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South African Journal of Libraries and Information Science

On-line version ISSN 2304-8263
Print version ISSN 0256-8861

SAJLIS vol.88 n.1 Pretoria  2022

http://dx.doi.org/10.7553/88-1-1962 

RESEARCH ARTICLES

 

Open Access Digital Repositories of Agricultural Sciences in Africa: a webometric study

 

 

Sanjib GhoshI; Bijan Kumar RoyII

IResearch Scholar at Dept. of LIS, the University of Burdwan, West Bengal, India. ghoshsanjiblib@gmail.com ORCID: 0000-0002-1941-8023
IIAssociate Professor at the University of Calcutta West Bengal, India. bkroylis@gmail.com ORCID: 0000-0001-9735-9586

 

 


ABSTRACT

The study examines different characteristics of link analysis and visibility of websites of agricultural digital repositories in Africa. The different link structures and the presence of web through different indicators like Internet access, web pages and link count are also highlighted. This study has used popular search engines Google) to analyse and measure the web presence of African agricultural repositories. An attempt has also been made to find out the correlation among the WIF and WISER indicators of selected agricultural repositories in Africa. The result shows that KARI e-repository-(Kenya Agricultural Research Institute) occupies the first place with 102467.5325 SWIF among 37 agricultural repositories in Africa. Again, it ranks 23rd position with 77 Web Pages and 02 In-link Web Pages and 0.025974026 RWIF. The findings of this study may guide webmasters and library professionals to identify the web presence of repositories and also help them to compare the repository websites of agricultural sciences in Africa by their WIF and WISER Rank.

Keywords: Digital Repositories, Agriculture, Web Impact Factor, WISER Rank, OpenDOAR, ROAR


 

 

1 Introduction

Agriculture plays a strategic role in the economic development of developing countries, and act as a backbone of an economy that provides the basic ingredients to mankind (Praburaj 2018). Agriculture provides the main source of food, income, and employment to rural populations of different countries (Monga 2012). The agricultural industry in Africa plays a leading role as a driver of economic transformation as witnessing unprecedented development of the continent. About 23% of the people in Africa depend on agriculture, with 30 to 60% of the total GDP of African continents coming from the agricultural sector and about 30% of the value comes from exports (Robert & Middleton 2018). Therefore, agriculture has become an important aspect in the development of the economic growth of any underdeveloped country. So agricultural knowledge that supports agriculture production, marketing, and post-harvest handling of agricultural products and management of natural resources plays an important role in the process of economic development in Africa. In the 21st century, due to the Open Access movement, all the academic and research organisations are trying to set up repositories to give free full-text access to their research outputs globally. The websites of agricultural digital repositories can be measured through webometric indicators and different web impact factors to show their global presence. The main objective of this study is to analyse and measure the web presence, as well as existence of different links such as self-link, external link of agricultural repository websites of Africa, using different indicators to show the global visibility of such repositories.

 

2 Agricultural repositories in Africa

Agriculture as a subject and discipline has become an important field of research among academic communities because it is one of the key sectors of an economy which provides the basic needs of people for food as well as larger employment opportunities and also helps to reform the economy of a country. In this point of view, different agricultural research Universities, Institutions, and development organisations of different countries have facilitated opportunities to disseminate their research outputs through the digital repositories based on their websites, so the research scholars can access the scholarly literature through the internet. According to OpenDOAR (A Global Directory of Open Access Repositories) and ROAR (Registry of Open Access Repositories), the total thirty-seven agricultural research organisations disseminate their research output through digital repositories based on their websites. The web presence of these repositories needs to be measured and their web activities evaluated through the help of different webometric indicators and different search engines.

