{"id":"https://openalex.org/W7114804969","doi":"https://doi.org/10.1145/3769526.3769634","title":"JONES-19: A Cultural Image Dataset Based on The Grammar of Ornament","display_name":"JONES-19: A Cultural Image Dataset Based on The Grammar of Ornament","publication_year":2025,"publication_date":"2025-09-25","ids":{"openalex":"https://openalex.org/W7114804969","doi":"https://doi.org/10.1145/3769526.3769634"},"language":null,"primary_location":{"id":"doi:10.1145/3769526.3769634","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3769526.3769634","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd International Conference on Culture and Computer Science: Remixing Analog and Digital","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3769526.3769634","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Linh Pham","orcid":"https://orcid.org/0009-0003-8484-1615"},"institutions":[{"id":"https://openalex.org/I33434090","display_name":"University of Massachusetts Boston","ror":"https://ror.org/04ydmy275","country_code":"US","type":"education","lineage":["https://openalex.org/I33434090"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Linh Pham","raw_affiliation_strings":["Harvard University, Allston (Boston), Massachusetts, USA"],"raw_orcid":"https://orcid.org/0009-0003-8484-1615","affiliations":[{"raw_affiliation_string":"Harvard University, Allston (Boston), Massachusetts, USA","institution_ids":["https://openalex.org/I33434090"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Stuart Shieber","orcid":"https://orcid.org/0000-0002-7733-8195"},"institutions":[{"id":"https://openalex.org/I33434090","display_name":"University of Massachusetts Boston","ror":"https://ror.org/04ydmy275","country_code":"US","type":"education","lineage":["https://openalex.org/I33434090"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Stuart Shieber","raw_affiliation_strings":["Harvard University, Allston (Boston), Massachusetts, USA"],"raw_orcid":"https://orcid.org/0000-0002-7733-8195","affiliations":[{"raw_affiliation_string":"Harvard University, Allston (Boston), Massachusetts, USA","institution_ids":["https://openalex.org/I33434090"]}]},{"author_position":"middle","author":{"id":null,"display_name":"David Alvarez-Melis","orcid":"https://orcid.org/0000-0002-9591-8986"},"institutions":[{"id":"https://openalex.org/I33434090","display_name":"University of Massachusetts Boston","ror":"https://ror.org/04ydmy275","country_code":"US","type":"education","lineage":["https://openalex.org/I33434090"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Alvarez-Melis","raw_affiliation_strings":["Harvard University, Allston (Boston), Massachusetts, USA"],"raw_orcid":"https://orcid.org/0000-0002-9591-8986","affiliations":[{"raw_affiliation_string":"Harvard University, Allston (Boston), Massachusetts, USA","institution_ids":["https://openalex.org/I33434090"]}]},{"author_position":"last","author":{"id":null,"display_name":"Alexandros Haridis","orcid":"https://orcid.org/0000-0003-0369-1428"},"institutions":[{"id":"https://openalex.org/I33434090","display_name":"University of Massachusetts Boston","ror":"https://ror.org/04ydmy275","country_code":"US","type":"education","lineage":["https://openalex.org/I33434090"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alexandros Haridis","raw_affiliation_strings":["Harvard University, Allston (Boston), Massachusetts, USA"],"raw_orcid":"https://orcid.org/0000-0003-0369-1428","affiliations":[{"raw_affiliation_string":"Harvard University, Allston (Boston), Massachusetts, USA","institution_ids":["https://openalex.org/I33434090"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I33434090"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.61860092,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12650","display_name":"Aesthetic Perception and Analysis","score":0.8305000066757202,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12650","display_name":"Aesthetic Perception and Analysis","score":0.8305000066757202,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.032099999487400055,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12496","display_name":"Color perception and design","score":0.009399999864399433,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7533000111579895},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.6470000147819519},{"id":"https://openalex.org/keywords/grammar","display_name":"Grammar","score":0.5722000002861023},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.5390999913215637},{"id":"https://openalex.org/keywords/documentation","display_name":"Documentation","score":0.505299985408783},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.48339998722076416},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4595000147819519},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4505999982357025}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7533000111579895},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6851000189781189},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6676999926567078},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.6470000147819519},{"id":"https://openalex.org/C26022165","wikidata":"https://www.