Library and Information Science Research
Electronic Journal ISSN 1058-6768
2000 Volume 10 Issue 2; September 30
Bi-annual LIBRES 10N2
Gilberto R, Sotolongo-Aguilar *, Carlos A. Suárez-Balseiro **, Maria V. Guzmán-Sánchez *
* The Finlay Institute; POBox 16017, Cod. 11600 La Habana, CUBA. E-mail: firstname.lastname@example.org
** Faculty of Communication, University of Havana Calle G, No.506, Vedado, La Habana 10600, La Habana, CUBA. E-mail: email@example.com
One of the challenges today for information professionals is to guide the way through huge volumes of information generated by different means. The birth and development of new disciplines such as “data mining” and “knowledge discovery”, shows the increasing importance of quantitative and qualitative analysis of huge corpora data (Dhar and Stein, 1997; Swanson and Smalheiser, 1997).
Bearing this in mind, bibliometric research becomes one of the fundamental tools used by information professionals in their quest of indicators; allowing them “critical appraisals” of scientific research, as well as interaction among researchers, institutions and knowledge areas.
The above reasons have conditioned increasing efforts for the systematization and standardization of methods and tools used in bibliometric research. Classical studies have supported the importance of clearly defining the problems in the field, emphasising the application of statistics as a key factor in discovering new knowledge (Egghe and Rousseau, 1990). For Glanzel (1996), bibliometrics is a complex discipline which, although it may be classified as a social science, is closely conditioned by pure and technological sciences. Therefore any methodological characterization requires well-documented data processing methods, a clear description of the sources and exact definition of indicators and, on the other hand, there is a need for an effective selection and integration of the applied technologies. Ravichandra Rao (1996) asserts that no bibliometric technique alone can be applied to all research, but instead different procedures should be used for different problems. Grivel, Polanco and Kaplan (1997) emphasize what they call “informatic infrastructure” where bibliometrics could develop all its potential. According to these authors, bibliometrics should have a methodology characterized by not only an adequate mathematical representation but also an effective “informatic architecture”.
Therefore bibliometric information systems are the workbench of bibliometric research. Being an important part of this field of endeavor, they require a flexible design in order to obtain accurate and customized indicators and should integrate new features resulting from the latest developments.
Many colleagues have found their way into bibliometrics by building in-house applications. At the end of the 80’s Terrence Brooks prepared a set of computer programs written in Turbo Pascal called the Bibliometrics Toolbox in order to measure the bibliometric aspects of a literature (Brooks, 1987; McLain, 1990). For Van Raan (1996) in Leiden the chosen name was “The Machine”; in CRRM they use a software suite, with DATAVIEW (Rostaing et al., 1996) as flagship, and the CUIB-METRIC system is proposed by specialists at the UNAM in Mexico (Portal and Thompson, 1994). There is the application of TOAK -Technology Opportunity Analysis Knowbot - at the Technology Policy & Assessment Center, at Georgia Institute of Technology, in Atlanta, USA (Porter and Detampel, 1995) and HENOCH (Grivel et al., 1997), and NEURODOC (Polanco et al., 1998) which are used at INIST in France. Bibexcel (Bibmap before Excel), developed by Professor Olle Persson, from Inforsk, Umeå University in Sweden, and BibTechMon (Kopcsa and Schiebel, 1998) are other available approaches. The work of Katz and Hicks (1997), Small (1998), and White and McCain (1998) takes the same direction. Finally we have to mention the experiences of Chen (1995), Lin (1995, 1997), and Orwing, Chen and Nunamaker (1997) in the application of artificial neural networks for bibliometric purposes based on the Kohonen’s self-organizing map (SOM), which is an orderly mapping of a high-dimensional, eventually structured distribution of data onto a regular low-dimensional grid. The Kohonen´s SOM is probably the best know network model geared towards unsupervised training and essentially consists of a regular grid of processing units or “neurons” associated with a model of some multidimensional observation to represent all the available observations with optimal accuracy, using a restricted set of models ordered on the grid so that similar models are close to each other and dissimilar models far from each other (Kohonen, et al., 1999; Kohonen, 1998).
However the problem arises of when generalization should be done. In-house applications are rarely well documented and their use by others becomes difficult. The result is that only the members of the team are able to replicate the use of such applications. The standardization fails and it is not only a handicap for practical research; it becomes a barrier for teaching purposes because many educational institutions are not be able to obtain and implement in-house applications to support bibliometric educational programs.
This problem may be overcome in part using a set of proprietary software, which is well-documented, widely available and more accessible than in-house applications. On the other hand, the validation of techniques is obvious and many teams of developers are continuously improving the performance of such software.
This paper describes a methodology based on the utilization of a set of proprietary software working together in order to perform bibliometric analysis of a literature. This methodology is explained as an open and flexible bibliometric information system in compliance with a simple modular design and connectivity for desktop work. It is useful for practical work as well as for education and training purposes. We envisage our task as seeking the integration of different available software with the objectives of consolidating an informatic infrastructure for our bibliometric research and developing standard methods that fit with this purpose.