2.1 Agricultural repository: present status

The Open Access movement has changed the scholarly communication process in the 21st century through the development of institutional repositories in different disciplines all over the world. Different developed countries like the USA, Europe, and UK are the key players in offering agricultural repositories. Developing countries have just started joining this new movement to provide free access to scholarly literature (Roy, Biswas & Mukhopadhyay 2016). Africa emerges as the fourth largest contributor (OpenDOAR 2019; ROAR 2019) only after Europe, North America, and Asia in the agricultural field. In 1991, Dr. Paul Ginsparg has developed arXiv (https://arxiv.org/), the first subject repository to provide access to e-prints in disciplines such as Mathematics and Physics. The ROAR (Registry of Open Access Repositories, December 2019) currently reports 4162 repositories of which 167 (4%) are from the 'Agricultural' field. Asia ranks 1st position and contributes 56 repositories, 42 in Europe, 28 from South America, 11 in North America, 20 from Africa, and 2 in Oceania (ROAR, 2019). Another database, OpenDOAR (Directory of Open Access Repositories 2019) has recorded 5179 repositories, of which 186 (3.59%) repositories are from 'Agriculture, Food and Veterinary'. Europe contributes 78 repositories, 46 America, 41 in Asia, 19 repositories from Africa, and 04 repositories from Oceania, which have identified in figure 1 and 2.

 

Figure 3

 

3 Scope and limitations

This analytical study is limited to all open access repositories of agricultural science in African countries registered in OpenDOAR (21 OARS) and ROAR (18 OARS) databases within December 2019. For this study, a total of 37 unique repositories have been finally selected from ROAR and OpenDOAR, after eliminating all common repositories. In OpenDOAR and ROAR, the 'agriculture' as a key subject covers different fields such as agriculture, food, veterinary science, plant culture, forestry, animal culture, aquaculture, fisheries, angling and hunting sports.

 

4 Purpose and objectives of the study

The purpose of this study is to examine different characteristics of link analysis and visibility of websites of agricultural digital repositories in Africa. The specific objectives are to:

analyse the selected OA agricultural repositories in Africa extracted from OpenDOAR and ROAR repository on the basis of their websites' activity;

trace and classify the domain of the selected open accesses agricultural repositories and find out various types of links, explore the web presence and calculate various web impact factors of websites of the selected agricultural repositories;

use WISER (Web Indicators for Science, Technology and Innovation Research) ranking method to assess the Web presence of the open access agricultural repositories on the web; and

compute the correlation between the ranking of WISER value and In-link WIF.

 

5 Review of literature

5.1 Webometrics is the quantitative study of the web and in this field several researchers have already conducted Webometric analysis of different fields. This review has been conducted under four broad headings viz. Webometrics development, Web Content Analysis, Web Link Analysis, Web Technology Analysis and Web Impact Factor. Bjorneborn and Ingwersen (2001) pointed out the framework for evaluating quality and content-based search engine coverage and performance. Web Impact Factors (Web-IF) measurement issues are also examined and outlined that transversal linkages may be an underappreciated beneficial effect of imperfect behavior, resulting in shorter pathways on the Web that could enhance the probability of encountering quality content in the intermediate web pages along the link path.

Bjorneborn and Ingwersen (2004) defined webometrics as generic sub-field of cybermetrics based on Informetric studies and the bibliometric approach belongs to Library and Information Science. Thelwall et al. (2008) studied Life Sciences research groups in Europe to assess the web connectivity using a commercial search engine which harnessed hyperlink data and used LexiURL for link analysis. It is supposed to be the first study which "applied" webometrics study for an external contract. Jalal, Biswas and Mukhopadhyay (2009) analysed websites of 13 Indian Institutes of Technology (IITs) and Indian Institutes of Management (IIMs) to determine the extent of the development of webometrics from bibliometrics. They reviewed the application, areas of webometrics research, the methodology adopted for data collection, techniques and tools of web analysis and the problems encountered in web research.

5.2 Web content analysis is one of the parameters of webometric analysis and many authors (Thelwall 2003, 2004; Thanuskodi 2012) have applied this technique in different fields to select the core journals. Thelwall (2003) introduced two web link count metrics such as in-links and out-links which is complemented to the Web Impact Factor. The in-links act as an average degree of online informal scholarly communication and information used by the academics and out-links act as a degree of web interconnection in a given university. Thanuskodi (2012) analysed the content of web page of libraries of institutes of national importance in India and applied the bibliometric methods to evaluate the contents, the link structures and other research areas in webometrics, and suggested that the webometric techniques are still at an experimental stage.

Webometrics is a term that refers to the study of all network-based communication through the use of informetric or other quantitative measurements. Citations analysis treats hyperlinks to and from other websites as "bibliographical citations" in conventional analysis. Rousseau (1997) first discovered power-law occurrences on the Web and established the phrase. Thelwall (2001) applied the external Web Impact Factor of universities in Britain to know the relationship between academic hyperlinks and research activity. Ortega and Aguillo (2007) compared the link relationships of 23 Finnish, 11 Danish and 28 Swedish academic web domains with the European one in the Nordic academic web. The results showed that the Danish network had less visibility than other Nordic countries. Jalal, Biswas, and Mukhopadhyay (2010a) analysed the Web Impact Factors to investigate the effectiveness and relevance of Indian universities websites globally.

