Ivana Roche
email: ivana.roche@inist.fr

Dominique Besagni
email: dominique.besagni@inist.fr

Claire François
email: claire.francois@inist.fr

Marianne Hörlesberger
email: marianne.hoerlesberger@arcs.ac.at

Edgar Schiebel
email: edgar.schiebel@arcs.ac.at

Identification and Characterisation of Technological Topics in the Field of Molecular Biology

Keywords: S&T indicators for the identification of emerging fields; Secondary: Visualisation and science mapping emerging technologies, bibliometric indicators, terminology evolution, diffusion model, diachronic cluster analysis

Following up the European project PromTech the aim of which was to detect emerging technologies by studying the scientific literature, we chose one field, Molecular Biology, to identify and characterize emerging topics within that domain. We combined two analytical approaches: the first one introduces a model of the terminological evolution of the field based on bibliometric indicators and the second one operates a diachronic clustering analysis. Our objective is to bring answers to questions such as: Which technological aspects can be detected? Which of them are already established and which of them are new? How are the topics linked to each other?

Introduction

This paper focuses on methodological approaches for characterising specific topics within a technological field based on scientific literature data as used in the framework of the PromTech project. This project aimed to identify promising emerging technology fields on the observation that many new emerging technologies of the last decades have drawn on the technical application of physical knowledge. Indeed, there are many examples showing that advanced technology in Applied Sciences as well as in Life Sciences is increasingly linked to recent outcomes of research in Physics. The study of the intersection of Physics and these two scientific domains with bibliometric methods, validated by expert panels, led to the selection of ten most promising technological fields. In order to characterise them and analyse their potential evolution, we used the diachronic clustering analysis and the diffusion model, based on bibliometric indicators. This model uses a modified TF IDF approach and additionally evaluates the "home Technology" nature of keywords on the basis of probabilities, and distributes them in different diffusion stages and in semantic categories.

The questions we are trying to answer are: how can we identify and characterise important topics in a set of several thousand articles? Which technological aspects can be detected? Which of them are already established and which of them are new? How are the topics linked to each other?

After describing the diffusion model and the diachronic cluster analysis, we present the results obtained for one of the promising technological fields detected in the PromTech project: "Molecular Biology". The results are visualised with the software-tool Stanalyst ® [START_REF] Polanco | Stanalyst ® : An Integrated Environment for Clustering and Mapping Analysis on Science and Technology[END_REF].

Methodology

The high coverage of scientific literature in Physics and Natural Science, the detailed systematic of classification codes with more than 6,000 items as well as the high quality of the keywords where the reason why the authors used the PASCAL database for this investigation. In PromTech project, the data were extracted in function of their classification categories from the PASCAL bibliographic database. PASCAL contains more than 17 million bibliographic records. They are derived from the analysis of the scientific and technical international literature published predominantly in journals, conference proceedings, reports and dissertations but, in this work, we restricted our study to a corpus of publications coming from journals only. Each publication receives at least one but, if necessary, more than one classification categories related to different scientific fields. The queries operated in this work were exclusively based on the classification categories given in the database and assigned to the individual publications, either manually by scientific experts or automatically based on a content analysis. These classification categories belong to the PASCAL classification scheme, that is a taxonomy of every field and subfield of all the disciplines covered in the database. Each individual publication also benefits from a manual or automatic indexing by keywords. After validation by a scientific expert, that terminology is employed in our analysis.

Let us introduce the two methods we employed to follow the evolution of the technological field.

Firstly, the diffusion model was used to evaluate the term status in the considered technological field, comparing it with its status in the other technological fields where the terms are present and measuring their degree of emergence in order to provide a global field characterisation.

Secondly, the diachronic cluster analysis considered the content of the technological field by a clustering approach allowing to organize the data in subtopics and to analyse the links between them.

Diffusion model

Our starting hypothesis was that technologies have their roots in an invention which was created by one person or a group of scientists. After a specific invention is published, the respective knowledge is available to other scientists. If scientists are inspired by the new ideas, they make use of the new theories and findings, and publish their own research work. At that stage, a new terminology is created and also the use of methods, materials, natural science effects and applications are described in few publications. Unusual terms play a specific role in that stage of the development of a new technology. In later stages, many groups use the findings on a similar technology nomenclature. Bit by bit a well defined terminology is established but is still specific for this new field. In yet other stages, findings diffuse to other technologies and many terms show a more cross sectional property.

Following this diffusion model, a framework was constructed to analyse the stages of the selected technologies:

-STAGE 1: New terms created specifically for the technology can be found in few publications. New terms and terms established in other technologies occur seldom and they form a strong exotic cluster.

- Therefore, the diffusion model identifies three categories of terms: terms unusual in this topic, the established terms, and cross section terms. The allocation of a term in one of these categories is accomplished thanks to the application of a sequence of statistical filtering essentially based on two pragmatic approaches introduced to identify keywords describing a technological specificity.

First, the so called "home Technology" terms were defined. We assume words which are specific for a technology occurring with a higher probability in one technology field than in the others. The probability is defined by the frequency of one term in a technology field divided by the number of articles in this field. Then, this probability is calculated for the same term for other technology fields.

The technology field with the highest probability is declared as the home technology for the considered term.

Secondly, we used a measure for the cross section degree of a term in the selected technology. For this purpose, we introduced the Gini index, a measure of statistical dispersion most prominently used as a measure of inequality of income distribution or inequality of wealth distribution (see [START_REF] Gini | [END_REF]). The Gini index varies from 0 to 1. In our case, 0 means a completely uniform distribution of all terms and indicates that each term of the considered field occurs in all other fields. A Gini index of 1 tells us that all terms of the considered field occur only in this field, which means that no term of the considered field occurs in another field. Therefore, more general terms push down the Gini index. Particularly, in our context of emerging technologies, a Gini index value lower than 0.87 means that the considered field does not consist of specific terms.

The technology field is examined according to bibliometric statistics that give an overview of the publication activity, the diversity of the field, the specificity of technical terminology, and the cross section property.

Next, the keywords which form the framework of a technology field and the diffusion stages are selected. For this purpose the following procedure is defined:

1) Selection of all keywords of the technology field;

2) Selection of home technology terms of the technology field;

3) Selection of terms with a higher frequency (more than 2 occurrences); 4) Selection of terms with a higher Relative Term Frequency -Inverse Technology Frequency (RTF-ITechF) (see [START_REF] Schiebel | About the Identification of Technology Specific Keywords in Emerging Technologies: The Case of "Magnetoelectronics[END_REF]);

5) The remaining terms are categorised in the three stages of our diffusion model (unusual terms, established terms and cross section terms), using the Gini index and the RTF-ITechF (see [START_REF] Schiebel | About the Identification of Technology Specific Keywords in Emerging Technologies: The Case of "Magnetoelectronics[END_REF]);

Finally, each term belonging to the three diffusion stages is secondarily categorised, with expert's help, into "Natural science effect-", "Method-", "Material-", or "Application-" related (from now on named semantic categorization).

Diachronic cluster analysis

The diachronic cluster analysis is realized with the help of a clustering tool applying first a non-hierarchical clustering algorithm and then a principal component analysis to map the obtained clusters. This tool is implemented in the information analysis platform Stanalyst ® . Neurodoc ( [START_REF] Lelu | Modèles neuronaux pour l'analyse de données documentaires et textuelles[END_REF]; [START_REF] François | Hypertext Paradigm in the Field of Information Retrieval: A Neural Approach[END_REF]), the clustering tool we used, applies a non-hierarchical clustering algorithm, the axial K-means method, coming from the neuronal formalism of Kohonen's self-organizing maps, followed by a principal component analysis (PCA) in order to represent the obtained clusters on a 2-D map.

Our approach consists of:

-splitting the corpus in two successive time periods, namely 1996-1999 and 2000-2003;

-applying cluster analysis on the corpus of each period, in which documents are represented by the keywords existing in the bibliographic references;

-analysing the evolution between the two cluster sets and maps by examining the vocabulary related to the clusters of the two periods.

In order to analyse the evolution of the cluster vocabulary between the two considered periods, we build a comparison matrix pointing out the percent of keywords belonging to the second-period clusters and already existing in the first-period clusters. The cumulated percentage is also calculated for each secondperiod cluster. Using this matrix, we can identify different cluster behaviours: stability, fusion or splitting. Using the cluster maps, we can also detect status change of the clusters in the global network. These detected phenomena need to be validated by experts from the technological domain.

The cluster behaviours are calculated in the following way.

Let M be the comparison matrix: cluster and existing in cluster and for each line , the m arginal value is :

P jn i ij j ij i mm     
The analysis of the matrix M allows us the build the following hypotheses:

-Let (i,k) be a pair of homonymous clusters. The cluster i is supposed to be stable if:

1 ; and 1, 2

ik ij P m m j k j n     (1)
-Let (i,k) be a pair of homonymous clusters. The cluster i is supposed to be unstable if there exists at least one j respecting the condition:

1 ; an d 1, 2 ik ij P m m j k j n     (2)
-Clusters having new titles (titles not existing in the first period cluster list) and obeying the condition (1) are supposed to be stable;

-Clusters i with the lowest marginal values have less inheritance from first-period clusters and, we suppose they constitute new terminological notions;

-Clusters i with the highest marginal values are supposed to aggregate a great number of notions already existing in the first period clusters.

