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RelatedMaterial
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update: {"datacite_list"=>["IsSupplementedBy ", "IsSupplementedBy"]}
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2024-04-18T18:23:34Z
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update: {"datacite_list"=>["IsSupplementedBy ", "IsSupplementedBy"]}
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2024-04-18T18:23:34Z
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create: {"material_type"=>"Article", "availability"=>nil, "link"=>"https://doi.org/10.1038/s41597-022-01578-x", "uri"=>"10.1038/s41597-022-01578-x", "uri_type"=>"DOI", "citation"=>"Ke, Q., Liang, L., Ding, Y. et al. A dataset of mentorship in bioscience with semantic and demographic estimations. Sci Data 9, 467 (2022). https://doi.org/10.1038/s41597-022-01578-x", "dataset_id"=>536, "selected_type"=>"Article", "datacite_list"=>"IsCitedBy"}
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2022-08-08T16:45:04Z
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RelatedMaterial
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create: {"material_type"=>"Article", "availability"=>nil, "link"=>"https://arxiv.org/abs/2106.06487", "uri"=>"https://arxiv.org/abs/2106.06487", "uri_type"=>"URL", "citation"=>"Ke, Qing, Lizhen Liang, Ying Ding, Stephen V David and Daniel Ernesto Acuna. “A dataset of mentorship in science with semantic and demographic estimations.” ArXiv abs/2106.06487 (2021): n. pag.", "dataset_id"=>536, "selected_type"=>"Article", "datacite_list"=>"IsCitedBy"}
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2021-06-21T15:22:30Z
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RelatedMaterial
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create: {"material_type"=>"Article", "availability"=>nil, "link"=>"https://doi.org/10.1145/3461702.3462616", "uri"=>"10.1145/3461702.3462616", "uri_type"=>"DOI", "citation"=>"Acuna, Daniel E., and Lizhen Liang. 2021. “Are AI Ethics Conferences Different and More Diverse Compared to Traditional Computer Science Conferences?.” OSF Preprints. May 19. doi:10.1145/3461702.3462616.", "dataset_id"=>536, "selected_type"=>"Article", "datacite_list"=>"IsCitedBy"}
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2021-05-26T19:15:14Z
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RelatedMaterial
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create: {"material_type"=>"Article", "availability"=>nil, "link"=>"https://doi.org/10.1007/s11192-020-03826-6", "uri"=>"10.1007/s11192-020-03826-6", "uri_type"=>"DOI", "citation"=>"Kim, J., & Owen-Smith, J. (In print). ORCID-linked labeled data for evaluating author name\r\ndisambiguation at scale. Scientometrics. Doi: 10.1007/s11192-020-03826-6", "dataset_id"=>536, "selected_type"=>"Article", "datacite_list"=>"IsCitedBy"}
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2021-02-11T16:43:39Z
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Dataset
|
update: {"description"=>["Prepared by Vetle Torvik 2018-04-15\r\n\r\nThe dataset comes as a single tab-delimited ASCII encoded file, and should be about 717MB uncompressed.\r\n\r\n• How was the dataset created?\r\nFirst and lastnames of authors in the Author-ity 2009 dataset was processed through several tools to predict ethnicities and gender, including\r\n\r\nEthnea+Genni as described in:\r\n\r\n<i>Torvik VI, Agarwal S. Ethnea -- an instance-based ethnicity classifier based on geocoded author names in a large-scale bibliographic database. International Symposium on Science of Science March 22-23, 2016 - Library of Congress, Washington, DC, USA.