{"id":"https://openalex.org/W2941766203","doi":"https://doi.org/10.1145/3290605.3300356","title":"How Data Science Workers Work with Data","display_name":"How Data Science Workers Work with Data","publication_year":2019,"publication_date":"2019-04-29","ids":{"openalex":"https://openalex.org/W2941766203","doi":"https://doi.org/10.1145/3290605.3300356","mag":"2941766203"},"language":"en","primary_location":{"id":"doi:10.1145/3290605.3300356","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3290605.3300356","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5026028381","display_name":"Michael M\u00fcller","orcid":"https://orcid.org/0000-0001-7860-163X"},"institutions":[{"id":"https://openalex.org/I4210087032","display_name":"Cambridge Scientific (United States)","ror":"https://ror.org/001s4dh65","country_code":"US","type":"company","lineage":["https://openalex.org/I4210087032"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Michael Muller","raw_affiliation_strings":["IBM Research, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"IBM Research, Cambridge, MA, USA","institution_ids":["https://openalex.org/I4210087032"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048863131","display_name":"Ingrid Lange","orcid":null},"institutions":[{"id":"https://openalex.org/I4210087032","display_name":"Cambridge Scientific (United States)","ror":"https://ror.org/001s4dh65","country_code":"US","type":"company","lineage":["https://openalex.org/I4210087032"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ingrid Lange","raw_affiliation_strings":["IBM Research, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"IBM Research, Cambridge, MA, USA","institution_ids":["https://openalex.org/I4210087032"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062817658","display_name":"Dakuo Wang","orcid":"https://orcid.org/0000-0001-9371-9441"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dakuo Wang","raw_affiliation_strings":["IBM Research, Yorktown Heights, NY, USA"],"affiliations":[{"raw_affiliation_string":"IBM Research, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076670713","display_name":"David Piorkowski","orcid":"https://orcid.org/0000-0002-6740-4902"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"David Piorkowski","raw_affiliation_strings":["IBM Research, Yorktown Heights, NY, USA"],"affiliations":[{"raw_affiliation_string":"IBM Research, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077279308","display_name":"Jason Tsay","orcid":"https://orcid.org/0000-0002-8085-5708"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jason Tsay","raw_affiliation_strings":["IBM Research, Yorktown Heights, NY, USA"],"affiliations":[{"raw_affiliation_string":"IBM Research, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024320659","display_name":"Q. Vera Liao","orcid":"https://orcid.org/0000-0003-4543-7196"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Q. Vera Liao","raw_affiliation_strings":["IBM Research, Yorktown Heights, NY, USA"],"affiliations":[{"raw_affiliation_string":"IBM Research, Yorktown Heights, NY, USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082995130","display_name":"Casey Dugan","orcid":"https://orcid.org/0000-0002-1508-2091"},"institutions":[{"id":"https://openalex.org/I4210087032","display_name":"Cambridge Scientific (United States)","ror":"https://ror.org/001s4dh65","country_code":"US","type":"company","lineage":["https://openalex.org/I4210087032"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Casey Dugan","raw_affiliation_strings":["IBM Research, Cambridge, MA, USA"],"affiliations":[{"raw_affiliation_string":"IBM Research, Cambridge, MA, USA","institution_ids":["https://openalex.org/I4210087032"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110106297","display_name":"Thomas Erickson","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thomas Erickson","raw_affiliation_strings":["Unaffiliated, Minneapolis, MN, USA"],"affiliations":[{"raw_affiliation_string":"Unaffiliated, Minneapolis, MN, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5026028381"],"corresponding_institution_ids":["https://openalex.org/I4210087032"],"apc_list":null,"apc_paid":null,"fwci":12.975,"has_fulltext":false,"cited_by_count":255,"citation_normalized_percentile":{"value":0.98954902,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"15"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9973000288009644,"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"}},"topics":[{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9973000288009644,"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11986","display_name":"Scientific Computing and Data Management","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.777384877204895},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.7334920763969421},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6986206769943237},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.5465483665466309},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.546267569065094},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.5347026586532593},{"id":"https://openalex.org/keywords/e-science","display_name":"e-Science","score":0.5167750120162964},{"id":"https://openalex.org/keywords/work","display_name":"Work (physics)","score":0.5137796998023987},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4460594356060028},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2151588797569275},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12517020106315613},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.1232512891292572},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.08918246626853943}],"concepts":[{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.777384877204895},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.7334920763969421},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6986206769943237},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.5465483665466309},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.546267569065094},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5347026586532593},{"id":"https://openalex.org/C517757529","wikidata":"https://www.wikidata.org/wiki/Q1273268","display_name":"e-Science","level":3,"score":0.5167750120162964},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