At this point, scientific expertise is needed. We asked scientific experts for the validation of our hypotheses, for the analysis of the two cluster maps taking into account the cluster contents.

Results

Molecular Biology is a research field about Biology at the molecular level. There is a strong interaction with Genetics and Biochemistry. Many efforts are done to understand the mechanism of interactions in cells, the working of DNA, RNA and the regulation. Bibliometric analysis reveals that "Molecular Biology" is a field with high research activities with about 5,000 articles produced during the 1996-2003 period.

Diffusion model

In comparison to other technology fields (which were investigated in the PromTech project), the percentage of home terms (specific technical terminology), the number of terms per article (diversity of terms) and the average relative term frequency are quite low. That indicates a lot of research activities in various topics without a committed specific terminology. It seems that the terminology consists of many terms but is not established and is somewhat highly fluctuating. Contrary to the lower percentage of home terms, the value of the average Gini index shows however that the terminology is commonly used within the technology field of "Molecular Biology". The results of the selection procedure for the keyword categorisation into diffusion stages are showed in the Table 1. This table shows only the most important terms of the field "Molecular Biology". The constraint of a paper does not allow to present the whole long term list.

Indicator
We consider now the content of this table. In the category "Natural science effect" we notice the terms "Multiple diffraction", "Angular distribution", "Helix coil transition", and "Nuclear relaxation" for instance as "unusual terms". Each of these "unusual terms" has a RTF (Relative Term Frequency) less than or equal to 2%. The sample of the keywords in the category "Natural science effect" such as "Molecular force constants", "Isomerism", "Microgravity", and "Solvation" are allocated to diffusion stage "Established terms". Each of these keywords holds a RTF greater than 2%. The "Cross section terms" in this category such as "Brownian motion", "Photosynthesis", "Circular dichroism" and "Intermolecular forces" feature a Gini index less than or equal to 0.97.

Considering the category "Method" the sample of terms "Matrix assisted laser desorption ionization", "Moller Plesset partition", "Optical tweezer", and "X-ray topography" are the results for "unusual terms" with the RTF less than or equal to 2% as we already mentioned for "Natural science". The examples of keywords for the diffusion stage in this category are "Two-dimensional spectroscopy", "Heavy atom method", and "Liquid-liquid transformation", whereas the methods like "Molecular dynamics simulation", "XRD", "NMR spectroscopy", and "Lennard Jones model" are assigned to the stage "cross section terms" for this category.

The category "Material" shows us the sample of terms "Tissues", "Pinene", "Leaf oil", and "Lyotropic liquid crystals" for the "unusual terms" diffusion stage. "Biological macromolecule" is listed for the "established terms" stage, and "Polymers", "Essential oils", "Biological compound", and "Animal cells" in the stage "cross section terms".

In the category "application" we find the examples of terms "Instrument for chemical analysis", "Multilayer perceptrons", "Diagnostic techniques", and "drug delivery systems" in the stage "unusual terms". Terms with a RTF greater than 2% in the stage "established terms" are such as "Structure resolution" and "Viruses". Examples for terms with a Gini index less than or equal to 0.97 (cross section terms) in the category "Application" are "Molecular configurations", "Polymer solutions", "Biomolecular effects of radiation", and "Oxygen sensors".

The analysis reveals some special, probably new biophysical research fields like multilayer perceptrons or lyotropic liquid crystals and also some well known characteristics about this research field like molecular interaction, microgravity or important driving forces like diagnostics and drug delivery systems. But expected and established basic areas like gene expressions, micro arrays, polymerase chain reaction, expression cloning or others are not even home technology terms in "Molecular Biology". The reason could be that they are now more often used in related science fields like Medical Imaging, Biotechnology, Biodeterioration or others.

Diachronic cluster analysis

The diachronic cluster analysis applied on two periods, namely 1996-1999 and 2000-2003, allows us to determine which topics of the second period have roots in the first one and which topics are new in the second period.

After analysis of the results of classifications obtained with various numbers of classes over the two periods the expert chose that with 20 clusters in the first period and 20 clusters in the second period. In Table 3, we can see, for each period, the lists of cluster titles, their number of keywords (column KW) and the number of documents (column DOC). The 11 homonymous cluster pairs are highlighted.

Analysing the comparison matrix and the Table 3 we can singularize for the second period:

-eleven homonymous clusters, but only six of them seem to be stable ( On the map of both periods, as we can see in the Figures 1 and2, it is possible to singularize an interesting dichotomous configuration represented by two very strongly connected cluster networks associating about two thirds of the clusters. However, these networks have quite different characteristics. On the one hand, the topics present in the bigger one are related to the modelling and simulation of biological phenomena. On the other hand, the little network is very homogeneous and deals essentially with instrumentation topics. The remaining clusters are scattered in the map with no significant links with the two previously described networks.

FIRST PERIOD MAP

In the first period map (Figure 1), the bigger network is located in the left side and is formed around the cluster "Computerized simulation" that brought together two sub-networks:

-the first one is located in the upper side and constituted by the clusters "Molecular configurations", "Polymers" and "Fluctuations" related to physical characteristics of biological structures, specially macromolecules and membranes; -the second one, in the lower side, is formed by the clusters "Diffusion", "Stochastic process", "Neural networks", "Physiological models" and "Biological evolution" dealing with biological process analysis. This sub-network focuses on theoretical aspects as neuronal network studies produced in computer science field and on aspects related to the modelling of physiological process.

The smaller network, that is located in the lower right side, deals with applications of instrumentation techniques to therapeutic measuring.

SECOND PERIOD MAP

In the second period (Figure 2), this smaller network is now located in the upper left side. Its evolution shows an important stability in spite of a redistribution of the cluster contents: the clusters "Measuring methods" and "Fluorescence" have strong inheritance from "Instrumentation" cluster.

The bigger network, located in the right side of the map, shows the same global structure with its two sub-networks linked by means of the "Computerized simulation" cluster. As indicated previously, this cluster, number 6 in Table 3, has a strong inheritance from different clusters which is explained by its position in this network.

The stability of each sub-network structure can be observed:

-in the upper side, the sub-network organized around "Physiological models"; -in the bottom side, the sub-network centred on "Molecular configurations" cluster.

An in-depth analysis of these sub-networks allows to observe some interesting evolutions. Particularly, we observe a re-centring of the cluster couple "Neural networks" and "Stochastic processes" and a meaningful density increase of this sub-network with the coming out of a new cluster: "Brownian motion". This cluster is constituted by a majority of studies related to the development of Brownian engines that are devices working in a nanometric scale by which thermally activate processes are controlled and used to generate directed motion in space. The first period cluster "Biological evolution" that dealt with the modelling of biological evolution phenomena at genetic and environmental level has a growth of its application field towards principally the development of theoretical models to study processes positioned at the molecular level, in its principal heir in the second period, the cluster "Random process".

The second period cluster "Cellular biophysics" inherits from 11 first period clusters, principally: "Biosensors", "Biological evolution", "Diffusion", "Stochastic processes" and "Physiological models". As we can see in the first period map, theses clusters form a sub-network if we consider that the cluster "Biosensors", that is not linked, is near enough to allow its integration. This subnetwork deals with the analysis of biological processes. Finally, in the "Cellular biophysics" cluster we can find studies dealing with the utilisation of tumoral cells as behaviour model, that were absents in the first period clusters.

Convergences between the two analyses

The comparative analysis of the results of the two methodological approaches operated in this work, namely the diffusion model and the diachronic cluster analysis, reveals some quite interesting convergence points.

Let us consider the 124 home technology terms obtained by the model diffusion selection procedure (Table 1) and their distribution by on the one hand diffusion stage and on the other hand their presence in clusters of first period (P1), second period (P2), both periods or their absence in both periods. The results are showed in Table 4. We can observe, in the last column of Table 4 that more than a quarter of selected terms (30) are absent from the clusters of both periods. This set is formed by a majority of unusual and cross section terms (in total 94%). This absence can be explained if we consider that:

-the unusual terms correspond to fundamental new concepts not frequent enough to participate to a cluster constitution (as "optical trapping", "optical tweezers" or "Moller Plesset partition"), and -the cross section ones reflect concepts related to applications not yet consolidated and in consequence not frequent enough.

About a half of "home Technology" terms (57) are presented exclusively in P2 clusters. This set is constituted by a majority of unusual and cross section terms (in total 88%). The presence in the second period of a great number of unusual and cross section terms seems to be consistent with the observation in the second period cluster map that:

-in its left side a great number of heterogeneous isolated clusters contains a lot of unusual terms carrying innovation but not yet important enough to be associated to the central clusters, and -in its right side the clusters forming the network kernel and contain the cross section terms by which the diffusion to other technological fields occurs.

Fifteen terms (that is 12% of the "home Technology" terms) are present exclusively in P1 clusters. This smallest set is constituted by a majority (87%) of unusual and cross section terms. The important presence of unusual terms in P1 is at first sight unexpected. These terms can be considered as peripheral either representing topics which did not go on in the second period, or representing topics which went in others fields.

