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EUROSIS

Task 3.1

« Webmapping of science and society actors in Europe »

Final Report

http://webatlas.fr/exhibition/eurosis/

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EXECUTIVE SUMMARY ........................................................................................................................................... 3

INTRODUCTION ............................................................................................................................................................ 4

MAPPING THE WEB OF SIS IN 12 EUROPEAN COUNTRIES.............................................................................. 5

WHAT IS OUR APPROACH? ........................................................................................................................................... 5 Web mapping as a Sociology .........................................................................................................................5 New tools and methodology for a new data source: the web ....................................................................................6 Web mapping as Network Sciences...........................................................................................................................7

CONSTRAINTS ............................................................................................................................................................... 8 Differences between NCPs ........................................................................................................................................8 It is only a “Web picture” .........................................................................................................................................9 The set of countries ...................................................................................................................................................9

ABOUT THIS EXPERIMENT ........................................................................................................................................... 10 NCPs practicing the Web exploration.....................................................................................................................10 How works a distributed network of experts? .........................................................................................................11 Building a common description of the resources ....................................................................................................12

RESULTS........................................................................................................................................................................ 15

GENERAL PICTURE ...................................................................................................................................................... 15 Summary .............................................................................................................................................................15 General Map .......................................................................................................................................................16 Connectivity structure of the types of actors ........................................................................................16 Interactions between types of actors .......................................................................................................19

NATIONAL MAPS .......................................................................................................................................................... 20 Specificities .............................................................................................................................................................20 Armenia...................................................................................................................................................................22 Belgium ...................................................................................................................................................................25 Bulgaria ..................................................................................................................................................................29 Czech Republic........................................................................................................................................................32 Estonia ....................................................................................................................................................................36 Finland....................................................................................................................................................................40 France .....................................................................................................................................................................43 Hungary ..................................................................................................................................................................46 Italy .........................................................................................................................................................................49 Montenegro .............................................................................................................................................................52 Poland .....................................................................................................................................................................55 Portugal ..................................................................................................................................................................58

MAIN AXIS OF INTERPRETATION ................................................................................................................................ 60 Hierarchy emerging from countries' relations ........................................................................................................60

• Where do websites aggregate? ...................................................................................................................... 60 • Small-World in the countries ..................................................................................................................................... 60 • The signature of complexity....................................................................................................................................... 61 • Observing the hierarchy ............................................................................................................................................. 62 • Interpretation of the hierarchical relations.................................................................................................................. 63

FUTURE STEPS............................................................................................................................................................. 68

PROPOSITIONS FOR A WEB INFORMATION SYSTEM DEDICATED TO SIS (OR SIS²)........................................... 68

BIBLIOGRAPHY........................................................................................................................................................... 69

GLOSSARY .................................................................................................................................................................... 70

ANNEX 1 ......................................................................................................................................................................... 72

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EXECUTIVE SUMMARY Background and objectives

In 2007, the conjunction of two favourable elements made the EUROSIS Webmapping project possible: - a promising national study had just been carried in France on a related topic: it consisted in a webmapping of Science in Society stakeholders in France (SiSmap); - the publication of a call for proposal dedicated to the “Science in Society” National Contact Points (SiS NCPs) network. While the NCPs built up an answer to this call, it appeared that there was a need for them to get a better knowledge of science and society communities. The main objective of the project was to make it possible for SiS NCPs to acquire a better knowledge of science and society potential projects’ partners. Based on a specific webmapping of contact areas between actors of the science and society debate, the project aimed at providing NCPs with an adequate tool, in order them to visualize the patterns of the science and society relationship, and to understand which stakeholders, communities, grassroots protagonists, are the most represented and the most active in the science and society dialogue. Methodology and results

In order to reach these objectives, the project partners selected a subcontractor, namely the Web Atlas association. Together, they developed a back and forth process: • NCPs would provide various data on national SiS stakeholders • Web Atlas processed the collected data and correlated them so that findings take the shape

of graphic interactions corresponding to various SiS domains • NCPs analysed and adjusted the results according to an analysis grid • Web Atlas delivered a SiS’actors study. While defining the methodology, a series of possible biases appeared: • The first obvious one, which is our choice, is that this study is based on the Web only. As a

consequence, it does not show the “real” SiS components of each Member State, but the dynamic interactions between grassroot protagonists on the Web

• For each country, the task of collecting data has been made by the NCPs. However, NCPs have different backgrounds and knowledge of Science and Society communities. As a consequence, their better knowledge of specific SiS networks may have created heterogeneity and impacted the final mapping

• All participating partners were volunteers. As a result, the project did not benefit from a very coherent set of countries. This had a noticeable impact on the results.

The partners agreed on having the map address, at the national as well as European level: • Major themes (scientific and/or citizen) of the science and society relationships • Actors and communities engaged in these interactions • All categories of interactions, going from dialogue and cooperation to controversies and

open conflicts. The results the project are of two kinds: • A series of deliverables: a report on the Science in Society actors in Europe, a list of Science

and society stakeholders (NGOs, government, research centers...) with contacts details and 158 “printed” maps

• Scientific results. A comparative study of the national studies led by each SiS NCP in its own country shows strong national specificities, although general trends can be identified: a three-level hierarchical structure, universities and the world of academic research acting as a bridgehead or as a backbone in the overall architecture. This suggests a pregnant top-down system; our main recommendation would then be to facilitate more fluid two-way exchanges between the various layers of the system, in order to encourage bottom-up participation.

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Introduction

After a call for interest, organized by the French Science in Society National Contact Point (SiS NCP) as task leader of the Webmapping project, 12 NCPs chose to take part in the task “Webmapping Science in society actors” These NCPs are representatives of Armenia, Belgium, Bulgaria, Czech republic, Estonia, Finland, France, Hungary, Italy, Montenegro, Poland, Portugal (for the list of participants and their individual profile, see annex 1). These experts in the Science in society programme (7th framework programme) were actively involved all along the different stages of the task:

• They provided data (websites, tags...) • They took part in two two-day Workshops in Paris (26 and 27 May, 2008 and 1 and 2

December, 2008) • They validated the final deliverables.

The first aim of the EuroSiS Webmapping project is to explore and understand the large, open

and dynamic topics dedicated to science in society on the Web through the collective construction of a mapping tool. The results consist in a collection of SiS-maps including themes and actors at different levels of analysis (common European themes, international and national neighbourhoods SIS themes, particular themes through national and European dimensions…). It consists also in the elaboration of a common description schemes and a collective vocabulary for resources description among 12 NCPs involved.

As regards to methods and tools, the EUROSIS mapping project is based on :

a) Use of visualization for communication/promotion of EuroSiS project

b) Production of graphical synthesis of large sets of data

c) Extraction of visual patterns from web data analysis (centre, periphery, clusters, levels of hierarchy, borders…)

d) With different levels of data (zoom) and analysis of SIS European themes.

e) And preparing an information archiving process on specific topics and, beyond, a possible Web Information System (or portal) dedicated to European SIS domains.

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Mapping the web of SiS in 12 European countries

What is our approach?

Web mapping as a Sociology

One of our fundamentals is the “Actor-Network Theory” or ANT. Despite this name it is not a theory, but rather a methodology. Its main purpose is: ”just observe”, and tell what you see. And its difficulty takes place in this “just”, because as the French sociologist Bruno Latour says, it is not easy to “just” observe. We do not judge what is good or bad, who is right or wrong. We do not try to show something. We just observe what is happening between the actors of Science in Society.

The ANT makes us aware of one of the biggest issues in this kind of sociology: it is very hard to define what – or who – is an actor. At one moment one institution is an actor, but at another moment it is a network of people acting on their own. The actors involved in Science and Society split or gather depending on various reasons, including the issue they are confronted with. A laboratory may have a specific action in a specific field (ex: promoting nanotechnologies) and some of its researchers may act in another way (ex: alerting people about risks).

This is the first complication: how to describe a large amount of entities, when describing only

one entity is so difficult?

Let us say we have got our entities – many entities. How are they linked together? How to define the interactions – many interactions – between these entities? Let's say one actor is linked to another. Do they work together? Or does one actor give money to the other? Do they share common objectives? Or just know each other? Is this link positive (they cooperate) negative (they compete) or both (“coopetition”)?

This is our second complication: how to describe a large amount of interactions, when

describing only one interaction is so difficult?

Science already provides us with very relevant studies that deal with those complicated issues. But reality is not only complicated. It is also complex – and it is not the same thing. Even simple things can draw complex networks. We all know that bees are much simpler than the human being, but we do not succeed in understanding how the bee society acts, as a whole entity, as an actor-network. And it is not because bees are complicated: if we work enough we will understand any complicated thing. It is because their society is complex, and this means that it is out of our understanding – unless we have some help from adapted tools like computers. No matter how much time you spend to understand one only bee. It is in the complex network of their interactions that hides the answer.

How to explore and describe the geography of an open, dynamic and large scale hypertext

system such as the Web? Can we identify and map such a distributed organization? A web

mapping project, even with social network analysis, lead us to a very new kind of information

architecture as A.-L. Barabasi described it in “Linked” or D. Watts in “Six Degrees”. With

EURO-SIS data, we are far from a library…

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A lot of studies consider one or few types of actors, or one or few types of interactions. We know some things by studying an issue (ex: gender studies). We know some things by studying some type of actors (ex: media and agenda setting) or interactions (ex: the business model of universities). But it is not enough to get the big picture, because the way these studies link together is too complex to be easily understood. As a result the context is missing, so that it is even more difficult to get a perspective. The different actors and issues are not separated like suggest these studies, there are a lot of transversal links, from one actor involved in a field to another actor of another type in another field... And these problematic interactions are not always complicated to explain. What is the link between a politician involved in health issues in southern countries, and a scientific journalist involved in gender studies? They are married: a simple explanation but that does not fit in a simple grid of analyze. A lot of interactions between actors are due to specific cases, and we used to consider these unexpected links as something irrelevant. But this is an important part of the big picture we fulfill. This is a part of the complexity we have to deal with, and we must adopt a new approach to observe it.

