=Paper=
{{Paper
|id=Vol-2845/Paper_16.pdf
|storemode=property
|title=An Informetric Assessment of Various Research Fields Interactions on Base of Categorized Papers in Dimensions
|pdfUrl=https://ceur-ws.org/Vol-2845/Paper_16.pdf
|volume=Vol-2845
|authors=Serhiy Shtovba,Mykola Petrychko,Sergiy Rozhkov,Kostyantyn Kondrashov,Oksana Tereshchenkova,Maryna Falenkova,Hlib Horban,Ihor Kandyba,Mykhailo Dvoretskyi,Anzhela Boiko,Mykhailo Dvoretskyi,Svitlana Dvoretska,Hlib Horban,Yuriy Nezdoliy,Yaroslav Linder,Maksym Veres,Kateryna Kuzminova
|dblpUrl=https://dblp.org/rec/conf/iti2/ShtovbaP20
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==An Informetric Assessment of Various Research Fields Interactions on Base of Categorized Papers in Dimensions==
https://ceur-ws.org/Vol-2845/Paper_16.pdf
An Informetric Assessment of Various Research Fields
Interactions on Base of Categorized Papers in Dimensions
Serhiy Shtovba a, Mykola Petrychko b
a
Vasyl’ Stus Donetsk National University, 600-richchia str., 21, Vinnytsia, 21021, Ukraine
b
Vinnytsia National Technical University, Khmelnytske Shose, 95, Vinnytsia, 21021, Ukraine
Abstract
We estimated the level of interactions between all the pairs of the research groups and be-
tween all the pairs of the research divisions for 4 five-year periods on base of Dimensions da-
ta. The level of interactions is assessed by Jaccard index. Paired interaction for research divi-
sions shows that every consecutive five-year period has decreased irregularity of its distribu-
tion of interaction. Cumulative interaction shows that all research divisions tend to have their
stickiness index decreased over time. Paired interaction for research groups showed the same
results but difference between two consecutive periods is greater by a larger factor. On base
of interacting assessments we identified 5 research groups that have been suspiciously as-
signed to divisions in ANZSRC. Identified research groups have more similar research
groups in concurrent divisions with statistically significant level. Those 5 research groups al-
so have strong semantic ties with concurrent divisions, this can be an argument for adapting
of ANZSRC.
Keywords 1
Informetrics, research group, research division, interactions, interdisciplinary, Dimensions,
ANZSRC, Jaccard index, stickiness index, distribution, categorization.
1. Introduction
Interdisciplinarity is a fashion direction in modern education and science. Inter-disciplinarity is
impossible without interactions between research from various fields. For quantitative assessment of
level of interdisciplinarity, of level different research fields interactions a lot of approaches are pro-
posed in recent years.
Currently the most widely used approaches to quantitative measuring of interdisciplinarity use bib-
liometrics. They take into account co-authorships, collaborations, references, citations and co-
citations. The most common bibliometric technique for measuring inderdisciplinarity is some kind of
citation analysis. A citation environment is defined as all journals that cite or are cited by a specific
journal [1].
In [2] authors measure interdisciplinarity by relating papers’ cited journals to their corresponding
subject categories of Web of Science. Their metrics take into account the inter-relatedness of subject
categories. They use the following quantities deducted from citation of papers of a single subject cat-
egory: number of papers published in journals associated with this subject category; number of cites
by these papers to papers in journals associated with this subject category; number of cites to these
papers by papers in journal associated with this subject category.
Another approach based on citations is presented in [3]. It shows that the fraction of paper refer-
ences that point to work in other disciplines is increasing in both the natural and the social sciences
and the fraction that points to another specialty in the same discipline has a slight decline. The
IT&I-2020 Information Technology and Interactions, December 02–03, 2020, KNU Taras Shevchenko, Kyiv, Ukraine
EMAIL: shtovba@vntu.edu.ua, s.shtovba@donnu.edu.ua (S. Shtovba); mpetrychko@vntu.edu.ua, pet-
rychko.myckola@gmail.com (M. Petrychko)
ORCID: 0000-0003-1302-4899 (S. Shtovba); 0000-0001-6836-7843 (M. Petrychko).
