mapping and classification of agriculture in web of ... and...mapping and classification of...

Scientometrics (2016) Volume 109, Issue 2, pp 979–996

Mapping and classification of agriculture in Web of Science: other subject categories and

research fields may benefit

Tomaz Bartol 1, Gordana Budimir

2, Primoz Juznic

3, Karmen Stopar

1

Abstract Fields of science (FOS) can be used for the assessment of publishing patterns and scientific output. To

this end, WOS JCR (Web of Science/Journal Citation Reports) subject categories are often mapped to

Frascati-related OECD FOS (Organization for Economic Co-operation and Development). Although

WOS categories are widely employed, they reflect agriculture (one of six major FOS) less

comprehensively. Other fields may benefit from agricultural WOS mapping. The aim was to map all

articles produced nationally (Slovenia) by agricultural research groups, over two decades, to their

corresponding journals and categories in order to visualize the strength of links between the categories

and scatter of articles, based on WOS-linked raw data in COBISS/SciMet portal (Co-operative Online

Bibliographic System and Services/Science Metrics) and national CRIS - Slovenian Current Research

Information System (SICRIS). Agricultural groups are mapped into four subfields: Forestry & Wood

Science, Plant Production, Animal Production, and Veterinary Science. Food science is comprised as

either plant- or animal-product-related. On average, 50% of relevant articles are published outside the

scope of journals mapped to WOS agricultural categories. The other half are mapped mostly to OECD

Natural-, Medical- and Health Sciences, and Engineering-and-Technology. A few selected journals

and principal categories account for an important part of all relevant documents (core). Even many

core journals/categories as ascertained with power laws (Bradford's law) are not mapped to

agriculture. Research-evaluation based on these classifications may underestimate multidisciplinary

dimensions of agriculture, affecting its position among scientific fields and also subsequent funding if

established on such ranking.

Keywords:

classification, fields of science, research evaluation, power laws, agriculture, research groups

Introduction

Research Background and Motivation

In national research evaluation schemes, research fields and subfields are often evaluated uniformly

within each major field of science. Publication patterns, however, tend to be specific within each

individual field, such as agriculture, medicine, the social sciences, etc. Because of different publishing

practices it is thus often difficult to uniformly and consistently assess publication activity across these

fields. In an attempt at more objective assessments, the citation database Web of Science (WOS) uses

a principle of classifying journals into subject categories. WOS editors thus attempt to offer balanced

coverage within each individual category (Testa 2003). The procedures have been developed by

methods begun over 40 years ago. Categories were established, then new journals were assigned one

at a time based upon all relevant citation data (Pudovkin and Garfield 2002).

----

Self-archived authors' version of the paper:

Bartol, T.; Budimir, G.; Juznic, P. & Stopar, K. (2016). Mapping and classification of agriculture in Web of

Science: other subject categories and research fields may benefit.

Scientometrics, 109(2), 979-996. doi:10.1007/s11192-016-2071-6

The final paper is available at: http://link.springer.com/article/10.1007/s11192-016-2071-6

1 Agronomy Department, Biotechnical Faculty, University of Ljubljana, Jamnikarjeva 101, 1000 Ljubljana,

Slovenia 2 Institute of Information Science, Presernova 17, 2000 Maribor, Slovenia

3 Department of Library and Information Science and Book Studies, Faculty of Arts, University of Ljubljana,

Askerceva 2, 1000 Ljubljana, Slovenia


Despite the fact that WOS’ journal classification system, as well as coverage (Larsen and von Ins

2010), is frequently questioned, much scientometric research has been conducted through this system.

It offers the possibility of comparing experiments on similar principles over long periods of time and

has been mapped to different international classification schemes such as the Fields of Science and

Technology (FOS) classification (a.k.a Frascati classification) of the OECD (Organization for

Economic Co-operation and Development). The aim of classification is to ensure international

comparisons of R&D (Research and Development) expenditures in the public sector (OECD, 2007) in

OECD member countries. This scheme is used in many countries and is employed for evaluation

purposes in national current research information systems (CRIS). The appropriate mapping of OECD

FOS to the corresponding WOS categories has been put into practice by WOS editors. All WOS

categories are represented in the mapping (Thomson Reuters 2015). WOS is the most frequently

employed database for the purposes of research evaluation, complemented in the last decade by

Scopus. Both databases offer specific functionalities.

Agriculture is often presented and subsequently assessed as a principal scientific field. It contains

several subfields related to animal-, plant/crop-production & health, food & nutrition, and forestry,

according to all three well-known principal international information systems CAB Abstracts, Agris,

and Agricola. In national environments where most academic research is financed through publicly

funded schemes, it is of utmost importance that each major research field and subfield receives its due

share of attention according to its real scope, including a balanced allocation of resources in

agricultural research (Vanloqueren & Baret 2009). Such decision-making should take into account

different levels of national research, for example, output from different research groups (Jarneving

2009, Cova et al. 2015).

Our aim in this paper is to gain a better insight into the published output in agriculture, with an

emphasis on research groups that are active in the following major subfields within agriculture:

Forestry & Wood Science, Plant Production, Animal Production, and Veterinary Science, according to

the national categorization scheme in Slovenia, which is based on the aforementioned OECD/Frascati

FOS. The research groups are not strictly linked to an institution but instead connect scientists who

exhibit specific publishing characteristics in a given agricultural subfield. In this way, research-group-

based assessment can offset any bias of a particular agricultural institution. Agricultural institutions

can and do employ researchers who are not active in agricultural research. Also, agricultural

institutions range from mere teaching departments on one side or large research faculties on the other

side, and also include small and specialized research institutes. Such differences could in principle

hinder comparison.

Even though agriculture is a well-established major field of science, we believe that a substantial

part of its relevant research gets published outside its scope and that the current agriculture-related

records do not reflect comprehensively the many facets of activities in this scientific area. The widely

used WOS-related classification scheme and its generic categories may thus put agricultural sciences

at a disadvantage in view of research evaluation - if based solely on scientific fields. To this end, we

assess all articles (co)-authored by members of all Slovenian agricultural research groups, over almost

two decades. We map these articles to the corresponding WOS journal-categories in order to visualize

the strength of particular categories and to identify the links between these categories. In addition, we

assume that the distribution of both the journals as well as subjects (categories) follows some

characteristic principles of inverse relationship. Thus, we also aim to investigate how the core group of

agriculture-related articles or journals is attributed to the existing agricultural classifications, or,

perhaps, if the principal information resources are assigned to other major fields aside from

agriculture.