New tools and methodology for a new data source: the web

The idea of getting the big picture of complexity is not a new one. But something changed that makes it possible: Internet. We use the web as a field to collect a large amount of heterogeneous data. In the physical world it would be difficult (and very expensive) to collect data about many actors and their interactions. The fact is they belong to various spheres, and each of these spheres has its own rules. You cannot ask for information to a university the same way you ask it to a ministry, or a NGO, or a newspaper... And if you usually know quite easily which are the interactions inside one sphere (governmental, science, media...) it is far more difficult to get information about “transversal” interactions, from one sphere to another.

The web makes everyone projected to the same surface. A website is a website and all of them can be compared no matter which sphere they belong to. A hypertext link is a hypertext link. You probably will not know which “real” interaction has made this link possible, but it exists. This is a problem for detailed analysis, but here is the gain: all links fit to the big picture. Remember that the big picture is the context that we need. Once we get it, we can “dive” in data to get details.

Bruno Latour calls this “quali-quantitative analysis”. It is a classical methodology in network sciences. Our data are as large as they are detailed. We collect many websites but each of them contains a lot of useful information, including: who is this actor, where does it come from, who are its members and/or partners, what does this actor do... You can get some detailed analysis on some actors, but not all of them (because it is too much work). Where is it useful to get a detailed analysis? When you interpret a map, you may know where to “dig” or “dive” to the detailed level. The map is the context that gives details their meaning, and the details help you to get a better interpretation of the general level. That is why we always move our point of view from general to detailed, from detailed to general. It is neither a qualitative nor a quantitative approach. It is both of them, because the web is a new source of knowledge that makes it possible.

The quali-quantitative approach is the best methodology to understand the complexities we are confronted with. That is why when our first task is to collect, the second one is to expose data. Our goal is not to provide a statistical analysis, even if we actually use a lot of statistics. Our goal is to show the big picture, whatever its complexity is. Each map we build, each statistic we compute, is linked with the others so that you can switch your point of view from the general to the detailed and so on. We will provide some conclusions, but these data tell a lot more than we can possibly interpret in this report. The value of this kind of work is to expose many things that you can understand if you get involved in the field of Science in Society.

That is why our tools are not “big black boxes” that compute some list of numbers. They are made to show what is happening, and we altogether discuss the best way to show the data we have collected.

The main work that each NCP provided is collecting and discussing the relevance of the

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maps and the other statistical visualizations.

Web mapping as Network Sciences

A few years ago in the United States, it was decided to create a new discipline: the Network Sciences. The scientific work done by many researchers like A.-L. Barabasi, from whom this work is inspired, did not fit into another existing discipline. These researches started as mathematicians (like Duncan Watts) discovered a new kind of structure called “scale-free network”. This kind of structure can be considered as the signature of the complexity: it is a non-hierarchical network that we find in various fields like biology, ethology, sociology, economics, engineering... and the Web. These networks are special because they have a high degree of clustering (ex. The friends of my friends are probably my friends) but also a small diameter (the famous “six degrees of separation”).

We can tune each network between complete order and complete disorder (central feature

on the left). As expected on the Web, EuroSiS map belongs to a special class of networks with

two properties : composed with clusters and short distances between nodes (on the right,

general EuroSiS map at 2nd workshop).

A lot of strange properties stick to these structures (strongly connected components, power law distribution of degrees, the preferential attachment modeled by Barabasi...). We do not know so much about these strange objects but we can build tools to analyze them. The mathematical concept is the “graph” and we use these graphs to build our maps: sites connected by links. The “InfoViz” discipline helps us to build tools to manage graphs, altogether with computer sciences. And with these tools we design maps following the basics of semiotics (J. Bertin). As previously stated, the whole methodology can be understood in the perspective of the sociology and the Actor-Network Theory. All these disciplines are necessary to handle complex structures. That is why we consider this approach in the perspective of the interdisciplinary field of Network Sciences.

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The basis of network sciences is the graph theory, which is a branch of the Mathematics that tries to understand different aspects of the graphs: nodes connected by edges. At the beginning this field is close to geometry and topology, and is quite abstract (it applies to any “relational system”). European mathematicians Erdös and Renyi tried to explain why, when you make a random graph grow, a “strongly connected component” appears no matter which are the parameters. But these works did not help to understand complex networks until Watts and Strogatz understood that the “random graph” was not the good model. At this moment they worked about how animals can synchronize, and more precisely some crickets that emit a pulsating light. Their problem was: how do these insects observe each other, so that they perfectly synchronize through large distances? They tried to draw the network of the insects observing each other to understand how it was possible. The solution was found in a new type of networks they imagined, that is neither a regular nor a random graph, but something in-between. This graph has some shortcuts that break the symmetry and allow nodes at the opposite to be connected. These crickets were looking at their neighbors, excepted some of them looking far away to distant other crickets (shortcuts). And they noticed that this new model explained some animal phenomenons, but also some social ones like the famous “six degrees of separation” that connect almost everyone on earth (identified by Milgram); that is why this type of networks was called “small-world”.

After this discovery different works established that these “small-world” networks could be found in various places, like the power grid of the USA, neural networks, ecosystems and of course, Internet. The Web had to be studied, and various “network sciences” works were dedicated on it. Kleinberg's works on graphs were decisive there. He created his famous algorithm HITS that allows to build search engines, and he worked on the existence of “aggregates”, strongly connected clusters of resources that share the same topic. Barabasi is also a “father” of network sciences, as he tries to determine the diameter of the Web and conceptualized the “preferential attachment” as the source of the small-world networks (which he calls “scale-free networks”). His book “Linked” is a good synthesis of all these fields, and it has been read by a lot of people including many non-specialists. Tim Berners Lee, who created the HTML and the Web, leads a network-oriented program called “Web Science”. On his side Barabasi leads a program called “Network Sciences” at the Sante Fe Institute, that interests in the Web but also other applicative fields like cure for cancer or studies on biodiversity. Nowadays Web studies are only one branch of Network Sciences amongst other. Its concepts spread to various fields, now including Science in Society.

Constraints

Differences between NCPs

One of our goals is to compare the countries. But for each country the task of collecting data has been made by the NCPs. That is why the differences observed between countries could be due to differences between the NCPs. Indeed, some of the NCPs were specialist of the Science in Society fields whereas others were specialist of the FP7 procedures (for the NCPs profile, see annex 1). As a consequence, they all had a different approach of the way of collecting data.

This is an internal problem of our methodology, we could not avoid it. In a matter of facts, we observe such a bias. We observe it because we are aware of this issue since the beginning. We

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deal with it.

Our way to eliminate the main part of this bias is to actually compare the profiles of the countries, and to confront it to the NCPs. The NCPs are our experts and we count on them as objective analysts. In our workshops we discussed about these differences, and asked them for an interpretation of the differences between the data of each country. The self-evaluation came spontaneously and we had no difficulty to point to these biases.

As we will see, we are not able to provide a general analysis grid for all countries. This is one of our results: there are big differences between the way that each country “thinks” Science in Society. So the bias due to the differences between NCPs still remains in our analysis. But we will point to the biased results as far as we know them. We will deal with it to highlight the main real differences between countries' profiles. On a detailed level it is difficult to separate biases from true results. But most of our observations are so clear and general that it is not the case.

It is only a “Web picture”

We studied the Web. Not the “reality”. But our results are besides objective, and as real as they are scientific. Now let's clarify this point.

The Web is real. Naturally it is not the “physical world”, but we do not consider it as “virtual”. It is not a “virtual reality”, it is rather a “real virtuality”. We observe Websites of actors that really exist and these actors really published the information that we collect. These Websites are not “the actors” but we consider that they represent them in a certain way. It is useful to study the Web and that is why we do it, but we do not take an actor for its website.

We also consider the hypertext links as the only relation between actors that we observe. We do not know how to describe or give a value to such a link. It is neither good or bad, strong or weak... We just take it as the trace of an interaction, whatever it is. Because we do not need to analyze it at a detailed level, it is sufficient. But we can track for more details by “diving” into data and analyzing the context of the link (analyzing the page: what does it tell, what is the meaning of the link...).

The Web does not exist in a separated reality. What exist in the Web has good reasons to exist. The Web is produced by the “physical reality”. The Web is a distorted prism of the “reality”. At a general, non-detailed level, it is a useful representation of what “really” happens, despite the distortions (that we always try to manage). And it is not only that: it is the only way that we have to get the whole picture.

The set of countries

We asked for volunteers to participate to this task. As a result we did not get a very coherent set of countries. This has a noticeable impact on our maps.

The first problem is linked to the size of the countries. Large countries propose more relevant actors (and websites) than small countries. How to balance the amount of websites demanded? We chose to ask for the same amount of actors to each NCP. This means that small countries have more small actors: big countries selected only the “top” actors, those who often have the biggest websites.

This produces a map where each country has the same amount of actors, but their connectivity strongly varies. That is why the mean connectivity of each country has to be balanced with the size of the country: some smaller actors do not appear in the set of the biggest countries.

The second problem is about the countries that did not participate. This impacts the connectivity of smaller countries. We think that most of the big countries have a lot of interactions with various other countries. But smaller countries may have a large part of their interactions with few big countries close to them. As you will see Estonia has a lot of links with Finland. But some of our small countries like Montenegro look poorly connected because we do not have their main partners (Greece, Germany, Austria...). Due to the absence of several big countries, some small countries may look less connected than they are.

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About this experiment

NCPs practicing the Web exploration

NCPs are not supposed to be experts in watching, even if they are experts in the SiS field. Exploring the Web is a difficult task. It needs a lot of energy and beginners often feel lost in the huge amount of pages. Our goal is to help them to find their way in this ocean of information.

