Visualizing Gender Balance in Conferences
10.6092/issn.2532-8816/8585
La visualizzazione dei dati è un potente strumento per le borse di studio digitali, ma non senza le sue insidie. Sulla base della tesi di laurea "Visualizing Gender Balance" che mette a confronto dieci conferenze di informatica, diverse tecniche e strumenti di visualizzazione vengono sottoposti a una revisione critica. Il set di dati alla base delle visualizzazioni contiene dati che i ricercatori incontrano quotidianamente: informazioni bibliografiche. L'analisi di gruppi più ampi di autori che scrivono e pubblicano per conferenze in informatica cambia la nostra percezione del (im)balance di genere in quest'area di ricerca accademica. Ma solo un'attenta cura e visualizzazione può veramente rivelare ciò che accade dietro le quinte. Ancora più complicata, dettagliata e sfumata la visualizzazione, più diventa difficile per un occhio non addestrato interpretare i modelli.
text/html
2532-8816
8585
5
en
Sytze Van Herck
Umanistica Digitale
Bibliographic Analysis
Visualizing Gender Balance in Conferences
Text.Serial.Journal
https://umanisticadigitale.unibo.it/
Articles
2019-05-27
2018-10-01
2019-05-27
2020-05-19
Um. Dig.
Sytze Van Herck
2019/05/27
10.6092/issn.2532-8816/8585
Center for Contemporary Digital History, University of Luxembourg
2532-8816
5
Bibliographic Analysis
en
Visualizing Gender Balance in Conferences
https://umanisticadigitale.unibo.it/article/view/8585/9231
     Umanistica Digitale - ISSN:2532-8816 - n.5, 2019
S. Van Herck – Visualizing Gender Balance in Conferences
DOI: https://doi.org/10.6092/issn.2532-8816/8585



               Visualizing Gender Balance in Conferences


                                               Sytze Van Herck

                                           University of Luxembourg
                                          sytze.vanherck@uni.lu




           Abstract. Data visualization is a powerful tool for digital scholarship yet not without its
           pitfalls. Based on the dissertation “Visualizing Gender Balance” comparing ten computer
           science conferences, several visualization techniques and tools undergo a critical review.
           The dataset underlying the visualizations contains data researchers encounter daily:
           bibliographic information. Analyzing larger sets of authors writing and publishing for
           conferences in computer science changes our perception of the gender (im)balance in this
           academic research area. But only a careful curation and visualization can truly reveal what
           goes on behind the scenes. Still the more complicated, detailed and nuanced the
           visualization, the harder it becomes for an untrained eye to interpret the patterns.
           La visualizzazione dei dati è un potente strumento per gli studi informatici, ma non è
           priva di insidie. Sulla base della presentazione "Visualizing Gender Balance", che mette a
           confronto dieci diverse conferenze di informatica, vengono qui sottoposti a revisione
           critica diverse tecniche e strumenti di visualizzazione. Il dataset alla base delle
           visualizzazioni è costituito da dati con cui i ricercatori hanno a che fare quotidianamente:
           informazioni bibliografiche. L'analisi di ampi gruppi di autori che scrivono e pubblicano
           nel campo dell'informatica cambia la nostra percezione dello (s)quilibrio di genere in
           quest'area di ricerca accademica. Tuttavia, solo un'attenta cura può veramente rivelare ciò
           che accade dietro le quinte. Più complicata, dettagliata e sfumata è la visualizzazione,
           infatti, più diventa difficile per un occhio non addestrato interpretare i modelli.



           Introduction

As Virginia Valian discusses in her article Beyond Gender Schemas people generally fail to
recognize gender equity problems.1 When people do recognize gender disparities, they often
rely on four possible explanations. First, the pipeline problem refers to the decline in the
percentage of women from undergraduate to graduate to professional status. Second, the child-
care responsibility indicates that when child care is seen as women’s work they are more likely
to become part-time workers. The third problem of different values based on gender originates

1   25..


