Nourmohammadi keramatfar

Proceedings
of
10th International Conference on
Webometrics, Informetrics
and Scientometrics &
15th COLLNET Meeting 2014
net
2014
September 3-5, 2014
Technische Universität Ilmenau, Germany
Edited by
Bernd Markscheffel • Daniel Fischer •
Daniela Büttner • Hildrun Kretschmer

Proceedings
of
10th International Conference on
Webometrics, Informetrics and Scientometrics &
15th COLLNET Meeting 2014
September 3-5, 2014
Technische Universität Ilmenau, Germany
Edited by
Bernd Markscheffel,
Daniel Fischer,
Daniela Büttner and
Hildrun Kretschmer

Bernd Markscheffel,
Daniel Fischer,
Daniela Büttner and
Hildrun Kretschmer
Technische Universität Ilmenau
Fakultät für Wirtschaftswissenschaften und Medien
Institut für Wirtschaftsinformatik
P.O. Box 100565
98684 Ilmenau
Germany
bernd.markscheffel@tu-ilmenau.de
daniel.fischer@tu-ilmenau.de
daniela.buettner@tu-ilmenau.de
kretschmer.h@onlinehome.de
Ilmenau, 2014

10th International Conference on Webometrics, Informetrics and Scientometrics & 15th COLLNET Meeting 2014
v
Index
Index .......................................................................................................................................... v
Invited Papers ........................................................................................................................... 1
Eugene Garfield and Alexander Pudovkin ................................................................................. 3
Journal Impact Factor Reflects Citedness of the Majority of the Journal Papers
Liming Liang and Zhen Zhong .................................................................................................. 9
Uncited Papers, Unicited authors and Uncited Topics
Weiping Yue ............................................................................................................................ 17
A Scientometric Study on Collaboration between Academia and Industry –
Case studies of Chinese leading universities and companies
Hildrun Kretschmer and Theo Kretschmer .............................................................................. 21
Three-dimensional Visualization and Animation of Emerging Patterns by the
Process of Self-Organization in Collaboration Networks
I. K. Ravichandra Rao and K. S. Raghavan ............................................................................. 49
Seven years of COLLNET Journal of Scientometrics and Information Management
(2007 -2013)
Full Papers .............................................................................................................................. 69
Amir Reza Asnafi and Maryam Pakdaman Naeini .................................................................. 71
A Survey on Collaboration rate of authors in producing Scientific Papers in Quarterly
Journal of Information Technology Management during 2009-2014
André Calero Valdez, Anne Kathrin Schaar, Tobias Vaegs, Thomas Thiele,
Markus Kowalski, Susanne Aghassi, Ulrich Jansen, Wolfgang Schulz, Guenther Schuh,
Sabina Jeschke and Martina Ziefle ........................................................................................... 77
Scientific Cooperation Engineering Making Interdisciplinary Knowledge Available
within Research Facilities and to External Stakeholders
Arshia Kaul, Sujit Bhattacharya, Shilpa and Praveen Sharma ................................................. 87
Measuring Efficiency of Scientific Research
Ashkan Ebadi and Andrea Schiffauerova ................................................................................ 91
How do scientists collaborate? Assessing the impact of influencing factors

Barbara S. Lancho Barrantes .................................................................................................. 103
Benefits of scientific collaboration
Bernd Markscheffel and Johannes Schmidt ........................................................................... 109
A Bibliometric Indicator for the Consideration of Time Related Aspects Following
the Example of Twitters Influence Passivity Score
Bharvi Dutt and Khaiser Nikam ............................................................................................. 111
International Collaboration in Solar Cell Research in India
Carey Ming-Li Chen .............................................................................................................. 121
The Application of Funding Acknowledgment on the Path Analysis of Knowledge
Dissemination of Granted Researches
Carlos Olmeda-Gómez, María Antonia Ovalle-Perandones, Juan Gorraiz and
Christian Gumpenberger ........................................................................................................ 129
Excellence, merit and research team size: a library and information science case study
Chen Yue, Zhang Liwei, Wang Zhiqi, Liu Shengbo, Su Lixin and Hou Yu ......................... 139
Influential Bloggers and Active Bloggers on ScienceNet:
An Analysis of Popular Blogs
Chun Wang, ZhengYin Hu, Miaoling Chai and Hui Wang ................................................... 145
Legal Status Prediction for US Patents on Thermocouples
Divya Srivastava, Arvind Singh Kushwah and Mona Gupta ................................................. 153
An Analysis of Collaboration Pattern of Indian S & T Papers
(Published during 2005-09)
Divya Srivastava, Arvind Singh Kushwah and Mona Gupta ................................................. 163
Impact of Indian S&T Research Papers – Published during 2005-09:
through Citation Analysis
Divya Srivastava, Sandhya Diwakar and Ramesh Kundra .................................................... 173
Current status of Medical research across the Countries: India, China and Brazil
Farideh Osareh and Ismael Mostafavi .................................................................................... 179
Visualizing the co-authorship relations in surgery discipline outputs among Iranian
and Global cities
Fatemeh Helaliyan Motlagh and Mohammad Hassanzadeh .................................................. 191
Studying the status of knowledge management components in Petrochemical
Companies (case study: South Pars Energy Economic Special Zone » Assalouyeh «)
Fatemeh Nooshinfar, Aref Riahi and Elham Ahmadi ............................................................ 201
Study of Barriers to Scientific Collaboration of female Scientifics
(Case Study of Iranian Women members of University of Tehran)
vi