Twenty-two terms (that is 18% of the "home Technology" terms) are present in clusters of both periods. This set is formed by a majority of established terms and cross section terms (totally 90%). The presence of a great number of established and cross section terms is not really surprising. Indeed, if we consider that the diffusion to other technological fields begins with established terms, and becomes more marked with cross section terms, their presence in clusters of both periods should be considered consistent. It can also explain the very poor presence of unusual terms in clusters of both periods.

We also consider the cross distribution of the "home Technology" terms by diffusion stage (Unusual, Established or Cross section) and semantic categories (Natural science effect, Method, Material or Application) for both periods (Table 5). The number of unusual terms grows significantly in all the categories except in the Application one, with a lot of new terms not yet important enough to be associated to a central cluster and located in a great number of isolated heterogeneous clusters located in the marked region in the left side of P2 cluster map (Figure 2).

Category / diffusion stage

Concerning the established terms, we have an equilibrated situation between the two periods for all categories except the Natural science one. This situation is quite normal if we consider that established terms correspond to terminologically consolidated terms in the Molecular Biology field.

In relation to cross section terms, we have the strongest term growths in Material and especially in Application, that is the category facilitating the more the diffusion from "Molecular Biology" field to other technological fields. The clusters involved with application category terms are located in the neighbourhood of the P2 larger cluster network as we can see in the delimited region in the right side of P2 cluster map (Figure 2). In addition, we can suppose that the great number of clusters involved points out a behaviour with intrinsically dispersive tendency in accordance with the definition of these terms that announces their capacity to assure a broad diffusion to other technological fields.

Conclusion

It is a challenge to identify emerging technologies. "Molecular Biology" is a broad field and by applying the complementary methods presented here, it can be better characterised and described.

One particularity of our approach is the alternate utilisation of different bibliometric and/or informetric methods and scientific expertise. This expertise is necessary to validate or to complete the results obtained at each step of the work as well as to get the experts personal input on the matter at hand. This could be time consuming, but with our approach, the amount of data submitted to the experts' appreciation is limited, thus making their task notably easier.

The application of the diffusion model is a novel bibliometric approach giving a more in-depth view of the considered field. The introduced concept of home Technology, associating terms with technologies, allows the development of a new interesting analysis methodology based on the notion of terminology diffusion. The indicators we used such as term frequency, Gini index, relative term frequency, inverse technology frequency, helped working out the different features of a field. Based on these results, we were able to work out the field specifications.

The diachronic approach we adopted consists in splitting the corpus in two periods, applying a content cluster analysis for each period and then detecting the evolution of the topics by examination of the vocabulary related to the respective clusters.

Applying at the same time diachronic cluster analysis and diffusion model allows to confirm the results detected by each method and also to lead to a deeper understanding and characterization of the technological field, The diffusion model approach allows new interpretation of diachronic clustering results introducing external term categorizations.

It is to be noted that the conditions that make that study possible may disappear soon. Indeed, our bibliometric approach is based on the one hand on a multidisciplinary discriminating classification scheme and on the other hand on the possibility of indexing each bibliographic reference with several classification categories. These constraints are very strong and with the assisted indexing procedures currently used on an ever increasing share of the PASCAL database, we could no longer respect these constraints. In the future, if we were to reproduce or to generalize our approach, it would be necessary to develop an automatic categorization method for the bibliographic references along with a protocol to update the classification scheme.

A very interesting next step in this work would be to take explicitly into account the time variable in the diffusion model so as to be able to detect the passage of terms from one diffusion stage to another between time periods. We hope to verify if the results of both methods could be linked as shown in Table 6. Another attractive research axis could be the development of a clustering analysis method using an incremental approach. Such a method would allow to follow more precisely the evolution of a topic in function of the publication date of its related articles.

diffusing

  of clusters obta ined in the second period 1, ..., w here = num ber of clusters obta ined in the first period = percent of keyw ords belonging to

Figure 1 .

 1 Figure 1. First period cluster map of "Molecular Biology" technological field.

Figure 2 .

 2 Figure 2. Second period cluster map of "Molecular Biology" technological field.

  STAGE 2: Technology specific terms are established in the technology field and can occur together with established methods, materials, tools, and applications from other technology fields. They begin to diffuse to other technology fields.

-STAGE 3: This is the stage with the highest maturity. Technology specific terms are greatly established. Natural science effects, methods, tools, materials, and applications are highly accepted. They show a broad diffusion in other research or technology fields.

Table 1 .

 1 Results of the keyword selection procedure for the field "Molecular Biology".By applying the bibliometric indicators of Table1for identifying the diffusion stage and by the know-how of the experts for assigning the terms to the 4 semantic categories we get the results presented in Table2.

				Number of terms	Comment
			Field		6,787
		Home technology terms Unusual terms Established terms	1,843
		Term frequency > 2 (Not frequent technology (Frequent technology	248
	Categories	specific terms)	RTF-ITechF > 0.003 specific terms)	124 Cross section terms
	Natural science		Gini Index  0.97	56	cross section terms
	effect	Multiple diffraction	Gini Index > 0.97 Molecular force constants Brownian motion 90
		Angular distribution	RTF > 2% Isomerism	23 Photosynthesis technology specific established terms
		Helix coil transition	RTF  2% Microgravity	67 Circular dichroism technology specific unusual terms
		Nuclear relaxation		Solvation	Intermolecular forces
		…		…	…
		Matrix assisted laser		Two-dimensional	Molecular dynamics
	Method	desorption ionization	spectroscopy	simulation
		Moller Plesset partition	Heavy atom method	XRD
				Liquid-liquid
		Optical tweezer		transformation	NMR spectroscopy
		X-ray topography			Lennard Jones model
		…		…	…
	Material	Tissues		Biological macromolecule Polymers
		Pinene			Essential oils
		Leaf oil			Biological compound
		Lyotropic liquid crystals		Animal cells
		…		…	…
		Instrument for chemical		Molecular
	Application	analysis		Structure resolution	configurations
		Multilayer perceptrons	Viruses	Polymer solutions
					Biomolecular effects
		Diagnostic techniques		of radiation
		Drug delivery systems		Oxygen sensors
		…		…	…

Table 2 .

 2 Keywords sorted by diffusion stage and by semantic category.

Table 3 .

 3 Characteristics of the 2 sets of clusters.

Table 4 .

 4 Distribution of "home Technology" terms by diffusion stage, in function of their presence/absence in the clusters of each period.

		Exclusively in P1	Exclusively in P2	Present in clusters	Absence from
		clusters	clusters	of P1 & P2	clusters of P1 & P2
	Unusual	40%	56%	9%	57%
	Established	13%	12%	45%	6%
	Cross section	47%	32%	45%	37%
	Total nb. of terms	15	57	22	30

Table 5 .

 5 Distribution of "home Technology" terms by semantic category and by diffusion stage for each period.

		Natural science	Method	Material	Application
				First period
	Unusual	2	2	2	2
	Established	5	4	2	1
	Cross section	9	4	7	3
			Second period
	Unusual	15	10	11	1
	Established	13	4	2	2
	Cross section	10	6	13	6

Table 6 .

 6 Diffusion model and diachronic cluster analysis linkage.

Acknowledgment: This work has been realized thanks to the European project N° 15615 (NEST) -Sixth Research and Development Framework Plan of the European Union, during 2005-2007. The project acronym is PromTech, and the project full title is "Identification and Assessment of Promising and Emerging Technological Fields in Europe".


Edgar Schiebel
email: edgar.schiebel@arcs.ac.at2ivana.roche@inist.fr

Marianne Hörlesberger
email: marianne.hoerlesberger@arcs.ac.at

Ivana Roche

Claire François
email: claire.francois@inist.fr

Dominique Besagni
email: dominique.besagni@inist.fr

An advanced diffusion model to identify emergent research issues: the case of optoelectronic devices

Scientific progress in technology oriented research fields is made by incremental or fundamental inventions concerning natural science effects, materials, methods, tools and applications. Therefore our approach focuses on research activities of such technological elements on the basis of keywords in published articles. In this paper we show how emerging topics in the field of optoelectronic devices based on scientific literature data from the PASCAL-database can be identified. We use Results from PROMTECH project, whose principal objective was to produce a methodology allowing the identification of promising emerging technologies. In this project, the study of the intersection of Applied Sciences as well as Life (Biological & Medical) Sciences domains and Physics with bibliometric methods produced 45 candidate technological fields and the validation by expert panels led to a final selection of ten most promising ones. These 45 technologies were used as reference fields. In order to detect the emerging research, we combine two methodological approaches. The first one introduces a new modelling of field terminology evolution based on bibliometric indicators: the diffusion model and the second one is a diachronic cluster analysis. With the diffusion model we identified single keywords that represent a high dynamic of the mentioned technology elements. The cluster analysis was used to recombine articles, where the identified keywords were used to technological topics in the field of optoelectronic devices. This methodology allows us to answer the following questions: Which technological aspects within our considered field can be detected? Which of them are already established and which of them are new? How are the topics linked to each other?