Review of Literature

In our brief literature review here, we address articles that tackle agricultural and related mapping.

For the purposes of consistency and in view of different terminology relating to WOS (for example,

Science Citation Index Expanded, (SCI/SCIE), Web of Knowledge, Journal of Citation Reports (JCR),

Thomson Reuters) we refer to all of these WOS-associated items as WOS. The WOS-based

assessments are the most widely used method (Yan et al. 2013) which, although unreliable for

individual papers, produce good results for large numbers. An additional advantage is that the


categories are defined at the subdiscipline (subfield) level (Rafols et al. 2012). This is important for

the detection of specific publishing patterns. Namely, researchers belonging to a particular scientific

(sub) field, within a broader field, also publish outside that field (Abramo et al. 2012). Such profiles

are of primary importance in scientometric evaluation, since standards of scientometric indicators can

be set only within subfields (Glänzel and Schubert 2003).

Several studies have employed OECD-to-WOS classification mapping. Bornmann and Marx

(2015) assessed broader fields such as medical, agricultural and social sciences. Further, in an

assessment of South East Europe, Kutlaca et al. (2014) also addressed broader WOS-based areas

according to this mapping, including agriculture. The authors reiterated the significance of these data

in national R&D (Research and Development) policies. Both databases are sometimes used together to

offset disciplinary bias (Klavans and Boyack 2009). Sometimes only Scopus is used in the evaluation

of broad categories (Thelwall and Fairclough 2015).

Besides the internationally standardized OECD-to-WOS classification mapping, some studies use

several other ways of grouping categories in fields of science, each for specific purposes. For example,

Chavarro et al. (2014) mapped WOS categories to 18 'disciplines', including agriculture. Acosta et al.

(2014) mapped WOS categories to 12 broad disciplines, including agricultural and food sciences. The

categories were arranged into 20 'mega-fields' by Schoeneck et al. (2011) and into 18 'macro-

disciplines' by Gautam and Yanagiya (2012). On the other hand, instead of using broad database

categories, some authors design special search queries to identify more specific fields, for example,

agricultural and food science and technology (Borsi and Schubert 2011). All of these different

approaches identify scientific fields in different ways, so such assessments cannot be directly

compared.

Our research also investigates some other specific patterns of information distribution within the

subfields of agriculture. In informetric research, the distribution or scatter of items is frequently not

linear and exhibits patterns of the so called "power laws" of scatter, with an inverse proportional

relationship between the rank and frequency where a limited number of entities have high scientific

productivity or impact while the majority have low scientific productivity or impact (Yan et al. 2013).

Applying Bradford's law of scatter, Ren et al. (2013) found papers on water resources distributed in

more than 98 subject categories. Siegmeier and Möller (2013) detected power laws in organic farming.

Also using WOS classification, Toivanen (2014) tested power laws for 'hot-papers' and detected

increase in agricultural sciences.

We next provide some theoretical framework for the methods used in our research. For the

purposes of visualization, Aleixandre et al. (2013) used a network analysis package Pajek (Batagelj

and Mrvar 2012) in an experiment that was related to bibliometrics in agriculture (wine). Also using

Pajek, Gautam and Yanagiya (2012) included agriculture along with other WOS macro-disciplines and

complemented research by VOSviewer (developed by van Eck & Waltman, 2010) as another

visualization tool. As ways of counting, since there is no universally accepted criteria, different

methods can be applied, most notably fractional counting or whole counting is used. Also, some

ranking systems offer both indicators, full as well as fractional (Waltman et al. 2012). Each method

has strong and weak aspects, which have been discussed on many other occasions. In the system of

Journal Citation Reports, each journal has its ranking according to a category, regardless of the

number of categories to which it is assigned. Whole counting or multiple counting both avoid the

arbitrariness of assigning a multi-assigned journal to just one subject category (Yan et al. 2013).

Whole counting was also used by Jarneving (2009) in assessing national research.

Material and Methods

Our material was the published output of all research groups in the field of agricultural sciences in

Slovenia between 1996 and 2014 based on articles indexed by Web of Science Core Collection

(Science Citation Index Expanded (SCI- Expanded), Social Sciences Citation Index (SSCI), Arts &

Humanities Citation Index (A&HCI)) and its respective JCR (Journal Citation Reports) classification

of journals by Research Areas and Science Categories.

In Slovenia, research activities are organized into six major fields of science: (1) Natural Sciences

and Mathematics, (2) Engineering Sciences and Technologies, (3) Medical Sciences, (4) Agricultural


sciences (Biotechnical sciences), (5) Social Sciences, and (6) Humanities. This classification is

roughly based on the OECD Fields of Science as employed in the Slovenian Current Research

Information System (Slovenian CRIS or SICRIS). These respective classification codes are assigned

to individual researchers, research projects/programs, organizations as well as research groups. This

classification is used by the Slovenian Research Agency (SRA) for the purposes of statistics and

evaluation of publicly funded research through a nationally unified system of researcher

bibliographies. It has been used, for example, in the selection of research project proposals (Juznic et

al. 2010) and investigation of co-authorship network of Slovenian researchers (Ferligoj et al. 2015).

Researcher bibliographies are collected in the national bibliographic system COBISS (Co-operative

Online Bibliographic System and Services) and include scientific articles, proceedings papers,

monograph chapters, etc. Special attention is paid to items indexed by WOS, as these are employed as

an important criterion in the funding of grants. Records are linked to WOS through complex record-

matching algorithms. The records are controlled on several levels. Researchers are monitored through

unique author identifiers (codes). Unique codes are also used for the monitoring of research groups,

which are the subject of this analysis, and which are also classified according to the aforementioned

six fields of science. The major fields (first-digit level) are further subdivided into subfields.