Our theoretical model tells that the Web is organized in layers (see image above). The higher layers contain the biggest websites, associated to the most notorious actors. The more a website is in a higher layer, the more it is connected and the more it is general. That is what we usually call the highways of information. But low layers also contain a lot of relevant information. You may find there the websites of many actors that you do not know. These are more specialized websites, corresponding to various topical sub-categories. The higher layers with the most notorious actors are quite easy to explore, and they not contain original information: NCPs already know what is in there. The interesting but difficult part of the work is about collecting the websites in the lower layers. That is why we taught NCPs how to use the WebAtlas tools. We wanted them to confront with the lower layers and discover new resources.

The main tool is called Navicrawler. It is published by WebAtlas and available on their website. It is a free and open source software that works with the famous free browser Firefox. This tool is design to semi-automatically collect websites (and links between them) and to bring useful information about what is collected to the user.

WebAtlas usually teaches how to use the Navicrawler to researchers like sociologists. Several sessions of practice are generally enough to let these users explore the web on their own. The Navicrawler is complex but the basic functions are quite easy to understand. The difficulty does not come from the tool itself, but rather from the complexity of the Web. Even helped by the Navicrawler it is difficult to explore it. We had only one session to guide the NCPs (the first workshop). WebAtlas explained the main functions of the Navicrawler and presented theoretical models of the Web and the complexity. But NCPs had to practice on their side once back in their country. We had contacts by phone and e-mails, but we could not be there to help them practicing.

The NCPs have different backgrounds. Some of them were very interested by the methodology and the tools, because they saw that it could be useful for their own work, out of this project. Some NCPs were just following the methodology as a part of this task, and some of them had no problems while others found it difficult. Before the first workshop we asked NCPs to give us some URLs, and we made a map with it. The map was not very good but they had a sample of what would be done with their Navicrawler practice. This was a good point, and the NCPs got involved in the methodology during the first workshop. But after that those who had difficulties

Layers of the web:

- Higher layer: the most visible (Google, Microsoft, Adobe...)

- Medium layer: websites aggregate here (on-line communities...)

- Deep web: databases ...

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could not get the help they needed from us. They collected the URLs we needed by their own mean (search engines, databases, by their relational network...). It is not a big deal but we think that they did not benefit from an opportunity to discover new resources. As a result we asked some countries to get more resources after the second workshop, to balance the amount of websites-actors between countries.

Depending on the profile of the NCP, it may or not be relevant to improve their Web watching skills. For NCPs who are not involved in knowledge management, it is more difficult to stick to this task and benefit from it. This issue due to the various profiles of NCP could be resolved by asking NCP to get help from colleagues that get involved in knowledge management.

When WebAtlas teaches the practice of Navicrawler to researchers, they usually have difficulties. Compared to them, the NCP had a better understanding of this methodology. But researchers often work with other researchers, and they help each other so that they succeed in practicing. We think that it could be useful to make NCPs help each other. It is useful not only for the practice of Web exploration, but also for sharing knowledge about what is collected.

It is difficult and expensive to make all the NCPs travel to a single place to attend a workshop. If we had to make this work for every European country, it would even more difficult. That is why we think that it would be useful to propose local workshops, with only local countries, in addition to the common workshops. Each local workshop should be available for a group of 4 to 8 countries, like for example northern countries and Baltic republics. It would take place in a place where it is easy for these NCPs to travel. It would be useful for these countries to share their results because they probably collaborate a lot. And it would be the good way for us to help everyone practicing.

How works a distributed network of experts?

A distributed network of experts is a key-feature for monitoring SiS policies and societal issues in European Community. Building up an efficient network of observers is quite difficult and even more difficult in the first period of the system. We then had to take account of many differences between members of the new “community”.

In theory, a distributed system of observers (each one involved in identification and description of Web resources) “dialogues” with a central component (i.e. an online database accessible through a personal workspace). In this central component, a process of indexation and archiving contributes to a general library of documents. Centralization of data and computational processes allows graph calculations on a large set of documents, especially in order to build cartographies (European and national ones).

The EUROSIS system can be considered as a sum of local expertises: qualitative tasks to select and describe Web resources) collaborating in order to build a general corpus on which quantitative tasks are possible. In this way, the set of EUROSIS cartographies can be considered as the result of a quali-quantitative process.

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A distributed network is designed to fulfill collective goals (ex. European cartography and its common trends) as well as individual goals (national reporting, national cartography of SiS, local index of actors…).

The practice of such a distributed work implies the association of theory and technological experimentation. The actors of this network need to master the basis of the theory. It was not easy to transfer this knowledge because it is not always related to the profile of the NCPs. Nevertheless some theory is unavoidable as soon as we try to share knowledge about a new and complex object like Web mapping. The network needs to understand what it is working on in order to coordinate. The theory and the practice of tools benefit one another. The strength of such network resides in its capacity to coordinate the practices of actors, in their theoretical aspect as well as in their technological aspect.

The NCPs have to manually work on the corpus in order to guarantee its quality. We do not rely on automatic processes on this point. But automatic processes are needed to manage big masses of data. Manual as well as automatic processes are necessary. NCPs have to switch from one to another many times. The theory is required to be efficient on the manual process, while experimentations allow to successfully monitor automatic processes.

Building a common description of the resources

To get interesting data, we need to describe our resources. Each actor, represented by its website, has to be described. Some descriptions depend on facts, and it is easy to deal with these. The country of an actor is well determined, even if there are some exceptions. So as the social type: NGO, institution, university... But describing the actors by topics is far more difficult.

Each actor has an activity in one or several topics or themes. These can be ethics, gender studies, nanotechnologies... Our initial point of view was to let the main topics emerge. That is why we asked each NCP to describe its resources with free tags. Our idea was to build big topical clusters by grouping these tags. How to gather tags? Because each NCP could use many tags to describe each actor, we used the “co-occurring” criteria: two tags are link when they appear often together to describe actors. With this criterion we wanted to compute a graph and to observe emerging clusters.

The NCPs used 1200 different tags to describe their actors. But most of these tags meant the same thing. We manually gathered the synonymous tags. We then had a set of 200 tags. Take a look at the graph of co-occurrence:

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This graph is typically a scale-free network. No cluster emerges. There is a minority of hugely connected tags and a majority of poorly connected tags. Why are there no clusters?

We know that some clusters exist. We know that the “information society” is not the same thing as “gender issues”. We know that “nanotechnologies” is not the same topic as “nuclear energy”. Why every tag seems connected to every other tag? Because for each pair of tags, you will probably find an actor that is involved in both. You will find some actors that work on gender issues and information society. You will find someone involved in nanotechnologies and nuclear energy. This is typically the signature of complexity. Despite the fact that we tend to separate everything, there are a lot of transversal links that remind us that knowledge does not fit in a simple set of boxes.

So we failed to observe the topical clusters, because what we observed was the massive presence of trans-topical links. We could have used an algorithm to force the clustering, but it is not the good way to proceed if you want to understand how things work. So we tried to make the clusters ourselves. But if you look at these 200 tags you will see that they are clearly different one from another, even when they are close. Would you melt e-learning and e-government, or research funding and expertise? We thought that these questions were important enough to share them with the NCPs.

We discussed about this during the second workshop. And we underestimated the complexity of this once again... We started this work by an open discussion about the good way to gather the tags. But no common description emerged. When some NCPs proposed to gather some tags in a big topic, there always were other NCPs to find it irrelevant. Because this open discussion failed, we then tried something more constructive. We splitted the NCPs into three groups, each group working on a good list of topics to describe the corpus. And we then compared the results to find the common points. Two groups provided similar results whereas the last one came out with a very different approach.

• Groups 1 and 3: Agriculture, Chemistry, Communicating science, Economy and politics, Energy, Environment and sustainable development, Ethics, Food, Gender issues, Geology, GMOs genetics and biotechnologies, Health, ICT and telecommunication, Nanotechnologies, Physics, Science education, Security, Social sciences and culture, Transport.

• Group 2: Education methods, Ethics in science and technology, Gender, Governance of science, Science communication, Universities and public research bodies.

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Note that both sets of tags are closely related to the 7th Framwork programme (FP7). But as the first set describes the topics of science quite the same way as the FP7, the second set describes the topics of the Science in Society program of the FP7. Both sets have topics in common, like “gender”. But some tags take place in completely different perspectives. In the second set you will find “governance of science”. This tag is not in the first set, but it can be associated to any of these tags (governance of chemistry, governance of geology...). The perspective is different. The tags of the second set are about science, while the tags of the first set are parts of science.

After discussion, all NCPs agreed on the first set with some modifications due to the second set. We do not know if these tags are “the good ones”. There are probably other relevant sets, and we know that this choice is satisfying. So, finally we sticked to the FP7 perspective. And we could have done that since the beginning. Should we have?

We need to reduce some complexity of data to observe significant patterns in the maps. We want to look at the complexity of the interactions between actors, that is why the complexity of thematics is not our priority. We will build maps where each topic has its own color: if we have 200 topics then the map cannot be read. We need a very synthetic description of the topical aspect of actors.

But the question remains. Our conclusion is that another relevant approach of Science in Society could be the analysis of the topical complexity. We know that knowledge does not fit inside Dewey is classification. With the Web we can observe it and we could have some surprises.

By the way, we now know that it is a loss of time to work on the topics in a mapping of interactions. We need to reduce complexity somewhere, which is why we have to choose between analyzing interactions and analyzing the topical landscape. NCPs have different points of view on the good way to separate Science in Society in a set of topics. Maybe different countries just do not see the science as the same thing. That is why no common set of topics can simply emerge from this kind of distributed work. There will always be contradictory descriptions of the resources. Our chance is that the European Commission is making this work of coordination.

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Results

General Picture

Summary

The European SiS Network only exists as series of national SiS networks. But despite their differences, these networks share a common structure.

The core of the SiS network is the “Academic Science”: Universities and Secondary Schools, Research Centers, Sciences Centers and Museums... And it is important to notice that Media also participate to this core structure. Actors tend to interact a lot within the core like a community (the national scientific community).

