Towards journalometrical analysis of a scientific periodical: a case study
In this paper we use several approaches to analyse a scientific journal as a complex system and to make a possibly more complete description of its current state and evolution. Methods of complex networks theory, statistics, and queueing theory are used in this study. As a subject of the analysis...
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Інститут фізики конденсованих систем НАН України
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irk-123456789-1181912017-05-30T03:04:21Z Towards journalometrical analysis of a scientific periodical: a case study Mryglod, O. Holovatch, Yu. In this paper we use several approaches to analyse a scientific journal as a complex system and to make a possibly more complete description of its current state and evolution. Methods of complex networks theory, statistics, and queueing theory are used in this study. As a subject of the analysis we have chosen the journal “Condensed Matter Physics” (http://www.icmp.lviv.ua). In particular, based on the statistical data regarding the papers published in this journal since its foundation in 1993 up to now we have composed the co-authorship network and extracted its main quantitative characteristics. Further, we analyse the priorities of scientific trends reflected in the journal and its impact on the publications in other editions (the citation ratings). Moreover, to characterize an efficiency of the paper processing, we study the time dynamics of editorial processing in terms of queueing theory and human activity analysis. У цiй статтi ми використовуємо рiзнi пiдходи для аналiзу наукового журналу як складної системи та якомога вичерпнiшого опису її поточного стану та еволюцiї. Для вирiшення поставлених задач зокрема застосовними є методи теорiї складних мереж, статистики та теорiї черг. Об’єктом дослiджень ми обрали журнал “Condensed Matter Physics” (http://www.icmp.lviv.ua). Зокрема, на основi статистичних даних про статтi, опублiкованi у цьому виданнi вiд його заснування у 1993 роцi i дотепер, ми побудували мережу спiвавторства та визначили її основнi кiлькiснi характеристики. Ми провели аналiз прiоритетностi наукових напрямкiв, що вiдображенi в публiкацiях журналу, та рейтинг їх цитування в iнших виданнях. Для додаткової характеристики ефективностi редакцiйної обробки статей, ми вивчили її часову динамiку в рамках теорiї черг та аналiзу людської активностi. 2007 Article Towards journalometrical analysis of a scientific periodical: a case study / O. Mryglod, Yu. Holovatch // Condensed Matter Physics. — 2007. — Т. 10, № 2(50). — С. 129-142. — Бібліогр.: 19 назв. — англ. 1607-324X PACS: 02.10.Ox, 02.50.-r, 07.05.Kf, 89.75.-k DOI:10.5488/CMP.10.2.129 http://dspace.nbuv.gov.ua/handle/123456789/118191 en Condensed Matter Physics Інститут фізики конденсованих систем НАН України |
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Digital Library of Periodicals of National Academy of Sciences of Ukraine |
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English |
| description |
In this paper we use several approaches to analyse a scientific journal as a complex system and to make a
possibly more complete description of its current state and evolution. Methods of complex networks theory,
statistics, and queueing theory are used in this study. As a subject of the analysis we have chosen the journal
“Condensed Matter Physics” (http://www.icmp.lviv.ua). In particular, based on the statistical data regarding the
papers published in this journal since its foundation in 1993 up to now we have composed the co-authorship
network and extracted its main quantitative characteristics. Further, we analyse the priorities of scientific trends
reflected in the journal and its impact on the publications in other editions (the citation ratings). Moreover, to
characterize an efficiency of the paper processing, we study the time dynamics of editorial processing in terms
of queueing theory and human activity analysis. |
| format |
Article |
| author |
Mryglod, O. Holovatch, Yu. |
| spellingShingle |
Mryglod, O. Holovatch, Yu. Towards journalometrical analysis of a scientific periodical: a case study Condensed Matter Physics |
| author_facet |
Mryglod, O. Holovatch, Yu. |
| author_sort |
Mryglod, O. |
| title |
Towards journalometrical analysis of a scientific periodical: a case study |
| title_short |
Towards journalometrical analysis of a scientific periodical: a case study |
| title_full |
Towards journalometrical analysis of a scientific periodical: a case study |
| title_fullStr |
Towards journalometrical analysis of a scientific periodical: a case study |
| title_full_unstemmed |
Towards journalometrical analysis of a scientific periodical: a case study |
| title_sort |