                                                       91
      Umanistica Digitale - ISSN:2532-8816 - n.5, 2019

in survey data suggesting that men are more willing than women to forgo a balanced personal
life. Finally, the lack of acculturation refers to a presumed lack of understanding by women on
how to be successful.2
Furthermore, research confronting structural sexism and racism is often marginalized and
trivialized.3 In computational history the importance of gender dynamics are devalued
according to historians of technology such as Janet Abbate and Marie Hicks. 4 Similarly, racial
inclusion in STEM (science, technology, engineering and math) fields has only recently
received attention in both the book and the film on Hidden Figures.5 The concept of
intersectionality introduced by Kimberlé Crenshaw directs attention to the interaction of
multiple power structures such as race, sexuality, class and ability. 6 The intersectionality
framework provides both a methodological approach and lived experience of academics in the
field of digital humanities and computer science.
In the field of Digital Humanities, Nickoal Eichmann-Kalwara, Jeana Jorgensen and Scott B.
Weingart studied geographic, disciplinary and demographic diversity between 2000 and 2015
at the Alliance of Digital Humanities Organizations (ADHOs) conference. 7 Their analysis
reveals “a growing awareness of diversity-related issues, with moderate improvements in
regional diversity”, but a stagnation in gender diversity. 8 Although women occupy prominent
positions in the community’s core, they occupy less space in the much larger periphery of
authors.9 Yet the peer review process shows visible bias against authors with non-English
names.10 Furthermore biases around subject matter reflect gender disparities in specific
disciplines, a phenomenon explained by historian Lynn Hunt. 11 The feminization of a field is
usually paired with a decline in status and resources. 12 “There is a clear correlation between
relative pay and the proportion of women in a field: those academic fields that have attracted a
relatively high proportion of women pay less on average than those that have not attracted
women in the same numbers.” 13 So even though women are just as likely to get accepted as
men if they submit a presentation on the same topic, topics gendered towards women are less
likely to get accepted.14 Gender studies for instance has an acceptance rate of 60 percent,
whereas a male-skewed topic such as text analysis accepts 83 percent of submissions. 15 Barbara
Bordalejo’s minority report further demonstrates an Anglophone bias in Digital Humanities,

2    25..
3    21., ix.
4    21., ix; 15..
5    21., xi; 19..
6    21., xi; 8..
7    9..
8    9., 86.
9    9., 86.
10   9., 73.
11   9., 84.
12   9., 75; 16..
13   16..
14   9., 85.
15   9., 85.


                                              92
                                     S. Van Herck – Visualizing Gender Balance in Conferences

and includes personal attacks openly denigrating her work and feminist research by extension. 16
In the field of Computer Science, research participation and collaboration of female authors
increased by less than 0.1% per year between 2000 and 2015, and male researchers present
79% of actively publishing members in Computer Science conferences. 17 The European report
on She Figures mentions that “[women] made up 21% of those pursuing PhDs in computing
in 2012”, therefore remaining underrepresented within the subfield of computing. 18
Furthermore, “no progress has been made [between 2010 and 2013] to promote women to
grade A positions, (…) women remain relatively more present at lower level of the academic
career path”.19
In order to expand the analysis of gender inequality within the Computer Sciences and
compare the results to the geographic, disciplinary and demographic diversity in the Digital
Humanities, several data visualizations are included. As the graphic display of abstract
information, data visualization serves two purposes, namely sense-making or data analysis and
communication.20 As illustrated in the report of Malu A.C. Gatto on Making Research Useful:
Current Challenges and Good Practices in Data Visualisation, “academics have often struggled to
share their data with other actors and to disseminate their research findings to broader
audiences.”21 However, data visualization can truly advance research, not only as a
communication tool towards a broader audience, but especially as a tool that allows pre-
attentive processing of vast amounts of information. In short, “data visualization reduces
knowledge gaps.”22 Since conference data sets are often too large to process without the help of
visualizations, I would like to introduce several techniques.
Data visualization could provide one possible answer to Michael Jensen questions regarding
digital scholarship, asking: “how can we most appropriately support the creation and
presentation of intellectually interesting material, maximize its communicative and pedagogical
effectiveness, ensure its stability and continual engagement with the growing information
universe, and enhance the reputations and careers of its creators and sustainers?” 23 In order to
engage with the audience or reader, the design of the project should not be overlooked, even
though “necessity often dictates that we adopt and adapt tools and technologies that were
originally developed for other needs and audiences.” 24 I will illustrate the development of and
uses for visualizations in Digital Humanities research based on my own visualizations of the
gender balance in ten computer science conferences from 2000 until 2015.25



16   21..
17   2..
18   11., 20.
19   11., 130.
20   12., 2229.
21   13., 4.
22   13., 5.
23   17., 551.
24   18., 539.
25   26..