©️ 2020 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
159
measures are done using variety – the spread of references across disciplines and disparity – the “in-
tellectual distance” between disciplines in references. The key results they concluded is that interdis-
ciplinarity research takes time to have an impact and that some fields as well as countries are more
interdisciplinary than others.
A slightly less used approach for measuring interdisciplinarity using bibliometric uses collabora-
tions. In [4] authors used graph of collaboration between researches to investigate the interdiscipli-
narity of scientific fields. They estimate similarity between science categories using text similarity
between their descriptions. Their measure of interdisciplinarity is based on Stirling’s diversity index.
Some of the studies use text analysis to quantify the interaction and evolution of scientific disci-
plines. In [5] authors compare different measures of dissimilarity between scientific fields. These
measures are based on expert classification, citations and information theory.
All the above mentioned approaches require large information resources. Methods that based on
citation analysis, are pretty inertial. It is impossible to assess interdisciplinarity of a new paper instant-
ly, because citations take time to take place. As opposed to the mentioned approaches, paper [6] pro-
posed a new technique to assess the similarity between research fields. The technique applies to in-
formation resources of Dimensions with already categorized research papers. Dimensions was
launched in 2018 and many researchers started using it immediately for bibliometric research [7, 8, 9,
10]. Dimensions uses hierarchical system of research classification, included divisions at high level
and research group at low level. In paper [6] similarity of research groups is assessing via Jaccard
index.
The current paper extends idea of [6] in the following directions: 1) assessing the research interac-
tions at the high level – at level of divisions; 2) assessing the interdisciplinarity at the low level taking
into account only interactions with research groups from different divisions according to arguments of
[11] about various impacts for interdisciplinarity levels for various combinations of research fields;
3) improving the research classifying system on base of actual statistics of research fields interactions.
Analysis of the research interactions is carried for 4 five-year periods on base of papers dated 2001–
2020.
2. Research Classification System Used in Dimensions
Dimensions uses two-level version of ANZSRC. There are 22 research divisions at high level with
157 groups at low level. For example, division 08 Information and Computing Sciences contains the
following research groups:
0801 Artificial Intelligence and Image Processing;
0802 Computation Theory and Mathematics;
0803 Computer Software;
0804 Data Format;
0805 Distributed Computing;
0806 Information Systems;
0807 Library and Information Studies;
0899 Other Information and Computing Sciences.
Dimensions has indexed over 110M research papers. Each paper is assigned to one or several re-
search groups. Such categorization is carried out in by Dimensions itself by special software based on
machine learning guided by topic experts [12]. As training data Dimensions uses title and abstract of
the paper for this categorization.
3. Interaction between research divisions
Based on Dimensions data we can retrieve a number of papers assigned to each research division.
At the same time, we can get the number of above-mentioned papers that were assigned to several
research divisions simultaneously. By using this information we assess the interaction of any pair of
research divisions using 2 queries. According to [6] we use Jaccard index for assessing the interac-
tions of two research divisions A and B as follows:
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N A B
S ( A, B ) ,
N A N B N A B
where N A is a number of papers in division A;
N B is a number of papers in research division B;
N A B is a number of papers assigned to divisions A and B simultaneously.
For example, there are 353 347 papers assigned to division 08 Information and Computing Scienc-
es in 2019, and 11391 of them also belong to division 17 Psychology and Cognitive Sciences. Divi-
sion 17 consists of 178 156 papers published in 2019. Hence, the quantitative assessment of interac-
11391
tion of these divisions is S (08, 17) 0.021 .