towards journalometrical analysis of a scientific periodical: a case study |
| publisher |
Інститут фізики конденсованих систем НАН України |
| publishDate |
2007 |
| url |
http://dspace.nbuv.gov.ua/handle/123456789/118191 |
| citation_txt |
Towards journalometrical analysis of a scientific periodical: a case study / O. Mryglod, Yu. Holovatch // Condensed Matter Physics. — 2007. — Т. 10, № 2(50). — С. 129-142. — Бібліогр.: 19 назв. — англ. |
| series |
Condensed Matter Physics |
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AT mryglodo towardsjournalometricalanalysisofascientificperiodicalacasestudy AT holovatchyu towardsjournalometricalanalysisofascientificperiodicalacasestudy |
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2025-07-08T13:31:58Z |
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2025-07-08T13:31:58Z |
| _version_ |
1837085776177790976 |
| fulltext |
Condensed Matter Physics 2007, Vol. 10, No 2(50), pp. 129–141
Towards journalometrical analysis of a scientific
periodical: a case study
O.Mryglod1,2, Yu.Holovatch2,3
1 Lviv Polytechnic National University, 12 Bandery Str., 79013 Lviv, Ukraine
2 Institute for Condensed Matter Physics of the National Academy of Sciences of Ukraine,
1 Svientsitskii Str., 79011 Lviv, Ukraine
3 Institut für Theoretische Physik, Johannes Kepler Universität Linz, 69 Altenbergerstr., 4040 Linz, Austria
Received May 29, 2007
In this paper we use several approaches to analyse a scientific journal as a complex system and to make a
possibly more complete description of its current state and evolution. Methods of complex networks theory,
statistics, and queueing theory are used in this study. As a subject of the analysis we have chosen the journal
“Condensed Matter Physics” (http://www.icmp.lviv.ua). In particular, based on the statistical data regarding the
papers published in this journal since its foundation in 1993 up to now we have composed the co-authorship
network and extracted its main quantitative characteristics. Further, we analyse the priorities of scientific trends
reflected in the journal and its impact on the publications in other editions (the citation ratings). Moreover, to
characterize an efficiency of the paper processing, we study the time dynamics of editorial processing in terms
of queueing theory and human activity analysis.
Key words: complex systems, complex networks, co-authorship network, journal evaluation, human
dynamics
PACS: 02.10.Ox, 02.50.-r, 07.05.Kf, 89.75.-k
1. Introduction
It is a honor and pleasure for us to contribute by our paper to the jubilant fiftieth issue of
the journal “Condensed Matter Physics” (CMP) [1]. The history of this journal began in 1993
when it was founded by the Institute for Condensed Matter Physics of the National Academy
of Sciences of Ukraine. Soon the journal transformed into an international periodical, which is
currently recognized by the European Physical Society (since 2003) and is covered by ISI Master
Journal List (since August, 2005). Since the time of its foundation, 671 papers by authors from
44 countries have been published in the journal, see figure 1. The jubilee of the CMP is a good
incentive to present the results of the statistical analysis of its publications, paying attention
to their different features, ranging from their content, collaboration trends of the authors to the
efficiency of the paper processing procedure. Recently, a new term, journalometry, has appeared for
the complex quantitative analysis of scientific periodicals [2]. In particular, this complex approach
makes it possible to take into account different types of information about the journal, ranging
from the quantitative to the qualitative ones [3].
Another reason for analysing the statistics of publications in a scientific journal is that such an
analysis permits to shed light on different features of human activities as well as to quantify them.
To give an example, applying a complex network theory [4] to the analysis of co-authorship of the
papers published, one deals with a collaboration network, a subject of interest in social disciplines.
Similar tools applied to the analysis of the distribution of references that appear in the papers
published, lead to the so-called citation network, an example of information networks. Moreover,
as we shall discuss in our paper, the analysis of distribution of the waiting times of the papers
submitted to the journal is useful in understanding the origin of particular features of human
dynamics [5–10]. Although the analysed database allows us to make certain conclusions about
c© O.Mryglod, Yu.Holovatch 129
O.Mryglod, Yu.Holovatch
the statistical properties of the values considered, one should be aware of the natural limitations
imposed by the finiteness of data set – a typical situation, when a particular periodical is studied.