                                              93
      Umanistica Digitale - ISSN:2532-8816 - n.5, 2019


            Which Data?


               DBLP Digital Library
Both datasets used in this article indirectly trace back to the “Data Bases and Logic
Programming” (DBLP) Digital Library created by Michael Ley for his PhD research. 26 The
browsable DBLP collection started as formatted HyperText Markup Language (HTML) based
on tables of contents, where each authors’ name linked to a list of their publications and to a
list of co-authors and their personal pages. 27 In the Evolving Coverage of Computer Science Sub-
fields in the DBLP Digital Library Florian Reitz and Oliver Hoffmann discovered thematic
biases in the coverage of computer science, with a narrow focus on Databases, Information
Retrieval, Programming Languages, and Digital Libraries and Data Mining. 28 By 2005 the
DBLP collection covered 65% of computer science conferences mentioned in the list created
by Reitz and Hoffmann.29


               Women in Computer Science research
The dataset used in this research was created by Swati Agarwal et al. in the context of an article
on Women in computer science research: what is the bibliography data telling us? 30 and can be
accessed online via Mendeley Data. 31 The data was retrieved on September 17, 2015 from the
DBLP bibliography database and includes the last 16 years (2000 – 2015) of publication
records from 81 Computer Science conferences. 32 For each article the dataset contains the year,
conference abbreviation, publisher and unique doi, as well as the domains defined by the
authors for each conference.33 The Author and Editor tables in the dataset provide the position
of the author in the paper or the position of the editor in the conference proceedings, a unique
name, their gender and the probability of a name being male, female or undetermined. 34
Finally, the dataset includes information about the affiliation of each author and for each paper
such as the name, type (Industry or Academic Institution) and country of each affiliation, as
well as the latitude and longitude.35


               Citation Network
For the citation network I needed a second dataset created by Swati Agarwal et Al. with a focus

26   20..
27   20..
28   24..
29   24..
30   2..
31   4..
32   2..
33   4..
34   4..
35   4..


                                               94
                                      S. Van Herck – Visualizing Gender Balance in Conferences

on seven ACM SIGWEB series of conferences36 which is also available via Mendeley. 37 The data
was again derived from the snapshot of the DBLP collection on September 17, 2015 covering
16 years of not seven, but eight SIGWEB conferences. 38 The most important addition to the
dataset is the Cited_By table where an identifier for paper A links to the identifier of the article
B that cited paper A.39 Although the data structure was more complicated, the information
about the papers, authors and their affiliation was still included. In order to test the citation
network visualizations, I only selected the first 1000 papers and their 716 related authors.


                Why bibliographic data?
Originally bibliographies improved the process of browsing through collections of books,
articles, journals and proceedings either per genre, per country or per language and mostly for
academic researchers and librarians. When these collections became accessible online, they
provided insights into academia through visualizations. 40 Bibliographic databases store and
provide rich information on both co-authorship and the citation networks of academics. 41
Several tools use this data to uncover research area evolutions and communities that show
current trends in scientific research, as well as academic social networks. 42



            What’s (not) behind the data?


                Human or algorithmic selection
A dataset is inherently curated and therefore leaves out other information. Whether data is
selected by a human or an algorithm, the selection or parameters could be biased. For example,
as I mentioned earlier, the DBLP Digital Library focusses only on certain areas of Computer
Science research.43 Furthermore, the creators of the first database on Women in Computer
Science Research manually assigned each conference to a certain sub-field such as Software
Engineering (SE), Data Engineering (DE), and Theory (TH), as well as Computer Science
(CS) in general.44 In Mind the Gap: Gender and Computer Science Conferences Antonio Fiscarelli
and I applied topic modelling to prevent such a subjective judgement on research areas, but we
still had to decide on a name for each category. 45 For the visualizations my co-supervisor and I
have made another subjective selection of ten different conferences ranging from

36   3..
37   4..
38   4..
39   4..
40   26..
41   28..
42   28..
43   24..
44   2..
45   27..