We evaluated all WOS-indexed articles that were co-authored during 1996-2014 by at least one

Slovenian scientist registered in an agricultural research group in 2015. We matched the scientific

output to the publishing activity in WOS according to OECD Category to Web of Science Category

Mapping 2012 (Thomson Reuters 2015). Corresponding to this scheme, all WOS categories are

included (Table 1). These particular agricultural categories were at the center of our study. Relations

to other possible (non-agricultural) categories were also investigated, as the categories can be scattered

across multiple fields of science.

Table 1 OECD Category to Web of Science Category Mapping (WOS category) for Agricultural Sciences

Code Description (WOS category) Code Description (WOS category)

AF AGRICULTURAL ECONOMICS & POLICY JY FOOD SCIENCE & TECHNOLOGY

AE AGRICULTURAL ENGINEERING KA FORESTRY

AD AGRICULTURE, DAIRY & ANIMAL SCIENCE MU HORTICULTURE

AH AGRICULTURE, MULTIDISCIPLINARY XE SOIL SCIENCE

AM AGRONOMY ZC VETERINARY SCIENCES

JU FISHERIES

Agricultural groups self-classified their principal subject area according to one of the following

principal agricultural subfields (second digit level): B01 Forestry, wood and paper technology, B02

Animal production, B03 Plant production, and B04 Veterinarian medicine. The letter B denotes

'agriculture' in the national decoder for the six major scientific fields. Based on the above Web of

Science Category Mapping (Table 1) the four agricultural groups are comprehensively represented.

Regarding the WOS category Food Science & Technology there exist no distinct 'food' research

groups in the agricultural research scheme of Slovenia. Researchers active in food sciences designated

their research as pertaining to either 'animal production'/B02 or 'plant production'/B03. This distinction

is contingent upon the basic research materials: foods of animal origin, and foods of plant origin. In

this sense, the WOS Food Science & Technology can be associated with research groups (Table 2)

pertaining to either Plant Production or Animal Production.

After the identification of agricultural groups we created an experimental database of articles that

had been co-authored by at least one active member of each group. Computer specialists at IZUM

(Slovenian Institute of Information Science) prepared a script which included all of the required data,

for example, article's bibliographic data in separate columns (title, journal title, unique journal

identifier ISSN (International Standard Serial Number), year, etc.), including two-digit WOS journal

category code. These data were derived from the national portal COBISS/SciMet, which enabled

viewing, analyzing and processing of the data as linked to Web of Science. Journal codes were

verified for each particular year under study. Namely, it is possible that a journal, at a certain point in

time, modifies or adds a category. Thus, we have identified all categories assigned to a journal, for

each respective year under study, and conducted journal-to-category mapping. With the help of a

decoder, which we developed for this purpose, we transposed all codes into full category names.


Based on this database of all unique articles, we then created four databases (one pertaining to each

subfield). Table 2 presents the number of research groups in each of the four fields, number of

researchers involved in each field, number of all articles in the subfield and the three most productive

or principal groups in each subfield according to the number of articles. We identified 67 active

groups. The research outcomes are dominated by a few highly productive groups. Owing to the fact

that each researcher in Slovenia is affiliated (with a few minor exceptions) only to one research group,

the numbers given represent unique researchers in each group.

Table 2 Number of research groups in Slovenia, researchers, and number of all articles in the four

respective research subfields, and the first three most productive groups by the number of articles in

each respective subfield

Research field Research

Groups

Researchers All

Articles

1. group

articles

2. group

articles

3. group

articles

Forestry & Wood Science 11 163 779 374 181 95

Plant Production 25 236 1161 216 165 163

Animal Production 14 208 820 346 104 94

Veterinary Science 17 122 660 167 126 85

For further processing of data in each of the four subfield databases shown in Table 2, we

employed the toolbox Bibexcel (Persson 2011), followed by visualization using the program Pajek

(Batagelj and Mrvar, 2012). First, we calculated frequency distributions and conducted a subsequent

analysis of co-occurrences. In this routine, Bibexcel matched pairs of units from the same metadata

field. As a similarity measure, we then used the frequency (raw counts) of co-occurrences of

categories. We applied the whole counting method - also for the purposes of consistency. It has been

used in previous evaluation of OECD-WOS harmonized research activities in Slovenia.

Thus, if a journal was classified with more than one category, we mapped the journal to each

category and counted each category once. For a detailed visualization, we selected only the more

frequently occurring subject categories (40 such categories) in WOS, in each subfield (for example,

animal production, plant production ...). Some marginal categories occurred with such low frequency

that they could not be reasonably represented. We prepared the visualization with the network analysis

package Pajek. In the visualization maps, we represent the categories with circles (nodes or vertices)

where the size of a circle depends on the number articles connected in each particular category. The

distances between circles indicate the relatedness in the sense of co-occurrence. The tie lines represent

the strength of ties between the pairs of categories. This visualization facilitates easy assessment of the

strengths of the links (co-classification).

In addition to investigating the relationships between subject categories and between the four

agricultural subfields, we also explored additional patterns in the information, such as the relationship

between rank and frequency (the "power-law"). In informetric distributions, these relationships are

frequently inversely proportional. According to the well-known Bradford's law of scatter of articles in

journals (Bradford 1934), the journals in a scientific field can be distributed into three groups or zones.

Accordingly, the first zone (core or nucleus) of journal titles is usually very small, and the third group

is the largest with, for example, only one or two articles pertaining to each distinctive journal title.

Each zone contains, roughly, one-third of all articles in a field. The number of journals progresses

geometrically and not linearly. Accordingly, the number of articles per journal falls. The total numbers

in respective zones depend on a scientific field or specialty.

In each of the four subfields, we tested this theoretical power law model on journals as well as

respective categories. The results are represented by non-linear (geometrical) inversely-proportional

curves which are consistent with such laws. In our Results section that follows, we first present the

inverse-proportional patterns of journals - they provide an exploratory insight into the publication

patterns in each respective field. This is followed by visualization of co-occurrences of categories in

each subfield. The Results section finishes with the presentation of inverse-proportional distribution of

categories as this gives and additional important elucidation of the co-occurrences of categories.