Introduction

Early recognition of new and alternative products and production processes is a strategic necessity contributing to timely assessment and decision-making. Emerging technologies are essential to advances in research, industry and society. But the detection of emerging technologies remains quite a problem. Studies in a large range of domains in society, econmomy, ecology, politics, etc. were performed (Kajikawa Y., Yoshikawa J., Takeda Y., analysed the spread or migration of an issue from pure science over applied science to public awareness. They counted the occurrence of a keyword over time in different online databases that cover publications with the mentioned different categories. Additionally they suggested a procedure to identify growing issues in terms of time gradients of the number of published articles where single keywords occur. The selection tree introduced by Armstrong & Green (2007) gives an additional picture of the landscape of the forecasting methods that could be employed to detect these emerging technologies. It illustrates the dichotomy between judgmental and quantitative forecasting methods and shows the great diversity of existing approach like the Delphi or the Nominal Group Technique ones. Forecasts are obtained in a structured way from two or more experts. Other methods combine expert domain knowledge and statistical techniques and allow the identification of causal forces acting on trends. When the available collected data is enough to apply quantitative methods, an important question remains. It concerns the type of the data employed to forecasting: scientific literature databases and patent databases are the most often used sources to detect new themes by bibliometric analysis. They apply statistical techniques comparatively simple as growth curves analysis or more sophisticated as clustering or network analysis [START_REF] Noyons | Science maps within a science policy context[END_REF][START_REF] Daim | Forecasting emerging technologies: Use of bibliometrics and patent analysis[END_REF][START_REF] Mogoutov | Data search strategy for science and technology emergence: A scalable and evolutionary query for nanotechnology tracking[END_REF][START_REF] Kajikawa | Tracking emerging technologies in energy research: Toward a roadmap for sustainable energy[END_REF]. The addition of a third type of data coming from state-funded research grants is very interesting and allows, if the three sources of information are analyzed as a whole and not as separate entities, to gain an understanding of the triple-helix interfaces between university, industry and government [START_REF] Salerno | The role of funded projects content analysis in early stage disciplines exploration: The case of nanotechnology[END_REF][START_REF] Mogoutov | Biomedical innovation at the laboratory, clinical and commercial interface: A new method for ma pping research projects, publications and patents in the field of microarrays[END_REF]. As a technology consists of a bunch of physical effects, materials, methods and application we used a different approach. We analysed the growing number of publications in classes of the PASCAL database. These classes were considered to be a proxy for technologies and then after having identified the most growing classes, we examined the occurrence of all keywords of all articles in each class. The framework for this study was the PROMTECH project. It was financed by the European Commission. The principal objective in the project was to produce a methodology for the identification of promising emerging technologies. The basic hypothesis was derived on the observation that many new emerging technologies of the last decades have drawn on the technical application of physical knowledge. The growing relevance of Physics for technology can be explained by the increased complexity of today's technology. Indeed, there are many examples showing that advanced technology in applied sciences as well as in life sciences is increasingly linked to recent outcomes of research in Physics. In this project, the study of the intersection of applied sciences as well as life (biological & medical) sciences domains and physics with bibliometric methods, validated by expert panels, led to a final selection of ten most promising technological fields. The core idea of this contribution was to develop a methodology for detecting of new topics inside of broader technological fields. We choose the field "optoelectronic devices", because it is one of the most promising technology fields of the last decade. Light emitting diodes gain more applications in cars and housing lighting, OLED displays are introduced in electronic devices and consumer electronics. Optimisation of the luminous power and the tuning of the light spectrum are wwll known important research topics. In order to identify promising emerging research topics and their diffusion stage, we used two approaches and two datasets in two successive periods:

the "diffusion model", that is an approach that uses a bibliometric filter that distributes keywords in different diffusion stages in order to model the field terminology evolution [START_REF] Schiebel | About the identification of technology specific keywords in emerging technologies: The case of "Magnetoelectronics[END_REF] and the "diachronic cluster analysis", using the software-tool Stanalyst® [START_REF] Polanco | Stanalyst®: An integrated environment for clustering and mapping analysis on science and technology[END_REF], to identify research topics by recombining keywords by clustering papers.

Firstly we refer to the data acquisition. Secondly the applied methodologies are illustrated.

Thirdly the results of the applied methodologies to the field "optoelectronic devices" are presented. Finally we discuss the two approaches, summarize and conclude our discussion.

Data acquisition

The data framework is a sample recorded from the PASCAL database that was specifically adapted to the purpose of our approach. The PASCAL database provides a broad multidisciplinary coverage of scientific publications and contains, nowadays, about 17 million bibliographic records that are derived from the analysis of the scientific and technical international literature published predominantly in journals and conference proceedings.

In this work, we limited our study to a corpus of publications coming from journals only. The queries operated in this work were exclusively based on the classification categories given in the database and assigned to the individual publications, either manually by scientific experts or automatically based on a content analysis. These classification categories belong to the PASCAL classification scheme that is a taxonomy of every field and subfield of all the disciplines covered in the database. Each individual publication also benefits from a manual or automatic indexing by keywords. After a verification step, done by a scientific expert, that terminology can be employed in our analysis.

Methodology

In this section, we introduce the two methods we employed to follow the evolution of the technological field. In previous work, we presented their detailed description [START_REF] Roche | Identification and characterisation of technological topics in the field of Molecular Biology[END_REF].

Firstly, the diffusion model is used to evaluate the term status in the considered technological field, comparing them with theirs status in a reference set the other 45 technological fields identified in PROMTECH.

Secondly, the diachronic cluster analysis recombines the dynamic elements of the technological field by a clustering approach allowing to organize articles in sub-topics and to analyse links between them.

Diffusion model

The diffusion model is based on the assumption that new findings are published in articles in a research field. Keywords that describe the research discoveries occur in the first stage in an unusual manner. In the second stage the research intensifies and more established keywords are used in the home field and in a third stage the research results like natural science effects, methodologies, test arrangements, materials and applications diffuse to other research fields or from basic research to applied research like Lancaster F.W. , [START_REF] Lancaster | Bibliometric Techniques Applied to Issues Management: A Case Study[END_REF] mentioned in their publication.

In an earlier approach a bibliometric filter was used to assign the keywords of articles in three diffusion stages for the period from 1996 to 2003, [START_REF] Schiebel | About the identification of technology specific keywords in emerging technologies: The case of "Magnetoelectronics[END_REF] to get an idea about diffusing research topics during the whole period. In this paper a time dependency is introduced. The span of time is divided into two periods. Table 1 shows this new perspective.

Following the diffusion approach it can be assumed that keywords migrate from earlier stages in the first period to later stages in the second period like the following: 1. terms from stage 1 in the first period stay in this stage or migrate in the second period to stage 2 or stage 3;

2. terms from stage 2 in the first period stay in stage 2 or migrate to stage 3; 3. terms from stage 3 stay in stage 3. We defined pathways as follows: Pathway 1: the terms remain in stage 1 in both periods Pathway 2: terms move from stage 1 in first period to stage 2 in second period Pathway 3: terms move from stage 1 in first period to stage 3 in second period Pathway 4: the terms remain in stage 2 in both periods Pathway 5: terms move from stage 2 in first period to stage 3 in second period Pathway 6: the terms remain in stage three in both periods

The terms in the different stages were identified and the migration of terms between different stages over the two time periods where traced. This procedure has been performed for articles in the field "optoelectronic devices". Terms were extracted from the descriptor field. We constructed a diffusion matrix for each period by assigning the terms to the three stages. We used the bibliometric filter that was published in [START_REF] Schiebel | About the identification of technology specific keywords in emerging technologies: The case of "Magnetoelectronics[END_REF]. Table 2 shows the filter and the values for optoelectronic devices for the first period. As the technology filed is a part of the 45 reference technology fields the field name "optoelectronic devices" is the first selection criterion. Each condition reduced the number of keywords or classified them to be assigned to a diffusion stage.

Diachronic cluster analysis

The diachronic cluster analysis is based on the study of the evolution of cluster maps and contents obtained by a clustering method and considering data sets related to two, or more, successive time periods. In this work, after splitting the corpus in the two time periods I and II we applied a clustering algorithm on the corpus of each period, in which documents are represented by the keywords existing in the bibliographic references. The clustering tool we used [START_REF] Lelu | Hypertext paradigm in the field of information retrieval: A neural approach[END_REF][START_REF] Lelu | Modèles neuronaux pour l'analyse de données documentaires et textuelles[END_REF] applies a non-hierarchical, non-supervised clustering algorithm, the axial K-means method that allows producing clusters presenting particular characteristics:

-they can overlap because the clustering method allows a document or a key-word to belong to more than one cluster, -documents and key-words as the constituting elements of a cluster, are ranked by decreasing similarity with the cluster ideal type. Each cluster is homogeneous subset of the information contained in the analyzed corpus, corresponding to a topic. After the clustering, we represent graphically its results employing two methods: a principal component analysis and a connected component analysis. The first one produces a global map of the topics. The topics that are very far apart represent clusters very different with respect to the key-words defining them. The connected component analysis is then employed to show the relationships that exist between the clusters. It is a method based on the graph theory and defining the connected components representing the relative proximity between clusters. The distance between two clusters is defined as the cosine of the angle between the two axes representing the clusters. A lower and an upper threshold are defined as respectively the minimum and the maximum value of the cosine between the clusters. Nine intermediate levels are defined in that interval. We consider that, at a given level, two clusters are connected if the value of the cosine associated to them is within the range of values of this level. Finally, we analysed the evolution between the two cluster sets and the two maps by examining the vocabulary related to the clusters of each period using a comparison matrix [START_REF] Roche | Identification and characterisation of technological topics in the field of Molecular Biology[END_REF].