\r\nhttp://hdl.handle.net/2142/88927</i>\r\n\r\n<i>Smith, B., Singh, M., & Torvik, V. (2013). A search engine approach to estimating temporal changes in gender orientation of first names. Proceedings Of The ACM/IEEE Joint Conference On Digital Libraries, (JCDL 2013 - Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries), 199-208. doi:10.1145/2467696.2467720</i>\r\n\r\nEthnicSeer: http://singularity.ist.psu.edu/ethnicity\r\n<i>Treeratpituk P, Giles CL (2012). Name-Ethnicity Classification and Ethnicity-Sensitive Name Matching. Proceedings of the Twenty-Sixth Conference on Artificial Intelligence (pp. 1141-1147). AAAI-12. Toronto, ON, Canada</i>\r\n\r\nSexMachine 0.1.1: <a href=\"https://pypi.python.org/pypi/SexMachine/\">https://pypi.org/project/SexMachine</a>\r\n\r\nFirst names, for some Author-ity records lacking them, were harvested from outside bibliographic databases.\r\n\r\n• The code and back-end data is periodically updated and made available for query at <a href =\"http://abel.ischool.illinois.edu\">Torvik Research Group</a>\r\n\r\n• What is the format of the dataset?\r\nThe dataset contains 9,300,182 rows and 10 columns\r\n1. auid: unique ID for Authors in Author-ity 2009 (PMID_authorposition)\r\n2. name: full name used as input to EthnicSeer)\r\n3. EthnicSeer: predicted ethnicity; ARA, CHI, ENG, FRN, GER, IND, ITA, JAP, KOR, RUS, SPA, VIE, XXX\r\n4. prop: decimal between 0 and 1 reflecting the confidence of the EthnicSeer prediction\r\n5. lastname: used as input for Ethnea+Genni\r\n6. firstname: used as input for Ethnea+Genni\r\n7. Ethnea: predicted ethnicity; either one of 26 (AFRICAN, ARAB, BALTIC, CARIBBEAN, CHINESE, DUTCH, ENGLISH, FRENCH, GERMAN, GREEK, HISPANIC, HUNGARIAN, INDIAN, INDONESIAN, ISRAELI, ITALIAN, JAPANESE, KOREAN, MONGOLIAN, NORDIC, POLYNESIAN, ROMANIAN, SLAV, THAI, TURKISH, VIETNAMESE) or two ethnicities (e.g., SLAV-ENGLISH), or UNKNOWN (if no one or two dominant predictons), or TOOSHORT (if both first and last name are too short)\r\n8. Genni: predicted gender; 'F', 'M', or '-'\r\n9. SexMac: predicted gender based on third-party Python program (default settings except case_sensitive=False); female, mostly_female, andy, mostly_male, male)\r\n10. SSNgender: predicted gender based on US SSN data; 'F', 'M', or '-'\r\n", "Prepared by Vetle Torvik 2018-04-15\r\n\r\nThe dataset comes as a single tab-delimited ASCII encoded file, and should be about 717MB uncompressed.\r\n\r\n• How was the dataset created?\r\nFirst and last names of authors in the Author-ity 2009 dataset was processed through several tools to predict ethnicities and gender, including\r\n\r\nEthnea+Genni as described in:\r\n\r\n<i>Torvik VI, Agarwal S. Ethnea -- an instance-based ethnicity classifier based on geocoded author names in a large-scale bibliographic database. International Symposium on Science of Science March 22-23, 2016 - Library of Congress, Washington, DC, USA.\r\nhttp://hdl.handle.net/2142/88927</i>\r\n\r\n<i>Smith, B., Singh, M., & Torvik, V. (2013). A search engine approach to estimating temporal changes in gender orientation of first names. Proceedings Of The ACM/IEEE Joint Conference On Digital Libraries, (JCDL 2013 - Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries), 199-208. doi:10.1145/2467696.2467720</i>\r\n\r\nEthnicSeer: http://singularity.ist.psu.edu/ethnicity\r\n<i>Treeratpituk P, Giles CL (2012). Name-Ethnicity Classification and Ethnicity-Sensitive Name Matching. Proceedings of the Twenty-Sixth Conference on Artificial Intelligence (pp. 1141-1147). AAAI-12. Toronto, ON, Canada</i>\r\n\r\nSexMachine 0.1.1: <a href=\"https://pypi.python.org/pypi/SexMachine/\">https://pypi.org/project/SexMachine</a>\r\n\r\nFirst names, for some Author-ity records lacking them, were harvested from outside bibliographic databases.\r\n\r\n• The code and back-end data is periodically updated and made available for query at <a href =\"http://abel.ischool.illinois.edu\">Torvik Research Group</a>\r\n\r\n• What is the format of the dataset?