                                                95
      Umanistica Digitale - ISSN:2532-8816 - n.5, 2019

interdisciplinary (i.e. Computer-Human Interaction or CHI) to disciplinary (i.e. User-Interface
Software and Technology or UIST). Besides human interference with the data selection,
algorithms have enhanced the datasets with geographical information. The first dataset for
Women in Computer Science research combined several Application Programming Interfaces
(APIs) such as OpenStreetMap, Alchemy Language, Google Geocoding and Bing Geocoding
“to determine the type of affiliation (Industry or an Academic Institution)” and add the
coordinates of certain institutions. 46 Furthermore, the Genderize API determined the gender of
the authors based on their first name.47


               Assigning Gender
The Genderize API uses “big datasets of information, from user profiles across major social
networks” to determine the gender of a first name and “includes a certainty factor as well”. 48 In
Women in Computer Science Research the authors decided to include the gender of an author
only when their first name was known and the confidence score was over 60%. 49 Overall,
14,2% of authors did not have a gender in the dataset, whereas 69,1% of authors were
identified as male and only 16,7% as female. 50 The binary approach of such an algorithm
ignores the psychological and sociological use of the term gender, which originated in the
United States and signifies “the state of being male or female as expressed by social or cultural
distinctions and differences, rather than biological ones; the collective attributes of traits
associated with a particular sex; or determined as a result of one’s sex. Also: a (male or female)
group characterized in this way”. 51 Not only does an algorithm assign gender without taking
into account an individual’s agency to determine their gender on a spectrum rather than in
binary form, it also ignores change over time. Rather than ignoring gender in research
altogether, Eichmann-Kalwara et al. believe that “showing whether reviewers are less likely to
accept papers from authors who appear to be women can reveal entrenched biases, whether or
not the author actually identifies as a woman”.52



        What Data Visualization shows


               Data Processing
In order to test the data visualizations, several queries limited and structured the data further to
create smaller subsets per year and per conference for network visualizations, which were
combined to visualize authorship demographics and the evolution of the gender balance across

46   2..
47   2..
48   ‘Genderize.Io | Determine the Gender of a First Name’.
49   2..
50   2..
51   Oxford University Press, ‘Gender’.
52   10..


                                                  96
                                          S. Van Herck – Visualizing Gender Balance in Conferences

conferences and over the years. The data processing fell into three main steps:
     •    Querying. In order to perform the queries easily, I connected to the databases in
          Python and stored the results in variables. The queries differed for each single
          visualization, since they all required different information.
     •    Formatting. The visualizations created based on JavaScript libraries such as Google
          Charts, Google Maps API, Protovis, or D3.js only accepted data in the JavaScript
          Object Notation or JSON. Tableau on the other hand accepted Excel-files.
     •    Importing. The JSON-files for the first visualizations were imported using the jQuery
          asynchronous JavaScript and XML or AJAX method. Tableau used a drag and drop
          interface.




Figure 1: The data processing process. Arrows represent scripts, while post-its represent files


              Co-authorship network visualization
The first network visualization was based on an arc visualization in Protovis and showed every
single author represented as a dot on a horizontal line. The size of the dot or node represented
the number of co-authors. The color represented their gender and the lines connected authors
working on the same paper. Grey represents an unknown gender, pink represents female and
blue stands for male authors.
Because the dataset contains 48 877 authors in total for the selection of ten conferences, the co-
authorship network focuses on a specific year for a single conference. 2 shows the situation at
the relatively small ACM User Interface Software and Technology (UIST) conference in 2003
with a particularly low representation of female authors. Furthermore, the single-authored
papers were all written by men, whereas both unknown and female authors all co-authored
with male authors.