Figure 1. An international collaboration of authors in “Condensed Matter Physics”. During the
period of 1993–2007 the authors from 44 countries contributed to the journal.
The structure of our paper is as follows. In the rest of this section we shall describe the database
of CMP publications we constructed. Section 2 is devoted to the analysis of the content of papers
published in CMP. First, we address the authors of the papers and construct the co-authorship
network, measuring its main characteristics. Then, we briefly discuss the main thematic trends
of the papers published and the way these papers are cited in other periodicals. In section 3 we
approach the analysis of the journal from another viewpoint. Here, the subject of our analysis
is not the content of a given paper but rather the way the paper is processed by the editorial
board. In particular, we analyse the statistics of time intervals between submission of a paper
and its acceptance and interpret the obtained results in terms of queuing theory. Conclusions and
outcome are given in section 3.
Before passing to a more detailed analysis of publications in CMP, let us briefly describe the
structure of the database we used as well as display several general characteristics that follow from
the database analysis. The structure of the database is shown in figure 2. It contains information
about (i) the authors and their affiliation, (ii) the content of the papers published in CMP, as well
as (iii) the data regarding the papers cited in CMP. In what follows, we shall make use of the two
first parts of the database, the last one, (iii), will be considered elsewhere.
During the period analysed, 892 authors published 671 papers in 49 issues of CMP, 477 authors
being from Ukraine (where the publishing institution is located) and 415 being from other countries.
The international cooperation increased with time. More than a half of all papers during the last
3 years were written by at least one foreign author (figure 3). The decreasing rank of international
co-authorship in CMP journal is as follows: Germany (39 common papers), Poland (27), Japan
(23), Russia (22), USA (22), Austria (15), France(12) etc. However, the data from the ISI database
(Web of Science, [11]) about the external citation of CMP journal between 1993 and 2006 showed1
that the authors from other countries cited the papers from CMP in the following decreasing order:
USA (13.45% of all citations), Germany (12.48%), France (9.75%), Italy (8.58%), Poland (8%),
Austria (4.68%), England (4.1%), Russia (4.1%) etc. The maximum number of all coauthors in
CMP per one person is 25 and the most active author has 29 publications here. 67 authors had no
1The analysis was performed in October, 2006
130
Analysis of a scientific periodical: a case study
Figure 2. The structure of “Condensed Matter Physics” journal’s database.
Figure 3. The number of papers published in CMP by only Ukrainian, only foreign author(s)
and joint publications.
coauthors in CMP at all.
Now, with the above described database at hand we shall perform a more detailed analysis of
the statistical parameters of the CMP publications trying to find internal relationships between
different parameters and their time dynamics.
2. Analysis of the content of the papers: co-authorship and fi elds
of research
In this section, we shall analyse several features connected with the content of the papers
published in CMP. Let us start from the analysis of the authorship of the papers. To this end it is
131
O.Mryglod, Yu.Holovatch
reasonable to apply the tools of the complex networks theory [4], a field that originates from (and
still may be considered as a part of) graph theory. Complex networks were paid much attention
at the end of 1990-ies, when it appeared that many network-like structures that exist in nature or
result from human activities possess remarkable properties which were not explained within the
then available mathematical framework. The most striking ones were the so-called small-world and
scale-free features. In many networks the average distance between any pair of nodes 〈`〉 appeared
to be very small compared to the network size N (more precisely, the network is said to possess
small-world properties when 〈`〉 grows with N slower than Na, a > 0). Correspondingly, if the
node degree distribution P (k) of a network is governed by a power law, the network is said to be
scale-free.
Figure 4. An example of a co-
authorship graph. In this cluster a
degree of (number of links attached
to) the central node is equal to six.
Lines of different thickness represent
the number of common papers.