But there is an extension to this core that we call a “bridge”, that gathers actors concerned by science: NGO-CSO, Web-specific actors (as Portals) and Policy Makers and Governmental Organizations. These actors do not necessarily constitute a community, but extend the core in different directions that allow other poorly connected actors (networks of organizations, personal websites, companies...) to connect to the core.

The bridge is the most important part of a SiS network because it allows science to spread out of its own sphere. The “academic” core is present in almost every national SiS network but it is remarkable that the strongest networks always have this extension of the core. We suggest that, if the first condition to build a strong network is to get a core academic subnetwork, the second condition is to extend it through civil society (NGO-CSO), independent actors (websites, portals) and Policy makers.

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General Map

The general map shows that every actor binds more to actors of the same country. That is why the clusters in the general map essentially represent countries (that is why we colored the actors by country).

General Map: actors aggregate by country (colors by country)

Naturally this result was expected. Actors interact more inside the same country, and this is strengthened by the fact that on the web, different languages tend to separate (and European countries have many different languages).

To understand how actors interact we have to look inside every country. We will discuss about the differences between national SiS networks in the next section. The good way is to look at interactions between different types of actors rather than different nationalities. The general map does not help us for this task, but national maps and statistics do. That is why we will rely a lot on the statistics about types of actors:

A) NGO-CSO B) Portal or other independent Websites C) Science centers and museums D) Research centers E) Advisory bodies F) Universities and secondary schools G) Network of organizations H) Policy makers and governmental organizations I) Media J) Events/projects K) Companies L) Subnational and local actors M) Personal websites N) International

Connectivity structure of the types of actors

The way an actor links to others strongly depends on its type. For example we observe that Universities bind a lot and constitute the backbone of several national SiS networks. They also tend to bind a lot with other types of actors such as Research Centers or Science Centers. On the contrary, companies do not link each other, and are poorly cited by other actors. What is more there are many Universities and Secondary Schools while there are few companies in our European SiS network. It is clear that Universities and Companies play a very different role,

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have very different types of interactions. When comparing these different types of actors, a global pattern emerges. This pattern may be interpreted as a hierarchy, and because our network is a “small-world” (or “complex”) one, we choose to represent this pattern as “connectivity layers”.

The three connectivity layers: pattern for the types of actors

We found three connectivity layers. It is important to understand that an actor is in a layer if it fits its profile, and that this profile is determined by how actors link inside the layer, and how they link with other layers. That means that the way to determine which actor is in which layer has to be made recursively. Nevertheless the profiles are strong and there are big differences between the three profiles.

– The higher layer is the easiest to determine. Its main characteristic is the following: only an actor in the higher layer is more or almost as much cited by other actors in the higher layer than it cites them. In other terms, there are more links from other layers to the higher layer than from the higher layer to the other layers, and actors in the higher layer link a lot together. Furthermore each type in the higher layer has many actors. This layer gathers the “academic science” part of SiS and Media:

– Universities and secondary schools – Media – Research Centers – Science Centers and Museums

– The intermediate layer contains other types of actors that are well connected, but they are less cited by the higher layer than they cite it. What is more, the intermediate types of actors have almost symmetric links with the lower layer. This layer gathers three types of actors:

– NGO-CSO – Portal or other independent websites – Policy makers and governmental organizations

– The lower layer contains poorly represented and poorly connected actors. In particular these types of actors have more links with other layers than with their own. These types of actors are:

– Network of organizations – Personal websites – Subnational and local actors – Advisory bodies – Companies – Events/Projects

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How types of actors link depending on the three layers

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Two things are remarkable in this structure. The higher you are, the more you link to actors in the same layer. And the higher you are, the more you are cited (by lower layers).

Interactions between types of actors

The higher layer (Universities, Research Centers, Science Centers and Media) is the backbone of the European SiS network. As a matter of facts the European SiS Network is illusory, it is rather a conglomerate of national SiS networks. In other words the higher layer is the backbone of a typical SiS network.

The common point of Universities, Research Centers and Science Centers is that Science is the main part of their mission, of their activity. We will call these categories “the academic science” (even if it is not exactly the case). Anyway these three categories share a theme and a common structure. They often draw a strong and common subnetwork in the national networks (see the analysis of the national maps for more information). They take part of the same structures but some countries are more Science-Centers-oriented (such as France) while other are more Universities-oriented (for instance, Finland). And we have to assume that Media actors are a part of this “academic science” structure (this category fits our criteria).

On the contrary the lower layer gathers actors that do not play an important role in the SiS network. But we have to take into account that each of these categories are poorly represented; it would have been difficult, for a category that gathers about 5% of the actors of the corpus, to be linked a lot by other types of actors. Nevertheless it may be statistically possible. It just means that none of the categories in the lower layer can be considered as a remarkably connected type of actor. In other words the links concentrate where many actors gather. The global strength of the SiS network does not come from a small amount of hyper connected actors, as in an explicitly hierarchical network, the strength of the SiS network comes from its strong subnetworks (the higher layer). This does not mean that there is no hierarchy, but it is what we may call a “2nd degree hierarchy”, a hierarchical structure that does not exclude distributed patterns, that is strengthened by these distributed structures. In this perspective the lower layer is just the “miscellaneous” part of the network.

Now that we have seen the role of the higher layer and the role of the lower layer, it is easier to understand the role of the intermediate layer. Even if they are well connected, these actors play a different role than the higher layer, for two reasons. First, the higher layer is exclusive: its characteristic is that you have to be as well cited by the higher layer than you cite it to be in it. In other words, the intermediate layer has more links to the higher layer than the higher layer has to it. On this point the intermediate layer is like the lower layer. But the intermediate layer has nevertheless many more links with the higher layer than the lower layer has, even if they are asymmetric. Second reason, the actors of the intermediate have almost as many links to than from the lower layer. For these two reasons we interpret the intermediate layer as a bridge between the lower layer and the higher layer.

The “academic science” is the backbone in the SiS network and the intermediate layer is a bridge to it. These intermediate actors represent those who, if they are not directly dedicated to science, plug to the academic community because they are concerned. NGO-CSO are often dedicated to environmental issues and then plug to the academic science on environment (see national analysis on this). Portal or other independent websites, meaning web-specific actors, are more topically diffuse but play by nature the role of a bridge (because it is the role of portals in an information system). And policy makers also play this role. The difference with the higher layer is that these actors are less science-dedicated, and more society- or politics-oriented. We may interpret the intermediate actors as “those who are concerned by science”.

– CORE : Academic Science (Universities and Secondary Schools, Research Centers, Science Centers and Museums, Media)

� Strong national subnetworks

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– BRIDGE : Science Concerned (NGO-CSO, Portals and other independent websites, Policy makers and Governmental Organizations)

� Well connected, extends the core as a bridge to other actors

– OTHER : Miscellaneous (Network of organization, Personal websites, Subnational and local actors, Advisory bodies, Companies, Events/Projects)

� Poorly connected, but links to the bridge and the core

National maps

Specificities

The national SiS networks draw various structures. There are big and small, strong and weak networks. But two tendencies emerge.

– The bigger and stronger networks tend to be more connected to other countries

– The strong national networks are always supported by an academic subnetwork, but the presence of NGO-CSOs and web-specific actors (such as portals or independent websites) makes the difference for the strongest.

Armenia

A quite poorly connected network, with a lot of Research Center and few Web-specific

actors

Few links with other countries

Belgium

A very strong network, with well distributed types of actors, and where Wallon and

Flemish actors tend to separate

Some dedicated subnetworks, in particular about Sustainable Development

Many links with other countries

Bulgaria

A strong network with many science institutions counterbalanced by many NGO-CSOs

Few links from other countries, but many links to other countries

Czech Republic

Inconsistent network with many science institutions counterbalanced by many NGO-

CSOs


Estonia

Strong network with many science institutions counterbalanced by many NGO-CSOs,

and many media

Some dedicated subnetworks about Environment


Finland

Very strong network where most of actors are science institutions, in particular

Universities.


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France

A quite weak network with many science institutions, in particular Science Centers

Museums tend to aggregate but most of them are totally disconnected, and there is a

subnetwork of NGO-CSOs about Environment and Democracy


Hungary

A strong network, with well distributed types of actors including many web-specific

actors that play an important role


Italy

A weak network with no institutional subnetwork


Montenegro

A very weak network with many companies and media, policy makers and advisory

bodies and some dedicated subnetworks, in particular to scientific institutions

Very few links with other countries

Poland

A quite weak network with many science institutions that draw a strong subnetwork


Portugal

A quite strong but very small network with many Science Centers that draw a strong

subnetwork


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Armenia

118 actors have been collected, which is few compared to other countries. We found 218 hypertext links between these actors, which means a low links density of 0,016.

Armenian actors are quite poorly connected together, compared to other countries.

Note that like for every country, there are many more internal links than links with actors in other countries (it is a general and quite obvious result).

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The most represented actors are Research Centers, and they massively occupy the Armenian SiS territory. There are also many companies and NGO-CSOs. There is no subnational or local actor, no portal or other independent websites, and no personal website, and very few media or networks of organizations. This may indicate that the Web in Armenia does not play the role of a public space for discussing Science.

The main Armenian actor in the national network is the National Academy of Sciences. It attracts 12% of the internal links. It is classified as a Research Center, and a quick look on the national map shows that it is the center of the research centers.

Zoom on the Research Centers cluster (in light blue) in the Armenian map

The National Academy of Sciences is also the main hub. 11% of the links to other Armenian actors come from it. This is due to the fact that it links to most of the Research Centers.

Research Centers do not constitute a strongly connected network, but the hierarchy is very strong. As you see in the zoom above, the structure draws a star and its center is the National Academy of Sciences. This means that the different institutes do not link together, probably because the structure reflects an institutional network where the only necessary link is the National Academy of Sciences itself.

Armenia has very few inbound links. It seems that the Armenian network is not a major resource for the European SiS network.

Armenia has quite few outbound links to other countries. The main target is the International or European level (67%). Armenia has strong interactions with no other country in our set (but maybe with other European countries that did not participate to this task).

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The most represented themes are “Governance, expertise, ethics” and “Information and communicating technologies”.