                                                     97
     Umanistica Digitale - ISSN:2532-8816 - n.5, 2019




                Figure 2: Protovis visualisation of the 2003 UIST conference




                     FIgure 3: Chord Diagram of the 2005 CHI conference co-
                     authorship network


In the second network visualization authors were again represented individually as a dot, but
this time arranged in the form of a circle and grouped by affiliation according to a hierarchical
edge-bundling example from D3.js. The lines show the relation between co-authors and when
hovering over an author, the incoming and outgoing links to co-authors are highlighted. In 3
the gender of authors is not included in the visualization of collaboration at the Computer-
Human Interaction (CHI) conference of 2005. The chord diagram does illustrate that authors
in this interdisciplinary research area will mostly collaborate within the same institution, or one
or more authors from a single external organization. Despite the limited selection of 1000
papers from the dataset, the chord diagram is very difficult to interpret due to the sheer
number of authors.
Unfortunately, network visualizations always run into the risk of cluttered screens, which is


                                                 98
                                       S. Van Herck – Visualizing Gender Balance in Conferences

especially true for larger networks. However, added interactivity and filtering the data allows
users to explore the results in a structured way. Another solution would be to cluster authors
either based on their institution or by research area to reduce the number of nodes. 53 Co-
authorship networks are less clear in displaying the gender balance, but color-coding nodes
according to gender does draw attention to the role of gender in collaboration. Furthermore,
the choice of colors is heavily based on culture in 2 with pink for women and blue for men. In
modern Western culture the color choice immediately conveys gender information, but it does
adhere to existing stereotypes and fails to take into account other cultural color conventions.


             Conference Demographics
First I experimented with simple bar and line charts provided by Google Charts API to
demonstrate the evolution of gender balance in computer science conferences over time and a
map visualization of all the affiliations included in the dataset using the Google Maps API.
These visualizations did not, however, allow for interactive exploration, so eventually
authorship demographics were visualized using the software platform of Tableau. Besides
studying the evolution of the gender balance per conference, other demographics include the
geographical location of authors based on their affiliation, as well as gender in relation to co-
authorship grouped by conference.
The first stacked bar chart shows the evolution of gender balance in ten computer science
conferences over a period of sixteen years. The horizontal axis was grouped per conference and
the stacked bars were color-coded according to gender, with blue representing male, orange
representing female and grey representing unknown. The interactive Tableau software provides
details-on-demand while hovering over the visualization and filtering per conference, year and
gender. Overall, research participation and collaboration of female authors increased by less
than 0.1% per year between 2000 and 2015 as previously mentioned.54




Figure 4: 100% stacked bar chart of the gender balance in ten CS conferences from 2000 to 2016


The percentage of male authors decreased in several interdisciplinary conferences such as CHI,

53 27..
54 2..


                                                 99
     Umanistica Digitale - ISSN:2532-8816 - n.5, 2019

and in the field of knowledge engineering including UIST and Knowledge Discovery and Data
mining (KDD) as well as software engineering with the International Conference on Software
Engineering (ICSE).55 Because of the relatively large proportion of authors where the gender is
unknown, the decrease in male authors does not necessarily indicate an increase in female
authors. Overall only the CHI conference has a relatively large percentage of female authors
representing between 25% and 30% of all authors. The lowest percentages of female authors
can be found at the Very Large DataBases (VLDB) and UIST conference.
The map visualization uses the same color scheme as the stacked bar chart, but gradually
changes from dark blue indicating a lack of female authors to bright orange representing 100%
female authorship on average. Each country also contains the exact number of authors affiliated
to institutions located in that country. Tableau uses the Mercator map projection, which means
that although the shape of countries is respected, area is not well represented. A common
critique of the Mercator projection is that Greenland is roughly the same size as the entire
continent of Africa. Furthermore, the map is Europe-centered and thus presents a eurocentric
view of the world. For example, the size of Russia immediately draws attention, but only 99
authors are affiliated with Russian institutions compared to 141 868 authors from the United
States.




Figure 5: Map of average percentage of female authors at ten CS conferences between 2000 and 2015.

Despite the relatively low number of authors, 33.3% of Russian-based researchers submitting
papers to CS conferences are identified as female, compared to only 4,94% of U.S.-based
researchers. The United States (38,2%) and China (7%) account for 45,2% of all authors of
which on average only 5% are female. Besides disciplinary differences in the gender balance,
the disparities are far bigger in the author affiliations from the United States and China than in
Russia. The different course in the history of computing of the former Soviet Union had the
opposite effect on gender balance in CS creating a previously female-dominated field. 56




55 27..
56 6..