The co-authorship network we are going to consider is
one of the examples of collaboration networks. In turn, the
latter belong to a special kind of social networks which
represent human collaboration patterns. Though social net-
works have a large history, the collaboration networks have
a more precise definition of connectivity and much more
data [12]. A special feature of collaboration networks is
the possibility of documenting all the facts of collabora-
tion. In a collaboration network, the particular individu-
als, groups of people or even organizations can be repre-
sented by nodes, which are connected by links (different
types of interactions between people). An example of a co-
authorship graph from CMP is given in figure 4. The nodes
of the graph correspond to the authors and a link between
two nodes means that the two scientists represented have
coauthored a paper in CMP. Different ways of links weighting can be performed: in non-weighed
networks link means at least one common paper for two authors; multiple lines or lines of different
thickness can represent the number of common papers, as it is done in figure 4; in addition, the col-
laboration strength of a link can be considered (for example, the strength of collaboration between
two authors is taken to be stronger than between three authors) [13]. The results of investigations
of scientific co-authorship networks have been presented in numerous papers. Collaboration graphs
for scientists were constructed for a variety of fields based on the large databases: MEDLINE
(published papers on biomedical research), the Los Alamos e-Print Archive (preprints in theoreti-
cal physics), databases maintained by the Mathematical Reviews journal (mathematical papers),
NCSTRL (preprints in computer science) [12,13], SPIRES (papers and preprints in high-energy
physics) [14] etc. The sizes of these databases range from 2 million papers to 13,000. The analysis
showed that co-authorship networks have the features of scale-free small-worlds: they have a short
mean distance between any two nodes, a large clustering coefficient, and node-degree distributions
close to a power law. Actually, the distributions of such networks are well fitted by power laws
with an exponential cutoff. One of the possible explanations of this cutoff is the finite set of data
and the natural limitations on the active working lifetime of a professional scientist [12].
The general number of nodes in co-authorship networks varies between scientific fields, their
wide or narrow specializations and periods of existence [12]. On the other hand, the average number
of co-authors per one paper to a greater degree depends on its type (experimental or theoretical).
Naturally, theoretical papers usually have a rather small numbers of coauthors, compared with the
experimental papers [14]. Besides, in the experimental science the general number of collaborators
is larger. As regards the CMP, we can clearly see the theoretical character of the journal: the
average number of co-authors per paper is equal to 2 and its maximum value is 8.
The co-authorship network based on the relatively small database consists of separate groups
of connected nodes (clusters). These fragments of the network can group the authors that work in
the same field of science. In each cluster every node can be reached from any other node. With the
growth of the size of the network, separate clusters tend to join. At some moment, there appears
a giant cluster which includes almost all the nodes (a giant component). The analysis of different
132
Analysis of a scientific periodical: a case study
Figure 5. Visualization of the co-authorship network (892 nodes) of the CMP journal (1993–
2007). Three different fragments can be distinguished: the main cluster with 219 nodes (at the
upper left), the next-largest clusters with 15 nodes (top right and bottom left). The rest of
the nodes are collected on the right below. The network was generated using Pajek network
visualization software [15].
co-authorship networks showed that the main cluster includes approximately 80% or 90% of all
the nodes [12,13]. The existence of such a cluster allows us to connect almost all the authors by
one or several chains of intermediate collaborators. The high level of connectedness provides a fast
spread of new scientific information and shows intensive private interactions between scientists. It is
interesting that the second-largest connected cluster is far smaller than the largest one. In figure 5
we show the co-authorship network of the CMP journal. The intensity of each co-authorship, defined
by the number of common papers, is shown by lines of different thickness. The main co-authorship
cluster of the CMP journal is shown in figure 6.
Hight clustering is one of the main features of social networks. The clustering of co-authorship
networks greatly depends on the number of papers with a few authors which automatically creates
cycles. Special parameters of a network may characterize its clustering level. To proceed further,
let us define the main observables used for a quantitative description of networks [4]. As noted
before, the degree ki of a node i is the number of the attached edges (see figure 4). The mean node
degree 〈k〉 characterizes the whole network:
〈k〉 =
1
N
N∑
i=1
ki , (1)
where the summation is performed over all N nodes of a network. For a non-weighed co-authorship
network, the mean node degree is the average number of coauthors of a particular scientist. The
node degree distribution P (k) provides a probability for a node to have a degree equal to k. The
form of the node degree distribution determines the network type. As noted above, the network is
133
O.Mryglod, Yu.Holovatch
Figure 6. The non-weighed main co-authorship cluster of the CMP journal has 219 nodes (ge-
neral number of nodes is 892). Nodes with different degree (different number of coauthors in
CMP) are denoted by circles of different colours and radii. The visualization was carried out
using Himmeli software [16].