Gayatri Paul and Swapan Deoghuria ..................................................................................... 209
Indian Journal of Physics: A scientometric analysis
Grant Lewison and Richard Sullivan ..................................................................................... 217
Conflicts of Interest Statements on Biomedical Papers
Hailong Wang and Minyu Wang ........................................................................................... 227
Core technology fields and innovation cooperation network of electric
vehicle industry
Hamideh Asadi and Mahsan Poorasadollahi .......................................................................... 237
Structure and Evolution of Library and Information Science in the top Countries
of Middle East in terms of Scientific Productions during the years of 1992-2012
Hamzehali Nourmohammadi and Abdalsamad Keramatfar .................................................. 247
The relation between the number of countries’ Rich Files on the web and countries’
economic development
Hamzehali Nourmohammadi, Mahdi Keramatfar and Abdalsamad Keramatfar ................... 257
Research in what fields? Determining Iran’s research priorities according to
their impact on economic development
Handaru Jati ............................................................................................................................ 265
Weight of Webometrics Criteria using Entropy Method
Hongfang Shao, Qi Yu and Zhiguang Duan .......................................................................... 269
Detecting the milestones of epigenetics development from 2002 to 2013:
a Scientometrics perspective
Hou Haiyan, Zhao Nannan, ZhangShanshan, Liang Yongxia and Hu Zhigang .................... 281
Characteristics of the development of NB converging technology
Jiang Chunlin, Liu Xue and Zhang Liwei .............................................................................. 293
Data Fetching and Group Characteristics Analysis Based on Sina Microblog
Jiang Chunlin, Zhang Liwei and Liu Xue .............................................................................. 301
Survey of the Editorial Board Members for Journals of Library and
Information Science in China
K. S. Raghavan and I. K. Ravichandra Rao ........................................................................... 309
Mapping Engineering Research in India
Leila Nemati-Anaraki and Roya Pournaghi ........................................................................... 317
The Effect of Geographical Proximity on Organizational Knowledge Sharing
Li Gu, Weichun Yan and Shule An ........................................................................................ 327
The Relationship between internet attention and market share of operation systems
for personal computers
vii

Liu Xiaomin, Sun Yuan and He Jing ..................................................................................... 335
Impact of articles in non-English language journals – A bibliometric analysis of
regional journals of China, Japan, France and Germany in Web of Science
Lutz Bornmann, Moritz Stefaner, Felix de Moya Anegón and Rüdiger Mutz ...................... 345
Ranking and mappping of universities and research-focused institutions worldwide:
The third release of www.excellencemapping.net
M.H. Biglu and M. A-Farhangi .............................................................................................. 353
Infometrics analysis of Scientific-literature in Pediatrics obesity
Marzieh Yari Zanganeh and Nadjla Hariri ............................................................................. 359
Transactions Reports Analysis Islamic Azad University Marvdasht – branch website:
A Case Study
Marzieh Yari Zanganeh and Sedigheh Mohammad ............................................................... 367
Use of Six Sigma Concept in University Libraries:
A Case Study of Fars province Medical Sciences Library University
Masaki Nishizawa and Yuan Sun ........................................................................................... 373
How is scientific research reported in newspapers? – Comparison between press
releases and two different national newspapers in Japan
Meera and Surendra Kumar Sahu .......................................................................................... 381
Research Output of University College of Medical Science, University of Delhi:
A Bibliometric Study
Mohammad Hassanzadeh and Babak Akhgar ........................................................................ 395
Relationship between Development Indicators and Contribution to the Science:
Experiences from Iran
Mursheda Begum and Grant Lewison .................................................................................... 403
European cancer research publications, 2002-13
Nabi Hasan and Mukhtiar Singh ............................................................................................ 413
Library and Information Science Research Output: A study based on Web of Science
R. D. Shelton and T. R. Fade ................................................................................................. 427
Which Scientometric Indicators Best Explain National Performance of
High-Tech Outputs?
Roya Pournaghi and Leila Nemati-Anaraki ........................................................................... 437
The Mutual Role of Scientometrics and Foresight
S. L. Sangam, Devika Madalli and Uma Patil ....................................................................... 449
Indicators to Measure Genetics Literature: A Comparative Study of Selected Countries
viii