Results

Inverse proportional distribution of principal journals in agricultural research fields

In order to offer exploratory insight into the publishing patterns of Slovenian scientists involved in

agricultural research groups between 1996 and 2014 we first present the cumulative results for the

articles and the respective journals involved. Table 3 presents, for each research field, the five

principal journals that published articles by the researchers in the corresponding groups. We identified

between 660 and 1161 articles in the respective fields, published in between 248 and 391 different

journals. We noticed that a substantial part of articles centers on only a few preferred journals.

Table 3 Top five journals (WOS abbreviation (WOS Quartile in 2014)) with the highest number of articles in the

four respective research field, all articles in the field, number of categories involved, and total number of

different journal titles

Forestry & Wood Science (B01) Plant Production (B03)

DRVNA IND (Q3) 38 J AGR FOOD CHEM (Q1) 43

EUR J WOOD PROD (Q2) 25 FOOD CHEM (Q1) 38

WOOD RES-SLOVAKIA (Q4) 24 PLANT DIS (Q1) 31

SUMAR LIST (Q4) 21 SCI HORTIC (Q2) 31

FOREST ECOL MANAG (Q1) 19 EUR J HORTIC SCI (Q4) 25

All articles 779 All articles 1161

No. of different categories 97 No. of different categories 91

Different journal titles 286 Different journal titles 391

Animal Production (B02) Veterinary Science (B04)

FOLIA MICROBIOL (Q4) 31 SLOV VET RES (Q4) 68

FOOD TECHNOL BIOTECH

(Q3) 23 ACTA VET HUNG (Q3) 28

ITAL J ANIM SCI (Q3) 17 ACTA VET-BEOGRAD (Q4) 22

ACTA CHIM SLOV (Q4) 17 ACTA VET BRNO (Q3) 19

MEAT SCI (Q1) 14 VET MICROBIOL (Q1) 16

All articles 820 All articles 660

No. of different categories 105 No. of different categories 69

Different journal titles 361 Different journal titles 248

In each field, the article share of the first five journals is high, and also very similar: 16 % in

Forestry & Wood Science, 14 % in Plant Production, and 12 % in Animal Production. In Veterinary

Science it is even higher (23 %), which is attributed to abundant publishing in the national journal

Slovenian Veterinary Research. This is in fact the only Slovenian WOS journal mapped to agricultural

sciences (category Veterinary Sciences). Interestingly, if we disregarded this national journal, the

counts for the first five journals in veterinary science would be 16 %, just as in the field of forestry.

The distribution of articles in journals exhibits some noteworthy characteristics of the inverse

proportional relationship between the rank and frequency, which is very similar in all four of the

research fields that we study here.


Fig. 1 Scatter of articles per journal title (number of articles per journal-title and the rank of respective

journals) in the four research fields

We elucidate these characteristics by curves that clearly show that a substantial part of the total

articles centers on only a few core journals in each field (Fig. 1). The specific journals that occupy the

respective ranks are different in each field. The five principal journals are shown in Table 3 and these

are all completely different. The long tails in Fig. 1 show that most journals published only one or two

articles. These findings are in an agreement with the Bradford's law of scatter. All four fields under

analysis exhibit these characteristics in a very similar way, although the journals are different. In fact,

the curves overlap so strongly that it is even difficult to distinctly present, in the same figure, the

curvature for each field. Only Veterinary sciences stand out, to some extent, on account of the

aforementioned national journal that 'stretches' the Y axis.

As shown in Table 1, only 11 different specific categories are provided for the classification of

agricultural sciences in the WOS journal classification scheme. Table 3, however, shows that in

Animal Production alone the articles were published in 361 different journals, which had been

classified with as many as 104 different WOS categories. Even though many journals are classified

with more than one category, sometimes pertaining to different major fields of science, it is clear that

many articles get published in such journals that are not associated with agriculture.

Assessment of the principal WOS categories and their links in each research field

We visualized the strength of the particular categories in each field (subfield) as well as links between

the categories. These categories are represented with circles (Figs. 2-5). Labels denote the original

names of WOS categories. The labels written out in upper case (for example, FORESTRY) represent

agricultural categories according to WOS (as harmonized with OECD FOS). The labels written out in

lower case (for example, environmental sciences) represent non-agricultural categories. The size of a

circle is contingent on the number of articles. Lines represent co-occurrence of categories. In each

field, the journals had been classified with more than 90 different categories, except for the field of

Veterinary Sciences, which was mapped to only 69 categories. The majority of rare categories occur

only twice or even once according to the above-mentioned inverse proportional relationship between

the rank and frequency.


Forestry and Wood Science

Researchers who participated in the forestry groups (co)authored 779 articles published in 286

different journals. Articles were scattered in 97 different journal categories. The highest number of

articles was published in journals mapped to WOS subject categories of Forestry and Materials

science, paper & wood followed by Plant sciences. Environmental sciences and Ecology also play an

important role. The category Forestry features as an agricultural category (upper-case in the Figs.)

according to WOS classification scheme. Evidently, wood-related topics are central to forestry

research even though the category Materials science, paper & wood is not a designated agricultural

category but is assigned to OECD major field of Engineering and Technology. However, wood is a

principal tree product linked to forest through technological processes. Strong links (co-occurrence)

between Forestry and Materials science, paper & wood indicate that these two categories are

frequently co-assigned to the same journal title. On the other hand, forests are composed of trees thus

possessing strong connections with Plants sciences, as well as Ecology, and Environmental sciences.

In WOS, these are assigned to OECD Natural Sciences. The journals classified with non-agricultural

categories thus occupy an important share of forestry-related publishing. Some other agricultural

categories are also involved indicating that scientists in forestry research groups in Slovenia are also

active in other specialized research fields, for example agricultural chemistry.

Fig. 2 Articles published in journals according to Web of Science categories (Forestry and Wood

Science).

Explanation for Fig. 2-5: Labels written out in upper case represent agricultural categories according

to WOS classification. If written out in lower case they represent non-agricultural categories. Circle-

size represents the number of articles in each particular category. The lines represent the links between

the pairs of categories.