.5137796998023987},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4460594356060028},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2151588797569275},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12517020106315613},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.1232512891292572},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.08918246626853943},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3290605.3300356","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3290605.3300356","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":91,"referenced_works":["https://openalex.org/W7205189","https://openalex.org/W409344806","https://openalex.org/W1006997171","https://openalex.org/W1500693574","https://openalex.org/W1533574204","https://openalex.org/W1535357018","https://openalex.org/W1548849615","https://openalex.org/W1563513531","https://openalex.org/W1602965708","https://openalex.org/W1747111568","https://openalex.org/W1781458262","https://openalex.org/W1950644452","https://openalex.org/W1966104470","https://openalex.org/W1967841763","https://openalex.org/W1972868357","https://openalex.org/W1982811126","https://openalex.org/W1987044943","https://openalex.org/W1991612274","https://openalex.org/W1995644500","https://openalex.org/W1999796615","https://openalex.org/W2001302828","https://openalex.org/W2005643425","https://openalex.org/W2014534734","https://openalex.org/W2021698503","https://openalex.org/W2036972370","https://openalex.org/W2040801501","https://openalex.org/W2047973478","https://openalex.org/W2050640900","https://openalex.org/W2060083971","https://openalex.org/W2060095252","https://openalex.org/W2060437593","https://openalex.org/W2061356498","https://openalex.org/W2064766209","https://openalex.org/W2065915498","https://openalex.org/W2086034674","https://openalex.org/W2087586120","https://openalex.org/W2096458486","https://openalex.org/W2106895292","https://openalex.org/W2108816886","https://openalex.org/W2110763597","https://openalex.org/W2120096569","https://openalex.org/W2127806924","https://openalex.org/W2132667707","https://openalex.org/W2137940381","https://openalex.org/W2144908498","https://openalex.org/W2160327429","https://openalex.org/W2162065809","https://openalex.org/W2162791924","https://openalex.org/W2172146051","https://openalex.org/W2182353144","https://openalex.org/W2182687482","https://openalex.org/W2182787248","https://openalex.org/W2208202786","https://openalex.org/W2289672065","https://openalex.org/W2354301041","https://openalex.org/W2406186340","https://openalex.org/W2465910587","https://openalex.org/W2496777941","https://openalex.org/W2527349292","https://openalex.org/W2565890363","https://openalex.org/W2568476927","https://openalex.org/W2574781439","https://openalex.org/W2588064451","https://openalex.org/W2588744990","https://openalex.org/W2588765774","https://openalex.org/W2611289684","https://openalex.org/W2611789916","https://openalex.org/W2616338822","https://openalex.org/W2623347270","https://openalex.org/W2716311441","https://openalex.org/W2744518080","https://openalex.org/W2783532463","https://openalex.org/W2807910285","https://openalex.org/W2808450727","https://openalex.org/W2809161078","https://openalex.org/W2809386502","https://openalex.org/W2849214662","https://openalex.org/W2887334214","https://openalex.org/W2906151105","https://openalex.org/W2964083839","https://openalex.org/W3004390862","https://openalex.org/W3022452870","https://openalex.org/W3101276022","https://openalex.org/W3124912334","https://openalex.org/W4211192987","https://openalex.org/W4211221028","https://openalex.org/W4246938793","https://openalex.org/W4298286109","https://openalex.org/W6614226208","https://openalex.org/W6676415402","https://openalex.org/W6686029465"],"related_works":["https://openalex.org/W2033236835","https://openalex.org/W2091936039","https://openalex.org/W188202134","https://openalex.org/W2156145604","https://openalex.org/W4250569211","https://openalex.org/W2287633205","https://openalex.org/W1487214958","https://openalex.org/W2167756108","https://openalex.org/W46160656","https://openalex.org/W2135042710"],"abstract_inverted_index":{"With":[0],"the":[1,71,79,89,163],"rise":[2],"of":[3,49,81,103,119,140,162],"big":[4],"data,":[5,100],"there":[6],"has":[7],"been":[8],"an":[9,18,137],"increasing":[10,19],"need":[11],"for":[12,21],"practitioners":[13],"in":[14,87],"this":[15,52,74],"space":[16],"and":[17,27,44,60,62,84,95,130,143,145],"opportunity":[20],"researchers":[22,86],"to":[23,31,114,154,159],"understand":[24],"their":[25,99,141,148],"workflows":[26],"design":[28],"new":[29,152],"tools":[30],"improve":[32],"it.":[33],"Data":[34,121,133],"science":[35,67,108,134],"is":[36],"often":[37],"described":[38],"as":[39,122,124,126,128,131],"data-driven,":[40],"comprising":[41],"unambiguous":[42],"data":[43,66,107,115,142,167],"proceeding":[45],"through":[46,101],"regularized":[47],"steps":[48],"analysis.":[50],"However,":[51],"view":[53],"focuses":[54],"more":[55],"on":[56,64,78],"abstract":[57],"processes,":[58,144],"pipelines,":[59],"workflows,":[61],"less":[63],"how":[65],"workers":[68,135],"engage":[69],"with":[70,98,105],"data.":[72,149],"In":[73],"paper,":[75],"we":[76],"build":[77],"work":[80,97],"other":[82],"CSCW":[83],"HCI":[85],"describing":[88],"ways":[90,153],"that":[91],"scientists,":[92],"scholars,":[93],"engineers,":[94],"others":[96],"analyses":[102],"interviews":[104],"21":[106],"professionals.":[109],"We":[110,150],"set":[111],"five":[112],"approaches":[113],"along":[116],"a":[117],"dimension":[118],"interventions:":[120],"given;":[123],"captured;":[125],"curated;":[127],"designed;":[129],"created.":[132],"develop":[136],"intuitive":[138],"sense":[139,161],"actively":[146],"shape":[147],"propose":[151],"apply":[155],"these":[156],"interventions":[157],"analytically,":[158],"make":[160],"complex":[164],"activities":[165],"around":[166],"practices.":[168]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":39},{"year":2024,"cited_by_count":42},{"year":2023,"cited_by_count":41},{"year":2022,"cited_by_count":51},{"year":2021,"cited_by_count":30},{"year":2020,"cited_by_count":38},{"year":2019,"cited_by_count":8}],"updated_date":"2026-04-14T08:04:32.555800","created_date":"2025-10-10T00:00:00"}