Results

The PASCAL database query delivered 3,871 articles in the field "optoelectronic devices". In the first period we had 1,797 documents and 2,345 different keywords among which 1,527 (65%) appear less than 3 times. The second period had 2,074 articles and 2,738 terms among which 1,762 (64%) occur less than 3 times.

Diffusion model

In the first period corpus, we had 2,345 different keywords. Their categorization into diffusion stages was performed by using the bibliometric filter presented in table 2. The TFIDFTech is a modified TFIDF measure. We take the relative frequency of articles as the local weight TF1 . The global weight IFD2 is defined by the inverse number of technologies.

We used a threshold that selected 250 to 300 keywords. The first third of these keywords with a Gini3 index smaller than 0,95 consisted of terms that occur in many of the 45 technologies therefore these were defined as keywords that were well established and broadly diffused to other technologies. The remaining two thirds of keywords were divided in two halves: keywords with a low relative frequency were seldom used and the remaining words occurred more often but mostly in the respective technology field.

It can be seen that the filter has led to three nearly equal numbers of keywords for each stage. The reason is that the thresholds have been chosen in a pragmatic way, because it can not be said where the exact borders between the stages of the diffusion model are. It is a fuzzy approach: it was assumed that from N keywords a third is in each stage. With this pragmatic choice it is not really visible if a diffusion of keywords really takes place. We just span a space to be able to observe migration. An answer can be given if we can identify individual keywords that change the diffusion stages from period one to period two. In the second period corpus, that contained articles of the period 2000 to 2003, we found 2,738 keywords that were categorized into diffusion stages by using the bibliometric filter of Table 4. The growth of articles from period I to period II was about 16 percent. Differences to the first period in the thresholds of the filter indicators were not really remarkable. In general, it can be said that the research field, "optoelectronic devices", consisted of several research topics in the context of sourcing, detecting and controlling light. There are some very special research topics like "Superconductor-insulator-superconductor mixers", so called SISmixers, that are used in space technology for the transforming of frequencies in space observation on one side of the spectrum of specialised research and LCD-displays, CCD devices for digital cameras or light emitting diodes on the other side of the spectrum of more applied research, see table 5.

Pathways of keywords

The dynamics in the research could be demonstrated by the pathways of the keywords.

Pathway 1 Pathway 1 reflected the research topics that stayed in the stage 1, as it is shown in table 6.

There are only 3 to 9 publications per keyword in the second period and the number stayed nearly stabile over the two periods. It represents research about topics like SIS-Mixers, where only a few people work worldwide, or devices like optical fiber couplers, that were used and mentioned but were not entity of intensive research.

It can be argued that there is a high dynamic in stage one: many keywords with a low frequency of articles show a high fluctuation: they appear in a period and disappear in a second one. This could be expected because this is a stage where keywords represent new research results of which many are not interesting in later periods and only a few survive in this incubator stage. Those who survive and stay in stage 1 represent research work that persist but does not excite more researchers. Pathway 2 Pathway 2 in table 7 indicates growing research dynamics because terms that occurred only seldom in the first period moved to the phase of established terms in the second period. One example were cadmium telluride, what was a semiconductor material used for thin film solar cells, so called "advanced thin film" modules. Its properties fitted perfectly to the solar spectrum. There was a high potential for high-efficiency solar cell modules with a low-cost manufacturing processes. The "quantum confined Stark effect" is used to shift the light spectrum in light emitting diodes, what is important for the optical tuning. Light emitting diodes have a very high application potential in daily life and technical applications therefore together with high potential in basic research there is a growing research interest. Keywords of this pathway survived the incubator phase and started to grow within the same technology field: they attracted other research groups. Who found the findings usefull for their own research or started new research. Keywords with a high number of articles like binary compound (23), p n heterojunctions ( 16) and Liquid Phase Epitaxy -LPE ( 16) have made their career. We found only two terms that moved from stage 1 in the first period to stage 3 in the second period. Jumping ahead from the incubator stage to the use in many other fields is a big step an. Research findings with this career should have overcome bottlenecks that were relevant in several other technological fields. The first keyword "optical tuning of light emission diodes" was very essential for LEDs to became bright enough for practical use in daily life or for special technical usage. The composition of the proper emitting spectrum was an important matter of research. The traditional semiconductor technology needed crystals but in blue LED technology the needed GaN had bad crystal properties therefore thin layers which ternary compounds were developed and are intensively investigated. This breakthrough can be tracked by the migration of "ternary compound" from stage 1 in period I to stage 3 in period II within only some years, see table 8. Vapour Deposition what is a technique for depositing thin layers of atoms onto a semiconductor wafer. A heavily growing field was the application of organic semiconductors in optoelectronics. Organic semiconductors are applied in optoelectronic devices such as organic light-emitting diodes (OLED), organic solar cells and organic field effect transistors (OFET), they can be easily produced and optoeletronic polymers allowed the construction of flexible displays. Terms in this pathway represent ongoing core research in the field of optoelectronic devices, that is stable over time and has the highest number of key terms coming from this home technology.

Pathway 5

In Pathway 5, see table 9 we found research topics that were established in the first period and migrated to applications in many other technology fields in the second period. One example is the already mentioned organic semiconductors and the thin film III-V semiconductors. Optical arrays are used in "storage and reproduction of information", "global pollution and environment", "radiotheraphy" etc. "semiconductor thin films" are important in "storage and reproduction of information" and "transistor technology". Research topics from this pathway gain importance in other technologies. They can be crucial for the development of new applications or new properties of other technology fields.

Pathway 6

Pathway 6 comprises broad applications of optoelectronic devises and research topics like CCD image sensors used in digital cameras but also natural science effects like the exciton. An exciton is a bound state of an electron in an isolator or a semiconductor and an imaginary particle called an electron hole. Of course, all optoelectronic devices like LED, photodetectors, infrared detectors, photodiodes, semiconductor lasers and used materials like cadmium compounds, Gallium and Indium Compounds are part of the cross section term in stage 3.

Diachronic cluster analysis

The diachronic cluster analysis applied on the two periods I and II, allows us to determine which topics of the second period have roots in the first one and which topics are new in the second period. The clustering algorithm produced 17 clusters involving 1,599 documents and 348 keywords in period I (P1), and 20 clusters relating 2,073 documents and 456 key-words in period II (P2). Let us notice that the very important reduction of the number of key-words associated to the clusters in both periods, around 50% with respect to the initial number of terms obtained directly from the query results, does not entail a significant loss of information at the level of the documents. Indeed we had 89% and 100% of the initial information corpora in the clusters of respectively P1 and P2.

Analysing the comparison matrix we can singularize for the second period:

-nine homonymous clusters, but only four of them seem to be stable, -two clusters with new titles but presenting characteristics of stability, -four clusters with low marginal values (clusters with a low inheritance from clusters of P1: are they new clusters?), -six clusters with high marginal values (clusters with a strong inheritance from different clusters of P1). Finally, we have detected a particular behaviour of two P1 clusters: they seem to supply at the same time and in the same way five clusters of the second period.

Having expressed these hypotheses we asked the scientific expert to validate them accordingly to both, the content of clusters, and the cluster organization in the maps. Figures 1 and2 show the cluster maps of respectively the first and the second period. In both maps, each dot corresponds to a cluster and each line gives the connexion level between pairs of clusters. Note that we considered here only the 5 highest levels (out of 10) corresponding to the strongest connexions between clusters. In Stanalyst®, the level of connexions is codecoloured. Since these colours do not come out in print, we mentioned the level next to the connexion. The analysis of the expert seems to confirm the relative stability of five P2 clusters, each of them coming exclusively from an only P1 cluster that can be its homonymous or not. They are: "Electroluminescent devices", "Elemental semiconductors", "Infrared imaging", "Bolometers" and "Photodetector". The keywords of P2 cluster "Light emitting diodes" come predominantly from its homonymous cluster in the first period, but also from others P1 clusters: "Laser diodes" and "Electroluminescent devices". The P1 clusters "Electroluminescent devices" and "Light emitting diodes" seem to "feed" their homonymous P2 clusters but also, and with important rates, the P2 clusters "Light emitting diodes", "Organic semiconductors", "Organic light emitting diodes" and "Electroluminescence". The analysis of the cluster maps obtained for the two periods confirms this observation. Indeed, the pair of clusters "Electroluminescent devices" and "Light emitting diodes" shows a notable evolution, coming from a marginal position in the P1 cluster map (see dotted ellipse in Figure 1) to a central role in the P2 cluster network (notice dotted ellipse in Figure 2) where they find themselves linked to the clusters which they "fed". In this network, the expert points out clusters "Organic light emitting diodes" and "Organic semiconductors" that deal with new themes coming from the Organic Electronics domain. The grouped position of these clusters on the cluster map is quite logic as well as their location in the central network that confirms the enlarging importance of these new technologies in the second period.