wikidata.org/wiki/Q8091","display_name":"Grammar","level":2,"score":0.5722000002861023},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.5390999913215637},{"id":"https://openalex.org/C56666940","wikidata":"https://www.wikidata.org/wiki/Q788790","display_name":"Documentation","level":2,"score":0.505299985408783},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5012000203132629},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.48339998722076416},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4595000147819519},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4505999982357025},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4244999885559082},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.42179998755455017},{"id":"https://openalex.org/C2776445246","wikidata":"https://www.wikidata.org/wiki/Q1792644","display_name":"Style (visual arts)","level":2,"score":0.39259999990463257},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37389999628067017},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.33649998903274536},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3257000148296356},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3050000071525574},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.30250000953674316},{"id":"https://openalex.org/C2776608160","wikidata":"https://www.wikidata.org/wiki/Q4785462","display_name":"Natural (archaeology)","level":2,"score":0.28450000286102295},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.28029999136924744},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C60671577","wikidata":"https://www.wikidata.org/wiki/Q210272","display_name":"Cultural heritage","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2572000026702881}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3769526.3769634","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3769526.3769634","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd International Conference on Culture and Computer Science: Remixing Analog and Digital","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3769526.3769634","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3769526.3769634","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd International Conference on Culture and Computer Science: Remixing Analog and Digital","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.6997394561767578,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2117539524","https://openalex.org/W2149498137","https://openalex.org/W2777073510","https://openalex.org/W2797977484","https://openalex.org/W3000374768","https://openalex.org/W3005743304","https://openalex.org/W3089563436","https://openalex.org/W4210703993","https://openalex.org/W4221114191","https://openalex.org/W4254058752","https://openalex.org/W4390874575","https://openalex.org/W4402343102"],"related_works":[],"abstract_inverted_index":{"We":[0],"introduce":[1],"JONES-19,":[2],"a":[3,44,64,98,105,136],"high-quality":[4],"image":[5,68,85],"dataset":[6,39,133,146,164],"documenting":[7],"1,901":[8],"ornament":[9],"designs":[10],"belonging":[11],"to":[12],"nineteen":[13],"human":[14],"cultures.":[15],"The":[16,29,38,163],"images":[17],"and":[18,51,60,62,84,95,122,134,150,165],"their":[19],"annotations":[20],"are":[21,169],"based":[22,87],"on":[23,88],"an":[24],"open":[25],"access":[26],"archive":[27],"of":[28,31,57,70,130,144],"Grammar":[30],"Ornament":[32],"(London,":[33],"1856),":[34],"by":[35],"Owen":[36],"Jones.":[37],"poses":[40],"numerous":[41],"challenges":[42],"as":[43,104,112],"benchmark":[45,106,139],"for":[46,52,107,147],"computer":[47],"vision":[48],"classification":[49,138],"tasks":[50],"research":[53,109],"at":[54],"the":[55,128,131,142,145],"intersection":[56],"machine":[58],"learning":[59],"art":[61],"design:":[63],"small":[65],"sample":[66],"size,":[67],"samples":[69],"human-designed":[71],"artifacts":[72],"rather":[73],"than":[74],"common":[75],"objects":[76],"in-context":[77],"or":[78],"natural":[79],"scenes,":[80],"imbalanced":[81],"class":[82],"distribution,":[83],"distinctions":[86],"fine":[89],"details":[90],"involving":[91],"line":[92],"patterns,":[93],"reliefs,":[94],"colors.":[96],"As":[97],"design-inspired":[99],"dataset,":[100],"JONES-19":[101,132],"can":[102],"serve":[103],"various":[108],"fields,":[110],"such":[111],"visual":[113],"recognition,":[114],"data-efficient":[115],"learning,":[116],"art-historical":[117],"research,":[118],"architectural":[119],"style":[120],"analysis,":[121],"cultural":[123,156],"heritage.":[124],"This":[125],"paper":[126],"describes":[127],"curation":[129],"reports":[135],"baseline":[137],"that":[140],"evaluates":[141],"suitability":[143],"training":[148],"classifiers":[149],"exposes":[151],"insights":[152],"into":[153],"inter-class":[154],"relationships\u2013particularly":[155],"similarities\u2013by":[157],"examining":[158],"patterns":[159],"in":[160],"misclassification":[161],"errors.":[162],"its":[166],"accompanying":[167],"documentation":[168],"available":[170],"at:":[171],"https://huggingface.co/datasets/harvardseas-cultural-ornaments/JONES-19.":[172]},"counts_by_year":[],"updated_date":"2025-12-11T23:13:37.075516","created_date":"2025-12-11T00:00:00"}