They developed a micro link topology for Indian universities, using WebCrawler i.e., SocSciBot and showed that all the NITs were closely related to each other, whereas nodes of State and Central universities were not linked significantly. To know the visibility and connectivity of 173 State universities in India, Shukla and Poluru (2012) analysed the websites of these universities using the WISER ranking method. Data were collected through Yahoo Site Explorer and Google Scholar. Sujithai, Maria and Jeyshankar (2013) analysed and compared the web pages of Indian Institute of Technology websites through a commercial search engine. The reliability of data was checked with Histogram and Scatter Plot which were analysed with SPSS software. The result revealed that External link of web pages were greater than other link pages. Using the Google search engine, Majhi and Das (2020) investigated several web impact factors of IDRs website in Southern Asia. They also ranked digital repositories of Southern Asia utilising different web Impact Factors and assessed the link network visualisation. Ghosh and Roy (2021a, 2021b & 2021c) analysed websites of different agricultural repositories in Asia, Europe, and the Oceania continents, based on Web Impact Factor and WISER value to determine their presence, as well as their visibility on the web.

Vaughan (2004b) in his study applied a set of measurements for evaluating the three different commercial search engines i.e., Google, AltaVista and Teoma to test their performance in the web. Vaughan and Zhang (2007) examined the websites of commercial, educational, governmental and organisational domains of U.S., China, Singapore, and Taiwan through random sampling by custom-built computer programs. And the result reveled that the sites of US domains got higher positions than other countries. Bar-Ilan (2008) evaluated the performance of search engine through a set of measures that provide guideline for testing search engines. Thelwall (2008b) compared the API of Google, Live Search, and Yahoo to find out the consistency and inconsistency of these three selected search engines and suggested that the quantitative findings from the three search engines are usually similar. However, there are some unanticipated inconsistencies in the number of different URLs, sites, and domains returned within the search results that consumers should be aware of. For 'hit count estimations' author suggested Google, and Yahoo! for Webometric uses.

Ingwersen (1998) analysed seven small and medium-scale national and four large web domains and six institutional websites for investigating the feasibility and reliability of calculating impact factors of these websites. The findings showed that Web-IFs for national and sector domains may be calculated with high confidence, while institutional Web-IFs might be handled with care. Smith (1999b) explained the WIF of web pages of Australasian universities for comparing the relative attractiveness of web spaces of Australasian universities and electronic journals (Author/LRW). Walia and Kaur (2008) investigated selected Indian library associations' websites to realise the presence of Indian library associations over the web. Babu, Jayshankar and Rao (2009) analysed the web impact factor of 34 state agricultural universities in India, based on three indicators related to domain systems of the websites, number of web pages and link pages, and different Impact Factor. Jalal, Biswas and Mukhopadhyay (2010b) examined the web presence and Web Impact Factor of selected Asian countries using different search engines like AltaVista, Google, Yahoo and MSN and the result revealed that China, Japan and India occupied the highest rank in compared to other Asian countries.

Thanuskodi (2011) in his study analysed and compared the WIF of private engineering colleges in Tamil Nadu using the AltaVista search engine due to its coverage in comparison to other commercial search engines and found that general information about homepage features is more in PEC, EEC, SCT and lease in RMKEC and SJCE. Islam and Alam (2011) conducted a study about the 44 private universities in Bangladesh to find out the impact of websites and their web impact factor based on the webometrics indicator. The result showed that the universities did not have much of an impact factor on the web and were not known internationally due to insufficient number of link pages. Walia and Gupta (2012) analysed web impact factors and the quantity of information available in the form of rich files on national library websites. The study discovered that the websites of the United States, Australia and the United Kingdom were more visible and had more materials than the websites of India, Namibia, and South Africa. Majhi and Das (2019) used the Web Impact Factor analysis to evaluate the websites of India's High Courts in order to determine their web presence.