Sandhya Diwakar and K. K. Singh ........................................................................................ 459
Analysis of the Financial Assistance to Non-ICMR Biomedical Scientists by Indian
Council of Medical Research (ICMR) 2009 - 2013
Shantanu Ganguly, P K Bhattacharya and Tanvi Sharma ...................................................... 465
Growth of Literature in Biofuels Research: A Resource Analysis
Shilpa, Arshia Kaul and Sujit Bhattacharya ........................................................................... 481
Salient Aspects of India’s Publication activity
Soheila Bagheri and Mohaddeseh Dokhtesmati ..................................................................... 485
Comparative study of outputs and scientific cooperation of world's countries in
Biomedical engineering field in Science Citation Index in the years 2002-2011 with
an emphasis on co-authorship networks
Tahereh Dehdarirad, Anna Villarroya and Maite Barrios ...................................................... 497
Women in Science and Higher Education: a bibliometric study
Tariq Ashraf ........................................................................................................................... 507
Pattern of Research & Citations: A Study of Three Central Universities
Located in Delhi-India
Thuraiyappah Pratheepan and W.A. Weerasooriya ............................................................... 529
International research collaboration of Sri Lanka in the last 02 decades (1994 – 2013)
based on the SCOPUS database
Umut Al and Zehra Taşkın ..................................................................................................... 539
Relationship between Economic Development and Intellectual Production
Umut Al, İrem Soydal, Umut Sezen and Orçun Madran ....................................................... 549
The Impact of Turkey in the Library and Information Science Literature
Vijayakumar M, Debojyoti Nath and Annapurna SM ........................................................... 559
A study on Indian collaboration among SAARC Countries using
Webometrics Methods
Wen-Yau Cathy Lin ............................................................................................................... 569
Comparative Study of Journal Impact Factor and Self-Citation Across Asian
International Journals
Xianwen Wang, Wenli Mao and Chen Liu ............................................................................ 575
Does The Open Access Advantage Exist? An Empirical Study on Citation and
Article View Data
Xiaoyu Zhu, Zeyuan Liu, Chaomei Chen and Haiyan Hou ................................................... 581
Statistical analysis on interlocking directorate in Chinese listed companies
ix

Yang Zhongkai, Xu Mengzhen and Hanshuang .................................................................... 587
Measurement and Changing Trends of Originality Index Value –
In view of NBER Patent Citation Database
Yunwei Chen, Yong Deng, Fang Chen, Chenjun Ding, Ying Zheng and Shu Fang ............. 597
A Co-author Based CCS Index Used for Evaluating Scientists’ Performance
Zhao Qu, Xiling Shen and Kun Ding ..................................................................................... 609
Comparative Analysis on Technologies between Chinese and American
Large-sized Oil Companies based on Patentometrics
Posters ................................................................................................................................... 619
List of Accepted Posters ......................................................................................................... 621
x

1
Invited Papers

2

The relation between the number of countries’ Rich Files on the
247
web and countries’ economic development
Hamzehali Nourmohammadi* and Abdalsamad Keramatfar**
*Shahed University, Tehran, Iran
nourmohammadi.h@gmail.com
**Scientometrics Section of SID, Tehran, Iran
keramatfar@mailfa.com
Introduction
All the activities related to measuring science started in early 20th century with the works of
people like Holm(Braun & others,1985), following Price’s attempts to display the relation
between scientific products and countries’ scientific development, using citation indexes for
examining countries’ scientific development expanded rapidly. In addition, late in 1960s, Price
demonstrated the correlation between countries’ scientific productivity and their GDP and
presented the relation between scientific dynamism and economic development (Noroozi
Chakoli, 2012). Within the past years this correlation has been confirmed by many researchers
like, Vinkler (2008) and Lee & others (2011), which both indicates the significance of
evaluation of researches’ findings and verifies its method which is using excessive citation
indexes. On the other hand, since the mid-1990s has emerged a new research field,
webometrics-“webometrics” itself was coined in 1997 (Almind and Ingwersen 1997),
investigating the nature and properties of the Web drawing on modern informetric
methodologies (Björneborn & Ingwersen, 2001). the value of webometrics quickly became
established through the Web Impact Factor, the key metric for measuring and analyzing website
hyperlinks (Thelwall, 2012). Also the need for timely and relevant web-based S&T indicators
has become more urgent (Scharnhorst & Wouters, 2006). Nourmohammadi and keramatfar
(2013) demonstrated that there exists a correlation between countries scientific production rank
and their Rich Files rank on the web and concluded that scientific evaluation of countries could
be done based on the number of their Rich Files on the web. According to what was mentioned
above, the main problem this study seeks to address is this; is there any relation between
countries’ Rich Files on the web and their economic development?
 Therefore, the questions this study addresses are as follows:
 What is the number of scientific production of world’s different countries?
 What is the number of different countries’ Rich Files on the web?
 What is the amount of GDP indicator of world’s different countries?
 What is the amount of correlation between countries’ scientific production rank and
their GDP rank in comparison with the correlation between countries’ Rich Files rank
and their GDP rank?
 How is the linear relation between the number of countries’ Rich Files and their GDP?
Methodology
This study is library-based and due to its use of Scientometrics methods lies within scope of
Webometrics Researches. Countries’ scientific production data was extracted from SCImago
and countries’ GDP data was extracted from World Bank. Countries’ Rich Files data was
extracted from Bing search engine in the following way; in order to search, the name of a given
country was chosen in the Advance Search section then using the formulae: filetype:pdf,

