Examining Accesses by Country, Language and Area of Knowledge ETD 2011 – Cape Town

Ana Pavani Laboratório de Automação de Museus, Bibliotecas Digitais e Arquivos Departamento de Engenharia Elétrica Pontifícia Universidade Católica do Rio de Janeiro Brazil ETD 2011 – South Africa

This work is a continuation of a work presented last year in Austin. The two works differ in the following aspects:  In 2010, there were 71 data sets and this work considers 85 (20% more)  East Timor was included because accesses from this country have started happening  The UNDP has changed the way HDI is computed, so this data has been updated, as well as the populations of the countries  My co-author left the university, so this time I am by myself

ETDs, PUC-Rio, BDTD & NDLTD

PUC-Rio Rio de Janeiro Brazil

PUC-Rio is a small private university. It is divided in 3 centers and each has graduate programs:  CTCH  CTCH (Humanities) – 6  CCS  CCS (Social Sciences) – 10  CTC  CTC (Science & Technology) – 10  The oldest graduate program (EE) started in  The newest graduate program is less than 5 years old.

Characteristics of PUC-Rio’s ETD program:  First published ETD – May 2000  ETDs became mandatory – Aug 2002  Number of ETDs – 5,694 (Jun 2011)  CTCH – 1,442  CCS – 1,291  CTC – 2,961  Yearly average number of defended T&Ds (*) – 590  (*) 2007, 2008, 2009 & 2010; (**) 2006, 2007, 2008 &  There is retrospective digitization.

 ETDs are made available in chapters (graduate school regulation – please, don’t ask me the reason!, but it will change as of Oct 2011)

AllCTCHCCSCTC Number of ETDs5,6941,4421,2912,961 Average number of ETDs – June 2004 to June 20113, ,938.3 Average number of partitions – June Average of averages number of partitions – June 2004 to June

PUC-Rio’s ETDs, BDTD (*) and NDLTD (**) :  Number of BDTD institutions – 97 (OAI-PMH data providers)  Number of BDTD metadata records – 170K + (BDTD is an OAI-PMH data and service provider)  BDTD records are/were harvested by OCLC and other institutions, and made available worldwide  Brazilian ETDs are the largest collection in Portuguese available worldwide  (*) BDTD – Biblioteca Digital de Teses e Dissertações = Brazilian Nat’l Consortium.  (**) You must know what NDLTD stands for!!!

Accesses to PUC-Rio’s ETDs:  Access logs saved since – Jun 2004  Number of monthly logs when article was written – 85

pt & es IN THE WORLD

WorldwideWestern Languages Internet Portuguese7 th 3 rd 6 th Spanish2 nd 1 St 3 rd pt is the official or one of the official languages of:  Angola  Brazil  Cape Verde  Equatorial Guinea (*)  East Timor (**)  Guinea-Bissau  Macau (***)  Mozambique  Portugal  Sao Tome and Principe es is the official or one of the official languages of:  Argentina  Bolivia  Chile  Colombia  Costa Rica  Cuba  Dominican Rep  Ecuador  El Salvador  Equatorial Guinea (*)  Guatemala  Honduras  Mexico  Nicaragua  Panama  Paraguay  Peru  Puerto Rico  Spain  Uruguay  Venezuela (*) es & pt official (**) less than 5% of the population know it; it was banned during the Indonesian rule (***) UNDP did not publish in the last report; other data were used

Assumptions for the analysis:  ETDs are very specialized items – people who seek ETDs are highly educated  es and pt are quite similar languages – educated people who can speak one can read the other  es and pt-speakers are potential readers of PUC- Rio’s ETDs  2 countries were not considered:  Brazil – is the home country  US – there are very large groups of es and pt-speaking persons but neither one is the language of the country

 2 groups were defined: international group  “international group” – all countries except Brazil and the US pt+es group  “pt+es group” – all countries that have pt and/or es as one of the official languages  Factors considered to influence accesses to ETDs:  Population size  Level of education  Access to the Internet

DEALING WITH COUNTRIES DIFFERENCES

Mexico has 110M inhabitants Sao Tome and Principe has 165K inhabitants Portugal and Spain are in Europe Argentina and Honduras are in Latin America Angola and Mozambique are in Africa Portugal has 10M inhabitants Spain has 45M inhabitants Equatorial Guinea has the 2 languages

Quantization of potential accesses from countries that are very different :  Need to find data on the factors that may influence accesses to ETDs:  Population size – easy  Level of education – difficult (literacy rates are easy!)  Access to the Internet – difficult  All data should be considered in the same time-frame  Knowledge that the second and the third factors are dependent on how developed countries are  Knowlede that it was necessary to combine the 3 factors