 

6 Methodology

To conduct the study, data were collected from the websites of selected agricultural digital repositories registered in ROAR and OpenDOAR using Google search engine. A total of 37 unique repositories were finally selected from 39 repositories in Africa and the collected data were analysed and interpreted keeping in mind the objective of the study. The methodology has two parts - i) webometric study which includes identifying the Web Impact factors, WISER rank analysis and ii) evaluating the correlation between the ranking of WISER value and In-link WIF.

6.1 Data collection through searching

For the present study, data were collected using Google's advanced queries to collect the approximate number of pages from the websites of 37 selected agricultural repositories of Africa during 15-24 December 2019 by using a suitable search engine, i.e., Google (www.google.com) that counts the number of pages in websites and number of pages linking to the websites. The following search statements were used to collect data for each of the 37 repository websites as:

site: url-this will extract the total number of web pages to the websites under the url.

link: url- this will retrieve the total number of web pages linking to the websites

link:url AND site:url-it will provide a complete report of a number of web pages under the websites that provide links from the same websites i.e., Self-Link pages.

link:url NOT site: URL - it will provide a complete report of a number of links incoming from other websites i.e InLink / Backlink pages.

link:url AND NOT site: URL- it will provide a complete report of a number of web pages not under the websites which provide links from the other websites i.e., External-Link pages. Based on the command syntax of Google, the above five retrieval arguments were applied to collect data of each Open Access Institutional Agricultural Digital Repositories in Africa.

Web search engines are commonly used in Webometric studies such as Yahoo (https://www.yahoo.com), Google (https://www.google.com), Hotbot (https://www.hotbot.com), Exalead (https://www.exalead.com) and Bing (https://www.bing.com). Advanced query syntax of different search engines helps to access web data and to obtain hyperlink counts.

For this study, the five special command syntaxes as per Table 1, were used for accessing the number of web pages, number of hyperlink web pages, number of self-link pages, number of external-link pages, and number of in-link pages from Google search engine.

6.2 Calculation of web impact factors

Most of the webometric study is based on the web impact factors (WIFs) of either simple WIF (WIFs) or revised WIF (WIFs). The calculation of WIF is as follows:

1. Simple WIF =Total number of links / hyperlinks (external-link and self-link web pages) (LWP)

(SWIF) Total number of web pages (NWP)

2. Self-link WIF = Total number self-link web pages

(SLWIF) Total number of web pages (NWP)

3. External-link WIF = Total number of external-link web pages

(ELWIF) Total number of web pages (NWP)

4. InLink / Revised WIF = Total number of in-link web pages

(ILWIF / RWIF) Total number of web pages (NWP)

Where A=Total number of web pages of a given site; B=Total number of external back links to a given site; C=Total number of self-link of a given site; D=total number of links to a given site.

6.3 Calculation of WISER INDEX VALUE

The activities of agricultural digital repositories are multi-dimensional and are reflected through their web presence. Almind and Ingwersen (1997) first used the term Web indicator. The WISER Ranking value is calculated through the combination of these four indicators viz. the number of in-links or external links, the number of web pages, the number of rich files in a web domain and the number of publications in Google scholar database based on the following formula where each one has a different weight:

Webometrics Rank (position) = 4*RankV + 2*RankS + 1*RankR + 1*RankSc;

Where, V=Visibility; S= Size; R= Rich Files and Sc= Google Scholar.

Aguillo, et al. (2008) has proposed the formula for WISER ranking as: WISER ranking = log (Visibility 50%) + log (Size 20%) + log (Rich files 15%) + log (Scholars 15%) as presented in Figure 4.

 

 

7 Data analysis and interpretation

WIF for each Agricultural digital repository has been calculated on the basis of formula given in Figure 4. These are WIF (simple) a ratio of the number of total link pages and number of web pages; WIF (Self link)-a ratio of number of total self-link pages and number of web pages; WIF (External link)-a ratio of number of total external link pages and number of web pages; WIF (Revised link)-a ratio of number of total in-link pages and number of web pages which reflex of the degree of impact of the domain spaces on the Web. A matrix may represent the calculation of WIF of different web spaces in different levels shown in tables 2 to 5.