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Belgium

204 actors have been collected, which is very much compared to other countries. We found 1489 hypertext links between these actors, which means a very high links density of 0,036. Note that density is expected to lower as the count of nodes increases; the contrary happens here and it is remarkable.

The Belgian SiS network is very strong, compared to other countries.

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The different categories of actors are well balanced. The most represented are NGO-CSOs, Portals and Research Centers. There are few media, and no companies. The “society” part is well represented, and with the strong presence of “web-specific” actors (portals, personal websites), it means that the Belgian SiS network is active and well developed.

The main Belgian actors in the national network are the Université Libre de Bruxelles and the Belgian Science Policy (cited respectively by 56 and 51 other Belgian actors).

Research.be is the main hub (links 91 Belgian actors) and Belgian Science Policy is the second one (56).

Research.be is the main portal

SiS in Belgium draws a single and well connected network, but some components appear. First we have to notice that the Flemish actors and the Walloon actors globally occupy different areas, besides they are well linked and often melted. This is not a surprise as the two communities do not share language (and we know that on the Web different languages

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separate).

Another component can be identified as actors involved in sustainable development (mainly NGO-CSOs and portals). They tend to separate from the rest of the actors involved in Science, but there are bridges that are actors involved in environment.

NGO-CSOs and Portals about Sustainable development

As we have seen, there are many NGO-CSOs in Belgium. Most of them distinguish from the main “science” component. We identify two small clusters of NGO-CSOs. The first is Dutch language and dedicated to philosophy in science. The second one is dedicated to women and gender issues.

NGO-CSOs cluster about philosophy (related to science)

NGO-CSOs cluster about women

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Belgium has very much inbound links (184). The main source is France (23%) but other countries are important too (Bulgaria, Finland, Hungary, Poland and International). Belgium is well linked by many countries and it denotes an important role in the European SiS network.

Belgium also has very much outbound links to other countries. The main target is also France (41%) and in second the International or European level (33%). Other countries are poorly cited by Belgian actors (excepted Finland, 10%). This reinforces the hierarchical importance of the Belgian SiS network, which is well cited despite that it does not cite many countries.

The most represented themes are “Communicating Science” and “Governance, expertise, ethics”.

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Bulgaria

135 actors have been collected, which is a lot compared to other countries. We found 581 hypertext links between these actors, which means a high links density of 0,032.

The Bulgarian SiS network is strong, compared to other countries.

The most represented are NGO-CSOs, Universities and Research Centers. Every other category is represented. The “science” part is strong but is well counterbalanced by NGO-CSOs.

The main Bulgarian actor in the national network is the Bulgarian Academy of Sciences (cited by 38 other Bulgarian actors). This actor is also the main hub (cites 43 Bulgarian

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actors).

Most of the other important websites are universities: they draw a strong subnetwork.

The Bulgarian Universities subnetwork is strong (actors in blue)

But the universities subnetwork is not strongly connected to the institutes (research centers). The research centers draw a very hierarchical structure around the Bulgarian Academy of Sciences. There are few connections between institutes. This is probably due to an institutional approach of building websites, where the main bridge has to be the BAS.

The hierarchical network of Research Centers draws a “star” around BAS

Bulgaria has few inbound links (51). The main sources are Poland (31%) and the International level (25%). Several countries do not cite Bulgaria (Czech R., Estonia, France, Italy). The Bulgarian SiS network is not a main resource for the European SiS network.

But Bulgaria has many outbound links (124) to other countries. The main target is International (38%) but other countries are also well cited (Belgium 15%, Hungary 13%, Poland 14%).

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The most represented themes are “Governance, expertise, ethics” and “Communicating Science” and “Science education”.

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Czech Republic

70 actors have been collected, which is very few compared to other countries. We found 51 hypertext links between these actors, which means a very low links density of 0,011.

The Czech SiS network is nearly inconsistent, compared to other countries. There is too few links between actors to bind them in a single component.

The Czech Republic SiS Network is inconsistent:

Actors are poorly connected, or totally disconnected (list on the left)

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The most represented actors are NGO-CSOs (30%). The “science” component is also well represented (Universities 23%, Research centers 17%, Science center and museums 13%). There are few companies and media and no advisory bodies, no events/projects, no personal websites and no portals. This means that the web as a public space is not developed at all in the Czech SiS Network.

The main Czech actor in the national network is the NGO-CSO Gender Studies (cited by 8 other Czech actors). The main hub is Czech Helsinki Committee (cites 6 Czech actors), and the Forum 50% is the second authority as well as the second hub (cited by 5 actors, cites 5 actors, in Czech Republic).

NGO-CSOs draw the main subnetwork, that gathers most of the internal links in Czech Republic. The three actors mentioned above take place in this subnetwork. It is the only part of the Czech SiS (non-)network where resources significantly gather.

The Czech NGO-CSO subnetwork

Czech Republic has very few inbound links (16). The main source is International (56%) and other sources are not significant (most countries do not cite Czech actors at all).

Czech Republic also has few outbound links to other countries (72). The main target is also International (38%) but other countries are cited too (Belgium 8%, Finland 10%, France 10%, Hungary 8%, Poland 8%, Portugal 14%).

The Czech SiS Network is not important in the European SiS landscape (it does not

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mean that “individual” actors do not play any role). It is remarkable that 9 actors (13%) have connections with actors in other countries while they have no connection in Czech Republic. This is probably a consequence of its very weak internal connectivity. Note : this situation probably makes that search engines do not give a good rank to the Czech SiS websites.

The most represented themes are “Communicating Science”, “Governance, expertise, ethics” and “Science Education”.

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Estonia

202 actors have been collected, which is very much compared to other countries. We found 981 hypertext links between these actors, which means a high links density of 0,024.

The Estonian SiS network is strong, compared to other countries.

The most represented are NGO-CSOs (18%), Media (17%), Portals (16%) and the “science” component (universities 13%, science centers 11%, research centers 16%). There are no advisory bodies, no companies, and no personal websites. The presence of media is remarkable. The Estonian SiS network is very specific, because it is almost exclusively composed of Science, Media, Portals and NGO-CSOs (total 91% of the actors).

The main Estonian actors in the national network are Eesti Päevaleht and Postimees (cited respectively by 37 and 36 other Estonian actors). These two actors are medias.

Nature Pages is the main hub (links 58 Estonian actors) and is a portal.

In general the authorities are Media and NGO-CSOs while hubs are (obviously) Portals, and also Media and NGO-CSOs. Media are very strong in the Estonian Network, but also NGO-CSOs.

The main authorities, that are media, are very central (as expected). They gather and occupy the center of the bottom-right area of the map.

Main authorities (media): Eesti Päevaleht and Postimees

They take place in the center of the biggest component.

On the contrary, the main hubs take place around a zone (that we call “bridge”) that connects the main component (bottom-right) and a smaller component (top-left). We will explain below.

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Main hub, a portal, below the bridge.

Second hub, a media above the bridge

The main component contains most of the Estonian actors, including museums, schools, universities, media... We may identify two centers. The first is constituted of the main authorities, and is closer to the center of the map (top-left of the component). The second one is the Estonian Ministry of Education and Research.

The main component. First center, top-left: Eesti Päevaleht and Postimees

Second center, bottom-right: Estonian Ministry of Education and Research

The smaller component is dedicated to Natural Parks. Most of its actors are Research Centers. And the “bridge” zone that connects the two components contains mainly actors dedicated to the Nature issues. It contains many NGO-CSOs.

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Smaller component (top-left of the map): Natural Parks (Research Centers).

Bridge (between the two components): NGO-CSOs about Nature.

Scheme of the structure of the Estonian SiS network

As observed in other studies1, the Natural Parks separate from the rest of the SiS network 1 WebCSTI by WebAtlas http://webatlas.fr/download/docs/WebCSTI10.pdf

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(besides bridges always exist). We suggest that it is due to the strength of the “Nature” field on the Web. The authorities are not cited a lot by Natural Parks. This makes this subnetwork distinguish on the map. But Hubs take these in account and it explains their situation. They connect to many actors including in the main component, the bridge and the Natural Park subnetwork.

The Estonian SiS network is very hierarchical. Many actors are disconnected (19%) but hubs and authorities have many more links than other actors.

Estonia has few inbound links (51). The main sources are Italy (25%) and Finland (24%). Other countries do not cite a lot Estonia. Despite its strong constitution, the Estonian SiS network is not a main resource for the European SiS network.

Estonia has very few outbound links to other countries. The main target is Finland (48%) and then the International or European level (24%). Other countries are poorly cited by Estonian actors. Estonia is clearly citing Finland in priority. We suggest that Finland plays the role of a bridge between Estonia other European actors. Furthermore, Estonian SiS network seems self-sufficient (national actors do not rely a lot on foreign actors to expand their local network).

The most represented theme are “Science Education”, “Communicating Science” and “Environment”.

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Finland

102 actors have been collected, which is very few compared to other countries. We found 902 hypertext links between these actors, which means a very high links density of 0,088. The density is the highest, by far, in our whole corpus (the second one is Belgium, 0,036).

The Finland SiS network is very strong (the strongest), compared to other countries.

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The different categories of actors are strongly unbalanced. The most represented are Universities (51%). There are few Science centers, media, portals and personal websites, and no companies, no events/projects, networks or subnational actors. The “web-specific” actors (portals, personal websites) are remarkably underrepresented while Universities occupy a huge part of the network. The Finnish SiS network is mainly academic, that makes it very strong but it does not work well as a public space compared to other countries.

The main Finnish actor in the national network is the University of Tampere (cited by 37 other Finnish actors), but we have to notice that many other universities are almost as well cited.

The Academy of Finland, the Finnish IT center for science and the Finnish science and technology information service are the main hubs (they link respectively 43, 42 and 41 Finnish actors). Contrary to the authorities these are not universities, but respectively a Policy maker, a NGO-CSO and a Portal.

