2016 07-19 gender in computational biology
TRANSCRIPT
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GENDER DISPARITY IN COMPUTATIONAL BIOLOGY
19 JULY, 2016
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2016-07-19
OUTLINE
▸ Gender differences in Publication (background)
▸ Computationally inferring gender
▸ Gender in Biology, Computational Biology and Computer Science
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2016-07-19
MEN PUBLISH MORE PAPERS THAN WOMEN
West JD, Jacquet J, King MM, Correll SJ, Bergstrom CT (2013) The Role of Gender in Scholarly Authorship. PLoS ONE 8(7): e66212. doi: 10.1371/journal.pone.0066212
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2016-07-19
MEN PUBLISH MORE PAPERS THAN WOMEN
http://fivethirtyeight.com/features/in-science-it-matters-that-women-come-last/
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2016-07-19
GENDER DISPARITY VARIES BY FIELD
West JD, Jacquet J, King MM, Correll SJ, Bergstrom CT (2013) The Role of Gender in Scholarly Authorship. PLoS ONE 8(7): e66212. doi: 10.1371/journal.pone.0066212
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2016-07-19
GENDER DISPARITY VARIES BY FIELD
West JD, Jacquet J, King MM, Correll SJ, Bergstrom CT (2013) The Role of Gender in Scholarly Authorship. PLoS ONE 8(7): e66212. doi: 10.1371/journal.pone.0066212
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2016-07-19
GENDER DISPARITY VARIES BY FIELD
West JD, Jacquet J, King MM, Correll SJ, Bergstrom CT (2013) The Role of Gender in Scholarly Authorship. PLoS ONE 8(7): e66212. doi: 10.1371/journal.pone.0066212
http://www.eigenfactor.org/gender/#
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2016-07-19
https://commons.wikimedia.org/wiki/File:Matilda_Effect.png
MEN GET MORE CREDIT FOR DISCOVERY THAN WOMEN
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2016-07-19
MEN ARE MORE LIKELY TO BE CITED THAN WOMEN
http://www.nature.com/news/bibliometrics-global-gender-disparities-in-science-1.14321
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2016-07-19
MEN CITE THEMSELVES MORE OFTEN THAN WOMEN
http://arxiv.org/abs/1607.00376
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2016-07-19
INFERRING GENDER FROM FIRST NAMES
West JD, Jacquet J, King MM, Correll SJ, Bergstrom CT (2013) The Role of Gender in Scholarly Authorship. PLoS ONE 8(7): e66212. doi: 10.1371/journal.pone.0066212
We use US Social Security Administration records to determine gender from first names. The US Social Security Administration website (http://www.ssa.gov/oact/babynames/) makes available the top 1000 names annually for each of the 153 million boys and 143 million girls born from 1880–2010. (These data acknowledge only two genders.) We assume we can identify an author's gender if the author's first name is associated with a single gender in social security records at least 95% of the time, as with ‘Mary’, or ‘John’. Otherwise, as with ‘Leslie’ or ‘Sidney’, we are unable to identify the gender and do not include that author in our analysis.
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2016-07-19
INFERRING GENDER FROM FIRST NAMES
“To provide the highest possible accuracy, we combine the data of multiple data sources. We use data from publicly available governmental sources and combine them with data we crawl from social networks, which provides you the best possible matches. Each name has to be verified by different sources to be added to our list.”
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2016-07-19
INFERRING GENDER FROM FIRST NAMES
“To provide the highest possible accuracy, we combine the data of multiple data sources. We use data from publicly available governmental sources and combine them with data we crawl from social networks, which provides you the best possible matches. Each name has to be verified by different sources to be added to our list.”
BENEFITS:More names available than from census records alone
Androgynous names have probabilities associated
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2016-07-19
INFERRING GENDER FROM FIRST NAMES
“To provide the highest possible accuracy, we combine the data of multiple data sources. We use data from publicly available governmental sources and combine them with data we crawl from social networks, which provides you the best possible matches. Each name has to be verified by different sources to be added to our list.”