353347 178156 11391
Jaccard index distributions for two research divisions within the four of the five-year periods are
shown in Figure 1. The x-axis corresponds to ranked list of pairs of divisions sorted ascending for
each period separately. The y-axis corresponds to their level of interaction plotted in log scale. Over-
all, we analyzed 231 pairs of divisions. Figure 1 shows that every consecutive five-year period has
decreased irregularity of its distribution of interaction. We can see that by observing that pairs of divi-
sions from the beginning of Jaccard index distribution has their interaction decreased over time and
the pairs at the end of the distribution on the contrary – increased. This observation is confirmed by
Gini coefficient that decreases in time in the following way:
Gini D ( 2001 2005 ) 0.739 ;
Gini D ( 2006 2010 ) 0.737 ;
Gini D ( 2011 2015 ) 0.733 ;
Gini D ( 2016 2020 ) 0.733 .
mean (S Q4 )
Also, we observe decrease of D – ratio of mean value of Jaccard indexes from
mean (S Q1 )
the fourth and the first quartiles as follows:
D ( 2001 2005 ) 434 .9 ;
D ( 2006 2010 ) 372 .1 ;
D ( 2011 2015 ) 288 .1 ;
D ( 2016 2020 ) 250 .7 .
Figure 1: Distributions of paired interaction between divisions
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Figure 2 shows the dynamic of interaction of the most tied divisions. For each five-year period 5
pairs of divisions with the greatest Jaccard index are selected. For the first three periods, we observe
the same leaders of divisions’ pairs:
20 Language, Communication and Culture & 21 History and Archaeology;
01 Mathematical Sciences & 02 Physical Sciences;
19 Studies in Creative Arts and Writing & 20 Language, Communication and Culture;
02 Physical Sciences & 09 Engineering;
21 History and Archaeology & 22 Philosophy and Religious Studies.
Figure 2: Dynamics of seven leaders among research divisions
Two last pairs of the leaders moved down to the 6-th and 10-th positions respectively in the last
period. The following pairs replaced them: 03 Chemical Sciences & 09 Engineering, and 05 Environ-
mental Sciences & 06 Biological Sciences. Among seven leaders the following pair 02 Physical Sci-
ences & 09 Engineering has the highest decrease in interaction. Its Jaccard index dropped from
0.1085 in 2001–2005 to 0.0600 in 2016–2020. The data for the last 2 years shows that the decrease
has stopped – Jaccard index increased by a tiny value from 0.0595 in 2019 to 0.0560 in 2020. Among
leaders, the highest increasing in interaction is demonstrated by pair 05 Environmental Sciences & 06
Biological Sciences. It moved down from the position #12 in the first period to position #4 in the last
period. The data for the last 2 years shows that interaction between divisions 05 and 06 continues to
grow with 4.8% per year rate.
Figure 3 shows dynamic for the most changeable pairs of research divisions. We considered only
pairs that at least in one of the periods has fallen into a quartile with the greatest values of Jaccard
index. Within these pairs, we chose the 6 most dynamic; the one that has their Jaccard index changed
the most in terms of increasing and decreasing between the first (2001-2005) and last (2016-2020)
periods. Among chosen pairs the most rapid decrease in interaction happened for divisions 14 Eco-
nomics & 22 Philosophy and Religious Studies – it has dropped from 0.031 in 2001-2005 to 0.0121 in
2016-2020. The data for the last 2 years shows that it continues to decrease with 9.4% per year rate. In
2020, Jaccard index for divisions 14 & 22 takes a value of 0.0096. For all time of observations, the
highest increase in interaction happens for divisions 03 Chemical Sciences & 10 Technology – from
0.0073 in 2001-2005 to 0.0241 in 2016-2020. However, in the last five-year period the increase has
stopped. The data for the last 2 years shows that decrease is present with a rate of -9% per year. In
2020, Jaccard index for divisions 03 Chemical Sciences & 10 Technology takes value of 0.0203. For
the last 2 years, interaction between divisions 03 Chemical Sciences & 05 Environmental Sciences has
162
increased drastically (24%). In 2020 Jaccard index for divisions 03 Chemical Sciences & 05 Environ-
mental Sciences takes a value of 0.024.
In addition to paired interaction, we analyzed the cumulative interaction of divisions. For this
stickiness index [5] is used. Stickiness index G(A) of division A is a sum of all Jaccard indexes be-
tween A and other divisions as follows:
G( A) S ( A, p) ,
p: pF, p A
where F denotes a set of research divisions.
