{"id":"https://openalex.org/W6907232248","doi":"https://doi.org/10.21227/e9eb-ma51","title":"Deduplication Index for Big Code Datasets","display_name":"Deduplication Index for Big Code Datasets","publication_year":2019,"publication_date":"2019-06-27","ids":{"openalex":"https://openalex.org/W6907232248","doi":"https://doi.org/10.21227/e9eb-ma51"},"language":"en","primary_location":{"id":"doi:10.21227/e9eb-ma51","is_oa":true,"landing_page_url":"https://doi.org/10.21227/e9eb-ma51","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"dataset"},"type":"dataset","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.21227/e9eb-ma51","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Allamanis, Miltiadis","orcid":"https://orcid.org/0000-0002-5819-9900"},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Allamanis, Miltiadis","raw_affiliation_strings":["Microsoft Research"],"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210164937"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.7322999835014343},{"id":"https://openalex.org/keywords/data-deduplication","display_name":"Data deduplication","score":0.6305999755859375},{"id":"https://openalex.org/keywords/index","display_name":"Index (typography)","score":0.5497999787330627},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.3853999972343445},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.3142000138759613}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8327000141143799},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.7322999835014343},{"id":"https://openalex.org/C32587265","wikidata":"https://www.wikidata.org/wiki/Q1182260","display_name":"Data deduplication","level":2,"score":0.6305999755859375},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.5497999787330627},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4426000118255615},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4077000021934509},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.3853999972343445},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3822000026702881},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3368000090122223},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.3142000138759613},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3102000057697296},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.28380000591278076}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21227/e9eb-ma51","is_oa":true,"landing_page_url":"https://doi.org/10.21227/e9eb-ma51","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"dataset"}],"best_oa_location":{"id":"doi:10.21227/e9eb-ma51","is_oa":true,"landing_page_url":"https://doi.org/10.21227/e9eb-ma51","pdf_url":null,"source":{"id":"https://openalex.org/S7407051695","display_name":"IEEE DataPort","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"dataset"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Code":[0,46,61],"duplicates":[1],"in":[2,44,56,63,74],"large":[3],"code":[4],"corpora":[5,24,43],"have":[6],"adverse":[7],"effects":[8],"on":[9,20],"the":[10,41],"evaluation":[11],"and":[12],"use":[13],"of":[14,40,60,67],"machine":[15],"learning":[16],"models":[17],"that":[18],"rely":[19],"them.":[21],"Most":[22],"existing":[23,42],"suffer":[25],"from":[26],"this":[27,52],"problem":[28],"to":[29,72],"some":[30,39],"extent.":[31],"This":[32],"dataset":[33,53],"contains":[34],"a":[35],"\"duplication\"":[36],"index":[37],"for":[38,50],"Big":[45],"research.":[47],"The":[48],"method":[49],"collecting":[51],"is":[54],"described":[55],"\"The":[57],"Adverse":[58],"Effects":[59],"Duplication":[62],"Machine":[64],"Learning":[65],"Models":[66],"Code\"":[68],"by":[69],"Allamanis":[70],"[ArXiV,":[71],"appear":[73],"SPLASH":[75],"2019].":[76]},"counts_by_year":[],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}