                                                100
                                       S. Van Herck – Visualizing Gender Balance in Conferences




Figure 6: Treemap of the gender balance for each paper in ten CS conferences from 2000 to 2016
In a tree map visualization showing the percentage of women per paper and grouped by
conference, the same color-coding and filters were again adopted to allow additional data
exploration. The ten larger blocks each represent a single conference, with size referencing the
number of authors. Within each conference block, a single block represents a paper and a small
block refers to a single-authored paper whereas larger rectangles reference co-authored papers.
The filters can limit the results to a smaller range of years or exclude some of the conferences to
get a clearer view of the data. Furthermore, hovering over a block provides the exact number of
authors for a single paper, as well as the percentage of female authors for that paper.
Although the first network visualization showed that women at the UIST conference of 2003
generally co-authored papers, the same does not apply to other conferences, given the high
concentration of female authors in the bottom-right corner of single-authored papers. Overall,
the multi-authored papers on the left and top of each conference rectangle have few if any
female authors. Collaboration thus occurs mostly between men, except at the CHI conference
(top right).



       What Data Visualization doesn’t show


             Accessibility and Compatibility
In order to visualize the data, free access to software or existing code is rarely guaranteed since
this software is often commercial. Furthermore, if support for software or existing code ends,
the visualization will likely disappear entirely. Even at the time of the creation some of the
online visualizations are not supported by the browser or could appear different and even
distorted depending on the screen size and browser. Regardless of challenges related to the
visualization software, the main value of data visualization lies in both facilitating pre-attentive
processing, as well as communicating results.




                                                101
     Umanistica Digitale - ISSN:2532-8816 - n.5, 2019

             User Analysis
While the expert might find certain types of visualizations very useful, some graphical
representations such as the tree map in 5 do not communicate anything to a larger audience
because it requires previous knowledge of the structure and parameters included in tree maps.
Therefore, I evaluated the communicative value of visualizations with twelve Digital
Humanities students at KU Leuven. The test users performed nine tasks based on the
visualizations in a think aloud study and afterwards rated the visualizations on a System
Usability Scale. Furthermore, an open-ended questionnaire allowed participants to express their
opinion concluding the evaluation.
Based on the user test I found that 5 required explanation, but most participants correctly
identified the number of co-authors and especially the topic of the conference as an influence
on the percentage of female authors in conference papers. 57 The co-authorship network
visualization displayed in 2 and 3 frustrated the users, although nearly all of them recognized
more collaboration between authors from different affiliations in the CHI conferences of 2005
compared to the UIST conference in 2003. 58 Overall, the “interactive components and filters”
greatly improved how comprehensive a visualization was.



          Conclusion

Rather than merely analyzing issues regarding the gender imbalance in computer science, this
paper studies how to identify and visualize such issues through co-authorship networks and
conference demographics. However, a researcher first needs to critically examine and
understand the data at the base of the study. Without understanding the data, understanding
the visualization becomes difficult if not impossible. In the particular case of the DBLP
bibliographic database, topics are biased towards Databases, Information Retrieval, etc.
Furthermore, algorithmic bias creeps in through automatically assigning gender to authors
based on their first name. Finally, human selection of ten specific conferences further narrows
down the dataset.
Based on two co-authorship networks and three conference demographic visualizations, we can
better understand the pitfalls of data visualization and at the same time study intersectionality
in computer science conferences. For instance, due to the unknown gender of some researchers,
a decrease in male authors does not necessarily indicate an increase in female authors in the
100% stacked bar chart. However, taking a closer look at collaboration through co-authorship
networks can only be done for a single conference and specific year since both the arc- and
hierarchical edge-bundling visualizations otherwise become too cluttered to read. The map
visualization then illustrates the difference in gender balance between Russian and American
institutions yet distorts the size of countries in favor of a Western-centric vision of the world.
Furthermore, the tree map visualization might combine a lot of information such as the

57 26..
58 26..