\r\nThe dataset contains 9,300,182 rows and 10 columns\r\n1. auid: unique ID for Authors in Author-ity 2009 (PMID_authorposition)\r\n2. name: full name used as input to EthnicSeer)\r\n3. EthnicSeer: predicted ethnicity; ARA, CHI, ENG, FRN, GER, IND, ITA, JAP, KOR, RUS, SPA, VIE, XXX\r\n4. prop: decimal between 0 and 1 reflecting the confidence of the EthnicSeer prediction\r\n5. lastname: used as input for Ethnea+Genni\r\n6. firstname: used as input for Ethnea+Genni\r\n7. Ethnea: predicted ethnicity; either one of 26 (AFRICAN, ARAB, BALTIC, CARIBBEAN, CHINESE, DUTCH, ENGLISH, FRENCH, GERMAN, GREEK, HISPANIC, HUNGARIAN, INDIAN, INDONESIAN, ISRAELI, ITALIAN, JAPANESE, KOREAN, MONGOLIAN, NORDIC, POLYNESIAN, ROMANIAN, SLAV, THAI, TURKISH, VIETNAMESE) or two ethnicities (e.g., SLAV-ENGLISH), or UNKNOWN (if no one or two dominant predictons), or TOOSHORT (if both first and last name are too short)\r\n8. Genni: predicted gender; 'F', 'M', or '-'\r\n9. SexMac: predicted gender based on third-party Python program (default settings except case_sensitive=False); female, mostly_female, andy, mostly_male, male)\r\n10. SSNgender: predicted gender based on US SSN data; 'F', 'M', or '-'\r\n"]}
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2018-12-05T21:47:04Z
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Dataset
|
update: {"keywords"=>["Androgyny; Bibliometrics; Data mining; Earch engine; Gender; Semantic orientation; Temporal prediction; Textual markers", "Androgyny; Bibliometrics; Data mining; Search engine; Gender; Semantic orientation; Temporal prediction; Textual markers"]}
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2018-12-04T17:56:06Z
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RelatedMaterial
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create: {"material_type"=>"Article", "availability"=>nil, "link"=>"https://doi.org/10.1007/s11192-018-2968-3", "uri"=>"10.1007/s11192-018-2968-3", "uri_type"=>"DOI", "citation"=>"Kim, J., Kim, J. & Owen-Smith, J. Scientometrics (2018). Generating automatically labeled data for author name disambiguation: an iterative clustering method. https://doi.org/10.1007/s11192-018-2968-3", "dataset_id"=>536, "selected_type"=>"Article", "datacite_list"=>"IsCitedBy"}
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2018-12-04T17:40:24Z
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RelatedMaterial
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destroy: {"material_type"=>"Article", "availability"=>nil, "link"=>"https://doi.org/10.1007/s11192-018-2968-3", "uri"=>"10.1007/s11192-018-2968-3", "uri_type"=>"DOI", "citation"=>"Kim, J., Kim, J. & Owen-Smith, J. Scientometrics (2018). Generating automatically labeled data for author name disambiguation: an iterative clustering method. https://doi.org/10.1007/s11192-018-2968-3", "dataset_id"=>536, "selected_type"=>"Article", "datacite_list"=>"IsCitedBy"}
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2018-12-04T16:29:38Z
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RelatedMaterial
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2018-12-04T16:29:38Z
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RelatedMaterial
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2018-12-04T15:55:44Z
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RelatedMaterial
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create: {"material_type"=>"Article", "availability"=>nil, "link"=>"https://doi.org/10.1007/s11192-018-2968-3", "uri"=>"10.1007/s11192-018-2968-3", "uri_type"=>"DOI", "citation"=>"Kim, J., Kim, J. & Owen-Smith, J. Scientometrics (2018). Generating automatically labeled data for author name disambiguation: an iterative clustering method. https://doi.org/10.1007/s11192-018-2968-3", "dataset_id"=>536, "selected_type"=>"Article", "datacite_list"=>"IsCitedBy"}
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2018-12-04T15:48:58Z
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RelatedMaterial
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create: {"material_type"=>"Article", "availability"=>nil, "link"=>"https://doi.org/10.1371/journal.pone.0195773", "uri"=>"10.1371/journal.pone.0195773", "uri_type"=>"DOI", "citation"=>"Mishra S, Fegley BD, Diesner J, Torvik VI (2018) Self-citation is the hallmark of productive authors, of any gender. PLoS ONE 13(9): e0195773. https://doi.org/10.1371/journal.pone.0195773", "dataset_id"=>536, "selected_type"=>"Article", "datacite_list"=>"IsSupplementTo"}