Figure 3: Dynamics for the most changeable research divisions between the first and last periods
Using stickiness indexes let’s find research divisions with the highest level of interaction. The dis-
tributions of stickiness indexes for each of the periods are shown in Figure 4. The leaders by sticki-
ness index are 09 Engineering and 16 Studies in Human Society for the all 4 periods.
Figure 4 shows that all research divisions tend to have their stickiness decreased over time. This
conclusion is confirmed by Gini index for stickiness distributions that decreases from 0.197 in the
first period to 0.188 in the last period. For all time of observations, the most rapid decrease in sticki-
ness index is demonstrated by the following research divisions:
22 Philosophy and Religious Studies – -34%;
02 Physical Sciences – -31%;
19 Studies in Creative Arts and Writing – -30%;
01 Mathematical Sciences – -26%;
12 Economics – -25%.
4. Interaction between research groups
In addition to research divisions based on Dimensions data we can retrieve a number of papers as-
signed with each research group. At the same time, we can get a number of above-mentioned papers
that were assigned to several research groups simultaneously. By using this data, we can assess the
level of interaction between two research groups in the same way as for divisions. For example, there
are 80115 papers assigned to group 0806 Information Systems in 2019, and 2934 of them also belong
to group 1503 Business and Management. Group 1503 consist of 59662 papers published in 2019.
163
Hence, the quantitative assessment of interactive between 0806 Information Systems and 1503 Busi-
2934
ness and Management is S (0806, 1503) 0.021 .
80115 59662 2934
Figure 4: Divisions’ stickiness indexes distribution
Figure 5 shows the Jaccard index distributions for pairs of research groups with different divisions
within the four of the five-year periods. We empirically set a threshold for a number of papers for a
research division to be 1000, so that research divisions with less than 1000 papers are not considered.
In total 145 research groups are considered, hence the interactive level of 10440 pairs of the research
groups for each five-year period were calculated. Most of the research groups have zero Jaccard in-
dex. Figure 4 shows the same results as with the research divisions’ interactions. But now the pairs at
the end of the distribution have their interactions increased by a larger factor which shows that as the
time goes on interdisciplinarity between research divisions increases. This can be seen by looking at
Figure 5 and observing that the first period 2001-2005 has 334 pairs of research divisions with Jac-
card index greater than zero. The last period has 551 pairs, which give an increase of 217 pairs over
the last 2 decades. The same effect shows Gini coefficient that decreases in time in the following way:
GiniG ( 2001 2005 ) 0.983 ; GiniG ( 2006 2010 ) 0.98 ;
GiniG ( 2011 2015 ) 0.978 ; GiniG ( 2016 2020 ) 0.975 .
Figure 6 shows the dynamic of the interaction for the most tied groups. We apply the same selec-
tion here as for the divisions. For the all four periods, we observe the following 3 leaders that always
stay in the top 5:
1205 Urban and Regional Planning & 1507 Transportation and Freight Services;
1903 Journalism and Professional Writing & 2001 Communication and Media Studies;
1902 Film, Television and Digital Media & 2001 Communication and Media Studies.
Some of the leaders increase interaction as time goes on, some decrease interaction, some switch
between increasing and decreasing interaction. A trend towards increasing interaction shows 0502
Environmental Science and Management & 0602 Ecology. This pair increases the interaction from
0.11 in the first period to 0.16 in the last period. A trend towards decreasing interaction show a few
pairs of groups: 1205 Urban and Regional Planning & 1507 Transportation and Freight Services,
1903 Journalism and Professional Writing & 2001 Communication and Media Studies, and 0702 An-
164
imal Production & 0908 Food Sciences. They decrease interaction for the first 3 periods but have a
slight increase for the last period. Hence, no leader changed its interactive level significantly.