                                              102
                                      S. Van Herck – Visualizing Gender Balance in Conferences

                                                  number of authors for each paper, the number
                                                  of papers at each conference and the gender
                                                  balance in co-authored papers. However, a tree
                                                  map visualization is not intuitive or easy to
                                                  interpret for anyone unfamiliar with both the
                                                  data and the visualization method.
                                                   The iterative process of creating and evaluating
                                                   visualizations is best explained through
                                                   Norman’s Action Cycle.59 In order to form
                                                   questions and find answers, the action cycle falls
                                                   into two gulfs. First a user or researcher needs to
                                                   set an intention and create an action plan
                                                   during the gulf of execution. If the action or in
Figure 7: I primi 100 concetti-chiave estratti dai this case the visualization shows an interesting
paper CLiC-it. La dimensione del font è            pattern, then a gulf of evaluation follows. The
proporzionale al peso normalizzato del concetto- perception of the visualization might lead to an
chiave.
                                                   interpretation which then needs to be evaluated
                                                   again. Interactivity thus allows further
exploration of the data by other researchers or the audience, while storytelling structures the
relations between different visualizations and guides the audience through the research in a few
clicks. The core value of visualizations for the Digital Humanities therefore lies in accelerating
data processing and raising possibilities for further research.



          References

     1. Abbate, Janet. 2012. Recoding Gender. Cambridge: The MIT Press.
     2. Agarwal, Swati, Nitish Mittal, Rohan Katyal, Ashish Sureka, and Denzil Correa.
        2016. “Women in Computer Science Research: What Is the Bibliography Data
        Telling Us?” SIGCAS Computers and Society 46 (1):7–19.
        https://doi.org/10.1145/2908216.2908218.
     3. Agarwal, Swati, Nitish Mittal, and Ashish Sureka. 2016. “A Glance at Seven ACM
        SIGWEB Series of Conferences.” SIGWEB Newsletter, no. Summer:1-10.
        https://doi.org/10.1145/2956573.2956578.
     4. Agarwal, Swati, Ashish Sureka, and Nitish Mittal. 2016. “DBLP Publications Records
        and ACM Metadata for SIGWEB Conferences” Mendeley Data 1.
        https://doi.org/10.17632/dn5d8fbkb9.1.
     5. Agarwal, Swati, Ashish Sureka, Nitish Mittal, Rohan Katyal, and Denzil Correa.
        2016. “DBLP Records and Entries for Key Computer Science Conferences” Mendeley

59 22..


                                                103
Umanistica Digitale - ISSN:2532-8816 - n.5, 2019

   Data 1. https://doi.org/10.17632/3p9w84t5mr.1.
6. Boykis, Vicki. 2017. “Being a Woman in Programming in the Soviet Union,” Github
   (blog), 22 February 2017. http://veekaybee.github.io/2017/02/22/being-a-woman-in-
   programming-in-the-soviet-union/.
7. Bordalejo, Barbara. 2018. “Minority Report. The Myth of Equality in Digital
   Humanities.” In Bodies of Information: Intersectional Feminism and the Digital
   Humanities, edited by E. Losh and J. Wernimont. Minneapolis: University of
   Minnesota Press. https://muse.jhu.edu/book/63071.
8. Crenshaw, Kimberle. 1991. “Mapping the Margins: Intersectionality, Identity Politics,
   and Violence against Women of Color.” Stanford Law Review 43 (6):1241–99.
   https://doi.org/10.2307/1229039.
9. Eichmann-Kalwara, Nickoal, Jeana Jorgensen, and Scott B. Weingart. 2016.
   “Representation at Digital Humanities Conferences (2000-2015).” Preprint,
   submitted 21/03/2016. https://doi.org/10.6084/m9.figshare.3120610.v1.
10. Eichmann-Kalwara, Jorgensen, Weingart. 2018. “Representation at Digital
    Humanities Conferences (2000-2015).” In Bodies of Information: Intersectional
    Feminism and the Digital Humanities, edited by E. Losh and J. Wernimont.
    Minneapolis: University of Minnesota Press. https://muse.jhu.edu/book/63071.
11. European Commission, Directorate-General for Research and Innovation. “She
    Figures 2015. Gender in Research and Innovation.” Website, 2 September 2016.
    https://publications.europa.eu/en/publication-detail/-/publication/f546dfed-41a9-
    11e6-af30-01aa75ed71a1.
12. Few, Stephen. 2013. “Data Visualization for Human Perception.” In The Encyclopedia
    of Human-Computer Interaction, edited by M. Soegaard and R. Friis Dam, 2nd
    edition. The Interaction Design Foundation. https://www.interaction-
    design.org/literature/book/the-encyclopedia-of-human-computer-interaction-2nd-
    ed/data-visualization-for-human-perception.
13. Gatto, Malu. 2015. Making Research Useful: Current Challenges and Good Practices in
    Data Visualisation. Oxford: Reuters Institute for the Study of Journalism.
14. ‘Genderize.Io | Determine the Gender of a First Name’. Website. https://genderize.io/.
15. Hicks, Marie. 2017. Programmed Inequality: How Britain Discarded Women
    Technologists and Lost Its Edge in Computing. Cambridge: MIT Press.
16. Hunt, Lynn. 1998. “Has the Battle Been Won? The Feminization of History.”
    Perspectives on History. https://www.historians.org/publications-and-
    directories/perspectives-on-history/may-1998/has-the-battle-been-won-the-
    feminization-of-history.
17. Jensen, Michael. 2004. “Intermediation and Its Malcontents: Validating
    Professionalism in the Age of Raw Dissemination.” In Companion to Digital