If expert analysis leads to assert that, from a macroscopic point of view, the considered field seems to be relatively stable between the two periods because it is dealing with optoelectronic devices based on mineral or organic semiconductors, a deepest look at the application level allows the expert to observe:

-in the P1 period, the applications dealing with photodetectors (imagery, measuring instruments, sensors,…) are dominant and the electroluminescent diodes are completely isolated; -in the P2 period, the electroluminescent diodes are dominant and are located in a central position in the cluster network. The analysis of the cluster contents consent the expert to detect, at the material level:

-the growing relevance of research dealing with organic semiconductors. This interest is justified by the flexibility of these materials allowing the production of flexible and miniaturized electronic devices (see clusters "Organic semiconductors", "Organic light emitting diodes" in P2); -the rise of nanotechnologies in electronics in order to achieve a greater circuit miniaturization (internal evolution of "Photodetectors" and "Gallium arsenides" clusters); -the renewed interest in Si-Ge semiconductors alloys and type III-V semiconductors caused by the research expansion on electroluminescent diodes already observed at the applications level (internal evolution of "Elemental semiconductors" cluster).

The analysis of the terminology associated to the P2 cluster "Contact resistance" gives an additional indication about the important role of electroluminescent diodes. Indeed, in this P2 cluster, the expert observes a massive presence of some keywords weakly represented in the first period (contact resistance, ohmic contact, solid solutions and semiconductor-metal boundaries). These notions are not new but their presence in the second period shows and confirms the growing number of optoelectronic device applications on electroluminescent diodes. Indeed, the contact quality between the diode and the electrodes is a very determinant question related to the diode performance. Finally, the analysis of the terminology associated to the cluster "Infrared imaging" present in both periods, points out a very interesting trend. This cluster deals with the application of optoelectronics to the formation of images using infrared radiation, and to the processing of the acquired images for the purposes of measurements, identification or tracking. In P1, the infrared imaging technology is associated essentially to military applications and, in P2, the expert observes, with significant frequencies, the presence of keywords related to Biology and remote sensing. This singular observation allows the expert to move forward a hypothesis of either a technological transfer process from military usages to civil ones or, at least, an extension of the infrared imaging application field.

Convergences between the two analyses

The application of the diffusion model allows to select the most important terms of the field vocabulary: 288 keywords out of 2,345 in the first period and 273 keywords out of 2,738 in the second one. These selected terms are indexing the almost totality of the corpora: 1,788 documents out of 1,797 in period P1 and 2,069 documents out of 2,074 in period P2. In this section, we compare, for the period P2, the cluster map obtained from the whole corpus (WC), presented in Figure 2, and the one we got by employing only the terms selected by the diffusion model (DM), presented in Figure 3. First of all, we observed that the clustering results obtained with these selected terms are very close to the results produced from the whole corpus, although the clusters are more generic with a larger overlap between them. Then we particularly focused on the analysis, for each cluster, of the evolution of their specificity degree. The specificity degree is defined as the ratio between the number of documents present exclusively in the cluster and the total number of documents contributing to the cluster. A higher value of specificity degree means that the cluster has an isolated position very weakly linked with the rest of the cluster network. Mainly we observed three types of behaviour of the evolution of the cluster specificity degree: a) clusters maintaining it, b) clusters where it declines moderately, and c) clusters where it decreases strongly. The first type corresponds to clusters having, in DM and WC maps, an isolated position. An interesting example is the cluster "Infrared imaging" that basically does not change its role in the cluster network.

In the second type, we are going to find clusters that in spite of a loss of specificity did not fundamentally change their position in the cluster network. It is the case of the clusters "Gallium arsenides", "Photodetectors" or "Electroluminescent devices" that indeed have relatively preserved their position in the cluster network.

The third type consists on clusters either presenting an intensification of their links to the rest of the network or completely changing their status from an isolated position to a linked one.

As a notable example we have the cluster "Instrumentation" that presents the same number of documents in DM and WC but that shares them with a great number of other clusters in DM and wins in connectivity. But we have also the cluster "Bolometers" which remains isolated while receiving, in DM, a very important number of documents from other clusters more strongly connected. Another interesting case is the cluster "Organic light emitting diodes" that is the only cluster with an increase of the specificity degree. This could explain its location in the cluster network of DM that shows this cluster slightly less connected.

From an other point of view, the analysis of the results obtained from the two methodological approaches operated in this work reveals some quite interesting convergence points. Let us consider the terms of the six pathways presented in table 1. According to their diffusion stage, the terms of the first period do not diffuse in a homogeneous way to the second one. Only 26% of the unusual terms (26 / 100) occur in the pathways 1, 2 and 3. 54% of the crosssection terms (50 / 92) stay in their stage (pathway 6), and 60% of the established terms (58 / 96) occur in the pathways 4 and 5.

The distribution of the terms present in the six pathways according to their presence in the clusters of both periods is given in Table 10. For example, in the first cell, the fraction 2/13 means that 2 terms among the 13 of the pathway 1 (table 6) are present in at least one cluster of each period. The inheritances between the same diffusion stage are the strongest specially for established and cross-section ones.

In order to analyse the role of these terms in the thematic structuring of the technological field we can also analyse the projection of the terms of the pathways on the cluster maps of the two periods. Concerning the terms of pathways 1 and 2 we can observe the stability of their location in isolated clusters in both periods. These unusual terms correspond to fundamental new concepts not frequent enough to participate to a cluster constitution. However we can observe that one of the two terms of pathway 1 in both periods (superconducting microbridges) seems to migrate from an isolated cluster ("Superconducting microwave devices") in period P1 to a cluster ("Bolometers") located in a quite important sub-network of clusters in period P2. Conversely, the term of pathway 3 in both periods (ternary compounds) declines, going from a very strongly positioned cluster ("III-V semiconductors") in period P1 to an isolated cluster ("Ternary compounds"). The terms of the pathways 4 and 6 appear, in the two period maps, dispersed in both isolated and linked clusters with nevertheless, in the second period, a low concentration in the isolated clusters.

The spread of this great number of established and cross-section terms all over the clusters is not really surprising. Indeed, this characteristic shows that these terms are representative of all the clusters. Finally, the terms of the pathway 5 are, in both periods, concentrated in the principal cluster network. A deeper analysis is necessary to explain this observation.

Conclusions

The approach of alternate utilisation of different bibliometric and/or informetric methods and scientific expertise helps validating and completing the results obtained at each step of the work as well as to get the experts personal input on the matter at hand.

For the diffusion model it can be said that the bibliometric filter to assign keywords to stages and the splitting of the article corpus in two periods opens a view insight of the emergence of research topics in a technology. At a first glance it is a formal taxonomy to disassemble what is called a technology in its "atoms" of research and traces the breakthroughs if they happen. However, it is not a prognosis neither a prediction but terms in the unusual phase show potentials of future developments in a technology. To discuss well-founded emergences and research potentials it is important to mirror the results with experts who are part of the science producing system in the reflected technology.

The diachronic cluster approach allows to have a global view of the field landscape at two successive time periods. The analysis of the cluster contents and their relative position on the cluster maps supplies indications about their similarity with respect to their respective associated keywords. The observation of cluster maps allows to detect exceptional topics and interesting topic sub-sets. Let us point out nevertheless that the expert validation remains, at each step, absolutely necessary to validate and position the analysis results in the field context by giving them a scientific foundation.

The results coming from diffusion model lead to select the most important vocabulary without disturbing the cluster maps organization, and produce a new interpretation of diachronic clustering results by introducing a new term categorization that can be projected on the clusters.

very actively to the different project steps by bringing us their scientific and documentary expertises.

Pathway 3

 3 Terms of Pathway 3 are most important to observe breakthroughs in research and technological development.

Figure 1 .

 1 Figure 1. First period cluster map of "Optoelectronic devices" technological field.

Figure 2 .

 2 Figure 2. Second period cluster map of "Optoelectronic devices" technological field.

Figure 3 .

 3 Figure 3. Second period cluster map of "Optoelectronic devices" technological field obtained from terms selected by diffusion model.