Table 2 illustrates the rank distribution of agricultural digital repositories in Africa according to their Simple Web Impact Factor (SWIF). By dividing the number of link pages by the number of web pages, the SLWIF for each repository has been calculated. The KARI e-repository occupies first place with 102467.5325% SWIF. The second and third places go to the Institutional Repository of the University of Limpopo (ULSpace), and the Repository of the Agricultural Research Corporation (ARC) - Sudan. The University of Nairobi Digital Repository (13,61000), the University of Cape Coast Institutional Repository (789000), Khartoum Space (274000), and the University of Dar es Salaam Research Repository (234000) have more web pages than the three agricultural repositories mentioned above, but they ranked 26th, 14th, 30th, and 32nd, respectively, based on their simple link web impact factor.

The ranking of Agricultural Repositories in Africa is based on their Self Link Web Impact Factor as showed in Table 3. Repository of the Egyptian Agricultural Research Centre, Egypt occupies the first place with 4490 Self Link Pages and 9890 web pages with 0.453993933 % SWIF. AgriSearch Repository (Dept of Agricultural Research, Lesotho) and Institutional Repository of Federal University of Technology, Minna ranked 2nd and 3rd place with SWIF of 0.428571429 and 0.316326531 respectively. The University of Nairobi Digital Repository (1361000), University of Cape Coast Institutional Repository (789000), Khartoumspace (274000), University of Dar es Salaam Research Repository (234000), Sokoine University of Agriculture Institutional Repository (165000) have a greater number of web pages compared to all other repositories. These repositories ranked 26th, 33th, 23rd, 29th, 28th position respectively due to their insufficient number of Link Pages compared to their number of web pages.

Table 4 reveals the rank distribution of African Open Access Agricultural Digital Repositories based on their External Link Web Impact Factor (ELWIF). AgriSearch Repository (Dept of Agricultural Research, Lesotho) occupies the first place with 07 web pages, 03 link pages and its ELWIF is 0.428571429. Repository of the Agricultural Research Corporation of Sudan and IDEP Document Server of African Inst. for Economic Development and Planning have ranked 2nd and 3rd position with the EWIF as 0.351351351 and 0.295081967 respectively. AgriSearch Repository (Dept of Agricultural Research, Lesotho) is in the 2nd position with the SELWIF as 0.428571429.

Table 5 exhibits the rank distribution of the 37 African Open Access Agricultural Digital Repositories according to their revised web impact factor (RWIF). It has been calculated by putting the following formula i.e., Revised Web Impact Factor=E/A Where E=Internal Link Web Page and A=Number of Web Page. AgriSearch Repository (Dept of Agricultural Research, Lesotho) ranks first position with 07 web pages and 03 in-link web pages and 0.428571429 % RWIF; followed by Repository of the Egyptian Agricultural Research Centre, Egypt with 9890 web pages and 3350 In-link web pages and 0.338726%. Institutional Repository of the University of Limpopo (ULSpace) occupies 3rd position with 0.257796258%. The University of Nairobi Digital Repository and UPSpace Repository again have the maximum number of In-link Pages (i.e. 32100 & 29200) ranked 24th and 7th, position due to their less impact factor compared to all other repositories.

 

8 Calculation of WISER Rank

According to the WISER (Web Indicator for Science, Technology and Innovation Research) ranking method, the four indicators namely Size (S), Visibility (V), Rich Files (R) and Scholar (Sc) are used and have been given different weights to each indicator to calculate the rank of repositories. This ranking method is used to know the visibility and connectivity of the open access agricultural repositories on the web. The WISER Rank is calculated by using the following formula: WISER Rank = log (Visibility 50%) + log (Size 20% +log (Rich Files 15%) + log (Scholar 15%) recommended by the World Webometrics Group (http://www.webometrics.info/en/Methodology). The WISER Rank of selected repositories is presented in Table 6.

WISER rank of selected repositories is shown in Table 6. Here, the University of Nairobi Digital Repository occupies the highest rank, followed by UPSpace - the IR of the University of Pretoria and Khartoum space - Repository of the University of Khartoum. Also, Table 6 shows data of the total rich file with the sum of PDF, PPT, DOC., as well as the number of citations covered in the Google Scholar database of such repositories. The correlation between ranking of WISER and WIF (in-link) is presented in Table 7.

Hence, the Mean for the variable (X & Y) can be calculated as:

N

Xbar = 1/N Σxi =1/N(x1+x2+.........+ xN).

i=1

In this case, mean (X & Y) are the same i.e. Xbar = Ybar =19. Standard deviation is calculated with the help of the following formula: N

σ x = Sqrt [1/N Σ (Xi -Xbar)2]

i=1 Where N=37.