                                       104
                                     S. Van Herck – Visualizing Gender Balance in Conferences

        Humanities, edited by S. Schreibman, R. Siemens, and J.Unsworth. Oxford: Blackwell
        Publishing Professional. http://www.digitalhumanities.org/companion/.
     18. Kirschenbaum, Matthew G. 2004. ““So the Colors Cover the Wires”: Interface,
         Aesthetics, and Usability.” In Companion to Digital Humanities, edited by S.
         Schreibman, R. Siemens, and J. Unsworth. Oxford: Blackwell Publishing Professional.
         http://www.digitalhumanities.org/companion/.
     19. Lee Shetterly, Margot. 2016. Hidden Figures: The American Dream and the Untold
         Story of the Black Women Mathematicians Who Helped Win the Space Race. New York:
         William Morrow.
     20. Ley, Michael. 2002. “The DBLP Computer Science Bibliography: Evolution,
         Research Issues, Perspectives.” In Proceedings of the 9th International Symposium on
         String Processing and Information Retrieval, 1–10. Berlin: Springer-Verlag.
         http://dl.acm.org/citation.cfm?id=646491.694954.
     21. Losh, Elizabeth, and Jacqueline Wernimont. 2018. Bodies of Information: Intersectional
         Feminism and the Digital Humanities. Minneapolis: University of Minnesota Press.
         https://muse.jhu.edu/book/63071.
     22. Norman, Donald A. 2014. The Design of Everyday Things, Revised And Expanded
         Edition. Cambridge, MA: MIT Press. https://mitpress.mit.edu/books/design-
         everyday-things-revised-and-expanded-edition.
     23. Oxford English Dictionary, s.v. “Gender”.
     24. Reitz, Florian, and Oliver Hoffmann. 2010. “An Analysis of the Evolving Coverage of
         Computer Science Sub-Fields in the DBLP Digital Library.” In Research and Advanced
         Technology for Digital Libraries, edited by M. Lalmas, J. Jose, A. Rauber, F. Sebastiani,
         and I. Frommholz, 216–27. Berlin: Springer.
     25. Valian, Virginia. 2005. ‘Beyond Gender Schemas: Improving the Advancement of
         Women in Academia’, Hypatia 20 (3): 198-215.
     26. Van Herck, Sytze. 2017. “Visualising Gender Balance. Ten Computer Science
         Conferences and the Digital Humanities Conference Compared.” KU Leuven,
         Faculteit Wetenschappen, Master of Digital Humanities.
     27. Van Herck, Sytze, and Antonio Maria Fiscarelli. 2018. “Mind the Gap Gender and
         Computer Science Conferences.” In This Changes Everything – ICT and Climate
         Change: What Can We Do?, edited by D. Kreps, C. Ess, L. Leenen, and K. Kimppa,
         232–49. Berlin: Springer International Publishing.
     28. Wu, Meng Qi Yelena, Robert Faris, and Kwan-Liu Ma. 2013. “Visual Exploration of
         Academic Career Paths.” In 2013 IEEE/ACM International Conference on Advances in
         Social Networks Analysis and Mining (ASONAM 2013), 779–86.
         https://doi.org/10.1145/2492517.2492638.
Last URLs access: 30 September 2018


                                              105