filetype:doc, filetype:ppt, the number of Rich Files was determined. Correlation Test was
carried out using SPSS19, and Regression Test was carried out using Excell2007. Research
Society included all the world countries for which there is the possibility of specific search in
Bing search engine. Data was extracted in the second half of August 2013.
Theoretical framework
Nowadays scientific production is measured based on excessive citation indexes that present
bibliographical information of different kinds of scientific productions, because citation index
makes identifying and recovering valid information about subject areas possible and provides
citation information that relates papers and indicates the degree of validity of papers to a great
extent(Noroozi Chakoli, 2012). Using the number of countries’ scientific productions in order
to evaluate their scientific development by experts is done by the two large databases ESA and
SCImago, the former using Web of Knowledge data and the latter using Scopus data.
Along with developments in bibliometrics and emergence of Webometrics some attempts were
made to use the web for scientific evaluation. Webometrics is the quantitative analysis of web
phenomenon using informetric methods (Noroozi Chakoli, 2102). A useful database in this field
is Webometrics (http://webometrics.info) that has been evaluating universities across the world
according to their website since 2007. One of the indicators of this database is the number of
universities’ Rich Files on the web. Rich Files include PDF, DOC, and PPT; these files have
been chosen because the majority of scientific productions are published in one of these
formats. Nourmohammadi & Keramatfar (2013) by demonstrating the correlation between the
number of countries’ Rich Files on the web and the number of their scientific production
proposed that Rich Files can be used for evaluating countries’ scientific development. In this
study, the authors examine Nourmohammadi & Keramatfar’s proposal and by examining its
correlation with countries’ economic development compare this method with excessive citation
indexes method.
Findings
The findings will be presented in four sections according to the questions put forward in the
introduction.
1. What is the number of scientific production of world’s different countries?
Table No1 shows the number of world countries’ scientific productions in SCImago. USA, UK,
and Japan are ranked first, second, and third.
248
Table 1. The number of countries’ document in SCImago
Country Documents Country Documents Country Documents
United
6,149,455 Portugal 117,469 Philippines 11,326
States
United
Kingdom
1,711,878 New Zealand 114,495 Puerto Rico 9,862
Japan 1,604,017 South Africa 107,976 Iceland 9,285
Germany 1,581,429 Argentina 105,216 Latvia 8,396
France 1,141,005 Hungary 100,137 Armenia 8,054
Canada 885,197 Ukraine 98,083 Peru 7,516
Italy 851,692 Ireland 91,125 Oman 6,875
Spain 665,977 Romania 76,361 Georgia 6,381

249
Country Documents Country Documents Country Documents
India 634,472 Egypt 75,610 Azerbaijan 6,135
Australia 592,533 Malaysia 75,530 Costa Rica 5,711
Russian
527,442 Thailand 69,637 Luxembourg 5,121
Federation
South Korea 497,681 Chile 58,768 Iraq 4,420
Netherlands 487,784 Slovakia 49,863 Macedonia 4,401
Brazil 391,589 Croatia 49,462 Qatar 4,398
Taiwan 351,610 Pakistan 47,443 Ecuador 3,887
Switzerland 350,253 Saudi Arabia 46,167 Bosnia and
Herzegovina
3,524
Sweden 337,135 Slovenia 44,142 Syrian Arab
Republic
3,379
Poland 304,003 Tunisia 32,250 Panama 3,043
Turkey 267,902 Colombia 28,817 Bahrain 2,817
Belgium 265,913 Morocco 23,446 Libyan Arab
Jamahiriya
2,304
Israel 204,262 Lithuania 21,098 Bolivia 2,298
Austria 188,440 Algeria 21,059 Malta 2,029
Denmark 183,880 Serbia 21,011 Yemen 1,395
Finland 170,476 Jordan 17,126 Guatemala 1,296
Greece 160,760 Estonia 16,573 Albania 1,229
Iran 159,046 Indonesia 16,139 Nicaragua 818
Mexico 144,997 United Arab
Emirates
15,698 Paraguay 776
Hong Kong 144,935 Kenya 14,765 El Salvador 768
Czech
142,090 Viet Nam 13,172 Dominican
606
Republic
Republic
Norway 141,143 Kuwait 12,254 Honduras 595
Singapore 126,881 Lebanon 11,672
2. What is the number of different countries’ Rich Files on the web?
Table No2 shows the number of Rich Files for different world countries, with USA, Japan, and
Italy having the highest number of Rich Files on the web respectively.
Table 2. The number of countries’ Rich Files on the web
Country PDF DOC PPT SUM
Albania 16100 6720 71 22891
Algeria 46200 5130 1220 52550
Argentina 1190000 158000 25400 1373400
Armenia 13300 3190 1530 18020
Australia 2960000 171000 18800 3149800
Austria 1090000 42800 8560 1141360

Azerbaijan 12000 4490 61 16551
Bahrain 7820 101 44 7965
Belgium 1280000 98900 16600 1395500
Bolivia 64200 7610 1350 73160
Bosnia and Herzegovina 68300 11000 1480 80780
Brazil 4800000 399000 100000 5299000
Canada 4370000 202000 67900 4639900
Chile 639000 67400 22500 728900
Colombia 872000 93800 14100 979900
Costa Rica 127000 24600 14100 165700
Croatia 377000 54200 13700 444900
Czech Republic 708000 101000 22700 831700
Denmark 1070000 74200 11500 1155700
Dominican Republic 42900 2490 734 46124
Ecuador 169000 17200 3970 190170
Egypt 49400 11400 4550 65350
El Salvador 51700 2810 1040 55550
Estonia 161000 23500 8010 192510
Finland 883000 46300 11900 941200
France 5930000 351000 88300 6369300
Georgia 25500 4530 641 30671
Germany 8320000 264000 121000 8705000
Greece 553000 89900 11500 654400
Guatemala 69400 3610 1090 74100
Honduras 24800 1000 90 25890
Hong Kong S.A.R. 704000 60000 21000 785000
Hungary 672000 137000 24500 833500
Iceland 54200 4270 2230 60700
India 1500000 105000 2230 1607230
Indonesia 669000 83400 27600 780000
Iran 536000 93400 19800 649200
Iraq 14800 5460 66 20326
Ireland 528000 47700 8940 584640
Israel 387000 215000 35800 637800
Italy 9660000 978000 120000 10758000
Japan 13100000 444000 39400 13583400
Jordan 24700 9700 4050 38450
Kenya 27700 2490 792 30982
Kuwait 13100 1890 63 15053
250