Plant Production

Researchers in plant production groups (co)authored 1161 articles published in 391 different journal

titles. Articles were scattered across 91 different journal categories. Interestingly, the largest group of

articles is represented by non-agricultural category Plant sciences (OECD Natural Sciences). As many

as 260 articles were published in journals that had been mapped to this category. Even though many of

those had been co-classified with Horticulture or Agronomy, more than half had not been co-classified

with any of the agricultural categories. Environmental sciences also play an important role (OECD

Natural Sciences). Another group emerged, namely the one revolving around the topics of Food

science & technology in connection with Agriculture, multidisciplinary, and which is obviously also


linked to Chemistry, applied (OECD Natural Sciences) and related Nutrition & dietetics (OECD

Medical and Health Sciences). In the scope of plant production research groups, it is possible to

discern two major venues of research: one related to plants as an agricultural produce aimed at human

consumption, and the one reflecting a more biological frame of plants, for example plant physiology

or plant diseases and pests, and respective agricultural implications.

Fig. 3 Articles published in journals according to Web of Science categories (Plant production); see

Fig. 2 for explanation

Animal Production

Researchers who participated in animal production groups (co)authored 820 articles in 361 different

journals. Articles were scattered across 104 different journal categories. The largest group of articles is

mapped to agricultural category Agriculture, dairy & animal science, followed by Food Science &

technology and Veterinary sciences. Veterinary topics play a significant role in this group given the

strong relationships between animal production and animal health, and also the safety of products of

animal origin. We also observed strong publishing in journals classified with non-agricultural

Microbiology (OECD Natural Sciences) as well as Biotechnology & applied microbiology (OECD

Engineering and Technology), the latter also being connected with Food Science & technology. Many

categories are also mapped to OECD Medical and Health Sciences. As was explained in the Methods

section, there are no distinctive 'food production' research groups in the agricultural research scheme

of Slovenia. Researchers active in the fields of food processing and human nutrition most frequently

designate their research groups as pertaining to either animal production or plant production.

Consequently, in animal production the Food Science & technology is strongly connected with

Agriculture, dairy & animal science.


Fig. 4 Articles published in journals according to Web of Science categories (Animal production); see


Veterinary Science

Researchers active in veterinary science research groups (co)authored 660 articles in 248 different

journals. Articles occur in 69 different journal categories. Veterinary sciences possess a very obvious

central (agricultural) generic category of Veterinary sciences. This is visualized by the fairly obvious

largest circle in Fig. 5. Among those, almost seventy were published in the journal Slovenian

Veterinary Research, which accounts for 10 % of all articles. This national journal was included in

WOS in 2008 and is the only 'agricultural' WOS-indexed journal in Slovenia. The researchers in

animal production research groups also publish in this journal, although to a lesser extent. The

category Veterinary sciences is strongly linked to agricultural category Agriculture, dairy & animal

science, however, the links with a non-agricultural category Microbiology (OECD Natural Sciences)

are even more pronounced. Researchers in veterinary sciences publish very frequently in categories

which are mapped to OECD Medical and Health Sciences (Toxicology, Pharmacology & pharmacy,

Endocrinology & metabolism ...) which is expected given the essentially medical nature of veterinary

sciences. Some important publishing is also conducted in the frame of other categories within the

OECD Natural Sciences (Biotechnology & applied microbiology, Biochemical research methods,

Biochemistry & molecular biology).


Fig. 5 Articles published in journals according to Web of Science categories (Veterinary Science); see


Inverse proportional distribution of WOS categories

In the first subsection of the Results section, we investigated the patterns of the scatter of articles in

different journals without regard to a particular journal category. We found well-expressed

characteristics of the inverse proportional relationship between the rank and frequency (Fig. 1). In this

final subsection we have also ascertained similar characteristics relative to WOS categories. This

design is not completely matched to the aforementioned Bradford law, as each journal can be indexed

by two or sometimes more categories. And yet, similar Bradford-like characteristics can be observed.

We note again an inversely proportional relationship between the rank and frequency in all four

fields under study (Fig. 6). Moreover, the categories that occupy rank 1 are different in each field. For

example, in Veterinary sciences, the rank 1 is taken by the "generic" WOS category Veterinary

Sciences since the core articles are published in a national and regional veterinary journals (shown in

Table 3) which are all mapped to the Veterinary sciences. The prevalence of this principal category is

best visible in Fig. 5 where that respective circle is the largest. On the other hand, in Plant production,

the rank 1 is occupied by the WOS category Plant sciences, which is not a WOS agricultural category.

In Fig. 3 this category is represented by the largest circle. In general, all four fields show essentially a

similar inverse distribution: a few categories account for a high share of all documents, whereas most

categories are assigned to very few documents each.


Fig. 6 Scatter of categories (number of articles per category and the rank of the respective category) in

the four research fields: Forestry and Wood Science, Animal Production, Plant Production, and

Veterinary Science

Figs. 2, 3, 4, and 5 show only the more frequent and thus more representative categories, which are

ranked higher, accordingly, in Fig. 6. As was explained with each Figure, the most frequent categories

are not always mapped to agricultural sciences. Certain non-agricultural categories frequently co-occur

with agricultural categories in the same journal. However, many journals (papers) are only mapped to

non-agricultural categories. Note that the agricultural categories in Figs. 2, 3, 4, and 5 are spelled out

in upper case, and non-agricultural categories in lower case.

Finally, we ascertained the articles that would not be identified as such if based on WOS

categorization scheme for agricultural sciences. An approximation of the share of such articles in each

field follows: Forestry & Wood Science (65 %), Plant Production (50 %), Animal production (53 %),

Veterinary sciences (41 %). For example, in the field of forestry, a significant portion of articles was

mapped to journals mapped only to Materials science, paper & wood, Plant sciences, Environmental

sciences and Ecology. Here we point out that the WOS category Materials science, paper & wood is

mapped to WOS Research Area of Materials science and to the respective OECD Engineering and

Technology. Wood technologies are invariably mapped to Forestry in all major agricultural

classification schemes (CAB/Cabicodes, US National Agricultural Library Subject Category Codes,

Agris/FAO Subject Categories) as pertaining to forest products and respective processing thereof. It

seems that the WOS classification scheme aims at offsetting this challenge by frequently co-assigning

both Materials science, paper & wood as well as Forestry to the same journal, sometimes only after a

certain point in time as is, for example, the case with the European Journal of Wood and Wood

Products (a.k.a. Holz als Roh und Werkstoff). Many generic wood-related journals, however, remain

unmapped to Forestry.