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2018-09-29T15:43:38Z
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Dataset
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update: {"description"=>["Prepared by Vetle Torvik April 5, 2018\r\n\r\nThe dataset comes as a single tab-delimited ASCII encoded file, and should be about 717MB uncompressed.\r\n\r\n• How was the dataset created?\r\nFirst and lastnames of authors in the Author-ity 2009 dataset was processed through several tools to predict ethnicities and gender, including\r\n\r\nEthnea+Genni as described in:\r\n\r\n<i>Torvik VI, Agarwal S. Ethnea -- an instance-based ethnicity classifier based on geocoded author names in a large-scale bibliographic database. International Symposium on Science of Science March 22-23, 2016 - Library of Congress, Washington, DC, USA.\r\nhttp://hdl.handle.net/2142/88927</i>\r\n\r\n<i>Smith, B., Singh, M., & Torvik, V. (2013). A search engine approach to estimating temporal changes in gender orientation of first names. Proceedings Of The ACM/IEEE Joint Conference On Digital Libraries, (JCDL 2013 - Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries), 199-208. doi:10.1145/2467696.2467720</i>\r\n\r\nEthnicSeer: http://singularity.ist.psu.edu/ethnicity\r\n<i>Treeratpituk P, Giles CL (2012). Name-Ethnicity Classification and Ethnicity-Sensitive Name Matching. Proceedings of the Twenty-Sixth Conference on Artificial Intelligence (pp. 1141-1147). AAAI-12. Toronto, ON, Canada</i>\r\n\r\nSexMachine 0.1.1: <a href=\"https://pypi.python.org/pypi/SexMachine/\">https://pypi.org/project/SexMachine</a>\r\n\r\nFirst names, for some Author-ity records lacking them, were harvested from outside bibliographic databases.\r\n\r\n• The code and back-end data is periodically updated and made available for query at <a href =\"http://abel.ischool.illinois.edu\">Torvik Research Group</a>\r\n\r\n• What is the format of the dataset?\r\nThe dataset contains 9,300,182 rows and 10 columns\r\n1. auid: unique ID for Authors in Author-ity 2009 (PMID_authorposition)\r\n2. name: full name used as input to EthnicSeer)\r\n3. EthnicSeer: predicted ethnicity; ARA, CHI, ENG, FRN, GER, IND, ITA, JAP, KOR, RUS, SPA, VIE, XXX\r\n4. prop: decimal between 0 and 1 reflecting the confidence of the EthnicSeer prediction\r\n5. lastname: used as input for Ethnea+Genni\r\n6. firstname: used as input for Ethnea+Genni\r\n7. Ethnea: predicted ethnicity; either one of 26 (AFRICAN, ARAB, BALTIC, CARIBBEAN, CHINESE, DUTCH, ENGLISH, FRENCH, GERMAN, GREEK, HISPANIC, HUNGARIAN, INDIAN, INDONESIAN, ISRAELI, ITALIAN, JAPANESE, KOREAN, MONGOLIAN, NORDIC, POLYNESIAN, ROMANIAN, SLAV, THAI, TURKISH, VIETNAMESE) or two ethnicities (e.g., SLAV-ENGLISH), or UNKNOWN (if no one or two dominant predictons), or TOOSHORT (if both first and last name are too short)\r\n8. Genni: predicted gender; 'F', 'M', or '-'\r\n9. SexMac: predicted gender based on third-party Python program (default settings except case_sensitive=False); female, mostly_female, andy, mostly_male, male)\r\n10. SSNgender: predicted gender based on US SSN data; 'F', 'M', or '-'\r\n", "Prepared by Vetle Torvik 2018-04-15\r\n\r\nThe dataset comes as a single tab-delimited ASCII encoded file, and should be about 717MB uncompressed.\r\n\r\n• How was the dataset created?\r\nFirst and lastnames of authors in the Author-ity 2009 dataset was processed through several tools to predict ethnicities and gender, including\r\n\r\nEthnea+Genni as described in:\r\n\r\n<i>Torvik VI, Agarwal S. Ethnea -- an instance-based ethnicity classifier based on geocoded author names in a large-scale bibliographic database. International Symposium on Science of Science March 22-23, 2016 - Library of Congress, Washington, DC, USA.