said to be scale-free when its node degree distribution follows a power-law:
P (k) ∼ k−γ , γ > 0. (2)
In our case the node degree distribution is the distribution of the number of collaborators of a
scientist. The analysis of co-authorship based on biomedical, physical and mathematical databases
showed that the corresponding distributions are fat-tailed [13]. The node degree distribution for
the CMP journal is shown in figure 7. In spite of a small number of data points one definitely sees
a tendency towards a power law behaviour (2) with an exponent γ ' 3.25. Therefore, we conclude
134
Analysis of a scientific periodical: a case study
that the corresponding network is scale-free.
Figure 7. The node degree distribution of the co-authorship network of the CMP journal.
The clustering coefficient Ci of a node i shows the probability of the nearest neighbours of this
node to be connected. It is defined as:
Ci =
2Ei
ki(ki − 1)
, (3)
where Ei is the number of the existing connections between the nearest neighbors of the node i
of ki degree. Respectively, the mean clustering coefficient 〈C〉 characterizing the whole network is
defined as:
〈C〉 =
1
N
N∑
i=1
Ci . (4)
The clustering reflects a special way of network correlation. The clustering coefficient of a complete
graph is equal to one, whereas the clustering coefficient of a tree is zero. For the Erdös-Rényi
classical random graph (constituted by N nodes randomly connected by L links) the clustering
coefficient is equal to:
Cr =
2L
N2
. (5)
Compared to a random graph, the scale-free networks have a significantly larger value of the
mean clustering coefficient which proves their high correlation. In table 1 we give the mean value
〈C〉 of the CMP journal co-authorship network compared to Cr of the random graph of an equiv-
alent size. The clustering coefficient, found for the co-authorship network in physics (based on
the publication in Los Alamos E-print Archive [17]) between the years 1995 and 1999 is equal
to 0.43 [13]; for its cond-mat part (2000–2005) 〈C〉 ≈ 0.73 [18]. The mathematical co-authorship
network of the Mathematical Reviews journal has a clustering coefficient equal to 0.15 [13]. The
smallest value of 〈C〉, 0.066, characterizes the biomedical field (1995–1999) [13]. The clustering
coefficient of the CMP journal is 0.607 (see table 1).
Table 1. The numerical characteristics of the co-authorship network of the CMP journal. N :
number of nodes; L: number of links; 〈k〉, kmax: the mean and maximal node degree, respectively;
〈C〉, 〈C〉/Cr: the mean clustering coefficient and the ratio between clustering coefficients of a
given network and of a random graph of the same size; 〈l〉, lmax: the mean and maximal shortest
path length.
Parameter N L kmax 〈k〉 〈C〉 〈C〉/Cr 〈l〉 lmax
Value 892 1300 25 2.915 0.607 185.3 4.783 10
135
O.Mryglod, Yu.Holovatch
In any connected fragment of the co-authorship network it is possible to find the chains of
intermediate collaborators between any two authors. The results of calculations show a very small
lengths of the shortest paths between any two nodes: its average value is close to 6 [12]. The length
of the shortest path lij between the nodes i and j is equal to the minimal number of links which
should be passed to reach the j from i. For a connected network, the mean shortest path length is
defined as:
〈l〉 =
2
N(N − 1)
∑
i>j
lij . (6)
For well-connected networks the value of 〈l〉 is not large: for the above mentioned database in
physics [17] 〈l〉 ≈ 5.9 for all data and 〈l〉 ≈ 6.4 for its cond-mat part [12]. Measurements of the
mean shortest path of the papers submitted during 2000–2005 to the cond-mat part of the Los
Alamos E-print Archive resulted in the value 〈l〉 ≈ 3.62 [18]. Our value of 〈l〉 for CMP does not
differ essentially (see table 1). For small-world networks the value of 〈l〉 scales logarithmically or
slower with network size [4]. Figure 8 shows how 〈l〉 changes with an increasing size of the CMP
co-authorship network. Starting from a certain value of N (approximately after N = 550) new
nodes continue to appear but the mean distance between them remains 〈l〉 ≈ 4.7.
