Nourmohammadi keramatfar

Proceedings of

10th International Conference on Webometrics, Informetrics

and Scientometrics &15th COLLNET Meeting 2014

2014

September 3-5, 2014 Technische Universität Ilmenau, Germany

Edited byBernd Markscheffel • Daniel Fischer • Daniela Büttner • Hildrun Kretschmer

net

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

[email protected] [email protected] [email protected] [email protected]

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


vi

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)


vii

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


viii

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


ix

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


x

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


1

Invited Papers


2


247

The relation between the number of countries’ Rich Files on the web and countries’ economic development

Hamzehali Nourmohammadi* and Abdalsamad Keramatfar**

*Shahed University, Tehran, Iran [email protected]

**Scientometrics Section of SID, Tehran, Iran [email protected]

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,


248

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.

Table 1. The number of countries’ document in SCImago

Country Documents Country Documents Country Documents

United States

6,149,455 Portugal 117,469 Philippines 11,326

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 Federation

527,442 Thailand 69,637 Luxembourg 5,121

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 Republic

142,090 Viet Nam 13,172 Dominican Republic

606

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


250


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


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


252


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.

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 Herzegovina

17047582419.997 New Zealand -

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.

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Research in what fields? Determining Iran’s research priorities according to their impact on economic development

Hamzehali Nourmohammadi*, Mahdi Keramatfar** and Abdalsamad Keramatfar***

*Shahed University, Tehran, Iran [email protected]

**Tarbiyat Modaress University, Tehran, Iran [email protected]

***Scientometrics Section of SID, Tehran, Iran [email protected]

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.


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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

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:

Y α Y β X u

So this linear model is estimated and the significant assumption is tested. If the assumption coefficients of X i.e. β being zero Confirm then X is not Granger cause of Y . In fact if the being zero assumption of test is rejected X is cause of Y . Since there is a time gap between


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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.

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 Science

40 53 54 56 79 90 115 219 277 412 518 648 101 1117 139 1956

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 Science

26 33 33 41 45 62 109 136 197 245 347 583 693 1031 1281 2131

Health Professions

1 - 1 3 1 7 5 12 35 41 52 58 63 62 82 107


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Subject Area

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


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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 Finance

0.016 0.1455

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 Pharmaceutics

0.0284 0.5019

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 Engineering

0.003 0.17


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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 and Quality

0.29 0.007

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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