Some very essential categories are not classed as 'agricultural', most notably Plant sciences (OECD

Natural sciences). We need to reiterate that the journals mapped to WOS category Plant sciences and

which are not co-classified with an agricultural category play a crucial role in the dissemination of

outcomes in agricultural plant production. Many are ranked in the first Quartile of the JCR Impact

Factor. Some other essential WOS categories, of relevance to agriculture, and which are not co-

assigned with agricultural categories are mapped to OECD Engineering and Technology, or OECD

Medical and Health Sciences. It is thus evident that an important part of legitimate agricultural

research will evade detection if based solely on this classification. Let us restate here, in the case of the


Slovenian agriculture-related research as much as 50 % of research results are published outside the

scope of agricultural sciences and are not detectable through these schemes.

Discussion

In many countries, database classification systems are regularly used to evaluate the productivity of

scientific fields. Most of this sort of evaluation takes into account the given classification schemes

provided by database managers. However, an obvious question arises here: which database or

information system to choose. For many reasons, Google Scholar is not useful, also as the documents

there are not indexed or classified according to scientific fields. On the other hand, in the case of

agriculture, there exist major international bibliographic databases that are dedicated principally to

agriculture, such as Agris (FAO/Food and Agriculture Organization), Agricola (US/National

Agricultural Library), and CAB Abstracts (CABI - Centre for Agriculture and Biosciences

International), the latter being the principal global resource for agriculture-related information. These

three databases, however, catalog many different document types in many languages, on low

restrictive principles, and are not usually used for evaluation purposes. Also, specialized databases

cannot be used for a comparison of different scientific fields. Thus, the comprehensive citation

databases (Web of Science in our case) remain the only reasonable choice.

Despite frequently discussed limitations of schemes in such databases, they nevertheless form the

basis for a "uniform" comparison of research outcomes. Our WOS-based results indicate limitations in

the use of these schemes, which lies in a possible underrepresentation of some broader fields of

science, agriculture in the specific case we studied here. While the documents retrieved within

applicable categories will be relevant, many other possibly relevant documents will escape notice. In

addition, agricultural research papers published outside the scope of agricultural categories will

"boost" the counts in other areas of research. As some previous authors have remarked: "one field's

loss will be another field's gain" (Aksnes et al. 2000). A similar research based on Scopus, however,

would face other limitations. In this case, the field of agriculture would also include biology (Scopus

category of 'Agricultural and Biological Sciences'). Such an assembly would then be too broad. As

authors noted more in general, with a small granularity level the field is too heterogeneous (Ruiz-

Castillo and Waltman 2015).

In the WOS-based evaluations, the assessments frequently focus on categories that are mapped to

OECD/Frascati fields of science which can be generated from WOS, but not from Scopus, as the latter

is not sufficiently detailed (Kutlaca et al. 2014). Scopus also seems less robust in some other aspects,

such as disambiguation of article titles (Valderrama-Zurián et al. 2015), although it does offer more

coverage of social sciences and humanities and an enhanced coverage of more local or regional

journals (Bartol et al. 2014) which, however, also implies a less restrictive coverage. Our study has

thus been performed on WOS as this database still seems to offer a good possibility to uniformly

assess the agricultural groups under study.

Related scientometric research frequently focuses on institutions. However, in these cases the data-

disambiguation (correct identification of relevant items) presents many challenges (Huang et al. 2014).

In our study, these limitations were offset by the selection of research groups that was based on "raw

data" derived from the portal COBISS/SciMet regulated through a rigorous authority-control of WOS

data disambiguation. It is linked to the Slovenian national CRIS (SICRIS), which has been frequently

used for the identification of national actors in various scientometric assessments (Vilar et al. 2012).

The portal enables efficient downloading and subsequent mapping and visualization of all relevant

records, and subsequent evaluation of all nationally established research groups - as active in the field

of agriculture. The significance of research groups has also been pointed out, for example, by Albarrán

et al. (2011). As has been observed by Bourke and Butler (1998), research activities organized around

groups cut across boundaries established by academic structures. The "crossing of boundaries", in our

case, was possible by the additional topical enrichment of data on the level of a broader field as well as

a lower more specialized level (or subfield), the importance of which was also emphasized by Abramo

et al. (2012) and Glänzel and Schubert (2003).

The significance of other research fields for the dissemination of agriculture-related results has

been detected by other authors. Rinia et al. (2002) noted that the number of agricultural contributions

in articles in journals in basic life sciences are perhaps even more important than those in the

discipline of agriculture. Plant Sciences were linked to agriculture by Gautam and Yanagiya (2012)


and Klavans and Boyack (2009), as well as Jonkers (2009). Relationships between agriculture and

biology, environmental science, and biomedicine were noted by Zhang et al. (2010) and Morillo et al.

(2003). In our study, we were able to establish such links fairly precisely. In Forestry and Wood

Science, a crucial part is played by journals mapped to the major fields (OECD) of Engineering and

Technology as well as Natural Sciences. In Animal Production, OECD Medical Sciences are also

important. In Plant Production, many journals are mapped only to OECD Natural Sciences and

respective "non-agricultural" plant sciences. Veterinary Sciences exhibit very strong publishing

participation in OECD Natural Sciences as well as Medical Sciences. We also note that Slovenian

agricultural scientists publish predominantly in non-domestic publications (Bartol 2010). WOS

indexes only one Slovenian publication mapped to agricultural categories.

We have assessed the records we use in line with power-laws, which in scientometric experiments

frequently proceed from the Pareto model (Glänzel et al. 2014). We tested the scatter of information

using the so-called Bradford type of inverse proportional distribution. All four agricultural fields in

our study exhibit very similar non-linear curves. In all four fields, a few principal (core) journals

account for roughly one third of all articles published. On the other hand, majority of journals

published only one such article. This type of scatter is also evident in journal categories. What is more

telling, however, is that the first core-zone also contains journals that have not been classified with any

of the agricultural categories of the WOS categorization.