Figure 8. The change of the mean shortest path length 〈l〉 with an increase of the network size
N in the log-linear scale.
Another value that characterizes the network size is the maximal shortest path length, lmax.
For our network, the maximal shortest path lmax = 10 connects the nodes of M.R. Tuzyak and
M. Weigel, see table 1. Let us note that the average value of lmax for collaboration networks
discussed above is between 20 and 30 and it depends on the field of science [13]; for physics
〈lmax〉 ≈ 20.
The problems connected with the shortest paths in collaboration networks were the subject of
the analysis in [13]. It was shown that on average 64% of an individual’s shortest paths run through
the best-connected of the nearest collaborators. This fact shows the existence of very important
nodes in co-authorship networks, which can represent the most communicative and active scientists.
Another interesting parameter is “transitivity” which shows the probability that two coauthors of
a scientist have themselves coauthored a paper [13]. In other words, if two scientists have at least
one common coauthor, they have a high probability of becoming coauthors in future.
A careful observation of the main co-authorship cluster of CMP (figure 6) makes it possible to
single out its strongly-connected fragments. The possible reason for the existence of such connected
groups is the common scientific interest of these authors. In other words, one can visually recognize
the thematic trends of the CMP journal. Another numerical data showing the priority fields of
research in the CMP is the statistics of PACS numbers. Table 2 represents the top of the 10
most frequent PACS number in the papers of the CMP journal. Finally, the data from the ISI
database (Web of Science, [11]) regarding the external citations can show which thematic fields of
the CMP journal are most useful for the scientists. The decreasing rank of subject categories that
136
Analysis of a scientific periodical: a case study
Table 2. The top of the 10 most frequent PACS numbers in the papers of CMP journal.
The name of the field PACS number Frequency
Statistical physics, thermodynamics, and nonlinear dynamical sys-
tems
05 259
Structure of solids and liquids; crystallography 61 154
Electronic structure of bulk materials 71 139
Equations of state, phase equilibria, and phase transitions 64 124
Dielectrics, piezoelectrics, and ferroelectrics and their properties 77 116
Magnetic properties and materials 75 73
Physical chemistry and chemical physics 82 66
Superconductivity 74 58
Surfaces and interfaces; thin films and low-dimensional systems (struc-
ture and nonelectronic properties)
68 49
Physics of plasmas and electric discharges 52 39
cited CMP: physics, condensed matter (33.92% of all citations), physics, mathematical (14.43%),
physics, multidisciplinary (14.04%), physics, atomic, molecular & chemical (12.87%), chemistry,
physical (10.72%), physics, applied (9.75%), physics, fluids & plasmas (9.36%), materials science,
multidisciplinary (7.21%), electrochemistry (5.26%), chemistry, analytical (2.92%), polymer science
(2.53%).
3. Analysis of papers processing
In this section, we approach the analysis of the CMP journal from quite a different point of view.
Here, the subject of the study will be the way the papers submitted to CMP are processed by the
editorial board. A schematic process of editorial processing is shown in figure 9. Upon submission
and consideration by one of the editors, the paper is sent to the reviewers, then revised (if necessary)
and, finally, accepted. On each of the above stages the paper may be rejected. However, typically
the information concerning the rejected papers is not publicly available. Therefore, we define the
waiting time of a paper, τw, as the difference between the dates of the paper final acceptance, τa,
and the paper reception τr:
τw = τa − τr . (7)
Both τa and τr are often displayed in the paper. Therefore, the features to be analysed for the CMP
may be also checked for other periodicals [10]. We shall be interested in the distribution P (τw) of
the papers submitted to the CMP.
Figure 9. The schematic process of editorial processing of papers. t shows the time arrow.