Finland has very much inbound links (141). The main source is Poland (30%) but other countries are important too (Estonia 18%, Belgium 13%, Hungary 11%). Finland is well linked by many countries and it denotes an important role in the European SiS network.

Finland also has very much outbound links to other countries. The main target is also Poland (26%) and in second the International or European level (23%), and after Belgium (17%) and Hungary (16%). Armenia, Czech Republic, Italy and Montenegro are not (or very poorly) cited by Finnish actors. The Finnish connections with other countries are almost symmetric, even if Finland is a little bit more selective when citing other foreign actors.

As expected, the actors that point the most to foreign actors, as well as the actors that are the most cited by foreign actors, are mainly Universities. The Universities subnetwork is so strong and massive that it determines the global profile of the Finland SiS network. In particular the Universities subnetwork is well distributed and not strongly hierarchical. Most of the universities are very well connected, so that no particular hub or authority emerges. We suggest that this behavior of distributing connectivity might explain the symmetric connectivity profile of Finland amongst other countries (each country points to Finland with almost the same amount of links as it is cited by).

The Finland SiS network is typical of a regular, strongly connected, weakly hierarchical network. This might be due to a good mastering of the connectivity policies by most of the actors. But it also denotes that no public initiative emerges in this part of the Web, so that there is no independent subnetwork.

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The universities network in Finland is massive and well distributed.

The most represented themes is “Science Education” (according to the importance of universities).

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France

203 actors have been collected, which is very much compared to other countries. We found 676 hypertext links between these actors, which means a low links density of 0,016.

The France SiS network is quite weak, compared to other countries, and it is partly due to the amount of disconnected actors.

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The most represented actors are Science centers. There are few companies, and no portal or independent website. The part of Network of organizations is important compared to other countries.

The main French actors in the national network is the Cité des Sciences et de l'Industrie (cited by 47 other French actors). Agrobiosciences is the main hub (links 37 French actors).

The relative weakness of the French SiS network is partly due to its important count of disconnected actors (26%). The remaining connected actors draw a quite strong network. Amongst the disconnected actors we observe an important proportion of museums. These might be expected to constitute a subnetwork, but it is the exact opposite. Many of the museums ignore (and are ignored by) the other SiS actors. By the way there are some connected museums, and they draw a small subnetwork. This also means that they do not connect to actors of another type.

Most of the French museums are connected to no other actor

The remaining connected museums draw a small subnetwork

Two components distinguish in the French SiS network. The first (and main) component contains many “CSTI” labeled actors (Scientific, Technical and Industrial Culture). This component occupies the bottom-right part of the national map. The second component occupies the top-left part of the map. This component contains a subnetwork of NGO-CSOs. These two components give two different orientations to the France SiS network, but these are not clusters and many actors are out of these components. The main hub, Agrobiosciences, is close to the NGO-CSOs component, but the main authority, Cité des Sciences et de l'Industrie, is in between at the center of the map.

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The “Scientific, Technical and Industrial Culture” component

The NGO-CSOs (green) component

France has very much inbound links (146). The main source is, by far, Belgium (53%). Almost every country has at least 1 link to France, but otherwise it is not well linked by other countries. France SiS Network is a noticeable resource in the European SiS network.

France has many outbound links to other countries (74), but it remains less than inbound links (it is the only country with Hungary). The main target is also Belgium (58%) but most of the countries are not cited by France (Armenia, Bulgaria, Estonia, Hungary, Montenegro, Poland, Portugal). This gives importance to the France SiS network, that is quite well cited despite that it does not cite many countries.

The most represented theme is “Communicating Science”.

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Hungary

138 actors have been collected, which is a lot compared to other countries. We found 545 hypertext links between these actors, which means a high links density of 0,029.

The Hungarian SiS network is strong, compared to other countries.

The different categories of actors are well balanced. The most represented are NGO-CSOs

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(18%) and Universities. There are few advisory bodies and events/projects, and no networks of organizations. The “web-specific” actors are well represented (portals, personal websites) while “science” is not overrepresented. The Hungarian SiS network is active and well developed.

The main Hungarian actor in the national network is the Hungarian Academy of Sciences (cited by 33 other Hungarian actors).

The Institute for Political Sciences is the main hub (links 39 Hungarian actors).

NKTH is the second authority as well as the second hub (cited by 24 actors and pointing to 28).

The left part of the map contains more “science” websites, while the right side contains more Portals and NGO-CSO. The left part is science-oriented while the right side is society-oriented. But despite this distinction, the two parts are interconnected in a single national cluster.

Structure of the Hungary SiS Network: two interconnected parts

Hungary has many inbound links (88). The main sources are Finland (25%), Poland (20%) and Bulgaria (18%). Bulgaria is linked by many countries and it denotes a noticeable importance in the European SiS network.

Hungary also has many outbound links to other countries. The main targets are International (28%) and Belgium (26%), and also Finland (17%).

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The most represented themes are “Governance, expertise, ethics” and “Communicating Science”.

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Italy

118 actors have been collected, which is few compared to other countries. We found 136 hypertext links between these actors, which means a very low links density of 0,010.

The Italy SiS network is very weak, compared to other countries.

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The most represented actors are Portals (19%) and Science centers (19%). There are many Subnational actors compared to other countries, and each other category is represented. The “society” part is well represented, and with the strong presence of “web-specific” actors (portals, personal websites), it means that the Italy SiS network is active.

Because the network is weak, there are no strong authorities. The most cited website has 7 inbound links, and the following 6 or 5. On the contrary there are strong hubs. EXPLORA-La tv della scienza is the main hub (links 33 Italian actors) and Torino scienza is the second one (29).

The Italy SiS network, organized around two hubs

SiS in Italy draws a single a poorly connected but quite well structured network, if we take into account many disconnected actors (39%). No specific cluster emerges, and in particular contrary to other countries, there is no strong subnetwork dedicated to scientific institutions. The network is organized around the two hubs, but there are still links between other actors.

The absence of an institutional network is remarkable. In other countries there is often a network of Universities that makes the national network stronger, but there are few Universities in the Italian corpus (is it due to the NCP's selection ?). Nevertheless it is also remarkable that various actors link each other (portals, subnational or local actors, media and events/projects).

Italy has few inbound links (37). The main sources are Belgium (22%) and France (19%).

Italy also has few outbound links to other countries (71). The main targets are International (30%) and France (27%).

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The most represented themes are “Communicating Science” and “Environment”.

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Montenegro

126 actors have been collected, which is very much compared to other countries. We found 118 hypertext links between these actors, which means a very low links density of 0,007.

The Montenegro SiS network is very weak, compared to other countries.

The most represented actors are Universities. There are few Science centers, few Portals, and no Network of organizations, no Personal website. The science is underrepresented while advisory bodies, companies and media are very well represented.

The main Montenegrin actors in the national network are the University of Montenegro and the Ministry of Education and Science (cited respectively by 11 and 10 other Montenegrin actors).

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Ministry of Education and Science is also the main hub (links 12 Montenegrin actors) but other actors have almost the same amount of outbound links.

Three components emerge from the Montenegro SiS network.

The first component is dedicated to telecommunications and engineering and contains many Companies and Media.

The Telecom and Engineering component in Montenegro SiS Network

The second component contains many Faculties dedicated to various fields, and linked together by the University of Montenegro (that is the best authority).

The Faculties component

The third component is quite similar to the second one, but different. It contains High Schools and is organized around the Ministry of Education and Science.

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The High Schools component

The Montenegro SiS network is very weak and contains many disconnected actors (47%). Nevertheless it is well structured, clearly organized in an institutional way (subnetworks by types of actors) where some institutions are the center of the network (Ministry of Education and Science, and University of Montenegro).

Montenegro has very few inbound links (5 links only), the minimum in our corpus of countries. It also has very few outbound links to other countries (22, also the minimum). The main target is International (45%). This poor external connectivity corresponds to the weakness of the network.

The most represented themes are “Governance, expertise, ethics” and “Communicating Science”.

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Poland

195 actors have been collected, which is a lot compared to other countries. We found 496 hypertext links between these actors, which means a low links density of 0,013.

The Poland SiS network is quite weak, compared to other countries.

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The most represented are Universities (42%). Each other category of actors is represented, and the “science” component represents more than the half of the corpus (59%).

The main Polish actors in the national network are the Ośrodek Przetwarzania Informacji OPI Warszawa, the Polish Academy of Sciences and Nauka Polska (cited respectively by 22, 21 and 20 other Polish actors).

Polskie Towarzystwo Fizyczne is the main hub (links 46 Polish actors).

Many actors are disconnected in the Poland SiS Network (29%). They are responsible for the weakness of the network. On the contrary the remaining, connected, actors draw a well connected network.

Poland: many disconnected actors (left) and a quite strong network (right).

This makes the Polish SiS network globally weak

The strong subnetwork is actually the network of the Universities. The strength of the Poland SiS network is due to the amount of academic links between Universities. This does not mean that no other type of actors is important. In particular the main hub is a NGO-CSO (Polskie Towarzystwo Fizyczne) and it links many universities. But we suggest that the lack of variety in this network may exclude some actors and explain why there are so many disconnected actors.

The main hub is a NGO-CSO

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Poland has many inbound links (86). The main source is Finland (42%), and Bulgaria is also an important source (20%) but other countries do not link much Poland. Poland is well recognized by some countries but its sources of links are not well diversified. This may be due to the academic orientation of the national network.

On the contrary Poland also has very much outbound links to various other countries. The main targets are also Finland (24%) and the International or European level (24%). Other countries are well cited by Polish actors like Belgium (14%), Bulgaria (9%) Hungary (10%). The academic network is active but the asymmetry between the inbound links and the outbound links (more various actors) denotes and intermediary position of Poland in the European SiS Network. It is a quite important resource for some countries but not widely recognized.

The most represented theme is “Communicating Science”.

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Portugal

65 actors have been collected, which is very few compared to other countries. We found 86 hypertext links between these actors, which means a high links density of 0,021. Note this high density is not necessary significant because there are few actors.