To summarize: as much as 50 % of all agriculture-related articles can be found in journals which

are not mapped to any of the agricultural categories. Natural Sciences seem to "profit" the most. The

real participation of agriculture is thus probably not comprehensively reflected in experiments and

such national "case-reports" which rely solely on simple schemes.

Conclusions

A few selected journals, as well as a few principal categories, account for an important part of all

relevant documents in each of the agricultural subfields under study. This pattern conforms to the

general principles of power laws of inverse proportional distribution of items. Even "core" journals as

well as categories are frequently not mapped to the applicable agricultural categories, contrary to what

one might expect. Half of relevant records scattered across hundreds of different journals and dozens

of categories would have eluded detection if established on these schemes. This may have critical

consequences in some national R&D evaluation systems where fields of science (agriculture in our

case) receive their share of attention, and subsequent funding, according to their position in citation

databases. The under-representation of agriculture we identify in simple ranking schemes, based on

the given "standard" classifications, serves to illustrate a specific case in which the total output of that

science is in fact much higher.

References

Abramo, G., D’Angelo, C. A., & Cicero, T. (2012). What is the appropriate length of the publication

period over which to assess research performance? Scientometrics, 93(3), 1005–1017.

doi:10.1007/s11192-012-0714-9

Acosta, M., Coronado, D., Ferrándiz, E., & León, M. D. (2014). Regional Scientific Production and

Specialization in Europe: The Role of HERD. European Planning Studies, 22(5), 949–974.

doi.org/10.1080/09654313.2012.752439

Aksnes, D., Olsen, T., & Seglen, P. (2000). Validation of Bibliometric Indicators in the Field of

Microbiology: A Norwegian Case Study. Scientometrics, 49(1), 7–22.

doi.org/10.1023/A:1005653006993

Albarrán, P., Ortuño, I., & Ruiz-Castillo, J. (2011). Average-based versus high- and low-impact

indicators for the evaluation of scientific distributions. Research Evaluation, 20(4), 325–339.

doi.org/10.3152/095820211X13164389670310

Aleixandre, J. L., Aleixandre-Tudó, J. L., Bolaños-Pizzaro, M., & Aleixandre-Benavent, R. (2013).

Mapping the scientific research on wine and health (2001–2011). Journal of Agricultural and

Food Chemistry, 61(49), 11871–11880. doi.org/10.1021/jf404394e

Bartol, T. (2010). Scientometric assessment of publishing patterns and performance indicators in

agriculture in the JCEA member countries. Journal of Central European Agriculture, 11(1), 1–9.


Bartol, T., Budimir, G., Dekleva-Smrekar, D., Pusnik, M., & Juznic, P. (2014). Assessment of

research fields in Scopus and Web of Science in the view of national research evaluation in

Slovenia. Scientometrics, 98(2), 1491–1504. doi.org/10.1007/s11192-013-1148-8

Batagelj, V., & Mrvar, A. (2012). Pajek. Programs for large networks analysis. Retrieved from

http://pajek.imfm.si/doku.php?id=pajek

Bornmann, L., & Marx, W. (2015). Methods for the generation of normalized citation impact scores in

bibliometrics: Which method best reflects the judgements of experts? Journal of Informetrics,

9(2), 408–418. doi.org/10.1016/j.joi.2015.01.006

Borsi, B., & Schubert, A. (2011). Agrifood research in Europe: a global perspective. Scientometrics,

86(1), 133–154. doi.org/10.1007/s11192-010-0235-3

Bourke, P., & Butler, L. (1998). Institutions and the map of science: matching university departments

and fields of research1. Research Policy, 26(6), 711–718. doi.org/10.1016/S0048-

7333(97)00046-2

Bradford, S. C. (1934). Sources of information on specific subject. Engineering, 137, 85–86.

Chavarro, D., Tang, P., & Rafols, I. (2014). Interdisciplinarity and research on local issues: evidence

from a developing country. Research Evaluation, 23(3), 195–209. doi.org/10.1093/reseval/rvu012

Cova, T. F. G. C., Jarmelo, S., Formosinho, S. J., Sergio Seixas de Melo, J., & Pais, A. A. C. C.

(2015). Unsupervised characterization of research institutions with task-force estimation. Journal

of Informetrics, 9(1), 59–68. doi.org/10.1016/j.joi.2014.11.005

Ferligoj, A., Kronegger, L., Mali, F., Snijders, T. A. B., & Doreian, P. (2015). Scientific collaboration

dynamics in a national scientific system. Scientometrics, 104(3), 985–1012.

doi.org/10.1007/s11192-015-1585-7

Gautam, P., & Yanagiya, R. (2012). Reflection of cross-disciplinary research at Creative Research

Institution (Hokkaido University) in the Web of Science database: appraisal and visualization

using bibliometry. Scientometrics, 93(1), 101–111. doi.org/10.1007/s11192-012-0655-3

Glänzel, W., & Schubert, A. (2003). A new classification scheme of science fields and subfields

designed for scientometric evaluation purposes. Scientometrics, 56(3), 357–367.

doi.org/10.1023/A:1022378804087

Glänzel, W., Thijs, B., & Debackere, K. (2014). The application of citation-based performance classes

to the disciplinary and multidisciplinary assessment in national comparison and institutional

research assessment. Scientometrics, 101(2), 939–952. doi.org/10.1007/s11192-014-1247-1

Huang, S., Yang, B., Yan, S., & Rousseau, R. (2014). Institution name disambiguation for research

assessment. Scientometrics, 99(3), 823–838. doi:10.1007/s11192-013-1214-2

Jarneving, B. (2009). The publication activity of Region Västra Götaland: a bibliometric study of an

administrative and political Swedish region during the period 1998-2006. Information Research,

14(2), Paper 397. Retrieved from http://www.informationr.net/ir/14-2/paper397.html

Jonkers, K. (2009). Models and orphans; concentration of the plant molecular life science research

agenda. Scientometrics, 83(1), 167–179. doi.org/10.1007/s11192-009-0024-z

Juznic, P., Peclin, S., Zaucer, M., Mandelj, T., Pusnik, M., & Demsar, F. (2010). Scientometric

indicators: peer-review, bibliometric methods and conflict of interests. Scientometrics, 85(2),

429–441.