251
Latvia 114000 51700 3510 169210
Lebanon 23300 3070 1150 27520
Libya 5230 81 31 5342
Lithuania 210000 63600 7910 281510
Luxembourg 78800 3640 766 83206
Macedonia 33100 4660 600 38360
Malaysia 378000 28600 6720 413320
Malta 24800 1690 1860 28350
Mexico 2770000 308000 40400 3118400
Morocco 64600 6720 1570 72890
Netherlands 3570000 252000 36800 3858800
New Zealand 592000 48100 8730 648830
Nicaragua 24800 2060 911 27771
Norway 682000 57600 15000 754600
Oman 7630 2390 47 10067
Pakistan 101000 11400 2100 114500
Panama 60100 4380 1230 65710
Paraguay 23900 2650 1570 28120
Peru 633000 80500 11700 725200
Philippines 77200 4990 1510 83700
Poland 3400000 715000 45800 4160800
Portugal 934000 33900 10200 978100
Puerto Rico 84000 8580 4970 97550
Qatar 12500 1490 61 14051
Romania 737000 152000 18500 907500
Russia 2140000 2150000 147000 4437000
Saudi Arabia 88400 38400 20800 147600
Serbia 187000 20000 6300 213300
Singapore 352000 19000 3960 374960
Slovakia 397000 63800 10400 471200
Slovenia 284000 45200 20800 350000
South Africa 852000 71700 11800 935500
South Korea 686000 30900 67700 784600
Spain 6310000 334000 80500 6724500
Sweden 2540000 148000 21200 2709200
Switzerland 2420000 88300 22100 2530400
Syria 9330 980 45 10355
Taiwan 1320000 603000 127000 2050000
Thailand 1220000 310000 57000 1587000

Tunisia 36100 2350 891 39341
Turkey 1030000 229000 44900 1303900
United Arab Emirates 47700 4830 1520 54050
Ukraine 243000 128000 7490 378490
United Kingdom 6730000 626000 108000 7464000
United States 47500000 3870000 1380000 52750000
Vietnam 141000 135000 4030 280030
Yemen 710 43 3 756
3. What is the amount of GDP indicator of world’s different countries?
Table No3 shows countries’ GDP with USA, Japan, and Germany having the highest GDP
respectively.
252
Table 3. Countries’ GDP
Country GDP Country GDP
Albania 13119013351.4499 Lebanon 42945273631.8408
Algeria 207955103846.43 Libya -
Argentina 474865096195.534 Lithuania 42245532390.1713
Armenia 9910387657.35811 Luxembourg 57117125224.9936
Australia 1520608083022.1 Macedonia 9663203711.45536
Austria 399649131196.966 Malaysia 303526203366.211
Azerbaijan 67197738734.7695 Malta 8721923076.92308
Bahrain - Mexico 1177271329643.86
Belgium 483709179737.722 Morocco 96729450169.498
Bolivia 27035110167.0902 Netherlands 772226793520.185
Bosnia and
17047582419.997 New Zealand -
Herzegovina
Brazil 2252664120777.39 Nicaragua 10507356837.651
Canada 1821424139311.45 Norway 499667211001.289
Chile 268313656098.796 Oman -
Colombia 369812739540.023 Pakistan 231181921489.54
Costa Rica 45127292711.0687 Panama 36252500000
Croatia 56441607483.0696 Paraguay 25502060502.1181
Czech Republic 195656544502.618 Peru 197110985681.958
Denmark 314242037116.962 Philippines 250265341493.171
Dominican Republic 58951239185.7506 Poland 489795486644.151
Ecuador 84532444000 Portugal 212454101311.391
Egypt 257285845358.245 Puerto Rico 101495811266
El Salvador 23786800000 Qatar -
Estonia 21854197100.7971 Romania 169395940257.194
Finland 250024427873.489 Russia 2014774938341.85

253
Country GDP Country GDP
France 2612878387760.35 Saudi Arabia -
Georgia 15829300978.6172 Serbia 37488935009.7878
Germany 3399588583183.34 Singapore 274701299733.694
Greece 249098684277.449 Slovakia 91619230769.2308
Guatemala 50806430481.5925 Slovenia 45469230769.5781
Honduras 17967497441.1464 South Africa 384312674445.534
Hong Kong S.A.R. 263259372904.956 South Korea 1129598273324.48
Hungary 125507525410.477 Spain 1349350732836.2
Iceland 13656532879.6765 Sweden 525742140221.402
India 1841717371769.71 Switzerland 632193558707.476
Indonesia 878043028442.369 Syria -
Iran - Taiwan -
Iraq 210279947255.575 Thailand 365564375701.58
Ireland 210330986079.969 Tunisia 45662043358.0705
Israel - Turkey 789257487307.029
Italy 2013263114238.88 Ukraine 176308825694.203
Japan 5959718262199.13 United Arab
Emirates
-
Jordan 31243324000 United
Kingdom
2435173775671.41
Kenya 37229405066.6773 United States 15684800000000
Kuwait - Vietnam 141669099289.418
Latvia 28373857404.0219 Yemen 35645823131.5726
4. What is the amount of correlation between countries’ scientific production rank and their
GDP rank in comparison with the correlation between countries’ Rich Files rank and
their GDP rank?
Tables No.4 and No.5 show the correlation between GDP and the two indicators of countries
scientific production rank and countries Rich Files rank.
Table 4. Correlation between countries’ scientific production rank and their GDP rank
in comparison
GDP
DOC Correlation
Coefficient
.879**
Sig. (2-tailed) .000
N 80
Correlation is significant at the 0.01 level (2-tailed)