\r\nhttp://hdl.handle.net/2142/88927</i>\r\n\r\n<i>Smith, B., Singh, M., & Torvik, V. (2013). A search engine approach to estimating temporal changes in gender orientation of first names. Proceedings Of The ACM/IEEE Joint Conference On Digital Libraries, (JCDL 2013 - Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries), 199-208. doi:10.1145/2467696.2467720</i>\r\n\r\nEthnicSeer: http://singularity.ist.psu.edu/ethnicity\r\n<i>Treeratpituk P, Giles CL (2012). Name-Ethnicity Classification and Ethnicity-Sensitive Name Matching. Proceedings of the Twenty-Sixth Conference on Artificial Intelligence (pp. 1141-1147). AAAI-12. Toronto, ON, Canada</i>\r\n\r\nSexMachine 0.1.1: <a href=\"https://pypi.python.org/pypi/SexMachine/\">https://pypi.org/project/SexMachine</a>\r\n\r\nFirst names, for some Author-ity records lacking them, were harvested from outside bibliographic databases.\r\n\r\n• The code and back-end data is periodically updated and made available for query at <a href =\"http://abel.ischool.illinois.edu\">Torvik Research Group</a>\r\n\r\n• What is the format of the dataset?\r\nThe dataset contains 9,300,182 rows and 10 columns\r\n1. auid: unique ID for Authors in Author-ity 2009 (PMID_authorposition)\r\n2. name: full name used as input to EthnicSeer)\r\n3. EthnicSeer: predicted ethnicity; ARA, CHI, ENG, FRN, GER, IND, ITA, JAP, KOR, RUS, SPA, VIE, XXX\r\n4. prop: decimal between 0 and 1 reflecting the confidence of the EthnicSeer prediction\r\n5. lastname: used as input for Ethnea+Genni\r\n6. firstname: used as input for Ethnea+Genni\r\n7. Ethnea: predicted ethnicity; either one of 26 (AFRICAN, ARAB, BALTIC, CARIBBEAN, CHINESE, DUTCH, ENGLISH, FRENCH, GERMAN, GREEK, HISPANIC, HUNGARIAN, INDIAN, INDONESIAN, ISRAELI, ITALIAN, JAPANESE, KOREAN, MONGOLIAN, NORDIC, POLYNESIAN, ROMANIAN, SLAV, THAI, TURKISH, VIETNAMESE) or two ethnicities (e.g., SLAV-ENGLISH), or UNKNOWN (if no one or two dominant predictons), or TOOSHORT (if both first and last name are too short)\r\n8. Genni: predicted gender; 'F', 'M', or '-'\r\n9. SexMac: predicted gender based on third-party Python program (default settings except case_sensitive=False); female, mostly_female, andy, mostly_male, male)\r\n10. SSNgender: predicted gender based on US SSN data; 'F', 'M', or '-'\r\n"]}
|
2018-04-23T19:33:56Z
|
|
Dataset
|
update: {"description"=>["Prepared by Vetle Torvik April 5, 2018\r\n\r\nThe dataset comes as a single tab-delimited ASCII encoded file, and should be about 717MB uncompressed.\r\n\r\n• How was the dataset created?\r\nFirst and lastnames of authors in the Author-ity 2009 dataset was processed through several tools to predict ethnicities and gender, including\r\n\r\nEthnea+Genni as described in:\r\n\r\n<i>Torvik VI, Agarwal S. Ethnea -- an instance-based ethnicity classifier based on geocoded author names in a large-scale bibliographic database. International Symposium on Science of Science March 22-23, 2016 - Library of Congress, Washington, DC, USA.\r\nhttp://hdl.handle.net/2142/88927</i>\r\n\r\n<i>Smith, B., Singh, M., & Torvik, V. (2013). A search engine approach to estimating temporal changes in gender orientation of first names. Proceedings Of The ACM/IEEE Joint Conference On Digital Libraries, (JCDL 2013 - Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries), 199-208. doi:10.1145/2467696.2467720</i>\r\n\r\nEthnicSeer: http://singularity.ist.psu.edu/ethnicity\r\n<i>Treeratpituk P, Giles CL (2012). Name-Ethnicity Classification and Ethnicity-Sensitive Name Matching. Proceedings of the Twenty-Sixth Conference on Artificial Intelligence (pp. 1141-1147). AAAI-12. Toronto, ON, Canada</i>\r\n\r\nSexMachine 0.1.1: < a href=\"https://pypi.python.org/pypi/SexMachine/\">https://pypi.org/project/SexMachine/</a>\r\n\r\nFirst names, for some Author-ity records lacking them, were harvested from outside bibliographic databases.\r\n\r\n• The code and back-end data is periodically updated and made available for query at <a href =\"http://abel.ischool.illinois.edu\">Torvik Research Group</a>\r\n\r\n• What is the format of the dataset?