Table 1 . Taxonomy of the migration pathways of research topics (descriptor terms) through different stages of the diffusion model in two time periods

 1

				Diffusion Model
		Stage 1		Stage 2		Stage 3
	Periods	Unusual Terms		Established Terms		Cross Section Terms
	1996 to 1999 (period I)	1	3	4	5	6
		2
	2000 to 2003 (period II)

Table 2 . Filter to identify stages in the period 1996 to 1999 Indicator Number of Terms Comment

 2

	Technology Field: Optoelectronic Devices	2,345
	Minimum Number of Articles: 3	817
	TFIDFTech > 0,0008	289
	High Diffusion: small Gini < 0,95	92	Stage 3 Terms
	Low Diffusion: large Gini > 0,95	197
	Relative Frequency > 0,0035	96	Stage 2 Terms
	Relative Frequency < 0,0035	101	Stage 1 Terms

Table 3 . Keywords by diffusion stage in period 1996 to 1999 (Selection)

 3

	Stage 1: Unusual Terms	Stage 2: Established Terms	Stage 3: Cross Section Terms
	Acousto-optical devices	Aerospace instrumentation	Absorption coefficients
	Aluminium Nitrides	Aluminium arsenides	Aluminium
	Automatic optical inspection	Aluminium compounds	Arrays
	Binary compound	Amorphous semiconductors	Avalanche breakdown
	Cadmium tellurides	Astronomical instruments	Bolometers
	Cerium additions	Astronomical telescopes	Cadmium compounds
	Chirp modulation	Avalanche photodiodes	Carrier lifetime
	Clutter	Brightness	Carrier mobility
	Colour displays	Carrier density	CCD image sensors
	Commerce	Chemical interdiffusion	Charge injection
	Conjugated polymer	Complete computer programs	Computerized simulation
	Coordinates	Conducting polymers	Doped materials
	…	…	…

Table 4 . Filter to identify stages in the period 2000 to 2003 Indicator Number of Terms Comment

 4

	Technology Field: Optoelectronic Devices	2,738
	Minimum Number of Articles: 3	976
	TFIDFTech > 0,0007	273
	High Diffusion: small Gini < 0,94	95	Stage 3 terms
	Low Diffusion: large Gini > 0,94	178
	Relative Frequency > 0,0045	82	Stage 2 terms
	Relative Frequency < 0,0045	96	Stage 1 terms

Table 5 . Keywords by diffusion stage in period 2000 to 2003 (Selection) Stage 1: Unusual Terms Stage 2: Established Terms Stage 3: Cross Section Terms

 5

	Aluminium antimonides	Aerospace instrumentation	Aluminium compounds
	Aluminium complexes	Aluminium arsenides	Amorphous semiconductors

Table 6 . Terms of pathway 1

 6

	Indium antimonides	Porous semiconductors
	Indium nitrides	Semiconductor laser
	Microcavity	Spectral response
	Narrow band gap semiconductors	Submillimeter wave antennas
	Nonradiative transitions	Superconducting microbridges
	Optical fiber couplers	Superconductor-insulator-superconductor mixers
	Photoresistors

Table 7 . Terms of pathway 2 with number of publications in period 2

 7

	Binary compound, 23	p n heterojunctions, 16
	Cadmium tellurides, 11	Photodiode, 14
	Far infrared radiation, 12	Quantum confined Stark effect, 10
	Injection laser, 11	Semiconductor-metal boundaries, 19
	LPE, 16	Ultraviolet photoelectron spectra, 16
	Mercury tellurides, 10

Table 8 . Terms of pathway 3 with number of publications in period 2

 8

	Optical tuning, 14	Ternary compound, 33
	Pathway 4
	Pathway 4 reflected established research in both time periods: Gallium arsenides and Gallium
	nitrides as basic materials for advanced thin film LED, MOCVD -Metal-Organic Chemical

Table 9 . Terms of pathway 4

 9

	Aerospace instrumentation	Gallium nitrides
	Aluminium arsenides	Ge-Si alloys
	Avalanche photodiodes	Indium arsenides
	Carrier density	Interface states
	Dark conductivity	LED displays
	Dislocation density	Light emitting devices
	Electroabsorption	Line intensity

Table 10 . Terms of pathway 5 with number of publications in period 2

 10

	Aluminium compounds 193	III-V semiconductors, 449
	Amorphous semiconductors, 21	II-VI semiconductors, 91
	Brightness, 64	Optical arrays, 33
	Chemical interdiffusion, 16	Radiation quenching, 13
	Conducting polymers, 82	Schottky barriers, 22
	Deep energy levels, 10	Semiconductor thin films, 54
	Elemental semiconductors, 92	Tunnel effect, 39

Table 11 . Distribution of the terms of the six pathways according to their presence in the clusters of both periods

 11

		Period 2
	Unusual	Established	Cross-section

The relative frequency TF is measured by nij/nj with i be the index for a key word, j be the index for the technology field, nij be the number of publications a key word i occurs in technology j and nj the total number of publications in technology fieldj. where the keyword occurs at least in one article.

The global weight IDF is measured by 1/ti, ti be the number of technologies where the key word i occurs at least in one publication.

The Gini Index is used as a measure for the inequality distribution of occurrence of a term in the technology fields. A high value indicates a low diffusion.

Acknowledgments: This work was carried out thanks to a European Union funding: Project N° 15615 (NEST) -6 th Research and Development Framework Plan. The project acronym is PROMTECH, and the project full title is "Identification and Assessment of Promising and Emerging Technological Fields in Europe". The Consortium was composed by the Austrian Research Centers GmbH -ARC (Vienna, Austria), the Fraunhofer Institut für Systemtechnik und Innovationsforschung (Karlsruhe, Germany) and the Institut de l'Information Scientifique et Technique - INIST-CNRS (Nancy, France). We would also to warmly thank our colleagues from ARC and INIST-CNRS, particularly Mrs. Nathalie Vedovotto, that took part


Jullien Agnès

Larrard François

An overview of recycling in road infrastructure in France from the OFRIR project

Keywords:

This paper presents an overview of recycling in road infrastructure based on the OFRIR project, OFRIR being the acronym of "Observatoire Français du Recyclage dans les Infrastructures Routières". The OFRIR project has been based on a literature review of recycling in road framework in France since 1980, considering both environmental, geotechnical road building and service requirements. The review is presented in an internet site (http://ofrir.lcpc.fr, mail : ofrir@lcpc.fr) for road national actors aiming at proposing classified, summarized and validated information in order to help recycling decisions. Besides, part of the delivered information is attached to a geographical presentation in order to highlight local resources and practice.

Besides, LCPC and French technical network of road public ministry, BRGM (French Geological and mining organism) and INERIS (French Institute for risk assessment) also significantly contributed to this project supported by METLTM

Introduction

LCPC (Central Laboratory for Roads and Bridges, a French public research institution) mainly used to deal with the construction of new infrastructure involving materials and structures. More recently attention has been focused on better management of the existing stock of infrastructures. The aim is to maintain and to adapt the existing structures to the evolution of requirements dealing with environment and safety. Therefore, it has obviously become important to optimise maintenance policies, to control natural risks and environmental impacts of infrastructures over their whole life cycle. Besides, recycling has to increase, especially in road pavement because of increasing care about environment and in order to meet the 2002 French legislation, which greatly limits landfill. Hence, only final waste products that is, products which cannot be recycled or reused with the available technology, will be allowed in waste disposals. Therefore a national project began in 2002, aiming at developing a database about materials that are recyclable in road infrastructures. Hence, in order to improve recycling development, it is relevant to bring reliable information to those -owners, site engineers, construction companies and material suppliers -who contribute to building. Environmental questions, usually considered as the most critical ones, are those for which transparency is the most necessary.

As for recycling in roads, nobody has at once the full knowledge in the field of geotechnic, pavement technology, environment, economy, and legal issues necessary for an optimal use of material resources. It is only by gathering the various specialists, with the view of favouring exchange and discussions that relevant collective work can be carried out and sustainable long term policies can emerge. Besides, road always imposed real size experiments, using road sections, testing materials or new techniques. Unfortunately, road section monitoring with time is not always possible for many years, especially if local actors change. Hence, when the topic concerns materials environmental assessment and associated impacts, which may arise either at medium or long term, such a turnover is very regrettable.

The OFRIR project ("Observatoire Français du Recyclage dans les Infrastructures Routières") started on the above considerations, after about one year before getting ready. It is an internet site (http://ofrir.lcpc.fr) for road national actors aiming at proposing classified, summarized and validated information in order to help recycling decisions, while noticing the different obstacles, especially the geotechnical and environmental ones. This paper thus presents some elements about (i) the general data of the site, (ii) the data gathered to present a state of the art for fifteen products over eighteen, and (iii) the data associated with local practice either concerning products sources at the local scale or products used in road infrastructure from in-situ experiments or road works.

Presentation of the project and data implementation principles

The aim was to realise a database in the framework of an Internet site, focused on products usable in road infrastructure [START_REF] Setra | Réseau routier national-Catalogue des structures types de chaussées[END_REF]. Indeed, it is still important to give validated information to those who contribute to building in the framework of various industrial domains. Environmental questions, often found to be the most difficult ones besides economical problems, are those for which objectivity is necessary. The OFRIR site ( http://ofrir.lcpc.fr) opened the 4 th august 2003 for public connexion (anonymous). As for the private site area, only for club members (see menu connexion / charte du club on the first page), it was available since the 1rst September 2003. At present LCPC is going on implementing the database with new data at last until 2006. Such an implementation implies to go on collecting data, either from literature or from new contributors registered to the OFRIR club.