254
Table 5. Correlation between countries’ Rich Files rank and their GDP rank
GDP
RICH Correlation
Coefficient
.897**
Sig. (2-tailed) .000
N 80
Correlation is significant at the 0.01 level (2-tailed)E
Conclusion
Nowadays web and web databases are the first and the most important source for researchers
to find information and web richness of every country as its scientific backbone is of highest
importance. Moreover, free access to information resources is the context for expanding
researches. Existence of scientific resources could be used as a criterion for scientific evaluation
(Nourmohammadi & Keramatfar, 2013). The present study sought to investigate the correlation
between countries’ Rich Files rank and their economic development rank. The findings indicate
that there is a high degree of correlation between the ranking of these two variable. Compared
with the correlation between countries’ scientific development Ranking and countries’
economic development ranking (that also has been showed by King.(2004) Price (1978) and
Kealey (1996), this correlation does have a higher amount that means this variable has a greater
correlation with economic development than science production indicator. The high degree of
correlation between this variable and economic development signifies the significance of web
as the context of research and free access to information resources. Moreover this correlation
demonstrates that this variable can be used along with other indicators to evaluate countries’
scientific development. Another point worth noticing is the fact that having access to web,
disregarding the initial expenses, is free and evaluation according to this can be easily done,
while having access to databases like Web of Knowledge and Scopus involves expenditure;
however, it should be taken into account that due to the dynamic nature of web and its constant
and rapid changes, Webometric results have always been tentative. Other researches following
this study can be concerned with the evaluation of the nature of these files and their types –
article, manual, handbook, book, etc.; meanwhile conducting causality test between these two
variables can result in helpful findings.
References
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256

Research in what fields? Determining Iran’s research priorities
257
according to their impact on economic development
Hamzehali Nourmohammadi*, Mahdi Keramatfar** and Abdalsamad Keramatfar***
*Shahed University, Tehran, Iran
nourmohammadi.h@gmail.com
**Tarbiyat Modaress University, Tehran, Iran
mkeramatfar@gmail.com
***Scientometrics Section of SID, Tehran, Iran
keramatfar@mailfa.com
Introduction
The ability to assess a country’s scientific situation is of pressing importance. Since all the
sciences do not have the same degree of application (Berer, 2012) and in a particular time an
economy can develop technology in a number of sections and it is difficult to predict which
technologies would more beneficial (Kealey, 1996), determining research priorities is a very
important issue for science and technology policy-makers (Lee et al, 2011). One of the Iran’s
attempts is the Country’s Comprehensive Scientific Plan document that in the third season
determines the country’s scientific and technological priorities. On the other hand, economic
issues have to be deal with effectively in making any decision related to science and technology
(Salter 2001). It is also of highest importance to decide which fields are economically worth
investing. Ray and Lal (2000) suggest that developed countries should investment in basic
research and developing countries should invest in education, infrastructures, and engineering
because these fields have the biggest impact on economic development. Vinkler (2008) holds
out the effect of development level on researches’ outputs and argues that the relation between
economic development and researches’ outputs differs in different countries; in developed
countries there is no significant relation between economic development and researches’s
outputs while in central and Eastern European countries there is more significant relation; he
argues that developed countries are more capable of supporting basic researches, therefore, their
researches includes basic researches and deals less with future researches. Chuang et al. (2010)
indicated that the research areas in which Singapore, Taiwan, and South Korea have been
working during the last decade have been engineering areas. Newly industrializing countries,
especially South Korea and Taiwan, have been focusing on understanding and spreading the
existing technology rather than producing new technology. Moreover, Japan’s policy of science
and technology is increasingly concerned with technologies with economic importance. Kealey
(1996) argues that concentration on basic science is not effective in advancing technology.
Since Iran is a developing country, and due to the presence of oil resources, research expenses
may be directed toward unimportant areas that have the least impact on economic development.
Thus, the present paper aims to determine which research area will have the most central effects
on the country’s economic development.
Research purposes
The main objective of this study is to determine of Iran’s research priorities according to their
impact on economic development.