\r\nThe dataset contains 9,300,182 rows and 10 columns\r\n1. auid: unique ID for Authors in Author-ity 2009 (PMID_authorposition)\r\n2. name: full name used as input to EthnicSeer)\r\n3. EthnicSeer: predicted ethnicity; ARA, CHI, ENG, FRN, GER, IND, ITA, JAP, KOR, RUS, SPA, VIE, XXX\r\n4. prop: decimal between 0 and 1 reflecting the confidence of the EthnicSeer prediction\r\n5. lastname: used as input for Ethnea+Genni\r\n6. firstname: used as input for Ethnea+Genni\r\n7. Ethnea: predicted ethnicity; either one of 26 (AFRICAN, ARAB, BALTIC, CARIBBEAN, CHINESE, DUTCH, ENGLISH, FRENCH, GERMAN, GREEK, HISPANIC, HUNGARIAN, INDIAN, INDONESIAN, ISRAELI, ITALIAN, JAPANESE, KOREAN, MONGOLIAN, NORDIC, POLYNESIAN, ROMANIAN, SLAV, THAI, TURKISH, VIETNAMESE) or two ethnicities (e.g., SLAV-ENGLISH), or UNKNOWN (if no one or two dominant predictons), or TOOSHORT (if both first and last name are too short)\r\n8. Genni: predicted gender; 'F', 'M', or '-'\r\n9. SexMac: predicted gender based on third-party Python program (default settings except case_sensitive=False); female, mostly_female, andy, mostly_male, male)\r\n10. SSNgender: predicted gender based on US SSN data; 'F', 'M', or '-'\r\n", "Prepared by Vetle Torvik April 5, 2018\r\n\r\nThe dataset comes as a single tab-delimited ASCII encoded file, and should be about 717MB uncompressed.\r\n\r\n• How was the dataset created?\r\nFirst and lastnames of authors in the Author-ity 2009 dataset was processed through several tools to predict ethnicities and gender, including\r\n\r\nEthnea+Genni as described in:\r\n\r\n<i>Torvik VI, Agarwal S. Ethnea -- an instance-based ethnicity classifier based on geocoded author names in a large-scale bibliographic database. International Symposium on Science of Science March 22-23, 2016 - Library of Congress, Washington, DC, USA.\r\nhttp://hdl.handle.net/2142/88927</i>\r\n\r\n<i>Smith, B., Singh, M., & Torvik, V. (2013). A search engine approach to estimating temporal changes in gender orientation of first names. Proceedings Of The ACM/IEEE Joint Conference On Digital Libraries, (JCDL 2013 - Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries), 199-208. doi:10.1145/2467696.2467720</i>\r\n\r\nEthnicSeer: http://singularity.ist.psu.edu/ethnicity\r\n<i>Treeratpituk P, Giles CL (2012). Name-Ethnicity Classification and Ethnicity-Sensitive Name Matching. Proceedings of the Twenty-Sixth Conference on Artificial Intelligence (pp. 1141-1147). AAAI-12. Toronto, ON, Canada</i>\r\n\r\nSexMachine 0.1.1: <a href=\"https://pypi.python.org/pypi/SexMachine/\">https://pypi.org/project/SexMachine</a>\r\n\r\nFirst names, for some Author-ity records lacking them, were harvested from outside bibliographic databases.\r\n\r\n• The code and back-end data is periodically updated and made available for query at <a href =\"http://abel.ischool.illinois.edu\">Torvik Research Group</a>\r\n\r\n• What is the format of the dataset?\r\nThe dataset contains 9,300,182 rows and 10 columns\r\n1. auid: unique ID for Authors in Author-ity 2009 (PMID_authorposition)\r\n2. name: full name used as input to EthnicSeer)\r\n3. EthnicSeer: predicted ethnicity; ARA, CHI, ENG, FRN, GER, IND, ITA, JAP, KOR, RUS, SPA, VIE, XXX\r\n4. prop: decimal between 0 and 1 reflecting the confidence of the EthnicSeer prediction\r\n5. lastname: used as input for Ethnea+Genni\r\n6. firstname: used as input for Ethnea+Genni\r\n7. Ethnea: predicted ethnicity; either one of 26 (AFRICAN, ARAB, BALTIC, CARIBBEAN, CHINESE, DUTCH, ENGLISH, FRENCH, GERMAN, GREEK, HISPANIC, HUNGARIAN, INDIAN, INDONESIAN, ISRAELI, ITALIAN, JAPANESE, KOREAN, MONGOLIAN, NORDIC, POLYNESIAN, ROMANIAN, SLAV, THAI, TURKISH, VIETNAMESE) or two ethnicities (e.g., SLAV-ENGLISH), or UNKNOWN (if no one or two dominant predictons), or TOOSHORT (if both first and last name are too short)\r\n8. Genni: predicted gender; 'F', 'M', or '-'\r\n9. SexMac: predicted gender based on third-party Python program (default settings except case_sensitive=False); female, mostly_female, andy, mostly_male, male)\r\n10. SSNgender: predicted gender based on US SSN data; 'F', 'M', or '-'\r\n"]}
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2018-04-23T19:33:07Z
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RelatedMaterial