The different categories of actors are strongly unbalanced. The most represented actors are the “science” component: Universities (31%), Science centers (25%) and Research centers (22%) for a total of 78%. There are no advisory bodies, no Events/projects, no Media, no NGO-CSO, no personal website, no Portal or independent website. The network collected by the NCP is essentially the academic network in Portugal. There is a quite strong academic subnetwork that contains well connected Science Centers and Universities, but the Portugal SiS Network remains globally weak (despite its good density).

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The main Portuguese actor in the national network is the Ciencia Viva (cited by 11 other Portuguese actors). Ciencia Viva is also the main hub (links 20 Portuguese actors).

Portugal has few inbound links (38). The main sources are Czech Republic (26%) and Poland (26%).

Portugal has very few outbound links to other countries (56). The main target is International (29%) and in second France (21%).

The most represented themes are “Governance, expertise, ethics”, “Communicating Science” and “Science Education” (note that this theme is overrepresented, and it is probably due to the important role of Science Centers in the National SiS Network).

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Main axis of interpretation

Hierarchy emerging from countries' relations

• Where do websites aggregate?

The structure of the links is strongly determined by the countries. As you can see in the general map, actors bind more when they are in the same country. But we have to look further... How do actors link in the same country? How do actors link from one country to another, how do countries link?

As we have seen, actors bind more inside the same country. But different things happen in different countries. Some countries have a very strong structure, like Estonia. The Estonian NCP has collected a lot of actors, and they strongly link each other: almost 10 links per website. Some other countries have a weaker structure, like Czech Republic: almost 2 links per website. But something does not vary: inside every country the websites tend to link to those of the same type.

We need to make again an important remark here. It is easy to understand that we talk only about websites that we actually collected. But do not forget that the links we talk about are only the links between the websites that we collected. This means that a website can have a lot of links to other sites, and appear weakly connected in our map. This is the case when these links point to websites out of our corpus. So when we say that Czech has only 2 links per websites, we talk about 2 links from a collected Czech website to another collected Czech website. This low number is not necessary abnormal. On the contrary it is significant that there are 10 links per website inside the Estonian corpus.

The topic of the websites (representing actors) is important to understand how they bind inside a country. We often observe strong networks of institutional / governmental websites, and strong networks of universities, but there are some exceptions. We observe sub-networks in each country. But the size of these subnetworks and their links vary a lot from one country to another. We will see later if we can determine different profiles. At the moment we will focus on the question of strong networks and subnetworks.

We have got some strong networks in countries and some subnetworks inside some of them. Websites aggregate at two different scales: the national level (one aggregate by country) and the local level (some sub-networks in a country). There is no strong network with websites of several countries. We might also think that the European level is one big aggregate; it is probably true but we cannot tell with these data.

Note that we will not tell anything here about the specific case of each country. We will stick to the general question of data interpretation, even if it requires taking a close look on some countries' data.

• Small-World in the countries

How different countries link each other? We first observe that countries link more when they are close. The typical case is Estonia binding to Finland. This could be a consequence of historical relations as well as the result of the similarities between the NCPs' profiles. These questions fall out of the scope of this study. We may presume that most of the countries that strongly link in the map have strong ties in the “real world”. But the relations between “big” and “small” countries are more subtle, and this reflects some hierarchy that exists in actual interactions at the national level. Because we did not analyze every country in Europe, it will be difficult to describe precisely this hierarchy. Nevertheless we need to explain why it exists and what it does.

When we observed the connectivity between countries, we were surprised to observe a “complexity effect”. We will explain what it means, but for the moment let's say that we realized that countries had a specific type of relations. We were surprised because the general map does not look like a typical complex network. The networks that we usually collect on the

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Web look like an incredible mess, where it is difficult to see where websites link together. Compared to that, the general map is well organized: each country clearly occupies its territory. What is more, countries obviously link to their neighbors. But this “obviously” was hiding something: the links between close countries are asymmetric. This appears when you compare the amount of inbound and outbound links from a given country to another. And what is more we observed that this asymmetry is not random at all. This is typical of small-world, or complex, networks.

• The signature of complexity

The structure of the inter-country links has the signature of complexity. This can be determined with two observations. On one hand, almost every country links to every other country. This point reflects the presence of many transversal links. On the other hand, the quantity of links between two countries strongly depends on the “size” of each country. The way countries link depending on their “size” is very specific, and we will take a close look on this.

These “transversal links” are well known in the science of networks. They are identified as “bridges” or “shortcuts”, because they allow to travel quickly through the entire graph, from one side to another. In the image above, you can see such transversal links crossing the graph. Without these, the graph would not be a “small world” one.

When we say “big” or “small” countries, or when we write about their “size”, we do not necessary mean the size of the geographic area they occupy. We first mean that a “big” country is one that has a lot of strongly connected actors and a lot of connections with other countries. On the Web, being “big” means that you have a lot of incoming links, a lot of access: many visitors and a high ranking in search engines. We may assume that the more a country is economically strong and populated, the more its research gets fundings, the more powerful its network is. It is a very rough approximation but it works well with our 12 countries; that is why we say “big” and “small” - but do not forget the quotes. We consider Finland as a “big” country and Armenia as a “small” country. This may draw a scale that tells us which country is “bigger” than which one: we certainly will not write the list, for obvious reasons! But we need to start our analysis with relations between clearly “big” and clearly “small” countries. We need this approach to observe a certain type of interaction that is typically complex.

Finland has many more actors than Armenia, but it also has more links per website than Armenia. That is why Finland has many more links than Armenia. Armenia has not many links with other countries, and it has a lot more outbound links than inbound links. Finland is “big” and Armenia is “small”.

Finland has quite the same amount of inbound as outbound links. Who is Finland pointing to? Mainly the international or European (IoE) actors that we have in our corpus. Few links are from IoE to Finland, many links are from Finland to IoE. If we assume that the IoE level is “bigger” than Finland, then we can say that Finland has many links to what is “bigger” while it has few links from it. And as a result, if we except links with the IoE level, Finland has more incoming links than outgoing. Then take a close look at this: in Finland many inbound links are from Estonia, but there are few links to Estonia. If we assume that Estonia is “smaller” than Finland, then we can say that Finland has many links from something “smaller” and few links to it. Most links are from the “small” to the “big”.

If we look at Armenia, we will see that there is no country that has many links to it. But there are many links from Armenia to the International or European level (IoE). Armenia too is

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pointing to something “bigger”.

If we observe all countries, we will always observe this phenomenon. Most of links are from the “small” to the “big”. We call this “attraction to the top”, and it is typical of the scale-free networks. The links tend to point to more connected nodes. Most of the links start in lower layers and lead to high layers. From “small” nodes to “big” ones. As a result we observe something else. The “small” nodes tend to have few inbound links, because there is nobody “smaller” that could point to them. As well “big” nodes tend to have many inbound links because there are many “smaller” nodes pointing to them. And this leads us to another consequence: there are few “big” nodes and many “small” nodes, drawing pyramidal structure. This is the signature of complexity:

• Some countries have few inbound links and more outbound links. We call these the “small” ones.

• Some countries have many inbound links from various sources and a less or equal amount of outbound nodes. We call these the “big” ones.

• There are more “small” than “big” ones. The “size” is totally relative, so that you are “big” or “small” only compared to others. And there is an important gradient between “big” and “small” ones.

• Observing the hierarchy

Let's illustrate that with the links between Finland and Poland. From the Finnish point of view, the situation is well balanced, and the Poland seems to be an equal partner: Poland has a good amount of links to Finland, and Finland has a good amount of links to Poland. The amount of links incoming as well as outgoing is well balanced.

Inbound and outbound Poland

But let's look at the Polish point of view. Poland is “smaller” than Finland, and we can see that because its connectivity profile is not well balanced. Contrary to Finland, Poland has a lot more outbound links as inbound. The more valuable links are those that come to you, not those that go out of you. If others put links to you on their website, then they recognize you, and you may become an authority. Despite its outbound links, Poland has not so many inbound links. And this is changing the balance with Finland. Because even if there is the same amount of in and out links with Finland, the part of Finland links is not equal. The part is more important in inbound links than in outbound links. Finland is actually the country (that we have) that points the most to Poland. But it is not the case for outbound links. Where do Polish links go? First, they go to the IoE level (attraction to the top) and then to Finland and Italy. Finland is not the main destination of the links. But it is the main source.

Inbound and outbound Poland

As we have seen, Poland and Finland seem balanced from the Finnish Point of View. But it is not the case in the Polish point of view. This is because Finland is well cited, and Poland is not as well cited. Actually Finland and Poland are not in the same layer of connectivity; Finland is in a higher layer of connectivity.

Now that we have explained how to observe this relation, we can look at it for every country. What do we observe? Finland is an important source of links for Hungary and Estonia, and as we have seen, Poland. But Poland is at its turn an important source of links for Armenia and Czech Republic. And we need to add that no country is an important source of links for Finland, and that Armenia as well as Czech Republic are an important source of links for no other country. We have here a nice example of hierarchy, which we can represent as a pyramid with Finland at its top.

Hierarchy Finland

This is the main hierarchy of our set of countries. It does not include all of the countries because they do not all have hierarchical relations. France and Belgium for example form a

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balanced couple.

Couple FRance-BElgium

Some other countries do not really have hierarchical relations. It is the case of Montenegro, because it is an important source of links for no country and it has not enough inbound links to tell who is an important source of links. It is also the case of Bulgaria, but despite it has many inbound links, no country plays a particularly important role there. As we have told before we have only a part of this hierarchy, and we cannot fully draw it. For example Bulgaria may be an important source of links for a country that we do not have, or some country that we do not have may be an important source of links for Finland. In particular it would be very interesting to get the countries that could play an important role for other countries, like United Kingdom, Germany, Greece...