Figure 5: Distributions of paired interaction between groups
Figure 6: Dynamics of six leaders among research groups
Figure 7 shows dynamic for the most changeable research groups. We use the same approach as
with the research divisions’ selection. Overall we analyzed the 6 most changeable research groups:
0399 Other Chemical Sciences & 0699 Other Biological Sciences;
0206 Quantum Physics & 0906 Electrical and Electronic Engineering;
0601 Biochemistry and Cell Biology & 1112 Oncology and Carcinogenesis;
165
0401 Atmospheric Sciences & 0912 Materials Engineering;
0202 Atomic, Molecular, Nuclear, Particle and Plasma Physics & 0307 Theoretical and Computa-
tional Chemistry;
0401 Atmospheric Sciences & 0901 Aerospace Engineering.
Among chosen pairs the most rapid increase in interaction happened for groups 0399 Other Chem-
ical Sciences & 0699 Other Biological Sciences – it has increased at 378% from 0.02113 in 2001-
2005 to 0.10121 in 2016-2020. In 2020 Jaccard index for this pair equals 0.1411. But, the year pace
fell, and became 9.1% for 2019–2020. The pair 0206 Quantum Physics & 0906 Electrical and Elec-
tronic Engineering has increased interaction at 288% from 0.00634 to 0.02464. In 2020 Jaccard index
for this pair equals 0.0258. But, the year pace fell, and became 7.1% for 2019–2020. The pair 0601
Biochemistry and Cell Biology & 1112 Oncology and Carcinogenesis has increased interaction from
0.00630 to 0.01869. The most rapid decrease in interactions is observed for the pair 0401 Atmospheric
Sciences & 0912 Materials Engineering with moving from 0.00834 to 0.00125 (about 84%). The
pairs 0202 Atomic, Molecular, Nuclear, Particle and Plasma Physics & 0307 Theoretical and Com-
putational Chemistry and 0401 Atmospheric Sciences & 0901 Aerospace Engineering show approxi-
mately the same decrease in interaction at about 80%.
Figure 7: Dynamics for the most changeable research groups between the first and last periods
We also observed that many pairs of research groups have moved from zero interaction to greater
than zero and vice versa. According to our calculations 235 pairs of research groups has exceeded
zero interaction and only 18 pairs has reached zero interaction when comparing the first (2001-2005)
and last (2016-2020) periods. It conforms to earlier evidence about the increase of interdisciplinarity
on the low level.
As well as with research divisions, we analyzed the cumulative interaction of groups. We used the
same formula of stickiness index but, naturally, applied to Jaccard indexes of research groups. The
distributions of stickiness indexes for each of the periods are shown in Figure 8. The leaders by stick-
iness index are:
0502 Environmental Science and Management;
2001 Communication and Media Studies;
1205 Urban and Regional Planning.
Figure 8 confirms to the previous conclusion regarding to divisions and shows that with time re-
search groups tend to have a more equally distributed stickiness index. It is also observed from the
166
number of research groups that increased their stickiness index from zero to greater than zero. 11 re-
search groups have increased their stickiness index from zero in the first period to positive number in
the last period, and only one research group has decreased it to zero. The same effect shows Gini in-
dex for the stickiness index that decreases from 0.4722 in the first period to 0.4519 in the last period.
For all time of observations, the most rapid decrease in stickiness index took place for the following
research groups:
1603 Demography – -92%;
1301 Education Systems – -83%;
0901 Aerospace Engineering – -71%;
0307 Theoretical and Computational Chemistry – -59%;
1101 Medical Biochemistry and Metabolomics – -59%.
Figure 8: Groups’ stickiness indexes distribution
5. Application for improvement of ANZSRC
ANZSRC as any hierarchical classifying system tries to combine the similar objects in the same
cluster and makes any two clusters dissimilar. Hence, a research group has to be more similar, has to
have higher interaction with the research groups inside its division, than with the research groups
from any other division. To formalize this thesis regarding to research group G11 from division D1
we introduce the following:
1) G12 and G13 denote research groups with highest interaction with G11 inside division D1 ,
and besides J (G11, G12 ) J (G11, G13) ;
2) G22 and G23 denote research groups from division D2 , which are most tied with the research
group G11 from division D1 , and besides J (G11, G22 ) J (G11, G23) .