MAJOR ISSUES:~50% of names have no gender information
Non-western names are less likely to have gender information
BENEFITS:More names available than from census records alone
Androgynous names have probabilities associated
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2016-07-19
GUESSES MATCH VERIFIED GENDERS
BMJ 2016; 352 doi: http://dx.doi.org/10.1136/bmj.i847 (Published 02 March 2016)
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2016-07-19
GUESSES MATCH VERIFIED GENDERS
BMJ 2016; 352 doi: http://dx.doi.org/10.1136/bmj.i847 (Published 02 March 2016)
Known
Known w/o
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P(f
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2016-07-19
OUTLINE
▸ Gender differences in Publication (background)
▸ Computationally inferring gender
▸ Gender in Biology, Computational Biology and Computer Science
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2016-07-19
MOTIVATION
West JD, Jacquet J, King MM, Correll SJ, Bergstrom CT (2013) The Role of Gender in Scholarly Authorship. PLoS ONE 8(7): e66212. doi: 10.1371/journal.pone.0066212
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2016-07-19
MOTIVATION
West JD, Jacquet J, King MM, Correll SJ, Bergstrom CT (2013) The Role of Gender in Scholarly Authorship. PLoS ONE 8(7): e66212. doi: 10.1371/journal.pone.0066212
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2016-07-19
MOTIVATION
West JD, Jacquet J, King MM, Correll SJ, Bergstrom CT (2013) The Role of Gender in Scholarly Authorship. PLoS ONE 8(7): e66212. doi: 10.1371/journal.pone.0066212
COULD BIOLOGY BE A PATH TO COMPUTATIONAL/
QUANTITATIVE SKILLS?
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2016-07-19
METHODOLOGY
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2016-07-19
METHODOLOGY
▸ Download article info from Pubmed
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2016-07-19
METHODOLOGY
▸ Download article info from Pubmed
▸ MeSH Terms: “Biology” and “Computational Biology”
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2016-07-19
METHODOLOGY
▸ Download article info from Pubmed
▸ MeSH Terms: “Biology” and “Computational Biology”
▸ 1997-2014
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2016-07-19
METHODOLOGY
▸ Download article info from Pubmed
▸ MeSH Terms: “Biology” and “Computational Biology”
▸ 1997-2014
▸ Parse XML documents for author names, dates etc
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2016-07-19
METHODOLOGY
▸ Download article info from Pubmed
▸ MeSH Terms: “Biology” and “Computational Biology”
▸ 1997-2014
▸ Parse XML documents for author names, dates etc
▸ http://nbviewer.jupyter.org/github/kescobo/gender-comp-bio/blob/name_stats/src/xml_parsing.ipynb
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2016-07-19
METHODOLOGY
▸ Download article info from Pubmed
▸ MeSH Terms: “Biology” and “Computational Biology”
▸ 1997-2014
▸ Parse XML documents for author names, dates etc
▸ http://nbviewer.jupyter.org/github/kescobo/gender-comp-bio/blob/name_stats/src/xml_parsing.ipynb
▸ Use Gender API names to guess genders
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2016-07-19
METHODOLOGY
PMID Date Journal Name Position Dataset P(Female) Count26251854 2015/08/06 JEmpirResHum
ResEthics Masaru last bio 0.01 345
26251854 2015/08/06 JEmpirResHumResEthics Ituro second bio NA 0
26251854 2015/08/06 JEmpirResHumResEthics Naoaki penulEmate bio 0 37
26251854 2015/08/06 JEmpirResHumResEthics Mayumi other bio 0.97 654
26152079 2015/07/08 ArchIntHistSci(Paris) Vallori first bio NA 0
26152076 2015/07/08 ArchIntHistSci(Paris) Pierre-Olivier first bio NA 0
26152076 2015/07/08 ArchIntHistSci(Paris) Bernardino last bio 0.02 627
26152075 2015/07/08 ArchIntHistSci(Paris) Dolores first bio 0.98 4438
26152074 2015/07/08 ArchIntHistSci(Paris) Simone first bio 0.33 54976
26031011 2015/06/02 Pak.J.Biol.Sci. Jamuna first bio 0.89 174
26031011 2015/06/02 Pak.J.Biol.Sci. Johanna last bio 0.98 15733
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2016-07-19
DATA
▸ Biology (1997-2014)
▸ Publications: 202,818
▸ Authors: 1,111,776
▸ Computational Biology (1997-2014)
▸ Publications: 42882
▸ Authors: 244,141
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2016-07-19
DATA
▸ Biology (1997-2014)