Other objects of this research include:
 Quantity in science productions in countries’ subject areas
 Quantity of GDP during different years
 Determining of relation between the country’s different subject areas of science
258
production and GDP
 The majors of the greatest impact on GDP in engineering field
Theatrical Framework
Today assessment of scientific papers is performed based on Citation Indexes that collect
bibliography information because these Indexes provide Ability to identify and recover valid
information of various subject areas and citation information that link the work to other works,
and To a large extent reflects the impact of the paper(Noroozi Chakoli, 2011). The most
important of these indexes are Web of Knowledge and Scopus. In 2007 Scimago Research
Group offered a tool based on Scopus data that provide ability to study and comparison of
scientific production in two main Unit, countries and journals. This tool divides all scientific
papers to 320 disciplines and 27 areas that provide ability to subjective analysis.
There is a broad literature in studying the relation between science and technology. Price(1967)
stated that academic researches Create a generation of researches and future researches of these
researchers and will cause economic prosperity also basic researches that usually performs by
universities are input of R&D activities. Jaffe(1989) showed that academic researches improve
industrial R&D. in fact providing basic research spending by government, many industrials do
not pay for basic research in development of technology and they will be able to use it, thus
social benefits will result. Diamond(1996) stated that science is Leader of Technology and
technology will lead to productivity and growth. Narin et al(1997) studied citation in patents to
scientific papers and showed that this type of citation grew and concluded that Technology is
based on science. Mansfield et al(1991) studied new goods and process and stated that 11% of
new product and 9% of new process could not be improved without academic research. Martin
et al (1996) stated the various types of contributions that publicly funded research makes to
economic growth:
1. Increasing the stock of useful knowledge;
2. Training skilled graduates;
3. Creating new scientific instrumentation and methodologies;
4. Forming networks and stimulating social inter- action;
5. Increasing the capacity for scientific and technological problem-solving;
6. Creating new firms.
On other hand, some of R&D researches publish a paper of their work in scientific journals, so
assessment of papers can obvious economic activities in R&D sectors. Overall Evidences show
that publicly funded basic research have many benefits (Salter&Martin, 2001).
One of the common tests in econometrics is Granger causality test. In The Granger causality
test for testing the hypothesis; "(X_t) is not Granger cause of (Y_t)" a (VAR) model is formed:
୩
Y୲ ൌ ෍ α୧
୧ୀଵ
୩
Y୲ି୧ ൅ ෍ β୧
୧ୀଵ
X୲ି୧ ൅ u୲
So this linear model is estimated and the significant assumption is tested. If the assumption
coefficients of X୲ି୧ i.e. β୧ being zero Confirm then X୲ is not Granger cause of Y୲. In fact if the
being zero assumption of test is rejected X୲ is cause of Y୲. Since there is a time gap between

publication and their impact (King, 2004), here we test the impact of science on welfare with a
lag.
Methodology
This study is applied and descriptive - based and due to its use of Scientometric methods. The
data related to the country’s scientific production were extracted from Scimago data base,
Country Search section. Data related to GDP were extracted from the World Bank’s data base.
In order to analyze the data Eviews7 was employed and stationary and Granger test were
administered. The data were gathered early in December 2013.
Findings
First Data is an indication of the country’s science production from 1996 to 2012 in Scimago
data base. As is seen, medical science has the highest share, engineering and chemistry rank
second and third.
259
Table1. Number of scientific production of Iran in different subjects 1996-2011
Subject
Area
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Agricultural
and Biological
Sciences
78 84 83 119 129 137 227 274 368 513 1134 1597 1846 2088 2574 3686
Arts and
Humanities
4 4 2 2 1 7 2 4 4 17 25 32 46 75 76 127
Biochemistry,
Genetics and
Molecular
Biology
70 80 82 115 131 182 244 337 449 558 816 1256 1474 1666 2009 2824
Business,
Management
and Accounting
4 2 6 4 6 1 4 8 13 17 26 33 77 108 153 217
Chemical
Engineering
51 74 72 86 114 135 181 229 320 454 612 792 939 1142 1457 1987
Chemistry 142 168 236 316 363 502 616 838 1 1271 1515 1931 2155 2622 3016 3605
Computer
40 53 54 56 79 90 115 219 277 412 518 648 101 1117 139 1956
Science
Decision
Sciences
12 14 15 8 17 11 17 16 29 57 72 93 146 212 238 276
Dentistry 2 - 1 3 9 5 9 19 22 22 32 63 83 116 117 137
Earth and
Planetary
Sciences
28 45 35 36 67 64 85 148 161 204 263 334 337 537 601 807
Economics,
Econometrics
and Finance
2 - 2 1 2 2 2 4 2 4 10 9 16 32 69 150
Energy 16 27 22 22 17 24 60 75 97 102 167 208 325 404 580 873
Engineering 133 163 161 176 245 331 464 766 1028 1106 1471 1687 2125 3554 4293 5761
Environmental
26 33 33 41 45 62 109 136 197 245 347 583 693 1031 1281 2131
Science
Health
Professions
1 - 1 3 1 7 5 12 35 41 52 58 63 62 82 107