Our approach consists of five modules based on proprietary software integrating the system. The modules perform the following functions:
(1) Bibliographic Searches
(2) File Conversion & Handling
(3) Bibliographic Reference Management
(4) Indicators including (experimental) Artificial Neural networking
(5) Bibliometric Analysis
Bibliographic Searches are conducted online or on CD-ROM. Resulting files are downloaded and converted by module (2) File Conversion & Handling. Resulting files are the input to module (3) Bibliographic Reference Management, where the standardization of the database is performed. Different fields under study or a combination of them are exported and saved as text files. Afterwards, those files are processed in module (4) Indicators. In this module several statistical analysis may be carried out to obtain the inputs for the bibliometric analysis in the module 5. Different scenarios could be implemented, varying the elements inside each module.
In our experience, for small and medium size corpora, the following packages appear to work very well:
1. Bibliographic Searches. Dependent on the topic of research e.g. in biomedicine SPIRS, WINSPIRS, both from Silver Platter, PubMed and Internet GratefulMed or The Query E-mail Retrieval System from NLM.
2. File Conversion & Handling. Resulting files are downloaded and treated by BiblioLink™ converting them according to a selected configuration that depends on host and fields to be studied. BiblioLinkä convert the files to Prociteä format.
3. Bibliographic Reference Manager. Procite™ (Research Information Systems Inc.), works very well for the managing of bibliographic references allowing standardization of data. Furthermore BiblioLinkä and Prociteä are totally integrated in their latest versions.
4. Indicators (obtained in this module). The functions of this module are performed by different statistical packages, e.g. Excel™ (Microsoft Corp.) and its complement xlStat™ (Stat@Com Inc.) and STATISTICA® (StatSoft Inc.). The former gives the possibility of profiting from all the built-in features of this program including graph and functions features. We have recently introduced an experimental submodule for Artificial Neural Networking. We used Viscovery ® SoMine from Eudaptics Software Gmbh for this purpose, allowing us to work without models and statistical assumptions by using the powerful Self-Organizing Maps (SOM) Technology. It leads to a very good representation of high-dimensional data by maintaining similarities implicit in the data.
5. Bibliometric analysis. Finally in this module the analysis of indicators is performed according to the aims of the particular task undertaken.
Results & Discussion
The above mentioned scenario operates according to the following procedure: bibliographic searches are conducted online or on CD-ROM. Resulting files are downloaded and treated by BiblioLink™ converting them according to a selected configuration that depends on host and fields to be studied. The resulting converted file is already in Procite™ format having the possibility to switch directly to the bibliographic reference management features of Procite™. Here standardization of the database is conducted. Many different treatments could take place including the building of authority lists with the contents of different fields including an authority list of all words in any field or in the whole database. The different fields under study or a combination of them are exported and saved as text files. Afterwards, Excel™ imports those files. Frequency analysis may then be performed aided by the Pivot Table feature of Excel™ complemented by built-in Analysis Functions available in the Tools Menu. With Excel™, using xlStat™ it is also possible to build the matrices that produce the input for cluster analysis, factor analysis, PCA, and multidimensional scaling and undertake these analysis. Alternatively, those matrices may be exported as Excel™ sheets and imported into STATISTICA® (StatSoft Inc.) and finally processed. More recently we have been experimenting with Viscovery SoMine. Beyond its visual exploration capabilities, Viscovery® also supports in-depth statistical analysis of data. The combination of the non-linear data representation of the SOM approach with classical statistical methods - such as regressions or principal component analysis (PCA) - results in the improvement of the final model in terms of precision and efficiency.
This system platform guarantees a comprehensive traceability of all data from the first data downloaded, to the last chart obtained. At the same time, consistent results are attained by means of the reproducibility at all the steps performed. Bibliographic data in the database could also be used for building-up formatted bibliographies.
This approach has been applied in different studies (Macías-Chapula et al., 1999; Guzmán-Sánchez et al., 1998; Sanz-Casado, et al., 1998) mainly focused on the biomedicine field and more specifically in the area of vaccines research. However, other domains such as library and information science or economics have been explored (Sanz-Casado et al., 1999; Sotolongo-Aguilar, 1999). Bibliometric output data of the system could perform, among others, the following activity and relations measurements:
1. Counts of papers by the following fields or a combination of them:
· Keywords (e.g. MESH)
· Documents types
· Country of publication
· Document types/papers
2. Co-occurrence matrices for multivariate analysis of the following fields or a combination of them:
· Document types
· Self-Organized-Maps for spatial representation of linear or multidimensional data
The benefits resulting from the developments outlined in this paper could be threefold. Besides integrating public domain software in a flexible modular design, comprehensive automated processing and data representation stages of research could be achieved in contrast to the cumbersome tasks that are performed by other means. This platform is supported on software which is widely used and regularly updated and upgraded; in contrast with adhoc software that becomes outdated very rapidly.