The standard deviations of X (i.e. a x) & Y (i.e. a y) is 10.6770782 and 10.6770782 respectively. The correlation coefficient is used to relate the strength and direction of linear relationship between two variables. The coefficient of determination represents the % of data closest to the line of best fit. Correlation will always between -1.0 and +1.0. If the correlation is positive, we have a positive relationship. If it is negative, the relationship is negative. The coefficient of determination (i.e., r2) is such that 0< r2 < 1, and denotes the strength of the linear association between x and y. The formula can be given as follows:

Mean (X) = mean(Y) = 19; σ x = 10.6770782 and σ y = 10.6770782 (for lower one Equation i.e. For r2) Therefore, the calculated value of r would be = +0.0566619, which implied that there is much association or closeness between two ranking methods where N is the number of pairs of data and R denotes the correlation coefficient, where a x is the standard deviation of X and a y standard deviation of Y.

 

9 Major findings

Calculating the Web Impact Factor, link analysis and WISER ranking of agricultural repositories in Africa is still an unexplored area of webometric research. The following are the major findings of this study i.e.:

Digital Repository websites of the University of Nairobi ranks top with 1361000 total web pages and 12300 total Google scholar citations.

The KARI e-repository-Kenya Agricultural Research Institute websites at the first rank with 77 (102467.5325%) simple links.

The repository websites of AgriSearch Repository (Dept of Agricultural Research, Lesotho) is on top with the 03 (0.428571429%) external links and 03(0.428571429%) external links.

The websites of the repository of the Egyptian Agricultural Research Centre, Egypt is on top with 9890

(0.453993933%) total self-links.

The repository websites of the KARI e-repository-Kenya Agricultural Research Institute at the first rank with the Simple Web Impact Factor (SWIF) are reflected in Table 3 and the Repository of the Agricultural Research Centre occupies the first place with 0.453993933% Self Web Impact Factor. The second and third place goes to the AgriSearch Repository (Dept of Agricultural Research, Lesotho), and the Institutional Repository of Federal University of Technology, Minna.

Repository of the Egyptian Agricultural Research Centre, Egypt occupies the first place with 4490 Self Link Pages and 9890 web pages with 0.453993933 SWIF.

AgriSearch Repository (Dept of Agricultural Research, Lesotho) occupies the first place with 07 web pages, 03 link pages and its ELWIF is 0.428571429.

AgriSearch Repository (Dept of Agricultural Research, Lesotho) \ ranked first position with 07 web pages and 03 in-link web pages and 0.428571429 RWIF.

The calculated value of r = +0.0566619, which is shown in Table 7. This implies that there is an association or closeness between the value of WISER Indicators and In-links WIF. Therefore, the number of web pages play a significant role in influencing the value of two ranking methods i.e., WIF and WISER of any repositories.

 

10 Conclusion

In this digital era, the web is playing a very significant role in the dissemination of scholarly literature. Repositories around the world maintain their websites to provide unrestricted access to research outputs on a global scale. The domain of agricultural repositories in Africa is no exception. Websites, as well as the Internet, play an integral part in digital repositories across the world, including Africa. Webometrics has become an important field through which information professionals analyse websites to find the best repositories. This study analyses the WIF and links of agricultural repository websites in Africa. Furthermore, it focuses on the rank of the WISER index value rather than link architectures, which is another field of Webometrics research. This analysis provides an overall idea of the distinct types of link pages and the visibility of repositories websites in Africa. It will enable the readers to identify and compare the repositories' websites by their WIF. It will also assist them in identifying a website's utility and its overall effect on the Web. In addition, self-links also reflect the logical structures of selected repositories used for organising web pages on the local server. The external link impact factor, on the other hand, has shown the connectivity and relationship of repositories' websites under study with the outside, as suggested by Ingwersen (1998). The In-Link Web impact factor as shown in Table 6 will help readers measure the visibility of respective repositories over the web. In addition, the correlation between the In-link WIF and WISER value as per Table 7, indicates the potency and weaknesses of selected websites, which will help scholars to improve their repositories. Therefore, the results of this investigation may be employed as a blueprint for evaluating repository websites all over the world, irrespective of subjects and disciplines.

 

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Received: 28 August 2020
Accepted: 21 March 2022

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