Subject
Area
260
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Immunology
and
Microbiology
19 22 23 33 41 50 72 114 117 171 263 325 412 446 688 898
Materials
Science
63 86 88 109 144 208 277 405 528 711 937 1103 1619 2061 2599 3412
Mathematics 47 71 78 107 111 137 186 247 407 559 697 900 952 1299 157 2206
Medicine 124 188 165 161 194 276 480 720 827 1545 2346 305 3818 4499 5359 6684
Multi-disciplinary
11 12 14 23 22 13 33 30 62 48 138 216 511 620 522 1665
Neuroscience 10 10 9 15 17 22 31 45 56 67 107 156 176 196 218 309
Nursing - - 1 3 4 1 5 3 12 24 33 58 108 108 96 146
Pharmacology,
Toxicology and
Pharmaceutics
31 48 66 73 72 58 110 117 198 237 332 419 440 647 775 1169
Physics and
Astronomy
64 77 109 115 133 148 234 283 420 472 809 103 1357 1675 1939 2577
Psychology - 2 2 7 10 8 12 23 19 21 31 42 45 48 307 820
Social Sciences 10 5 8 8 11 9 29 48 48 75 106 150 190 306 653 1761
Veterinary 28 22 27 25 30 21 31 52 64 87 143 151 337 310 378 512
Second Data set shows the Iran’s GDP from 1996 to 2011.
Table2. GDP per capita of Iran 1996-2011
year GDP per capita
(current US$)
year GDP per
capita
(current
US$)
year GDP per
capita
(current
US$)
year GDP per
capita
(current
US$)
1996 1799.672 2004 2353.931 2000 1536.715 2008 4899.312
1997 1683.634 2005 2737.112 2001 1726.63 2009 4931.283
1998 1611.308 2006 3140.198 2002 1718.965 2010 5674.924
1999 1613.599 2007 3983.582 2003 1975.539 2011 6815.57
Third Data includes the results of Granger’s causal test for the country’s different subject areas
of science production, yellow cells indicate significance at the level of 0.05 and green cells
indicate significance at the level of 0.01. As is observed, nursing has had the greatest impact on
GDP, and at the same time, nursing has been influenced most by GDP.
Table3. Causality test in between different subject areas and GDP
causality
Causality direct Science production to GDP GDP to Science production
Agricultural and Biological
Sciences
0.4669 0.2276
Arts and Humanities 0.0163 0.0304
Biochemistry, Genetics and
Molecular Biology
0.0673 0.0327
Business, Management and
Accounting
0.0064 0.1396

261
causality
Causality direct Science production to GDP GDP to Science production
Chemical Engineering 0.0704 0.2858
Chemistry 0.0253 0.1613
Computer Science 0.7136 0.212
Decision Sciences 0.0513 0.0112
Dentistry 0.0499 0.0822
Earth and Planetary Sciences 0.0166 0.6185
Economics, Econometrics and
0.016 0.1455
Finance
Energy 0.0564 0.0181
Engineering 0.0024 0.4192
Environmental Science 0.0784 0.0134
Health Professions 0.9895 0.0412
Immunology and Microbiology 0.1873 0.4948
Materials Science 0.0283 0.2405
Mathematics 0.3154 0.0369
Medicine 0.2462 0.0697
Multidisciplinary 0.0052 0.0098
Neuroscience 0.2163 0.0198
Nursing 0.0002 0.0029
Pharmacology, Toxicology and
0.0284 0.5019
Pharmaceutics
Physics and Astronomy 0.1291 0.0168
Psychology 0.1354 0.2268
Social Sciences 0.0042 0.0292
Veterinary 0.0223 0.0182
As was mentioned before, each of the 27 separated areas in Scimago includes different majors,
in engineering field such a separation has been carried out. Table 4 indicates the result of causal
test for different engineering majors. Table 4 shows that eco-medicine engineering, civil
engineering, system and supervising engineering, industry and production engineering at the
level of 0.01, and mechanical engineering, material mechanics, and science of material at the
level of 0.05 have impact on GDP.
Table4. Causality test for different engineering areas
Subject Area Impact on GDP Impact of GDP
Aerospace Engineering 0.41 0.16
Architecture 0.32 0.001
Automotive Engineering 0.8 0.08
Bioengineering 0.0009 0.13
Construction 0.59 0.22
Civil and Structural
0.003 0.17
Engineering

262
Computational
Mechanics
0.06 0.86
Control and Systems
Engineering
0.008 0.12
Electrical and Electronic
Engineering
0.25 0.51
Engineering
(miscellaneous)
0.38 0.42
Industrial and
Manufacturing
Engineering
0.008 0.04
Mechanical Engineering 0.02 0.006
Mechanical Engineering 0.04 0.1
Media Technology 0.92 0.42
Ocean Engineering 0.99 0.0041
Safety, Risk, Reliability
0.29 0.007
and Quality
Chemical Engineering 0.0704 0.2858
Computer Science 0.7136 0.212
Material science 0.0283 0.2405
Conclusion
The major's eco-medicine engineering, civil engineering, system and supervising engineering,
industry and production engineering at the level of 0.01 and the major's mechanical engineering,
material mechanics, and science of material at the level of 0.05 have impact on GDP. In other
words, these majors should have research priority in Iran. Of course, it should be mentioned
that since industry and production engineering and mechanical engineering are affected by
GDP, it might mean that these sections have been financed. Being affected by GDP presented
above could be analyzed in this way: if an increase in GDP has had effects on a group or a
major, it probably means that GDP increase has been accompanied by budget increase in that
group or major, therefore, if the reverse relation, i.e. the effectiveness of that group or major in
GDP is not significant, continuing to increase the budget for that group or major cannot be
justified. Consequently, in engineering group majors like architecture engineering and safety
engineering do involve the risk and problem just mentioned and therefore investing in these
sectors is not justifiable.
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