▸ Publications: 202,818
▸ Authors: 1,111,776
▸ Computational Biology (1997-2014)
▸ Publications: 42882
▸ Authors: 244,141
▸ Unique Names: 74760
▸ % Names with unknown gender: 43.0%
▸ % Authors with unknown gender: 26.6%
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first
seco
ndoth
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penulti
mat
elas
t0.0
0.1
0.2
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0.4
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Primary Articles 1997-2014
Author Position
P(f
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e)BioComp
Error Bars: 95% confidence interval based on 1000 sample bootstrap
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first
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mat
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Primary Articles 1997-2014
Author Position
P(f
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Error Bars: 95% confidence interval based on 1000 sample bootstrap
first
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penulti
mat
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t0.0
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0.2
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Author Position
P(f
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Nature, Science, Cell 1997-2014
BioComp
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first
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t0.0
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Primary Articles 1997-2014
Author Position
P(f
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Error Bars: 95% confidence interval based on 1000 sample bootstrap
first
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mat
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Nature, Science, Cell 1997-2014
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PLoS Journals
PLoS Biol.PLoS Comput. Biol.
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first
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Author Position
P(f
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Error Bars: 95% confidence interval based on 1000 sample bootstrap
first
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Author Position
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Nature, Science, Cell 1997-2014
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2005 2010 20150.0
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P(f
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PLoS Journals
PLoS Biol.PLoS Comput. Biol.
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J. C
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Cou
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0.0
0.2
0.4
0.6
0.8
Journal
P(fe
mal
e)By Journal
![Page 37: 2016 07-19 Gender in Computational Biology](https://reader036.vdocuments.us/reader036/viewer/2022062503/58ed738d1a28ab29078b462f/html5/thumbnails/37.jpg)
2016-07-19
ARXIV DATA
2006 2008 2010 2012 20140.0
0.1
0.2
0.3
0.4
0.5
YearP(female)
Arxiv
Quant BioCS
first
seco
ndoth
er
penulti
mat
elas
t0.0
0.1
0.2
0.3
0.4
0.5
Author Position
P(f
emal
e)
Arxiv Articles 2007-2014
Quant. BioCS
![Page 38: 2016 07-19 Gender in Computational Biology](https://reader036.vdocuments.us/reader036/viewer/2022062503/58ed738d1a28ab29078b462f/html5/thumbnails/38.jpg)
2016-07-19
CONCLUSIONS
![Page 39: 2016 07-19 Gender in Computational Biology](https://reader036.vdocuments.us/reader036/viewer/2022062503/58ed738d1a28ab29078b462f/html5/thumbnails/39.jpg)
2016-07-19
CONCLUSIONS
▸ There are large gender disparities in publishing
![Page 40: 2016 07-19 Gender in Computational Biology](https://reader036.vdocuments.us/reader036/viewer/2022062503/58ed738d1a28ab29078b462f/html5/thumbnails/40.jpg)
2016-07-19
CONCLUSIONS
▸ There are large gender disparities in publishing
▸ Computational Biology has larger disparities than Biology as a whole
![Page 41: 2016 07-19 Gender in Computational Biology](https://reader036.vdocuments.us/reader036/viewer/2022062503/58ed738d1a28ab29078b462f/html5/thumbnails/41.jpg)
2016-07-19
CONCLUSIONS
▸ There are large gender disparities in publishing
▸ Computational Biology has larger disparities than Biology as a whole
▸ But maybe… better than computer science?