The distribution of waiting times during different kinds of human activities (sending letters,
e-mail communication, web-browsing, loaning books in a library, etc.) has been a subject of recent
interest [5–9]. In particular, it was found that different forms of human activities are characterized
by a waiting time distribution in the form of a power law:
P (τw) ∼ τ−α
w
. (8)
137
O.Mryglod, Yu.Holovatch
Moreover, the dynamics of different processes appear to be governed by different values of the
exponent α. The value α = 1 governs the waiting time distribution for web-browsing, e-mail
communication and library loans [7,8]; α = 3/2 for sending letters (obtained concerning the analysis
of correspondence of Einstein, Darwin, and Freud) [6,8]; α = 1.3 for stock broker activities [8]. To
explain this phenomenon, a model of the queuing process based on the priority principle has been
used [5,6,8]. Note, however, the existing disagreement between the predictions of [5–8] on the one
hand, and the assumption of a log-normal distribution P (τw) for e-mail communication patterns
[9], on the other hand.
In a recent study [10] we proposed to analyse the forms of waiting time distributions to charac-
terize editorial processing of scientific papers. To simplify the analysis we represented each process
of editorial consideration, referee review, communication between the participants and the modifi-
cation of materials being one service action. The analysis of the waiting time distributions of 2667
and 2692 papers published, respectively, in the journals “Physica A: Statistical Mechanics and its
Applications” (during the period 1975 – 2006) and “Physica B: Condensed Matter” (during the
period 1988 – early 2007) lead to the conclusions about two possible forms of the distribution [10]:
• power law with an exponential cutoff [8]:
P (τw) ∼ τ−b
w
e−
τw
τ0 , τ0 > 0, b = 1, (9)
where τ0 is the characteristic waiting time that depends on the rates of submission and task
execution [8];
• log-normal [9]:
P (τw) ∼ 1√
2πωτw
e
−[ln( τw
τc
)]
2
2ω
2 , ω > 0. (10)
where ln τc and ω are the mean and standard deviation of the ln τw.
Both distributions predict the same behaviour τ−1, differing only in the functional form of the
exponential correction.
Figure 10. Waiting time of the papers publi-
shed in CMP between years 1995–2007. Differ-
ent data points correspond to waiting times of
different papers. The mean waiting time (solid
line) has a tendency to decrease.
In figure 10 by the solid line we display the
time dynamics of the average waiting time of the
papers submitted to the CMP during the pe-
riod 1995–2007. Different data points correspond
to waiting times of different papers. The max-
imal time of the paper processing is equal to
τmax = 600 days (volume 9, No. 1(21), p. 175–
182), the minimal one is equal to 4 days (vol-
ume 7, No. 4, p. 829–844; volume 7, No. 4, p. 845–
858). As one can see from the figure, the mean
waiting time tends to decrease. Unfortunately,
only 159 out of all the papers published in CMP
contain the information on the dates of the pa-
pers final acceptance, τa, and the paper recep-
tion τr. Because of the poor statistics our results
show a significant data fluctuation. To obtain a
smoother curve, we analysed the corresponding
integral distribution:
P int(τw) =
τmax∑
t=τw
P (t). (11)
The corresponding curves (without normalization) are shown in figure 11 in the double logarithmic
and log-linear scales. A better linear fit (the absolute value of Pearson’s coefficient r is closer to 1)
on a log-linear scale suggests a rather exponential behaviour of the integral distribution curve.
138
Analysis of a scientific periodical: a case study
Figure 11. Unnormalized integral distributions of waiting times: the number of papers that have
waited more than τw days. Left plot: log-log scale, right plot: the same data in log-linear scale.
Conclusions
In this paper we have analysed the statistical properties of different data that may be used in
characterizing a scientific periodical. In our “case study” we have chosen the journal “Condensed
Matter Physics”. Our analysis consisted of two parts. In the first part we examined the features
connected with the content of the papers, whereas the subject of the second part was the paper
processing by the editorial board. By the content of the paper we mean all the information contained
in the text (authors, affiliations, fields of research, etc). From different characteristics that may
be extracted in order to quantify the content, we paid special attention to the authors and their
collaboration as well as to the main thematic trends of the papers. To this end, we have analysed
the CMP co-authorship network using the methods of complex networks theory. Besides, we made
use of the ISI database to extract the data regarding the citations of the papers published in CMP.
In the second part, where the paper processing was analysed, each paper was considered without
paying attention to its content. We were rather interested in the statistics of waiting times of the
submitted papers.