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create: {"material_type"=>"Article", "availability"=>nil, "link"=>"https://www.aaai.org/ocs/index.php/AAAI/AAAI12/paper/view/5180", "uri"=>"https://www.aaai.org/ocs/index.php/AAAI/AAAI12/paper/view/5180", "uri_type"=>"URL", "citation"=>"Treeratpituk P, Giles CL (2012). Name-Ethnicity Classification and Ethnicity-Sensitive Name Matching. Proceedings of the Twenty-Sixth Conference on Artificial Intelligence (pp. 1141-1147). AAAI-12. Toronto, ON, Canada", "dataset_id"=>536, "selected_type"=>"Article", "datacite_list"=>"IsSupplementedBy "}
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2018-04-23T19:30:15Z
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RelatedMaterial
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create: {"material_type"=>"Article", "availability"=>nil, "link"=>"https://doi.org/10.1145/2467696.2467720", "uri"=>"10.1145/2467696.2467720", "uri_type"=>"DOI", "citation"=>"Smith, B., Singh, M., & Torvik, V. (2013). A search engine approach to estimating temporal changes in gender orientation of first names. Proceedings Of The ACM/IEEE Joint Conference On Digital Libraries, (JCDL 2013 - Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries), 199-208. doi:10.1145/2467696.2467720", "dataset_id"=>536, "selected_type"=>"Article", "datacite_list"=>"IsSupplementedBy "}
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2018-04-23T19:30:15Z
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RelatedMaterial
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create: {"material_type"=>"Article", "availability"=>nil, "link"=>"http://hdl.handle.net/2142/88927", "uri"=>"hdl.handle.net/2142/88927", "uri_type"=>"Handle", "citation"=>"Torvik VI, Agarwal S. Ethnea -- an instance-based ethnicity classifier based on geocoded author names in a large-scale bibliographic database. International Symposium on Science of Science March 22-23, 2016 - Library of Congress, Washington, DC, USA. http://hdl.handle.net/2142/88927", "dataset_id"=>536, "selected_type"=>"Article", "datacite_list"=>"IsSupplementedBy"}
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2018-04-23T19:30:15Z
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Dataset
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update: {"description"=>["prepared by Vetle Torvik April 5, 2018\r\n\r\nThe dataset comes as a single tab-delimited ASCII encoded file, and should be about 717MB uncompressed.\r\n\r\nHow was the dataset created?\r\nFirst and lastnames of authors in the Author-ity 2009 dataset was processed through several tools to predict ethnicities and gender, including\r\n\r\nEthnea+Genni as described in:\r\n\r\nTorvik VI, Agarwal S. Ethnea -- an instance-based ethnicity classifier based on geocoded author names in a large-scale bibliographic database. International Symposium on Science of Science March 22-23, 2016 - Library of Congress, Washington, DC, USA.\r\nhttp://hdl.handle.net/2142/88927\r\n\r\nSmith BN, Singh M, Torvik VI (2013). A search engine approach to estimating temporal changes in gender orientation of first names. Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries (pp. 199-208). JCDL '13. Indianapolis, IN, USA.\r\n\r\nEthnicSeer: http://singularity.ist.psu.edu/ethnicity\r\n Treeratpituk P, Giles CL (2012). Name-Ethnicity Classification and Ethnicity-Sensitive Name Matching. Proceedings of the Twenty-Sixth Conference on Artificial Intelligence (pp. 1141-1147). AAAI-12. Toronto, ON, Canada\r\n\r\nSexMachine 0.1.1: https://pypi.python.org/pypi/SexMachine/\r\n\r\nFirst names, for some Author-ity records lacking them, were harvested from outside bibliographic databases.\r\n\r\nThe code and back-end data is periodically updated and made available for query here\r\nhttp://abel.ischool.illinois.edu\r\n\r\nWhat is the format of the dataset?