The database was developed as follows. Several teams of experts were created: the group of products specialists, the group of thematic specialists and the group of local coordinators. A steering committee including the project coordinators, the site development responsible as well as the site Webmaster was also designed. Part of the work was initially devoted to create a state of the art for each product and required a very wide literature data survey. For that purpose, the local coordinators sent data to the products experts. Besides, products experts were asked to use as numerous pertinent documents as possible. Then, once the state of the art writing period started, both the thematic experts and the site coordinators successively revised the documents in order to reach a rather homogenous content from one product to the other. Then, during a first phase, all texts giving state of the art for each product have been created, the texts being about 20 pages long. At the same time, the Internet site was developed and tested. Then a semi-automatic implementation method, including a workflow (Figure 1) for text revision process, especially realised to work with Internet tools has been developed. First of all, in order to obtain easy navigation conditions, the presentation of products state of the art has been proposed considering standard sections. This presentation has also another advantage, that is, once the text has already been fully implemented in the site, the procedure applied for its revision consists in changing only the section of interest. The site ergonomy is such that (see figure 2) :

-general data and information are provided in the various fields such as juridical, waste, mechanics, environment, health and of course road construction, -the states of the art are presented on the left screen side. Besides recycling products, there is also a state of the art called "reference". This specific state of the art focuses on natural classical materials used for roads infrastructure construction, in order to describe the standard of practice in public works, and then to remind usual rules for both materials and classical aggregates that conform to standards and specifications [START_REF] Setra | GTR (Guide Technique Réalisation des remblais et couches de forme), fascicule I[END_REF]; -the local data are attached to each product and are proposed using pictograms presented on a map of France at the administrative scale of departments.

Since the site opening, the revision has been done because of documents proposed to be on line whatever who made the proposal (any member of the club, products or thematic experts, anybody involved). Such changes arose because of changes of standards, changes of laws as it has been shown concerning aggregates and leaching characterisation. In all cases, each author who is a given product specialist, proposes a new version. Then all thematic referees revise this new version, that is:

-waste treatment expert, -geotechnics expert, -environment expert, -road works expert, -health expert, -road upper layers expert, -earthwork expert. The on-line documents are to be proposed by each product expert and are then examined if necessary by thematic expert. The necessity of reviewing by expert referees of OFRIR partners in this context is mainly dependent upon data and text sources. Then, the published documents like journal publications, already revised by scientific or technical specialists did not follow any revision procedure, whereas reports including industrial reports, research reports or any other report have been submitted to the review cycle.

OFRIR available data

At the beginning of the project the purpose of the writing step of the site was to prepare state of the art for different products suitable for road infrastructure use. At the same time, general features have been collected to explain rules and practice in the various fields involved for materials recycling in roads (figure 3). The total number of products to consider was eighteen, at first. The list of already available products is as follows (French name): Reference (Référence), Reclaimed concrete (matériaux de démolition), Sludge (boues de curage) , Reclaimed Asphalt Pavement/RAP (matériaux bitumineux recyclés), Coal fly ashes (cendres volantes), Mining wastes (déchets miniers), Blast furnace slags (laitiers de haut-fourneaux), Steel slags (laitiers d'aciérie), Nonferrous slags (laitiers non ferreux), MSWI bottom ash (MIOM), Phosphogypsum (Phosphogypses), Tires (pneus), Foundry sands (sables de fonderie), Shale (schistes).

Products state of the art and Local data

It is well known that damage phenomena under traffic cyclic loading depend upon material intrinsic mechanical properties. Therefore, once traffic is defined as the main parameter for road design, different initial thickness layers have to be used for road construction. The deepest layers are designed to remain mechanically stable during the infrastructure life and then have permanent thickness, while the upper layers are regularly removed and rebuilt with possible thickness changes, during roads life cycle. Maintenance operations, linked to traffic amplitude, are usually defined by guidelines or standards. Anyway, to obtain as homogeneous data as possible, for each product, the state of the art has been written considering data in relation with the points as follows.

-product definition, in order to precisely identify the industry from which it comes from, laying from public works to other industrial sectors such as steel industry for instance; -specific literature texts in the field such as, laws, circulars standards and technical guides; -products origin and the way it was processed to fulfil the required properties for road use; -physico-chemical characteristics of the products components, including elementary compounds and mineralogical composition, such details allows to be aware of possible leaching elements, as well as possible swelling ability, both able to change macroscopic environmental and mechanical performances;

-Environmental characteristics studied through dust sampling (airborne emissions) or laboratory leaching tests on the product after elaboration, or compacted; environmental road scale experiments to characterise the infrastructure aqueous release, -geotechnical properties, including the main resources characteristics depending upon the kind of use to be considered (aggregates, soils or binders), -health aspects, providing if any a piece of those information about risks and dangers, for instance linked to asbestos or tar containing products, -use in road construction, which details the type of use according to the classification (wearing course, binding course…).

Besides, local supply on the French territory, gives an overview of the available produced masses but it is only reported in some cases. Finally, in France, some of the products have already been used since 25 years, whereas some others have not been extensively recycled or reused.

As a first analysis of the collected data, it is obvious that the recent changes are mainly driven by the Europeans standards changes that are for aggregates and leaching characterisation. Finally, among secondary raw materials, some of them (i.e. MSWI bottom ash) have been used as road sub-base and sub-grade for several decades. However road performances back analysis has not been systematically performed because were was no legal obligation for it. Hence, such use was due to either a lack of classical local materials), or with the objective of decreasing ultimate waste. Classical tests have thus been performed (Los Angeles, Micro-Deval, bearing capacity...) to compare those products properties to the usually required properties for natural materials according to standards, and to classify them [START_REF] Afnor | Recueil de normes françaises Granulats[END_REF]. Hence it seemed interesting to propose, besides the state of the art for each a local presentation of the products sources and roadwork to allow data gathering for a wide range of places.

The road structure code of practice is still going on changing in order to add missing guidelines for by-products use to classical constitutive raw materials.

Club life and site implementation

Table 1 gives the number of references already available on-line for the club members both in the state of the art area and in the geographical presentation area either for production or for local road works experiments. As for the club members, among 113 members it is composed of 81 external members who do not belong to the administration financing the project. European members who have signed the "charte" of the club reach 8%. In the future other external members are expected to provide data, the principle for registration being to propose data for on-line use.

Conclusion

At the beginning of the project, a network of various experts was created to write state of the art of products usable in road infrastructures, from materials definition, chemical and mineralogical characteristics, environmental properties, geotechnical properties, health aspects, use specifications and practical knowledge.

Local resource availability as well as road works data is the part of the site to concentrate on fur future implementation.

During the OFRIR Internet site implementation period, this project aimed at gathering all motivated people, through the exchange data club. It will be a precious tool for literature surveying and for research results diffusion in the field of recycling. Transversal good governance, gathering all actors into a common consensus, has been expected from this project. Hence, the results of this collective work lead to contribute to sustainable development in the framework of roads infrastructure.

Figure 1 :

 1 Figure 1 : schematic view of the workflow process, with AD the site administrator, RP the product expert, RT the thematic experts (the whole), CO the co-ordinators, CP the steering committee, V0, V1,V2 and V3 the successive text version to be implemented.

Figure

  Figure 2 : site presentation

Figure 3 :

 3 Figure 3 : site general data.

  Besides, LCPC and French technical network of road public ministry, BRGM (Bureau de Recherche Géologique et Minière) and INERIS (Institut National de l'Environnement Industriel et des Risques) also significantly contributed to this project supported by METLTM/DRAST and DR (Ministère de l'Equipement, des Transports, du Logement, du Tourisme et de la Mer/Direction des Affaires Scientifiques et Techniques et Direction des routes), by MEDD/DPPR (Ministère de l'Ecologie et du Développement Durable/Direction de la Prévention des Pollutions et des Risques), by ADEME (Agence De l'Environnement et de la Maîtrise de l'Energie) and finally by SETRA (Service d'Etudes Techniques des Routes et Autoroutes).

Table 1 :

 1 some indicators on-line documents and club life.

	Product	State of the art	Local production	Local road works
	Reference (Référence)	8	Not required	Not required
	Reclaimed concrete (matériaux de démolition)	3	0	12
	Sludge (boues de curage)	14	0	0
	RAP (matériaux bitumineux recyclés)	13	0	0
	Coal fly ashes (cendres volantes)	1	0	0
	Mining wastes (déchets miniers)	11	0	0
	Blast furnace slag (laitiers de haut-fourneaux)	3	4	0
	Steel slag (laitiers d'aciérie)	9	0	0
	Nonferrous slag (laitiers non ferreux)	6	0	0
	MSWI bottom ash (MIOM)	15	59	16
	Phosphogypsum (Phosphogypses)	38	1	0
	Tires (pneus)		0	0
	Foundry sands (sables de fonderie)	3	14	0
	Shale (schistes)	8	21	0
	TOTAL	132	99	28

Acknowledgments

The authors thank Michel BERCOVICI and Christian JOBIC of LCPC -Service informatique for the site development and installation as well as Laurence LUMIERE of LCPC, Section Développement Durable for site implementation.

/DRAST and DR (Ministry of Transport and Construction/Scientifical office and Road direction), by MEDD/DPPR (Ministry of environment and sustainable development/ Pollution and risk direction), by ADEME (Energy and Environment Agengy) and finally by SETRA (Road and highway technical studies office).

have already decided to join the club members to contribute to this common knowledge.

This paper gives a general presentation of the actual available data. Then, a discussion based on a global analysis of collected data is presented. In particular