Assumption: research group G11 is assigned suspiciously to division D1 , if
J (G11, G12 ) J (G11, G22 ) AND J (G11, G13) J (G11, G23) . In this case, division D2 is more suitable for
research group G11 , than division D1 . Hence, ANZSRC may be improving its classifying system by
moving research group G11 from division D1 to division D2 .
167
We checked all the research groups on base of their Jaccard index for 2016-2020 data and found 5
problem research groups according to proposed Assumption (Table 1). For reliability reason only
research groups with J (G11, G23) 0.05 are considered.
According to Table 1 the following changes in ANZSRC are proposed:
1) move 2001 Communication and Media Studies from 20 Language, Communication and Culture
to 19 Studies in Creative Arts and Writing;
2) move 0909 Geomatic Engineering from 09 Engineering to 04 Earth Sciences;
3) move 1502 Banking, Finance and Investment from 15 Commerce, Management, Tourism and
Services to 14 Economics;
4) move 0105 Mathematical Physics from 01 Mathematical Sciences to 02 Physical Sciences;
5) move 0602 Ecology from 06 Biological Sciences to 05 Environmental Sciences.
Table 1
Problem research groups in ANZRSC
Problem Group Most tied groups inside the Jacard Most tied groups inside Jacard
division index concurrent division index
2001 Commu- 2002 Cultural Studies 0.0379 1903 Journalism and Profes- 0.2022
nication and sional Writing
Media Studies 2004 Linguistics 0.0076 1902 Film, Television and Dig- 0.1338
ital Media
0909 Geomatic 0901 Aerospace 0.0110 0406 Physical Geography and 0.0855
Engineering Engineering Environmental Geoscience
0906 Electrical and 0.0108 0404 Geophysics 0.0527
Electronic Engineering
1502 Banking, 1503 Business and 0.0304 11403 Econometrics 0.0503
Finance and Management
Investment 1507 Transportation and 0.0000 1402 Applied Economics 0.0502
Freight Services
0105 Mathe- 0102 Applied Mathematics 0.0386 0206 Quantum Physics 0.1040
matical Physics 0101 Pure Mathematics 0.0143 0203 Classical Physics 0.0515
0602 Ecology 0603 Evolutionary Biology 0.0599 0502 Environmental Science 0.1613
and Management
0607 Plant Biology 0.0522 0501 Ecological Applications 0.0655
6. Conclusions
We analyzed the interaction of research divisions and research groups for 4 five-year periods. The
level of interactions is assessed by Jaccard index. We analyzed paired and cumulative interaction for
both research divisions and research groups. Only research groups of different research divisions were
considered. Paired interaction allowed to find pairs that interact the most and analyze the behavior of
different interactions over time.
Using paired interaction we analyzed leaders and the most changeable research divisions and
groups in terms of interaction. Paired interaction for research divisions shows that every consecutive
five-year period has decreased irregularity of its distribution of interaction. This also confirms the
Gini index and the number of pairs with non-zero interaction. Cumulative interaction shows that all
research divisions tend to have their stickiness decreased over time. The same effect shows Gini index
168
for the stickiness index that decreases from 0.197 in the first period (2001-2005) to 0.188 in the last
period (2016-2020).
Paired interaction for research groups showed the same results but difference between two consec-
utive periods is greater by a larger factor. Cumulative interaction for research groups showed that with
time research groups tend to have a more equally distributed stickiness index. It is also observed from
the number of research groups that increased their stickiness index from zero to greater than zero and
Gini index.
On base of interacting assessments during 2016-2020 we identified 5 research groups that have
been assigned suspiciously to divisions in ANZSRC. They are as follows: 0105 Mathematical Phys-
ics, 0602 Ecology, 0909 Geomatic Engineering, 1502 Banking, Finance and Investment and 2001
Communication and Media Studies. All the mentioned research groups have at least a pair of more
similar research groups in concurrent divisions with statistically significant level. Those 5 research
groups also have strong semantic ties with concurrent divisions, this can be an argument for adapting
of ANZSRC.
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