\r\n1. auid: unique ID for Authors in Author-ity 2009 (PMID_authorposition)\r\n2. name: full name used as input to EthnicSeer)\r\n2. EthnicSeer: predicted ethnicity; ARA, CHI, ENG, FRN, GER, IND, ITA, JAP, KOR, RUS, SPA, VIE, XXX\r\n3. prop: decimal between 0 and 1 reflecting the confidence of the EthnicSeer prediction\r\n4. lastname: used as input for Ethnea+Genni\r\n5. firstname: used as input for Ethnea+Genni\r\n6. Ethnea: predicted ethnicity; either one of 26 (AFRICAN, ARAB, BALTIC, CARIBBEAN, CHINESE, DUTCH, ENGLISH, FRENCH, GERMAN, GREEK, HISPANIC, HUNGARIAN, INDIAN, INDONESIAN, ISRAELI, ITALIAN, JAPANESE, KOREAN, MONGOLIAN, NORDIC, POLYNESIAN, ROMANIAN, SLAV, THAI, TURKISH, VIETNAMESE) or two ethnicities (e.g., SLAV-ENGLISH), or UNKNOWN (if no one or two dominant predictons), or TOOSHORT (if both first and last name are too short)\r\n7. Genni: predicted gender; 'F', 'M', or '-'\r\n8. SexMac: predicted gender based on third-party Python program (default settings except case_sensitive=False); female, mostly_female, andy, mostly_male, male)\r\n9. SSNgender: predicted gender based on US SSN data; 'F', 'M', or '-'\r\n", "Prepared by Vetle Torvik April 5, 2018\r\n\r\nThe dataset comes as a single tab-delimited ASCII encoded file, and should be about 717MB uncompressed.\r\n\r\n• How was the dataset created?\r\nFirst and lastnames of authors in the Author-ity 2009 dataset was processed through several tools to predict ethnicities and gender, including\r\n\r\nEthnea+Genni as described in:\r\n\r\n<i>Torvik VI, Agarwal S. Ethnea -- an instance-based ethnicity classifier based on geocoded author names in a large-scale bibliographic database. International Symposium on Science of Science March 22-23, 2016 - Library of Congress, Washington, DC, USA.\r\nhttp://hdl.handle.net/2142/88927</i>\r\n\r\n<i>Smith, B., Singh, M., & Torvik, V. (2013). A search engine approach to estimating temporal changes in gender orientation of first names. Proceedings Of The ACM/IEEE Joint Conference On Digital Libraries, (JCDL 2013 - Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries), 199-208. doi:10.1145/2467696.2467720</i>\r\n\r\nEthnicSeer: http://singularity.ist.psu.edu/ethnicity\r\n<i>Treeratpituk P, Giles CL (2012). Name-Ethnicity Classification and Ethnicity-Sensitive Name Matching. Proceedings of the Twenty-Sixth Conference on Artificial Intelligence (pp. 1141-1147). AAAI-12. Toronto, ON, Canada</i>\r\n\r\nSexMachine 0.1.1: < a href=\"https://pypi.python.org/pypi/SexMachine/\">https://pypi.org/project/SexMachine/</a>\r\n\r\nFirst names, for some Author-ity records lacking them, were harvested from outside bibliographic databases.\r\n\r\n• The code and back-end data is periodically updated and made available for query at <a href =\"http://abel.ischool.illinois.edu\">Torvik Research Group</a>\r\n\r\n• What is the format of the dataset?\r\nThe dataset contains 9,300,182 rows and 10 columns\r\n1. auid: unique ID for Authors in Author-ity 2009 (PMID_authorposition)\r\n2. name: full name used as input to EthnicSeer)\r\n3. EthnicSeer: predicted ethnicity; ARA, CHI, ENG, FRN, GER, IND, ITA, JAP, KOR, RUS, SPA, VIE, XXX\r\n4. prop: decimal between 0 and 1 reflecting the confidence of the EthnicSeer prediction\r\n5. lastname: used as input for Ethnea+Genni\r\n6. firstname: used as input for Ethnea+Genni\r\n7. Ethnea: predicted ethnicity; either one of 26 (AFRICAN, ARAB, BALTIC, CARIBBEAN, CHINESE, DUTCH, ENGLISH, FRENCH, GERMAN, GREEK, HISPANIC, HUNGARIAN, INDIAN, INDONESIAN, ISRAELI, ITALIAN, JAPANESE, KOREAN, MONGOLIAN, NORDIC, POLYNESIAN, ROMANIAN, SLAV, THAI, TURKISH, VIETNAMESE) or two ethnicities (e.g., SLAV-ENGLISH), or UNKNOWN (if no one or two dominant predictons), or TOOSHORT (if both first and last name are too short)\r\n8. Genni: predicted gender; 'F', 'M', or '-'\r\n9. SexMac: predicted gender based on third-party Python program (default settings except case_sensitive=False); female, mostly_female, andy, mostly_male, male)\r\n10. SSNgender: predicted gender based on US SSN data; 'F', 'M', or '-'\r\n"], "keywords"=>["", "Androgyny; Bibliometrics; Data mining; Earch engine; Gender; Semantic orientation; Temporal prediction; Textual markers"], "version_comment"=>[nil, ""], "subject"=>["", "Social Sciences"]}
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2018-04-23T19:30:14Z
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