264f93cf3b0cfc4ede188a6300455c","dsName":"CORDA - 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