Decision on how to deal the countries differences:  Use UNDP’s HDI – Human Development Index that contains information on the second and the third factors (HDI combines indicators of life expectancy, education and income; the new way it is computed contains means years of schooling and expected years of schooling, going beyond literacy rates)  Decision to combine HDI with the population size Index I = Population x HDI

AllCTCH Total population420,281,00057,858,800 Average HDI Index I309,420,87125,114,111

Comments:  21 es-speaking and 10 pt-speaking countries (Equatorial Guinea was counted in both)  Average HDI for es-speaking countries is 34.16% higher than the other group  Population of the es-speaking countries is almost 7.4 times the population of the other group  Index I for the es-speaking group is times the same index for the pt-speaking group The expectation was to have many more accesses from es-speaking countries than from pt-speaking countries!!

WORKING WITH DATA AND RESULTS

Information:  Number of sets of data – 85 (one for each month)  For each set, 16 variables were computed (examples – number of countries, number of pt-speaking countries countries, total number of accesses, etc)  All data were computed for the complete set and for each of the 3 areas of knowledge

From the sets (collection and areas ) side

This analysis focused on the way the whole collection and each individual set – CTCH, CCS and CTC – were accessed from countries in different groups.

Results:  Total number of countries that accessed ETDs – 204  CTCH – 183  CCS – 183  CTC – 189 international group  Total number in the “international group” – 202  CTCH – 181  CCS – 181  CTC – 187

international group  Maximum number of countries in the “international group” in a month – 143  CTCH – 112  CCS – 108  CTC – 132 pt+es group  Maximum number of countries in the “pt+es group” in a month – 28 (maximum possible 30)  CTCH – 27  CCS – 27  CTC – 27

 Number of months with accesses from 100 or more countries – 42  CTCH – 18  CCS – 15  CTC – 32  Some percentages follow

% accessesAllCTCHCCSCTC from the international group in the international group from the es+pt-sepaking group in the es+pt-speaking group from pt-speaking countries in the international group from pt-speaking countries in the international group from Portugal in the es+pt-speaking group from Portugal in the pt-speaking group from Portugal

Comments:  Absolute values for CTC are higher – this area has the largest collection (higher than the sum of the others) international group  Percentages for CTC are lower, except for accesses from the “international group”  Is it more international?  It seems that language is not very important in C&T

From the accesses side

This analysis focused on the way accesses behaved for the complete collection and how they spread among the sets – CTCH, CCS and CTC. The collection and the sets have different profiles – numbers of ETDs and numbers of partitions. For this reason, normalization was necessary.

Quantization of potential accesses to sets of works with different profiles:  Sets are very different in:  Numbers of ETDs  Numbers of partitions per ETD  This means that numbers of accesses had to be normalized in order to compare accesses to the sets first attempt average ETDattracts  This work presents a first attempt to quantize the way the “average ETD” in a set “attracts” accesses

Decision on how to deal the sets differences:  Combine average numbers of ETDs with average of average numbers of partitions Index EI = 1 / (average number of ETDs x average of average numbers of partitions)

AllCTCHCCSCTC Index EI

Average numbers ofAllCTCHCCSCTC Accesses Accesses from the int group Accesses from the es+pt-speaking group Accesses from the pt-speaking countries Accesses from Portugal  numbers computed for the total numbers of acesses x index EI  numbers to be viewed as accumulated  monthly averages can be obtained dividing by 85

Comments:  When normalized data is consideredn, the average number of accesses (per ETD in Science & Technology) from the international group is the lowest among all  The same happens with accesses from the es+pt and pt-speaking groups, and Portugal as well  The reason is that ETDs in this group have the lowest average of accesses per ETD among the 3 subsets

 When normalized data is considered, the average number of accesses (per ETD in Humanities) from the international group is the highest among all  The same happens with accesses from the es+pt and pt-speaking groups, and Portugal as well  The reason is that ETDs in this group have the highest average of accesses per ETD among the 3 subsets

FINAL COMMENTS

 Percentage wise, international accesses are the most significant for ETDs in S&T average S&T ETD attracts average Humanities ETDattracts  At the same time, the “average S&T ETD” “attracts” less international accesses than ETDs in other areas of knowledge and the “average Humanities ETD” “attracts” the most  In all areas of knowledge, accesses from:  es- and/or pt-speaking countries are the most significant es+pt–group  pt-speaking countries are the most significant in the “es+pt–group”  Portugal are the most significant in the pt-group

attraction  New ways of defining “attraction” should be examined Results seem to indicate that language and HDI are important factors in accesses

Thank you! Muito obrigada!