The main results obtained in our analysis are given in sections 2 and 3. To summarize some
of them, let us mention that the co-authorship network of the CMP journal and the main cluster
of authors were considered. We concluded about the scale-free nature of this network as well as
its great level of connectivity. Moreover, a very positive tendency of the improving international
collaboration is observed. External ratings and evaluations should be also taken into consideration
in the full complex journal analysis. This information can be obtained from different international
information services and databases such as: Thomson ISI (Web of Science) [11], Scopus [19] and
others. Mainly, they include the external citation data which can be the base of the most known
quantitative criteria for the evaluation of scientific efficiency. Another result is connected with
the study of the editorial board’s activities. The editorial processing of incoming papers can be
considered as a process of human activity. Analyzing the statistics of waiting times of the papers
we can find a promising decrease of this parameter for CMP journal during the observed period.
Of course, our study does not cover the analysis of all the data that may be considered on
the basis of the database constructed. To give an example, we did not touch upon the analysis of
citation and co-citation networks, etc. Nevertheless, we think that our study might be useful as
far as the external evaluation of the journal is considered. Moreover, some of the numerical data
given in our paper can help to evolve a strategy to improve the work of the editorial board.
139
O.Mryglod, Yu.Holovatch
Acknowledgements
O.M. thanks the Johannes Kepler Universität Linz (Austria) for the possibility of getting infor-
mation from the ISI database and to Ihor Mryglod for the help rendered. We acknowledge useful
discussions with Christian von Ferber and Reinhard Folk.
References
1. The ISSN number of the journal “Condensed Matter Physics” is ISSN 1607–324X and the http address
reads: http://www.icmp.lviv.ua/journal/index.html
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30, 83–95. Note however that this reference analyses an evolution of a set of scientific journals, whereas
in our analysis we are rather interested in ’measuring’ a single journal.
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versity Press, Oxford, 2003; Holovatch Yu., Olemskoi O., von Ferber C., Holovatch T., Mryglod O.,
Olemskoi I., Palchykov V. Complex networks, J. Phys. Stud., 2006, 10, in press (in Ukrainian).
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6. Olivera J.G., Barabási A. Darwin and Einstein correspondence patterns, Nature, 2005, 437, 1251.
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tails in human dynamics. Phys. Rev. E, 2006, 73, 036127.
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arXiv:physics/0605027, v1 3 May 2006.
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2007, and unpublished.
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140
Analysis of a scientific periodical: a case study
Журналометричний аналiз наукового видання: дослiдження
часткового випадку
О.Мриглод1,2, Ю.Головач2,3
1 Нацiональний унiверситет “Львiвська полiтехнiка”, вул. Бандери 12, 79013 Львiв, Україна
2 Iнститут фiзики конденсованих систем НАН України,
вул. Свєнцiцького 1, 79011 Львiв, Україна
3 Iнститут теоретичної фiзики унiверситету Йогана Кеплера, Альтенбергерштрасе 69, 4040, Лiнц,
Австрiя
Отримано 29 травня 2007 р.
У цiй статтi ми використовуємо рiзнi пiдходи для аналiзу наукового журналу як складної системи
та якомога вичерпнiшого опису її поточного стану та еволюцiї. Для вирiшення поставлених задач
зокрема застосовними є методи теорiї складних мереж, статистики та теорiї черг. Об’єктом дослi-
джень ми обрали журнал “Condensed Matter Physics” (http://www.icmp.lviv.ua). Зокрема, на основi ста-
тистичних даних про статтi, опублiкованi у цьому виданнi вiд його заснування у 1993 роцi i дотепер,
ми побудували мережу спiвавторства та визначили її основнi кiлькiснi характеристики. Ми провели
аналiз прiоритетностi наукових напрямкiв, що вiдображенi в публiкацiях журналу, та рейтинг їх циту-
вання в iнших виданнях. Для додаткової характеристики ефективностi редакцiйної обробки статей,
ми вивчили її часову динамiку в рамках теорiї черг та аналiзу людської активностi.
Ключовi слова: складнi системи, складнi мережi, мережi спiвавторства, оцiнювання видань,
людська динамiка
PACS: 02.10.Ox, 02.50.-r, 07.05.Kf, 89.75.-k
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