Last but not least, teaching bibliometric research seems to benefit from this approach, bearing in mind its use of proprietary software available world wide, regularly updated and supported by well established developing teams. Improved bibliometric research practices must be supported by theoretical and practical education in which technologies have a key role. However, if the methods and tools used are not widely accessible and it is necessary to depend on an in-house application, the experience could be frustrating.
Finally we have to emphasise the fact that MOBIS-ProSoft is not new software, it is not a new in-house developed application; it is a methodological approach using a set of different proprietary applications working in modules. This methodology has been shown to be a working platform that could be up-graded, is flexible and has utility performance. Moreover it is a practical alternative for educators to improve their teaching programs. Improvements to MOBIS-ProSoft are foreseen. Participation in the testing of this methodology is welcome, as well as new ideas for incorporating modules or improving the existing system.
This appendix contains figures illustrating some features of the MOBIS-ProSoft (modular bibliometrics information system with proprietary software) approach.
Figure 1. Matrix-building module showing (partially): raw data (upper-half); frequency of ocurrence (lower half – left); coocurrence matrix (lower half – right)
One of the most interesting features of using the MOBIS-ProSoft approach, is the matrix-builder feature implemented by running biblio.xla module under xlStat™. A selected multiword field of the database, for instance the Subject field or the Author field, is exported as comma delimited text and after it is imported into Excel™. Then the biblio.xla module can be run. The resulting Excel™ sheet of the current workbook looks like the upper half of figure 1. Column A is manually added for assuring that all elements will be included in the matrix-building process. The results of running biblio.xla appears in the lower half of figure 1; at the left the frequency table is built up; after skipping one column the symmetric coocurrence matrix appears at the right. The square matrix can have a maximum size of 252 x 252. In the illustration is a data set from ISA (1966-1998) descriptors related to Library & Information Science Research Methods in Latin America & the Caribbean.
Figure 2. Dendogram resulting from clustering using the Ward method including clusters observation / clusters size
This illustration shows the results of clustering similarities e.g. Pearson Product Moment rp, using the Ward method. In this case country clustering according to the Activity Index from ISA (1966-1998) Library & Information Science in Latin America & the Caribbean. The illustration includes the Clusters Observation / Clusters Size for the best partition suggested by running the results i.e. three groups of size 5, 5 and 9, with the corresponding country names by group.
Figure 3. Map based on principal component analysis with Varimax transformation
This illustration shows the map corresponding to the same data set used in figure 2. In this case the display is a PCA map with Varimax transformation. The groups founded in the clustering procedure shown in figure 2 are highlighted.
Figure 4. Map based on multidimentional scaling
Figure 4 shows an MDS map which displays another view of the data set used in figures 2 and 3. In this case dissimilarities were calculated as 1- rp.
Figure 5. Self Organized Map (SOM) and windows of map-layers & values based on an Artificial Neural Network (ANN) trained with Kohonen unsupervised algorithm.
The most intriguing feature recently incorporated on an experimental basis to the MOBIS-ProSoft approach is the SOM-ANN. Again, as in figure 1, a data set from ISA (1966-1998) descriptors related to Library & Information Science Research Methods in Latin America & the Caribbean is used, in this case time series of descriptor occurrence for the years 1983-1984,1986, 1990-1998. Viscovery® SoMine was used for training the network based on 133 data records from same number of descriptors characterized by 12 components (dimensions or features). Forty four cycles in normal exact mode of training were needed for generating a map size of 100:61 with 1905 nodes that represent the trained network . Figure 5 shows a screenshot with some results. In the upper-left part of the screenshot is a map showing the clusters formed, at the upper-right the values of some of the year component of the cluster corresponding to bibliometrics that is located in the lower-left corner of the cluster map. The rest are all the map-layers corresponding to all the dimensions (one for each displayed from left-right top-bottom. A different tone of gray (different colors in the original) shows different “landscapes” views. The first year of the time series (left map in the first row of maps-layers) displays isolated spots of activity, while as time goes by (second row of maps-layers) the activity increases and non-linear correlation could be observed. Resulting data from the trained network could later be evaluated.
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LIBRES: Library and Information Science
Electronic Journal (ISSN 1058-6768) September 30, 2000
Volume 10 Issue 2.
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Gilberto R, Sotolongo-Aguilar
The Finlay Institute; POBox 16017, Cod. 11600 La Habana, CUBA. E-mail: firstname.lastname@example.org
Carlos A. Suárez-Balseiro
Faculty of Communication, University of Havana Calle G, No.506, Vedado, La Habana 10600, La Habana, CUBA. E-mail: email@example.com
Maria V. Guzmán-Sánchez
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 All the figures used in the appendix are related to the report prepared by Gilberto Sotolongo-Aguilar for the Scientific University Council of the University of Havana in 1999. This study focused on library and information science research methods in Latin America and the Caribbean from 1966 to 1998.