Klavans, R., & Boyack, K. W. (2009). Toward a consensus map of science. Journal of the American

Society for Information Science and Technology, 60(3), 455–476. doi.org/10.1002/asi.20991

Kutlaca, D., Babic, D., Zivkovic, L., & Strbac, D. (2014). Analysis of quantitative and qualitative

indicators of SEE countries scientific output. Scientometrics, 102(1), 247–265.

doi.org/10.1007/s11192-014-1290-y

Larsen, P., & von Ins, M. (2010). The rate of growth in scientific publication and the decline in

coverage provided by Science Citation Index. Scientometrics, 84(3), 575–603.

doi.org/10.1007/s11192-010-0202-z

Morillo, F., Bordons, M., & Gómez, I. (2003). Interdisciplinarity in science: A tentative typology of

disciplines and research areas. Journal of the American Society for Information Science and

Technology, 54(13), 1237–1249. doi.org/10.1002/asi.10326

OECD. (2007). OECD/OCDE. Revised field of science and technology (FOS) classification in the

Frascati manual. Retrieved from http://www.oecd.org/science/inno/38235147.pdf

Persson, O. (2010). Bibexcel – a toolbox for bibliometricians. Inforsk, Umea university.

http://www8.umu.se/inforsk/Bibexcel/. Accessed 10 November 2015


Pudovkin, A. I., & Garfield, E. (2002). Algorithmic procedure for finding semantically related

journals. Journal of the American Society for Information Science and Technology, 53(13), 1113–

1119. doi:10.1002/asi.10153

Rafols, I., Leydesdorff, L., O’Hare, A., Nightingale, P., & Stirling, A. (2012). How journal rankings

can suppress interdisciplinary research: A comparison between Innovation Studies and Business

& Management. Research Policy, 41(7), 1262–1282. doi.org/10.1016/j.respol.2012.03.015

Ren, J.-L., Lyu, P.-H., Wu, X.-M., Ma, F.-C., Wang, Z.-Z., & Yang, G. (2013). An informetric profile

of water resources management literatures. Water Resources Management, 27(13), 4679–4696.

doi.org/10.1007/s11269-013-0435-8

Rinia, E. J., Leeuwen, T. N. van, Bruins, E. E. W., Vuren, H. G. van, & Raan, A. F. J. van. (2002).

Measuring knowledge transfer between fields of science. Scientometrics, 54(3), 347–362.

doi.org/10.1023/A:1016078331752

Ruiz-Castillo, J., & Waltman, L. (2015). Field-normalized citation impact indicators using

algorithmically constructed classification systems of science. Journal of Informetrics, 9(1), 102–

117. doi.org/10.1016/j.joi.2014.11.010

Schoeneck, D. J., Porter, A. L., Kostoff, R. N., & Berger, E. M. (2011). Assessment of Brazil’s

research literature. Technology Analysis & Strategic Management, 23(6), 601–621.

doi.org/10.1080/09537325.2011.585029

Siegmeier, T., & Möller, D. (2013). Mapping research at the intersection of organic farming and

bioenergy - A scientometric review. Renewable and Sustainable Energy Reviews, 25, 197–204.

doi.org/10.1016/j.rser.2013.04.025

Testa, J. (2003). The Thomson ISI Journal Selection Process. Serials Review, 29(3), 210–212.

doi:10.1080/00987913.2003.10764831

Thelwall, M., & Fairclough, R. (2015). Geometric journal impact factors correcting for individual

highly cited articles. Journal of Informetrics, 9(2), 263–272. doi.org/10.1016/j.joi.2015.02.004

Thomson Reuters. (2015). InCites. Retrieved from http://ipscience-

help.thomsonreuters.com/incitesLive/globalComparisonsGroup/globalComparisons/subjAreaSch

emesGroup/oecd.html

Toivanen, H. (2014). The shift from theory to innovation: the evolution of Brazilian research frontiers

2005–2011. Technology Analysis & Strategic Management, 26(1), 105–119.

doi.org/10.1080/09537325.2013.850160

Valderrama-Zurián, J.-C., Aguilar-Moya, R., Melero-Fuentes, D., & Aleixandre-Benavent, R. (2015).

A systematic analysis of duplicate records in Scopus. Journal of Informetrics, 9(3), 570–576.

doi.org/10.1016/j.joi.2015.05.002

van Eck, N., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for

bibliometric mapping. Scientometrics, 84(2), 523–538. doi.org/10.1007/s11192-009-0146-3

Vanloqueren, G., & Baret, P. V. (2009). How agricultural research systems shape a technological

regime that develops genetic engineering but locks out agroecological innovations. Research

Policy, 38(6), 971–983. doi.org/10.1016/j.respol.2009.02.008

Vilar, P., Juznic, P., Bartol, T., & GreyNet, G. L. N. S. (2012). Information-seeking behaviour of

Slovenian researchers: implications for information services. The Grey Journal, 8(1), 43–53.

Waltman, L., Calero-Medina, C., Kosten, J., Noyons, E. C. M., Tijssen, R. J. W., van Eck, N. J., …

Wouters, P. (2012). The Leiden ranking 2011/2012: Data collection, indicators, and

interpretation. Journal of the American Society for Information Science and Technology, 63(12),

2419–2432. doi.org/10.1002/asi.22708

Yan, E., Ding, Y., Cronin, B., & Leydesdorff, L. (2013). A bird’s-eye view of scientific trading:

Dependency relations among fields of science. Journal of Informetrics, 7(2), 249–264.

doi.org/10.1016/j.joi.2012.11.008

Zhang, L., Liu, X., Janssens, F., Liang, L., & Glänzel, W. (2010). Subject clustering analysis based on

ISI category classification. Journal of Informetrics, 4(2), 185–193.

doi.org/10.1016/j.joi.2009.11.005

mapping and classification of agriculture in web of ... and...mapping and classification of...

Documents