Let is make an important remark about this hierarchy. One may think that it is an administrative, or institutional, hierarchy. It is probably not (only) the case. An “official” hierarchy is a tree-like structure. This means that you are linked with entity that is directly above you, and the entities that are directly below you. What is specific in these “classical” hierarchies is that you are not allowed to have relations with the “top”. You are not supposed to be in relation with the superior of the superior of your superior, you are supposed to use intermediates (your direct superior and that is it); and the same to the bottom of the hierarchy. We will call this a first degree hierarchy. The hierarchy we observe is different, it is a second degree hierarchy and that is why it is not probably (only) due to “institutional” relations. It is also the reason why it is difficult to observe. Compared to a 1st degree hierarchy, it is like if each entity is connected to all superior entities, and not only the entity strictly above. In particular every country is connected to the European on International level, which is the “top”, and in this 2nd degree hierarchy the top is the core. This makes so many links that it is difficult to detect the hierarchical relations, and you have to notice that countries are not connected in a tree-like structure. The links do not draw a 1st degree hierarchy. However, the distribution of the links makes another hierarchy emerges, the 2nd degree one. So there is no 1st degree hierarchy but a 2nd degree one, and we had to take a close look on links to observe it. That is why we call it “2nd degree”.

• Interpretation of the hierarchical relations

We cannot draw the full hierarchy and if we had the full set of European countries, our observations would certainly be different. But there is something that we would nevertheless observe: the hierarchical relation determines different layers. We do not think that we would discover a country “under” Armenia as well as “above” Finland. And clearly we have nothing like that in our set of countries, even if there are balanced relations like France and Belgium (the hierarchy is not strict).

At this point we have got to reformulate some things. It is not necessary to think on the basis of “big” and “small” countries. This was good for understanding and observing the phenomenon, but the relation is self sufficient. The relation determines which country is “bigger” or “above” which country, not the inverse. We are going to leave this and determine the specificities of this hierarchical relation from straight observations. The main criteria that we used for now is “who is an important source of links for who”. This works well but it is only a part of the phenomenon. Here is the big picture.

We have got to explain something that one may find paradoxical. A characteristic of the relation is that there are more bottom-up links than top-down. And another characteristic is that the upper country is an important source of links for the lower one. We have already seen that, with the relation between Finland and Poland, but it is useful to look at it again.

So there are more bottom-up links but there are top-down links that are important for the lower country. This is the point: it is not because top-down links are important for the lower country that there are many of them, or that they are important for the upper country. It is the contrary! The top-down links are not important for the upper country, because it has more links and because the outbound links are less important. This is precisely the problem of the lower countries: they get less inbound links while these are more important. The lower a

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country is, the more it lacks of inbound links.

Our hierarchy is originated in the direction of the links. This is the key: inbound links are rare. And there is a competition to get them, even if we do not see it at an eye's glimpse. Fundamentally the hierarchy is the consequence of this competition; we have got to assume that the hierarchy we observe reveals a competition between countries. And it is quite easy to understand it in the field of science, because we know that researchers struggle to be well cited. But we will discuss this analogy later. For now we will describe precisely what happens.

There can be a competition only if there is a limited resource to collect. This resource is the inbound link. And this is the limit: for every link there is only one node that is on the good side, the target side. As a caricature, we could say that each link has a winner (the target) and a loser (the source). The goal is to get many inbound links, and because it is a limited resource, we may think that those who get many inbound links prevent others to get enough. But as always when it is complex, it is more subtle... Because even if there is a limited amount of inbound links compared to the amount of links (the half is not it?), anybody can freely add more links. We can consider that when you publish links to others, you are making an investment. By adding more links (where you are on the wrong side, the source side) you are increasing the gap between your inbound links and your outbound links, and thus defining yourself as a “lower” actor. But you may hope that by doing this, you will get links back as target, where you are on the good side. What are the conditions to get these links?

We observe a specific relation between countries. This relation gathers this set of characteristics:

• The relation is asymmetric and we define its direction as vertical: the upper country and the lower country • The lower country has fewer inbound links than the upper one • The lower country has less various inbound links than the upper one • In proportion, there are more bottom-up links than top-down • The upper country is an important source of links for the lower one

And these relations chain each other so that we make these observations:

• The relations draw a partial and non-strict hierarchy, strongly determined by connectivity: if A�B and B�C we never observe C�A, knowing that we observe some D�E • We call the vertical levels connectivity layers and there are less countries in higher layers - the European/International layer is the highest • This relation is typical of scale-free (or small-world) networks

NB: do not confuse the hierarchical relation with the links: there are links between

unrelated countries

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The works of A.-L. Barabasi show that the hierarchy we observe may emerge from a behavior called “preferential attachment”. This happens when new nodes, for example freshly created websites, link to already strongly connected nodes, such as very famous and big websites. By doing this, they reinforce the gap between strongly connected nodes and poorly connected ones. In our case it is impossible to track back the genesis of the websites of our actors. But we know well some things that look like the preferential attachment. We know that it is a common behavior for small and badly known actors to put links to main and notorious actors. But it is a beginner mistake, because these links are not useful (since the target is already notorious) and because their source actor will not benefit from it, and certainly not by getting links back. And we also know that institutions usually do not put links to other non-institutional actors, especially when they are small, while they always require links to their websites (usually with their logo, as soon as they give money). This behavior is driven by the idea that a symbolic reward is needed in exchange to something, and the web is very sensitive to the symbolical transactions.

But there is a problem... Because the model of Barabasi predicts that “the winner takes all”: the destiny of such a system is to become a hyper-hierarchical network where there is one node in the center (the winner) that collects all the links from all other nodes. This is clearly not going to happen, neither to our countries nor to the Web in general. There is no clear explanation but WebAtlas' previous works suggest that it is due to the strength of communities. The countries play this role as we can see in the general map: the country level is so strong that it prevents main national actors from being lowered by foreign and bigger actors on the same field. Some cultural attraction is at play here, but it is mainly because there is a need for leading actors in every language. It is well-known that the Web is quite well separated depending on different languages... With the specific situation of the English language, unfortunately this study does not include United Kingdom.

The science is a community, and we will not discuss here on the details. The fact is that researchers usually collaborate. It is clear that when two countries collaborate a lot, like Finland and Estonia, they have many interactions that justify hypertext links. But this is not enough to prevent Finland from being in a higher connectivity layer than Estonia, and the main reason is that Finland has more various sources of links than Estonia. The hierarchy does not only emerge from the quantitative relations between countries, it mainly emerges because some countries have many partners while others have few (even if missing countries introduce a bias on this measure). And we insist on the notion of partnership, because it justifies reciprocal links. Like we have seen, unidirectional links usually are bottom-up and reinforce the gap between connectivity layers.

This is an important point on 2nd degree hierarchies. Even if actors try to have a balanced relation, and even if each pair of actors tries to have a balanced partnership, the 2nd degree hierarchy may emerge. It may emerge because even if the relations are balanced, their distribution tends to be unbalanced (to a certain point). Two actors may have equal interactions together, if one has more partners than the other, the relation is actually unbalanced. So if balancing interactions is a good way to regulate 1st degree hierarchies, it does not operate on 2nd degree hierarchies. What is more, the “preferential attachment” makes that the 2nd degree hierarchy tends to transform in a 1st degree one. As soon as a country has many partners, it tends to unbalance all of its relations (because it becomes more attractive). The 2nd degree hierarchy is more diffuse but stronger than the 1st degree one, so that it is more difficult to regulate. In our case the 2nd degree hierarchy was “hiding” behind a network that seemed well distributed, non-hierarchical, at first glance. We do not know if it is possible to counterbalance such a hierarchy. But we know that it is self-regulating to a certain point, elsewhere we would observe a “winner takes all” effect. We suggest that for countries that get few interactions, it is easier to get some from other poorly connected countries. This prevents some countries to lose all of their relations. Existing partnerships at the bottom of the hierarchy prevent the whole pyramid to become a hyper-hierarchical structure where the top (the “winner”) gets all of the connectivity (“takes all”). In the Web in general it is very clear that Google, if we call it the potential “winner”, does not prevent other websites to freely connect, even if a huge mass of users rely on search engines to access resources. Partnership, particularly in the lower levels, is a good strategy to get inbound links.

Of course the main goal of a partnership is not to get inbound links on your website!

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Nevertheless you would expect for it. Why do you expect to be linked? Because it brings more access to you. And it is not only because a hypertext link is a path for users to browse to your website. This is just the way access exists on the Web. What you want is access, whatever it looks like. It is about appearing in the papers of other researchers, if you are one. It is about being cited as the laboratory of a famous researcher, if you are a laboratory. It is about being cited as a support of a main event or project, if you are an institution. As soon as an actor publishes something, it is to be read. And to be read, this actor needs to be accessed. This is wider than just the Web. But in the Web symbolic transactions appear very well. We track interactions by analyzing the traces they leave: these traces are symbolic transactions, and the most obvious shape of a symbolic transaction in the Web is a hypertext link.

Depending on the situation, a symbolic transaction is incredibly cheap or incredibly expensive. Usually two persons do not make any effort to shake hands: it is almost nothing. But think of the effort to make the Israeli leader and Palestinian leader shake hands: this symbolical transaction is huge. We do not mean that the effort is made just for hands shaking. But without this effort, no symbolical transaction: this is the value of certain symbolic things. Such things happen with hypertext links. There is a story about one of the most famous Web designer. He wanted to make a big German car company put a link on its Website to another big German car company. The effort to make was so incredibly high that he never succeeded. At another level, these phenomenons happen on the Web of SiS in Europe.

An actor will not put a hypertext link to another without a good reason. Hypertext links exist only as traces leaved by interactions that worth the effort to put these links. Generally, just knowing the existence of an actor does not worth the effort to put a link to it. It seems clear to us that many of the actors of SiS know more other actors than they point to. We propose several reasons to put a link to another actor:

– It is asked by contract (links to institutions: bottom-up). In France, as soon as you

are supported by the CNRS (National Center for Scientific Research) you are obliged to put the

CNRS logo and usually the link (and the CNRS is the biggest authority in the Web of Science in

France).

– In the “links” page, the target is an important actor (often, meaning “at least as important as me”: mainly bottom-up). Typically, NGO's will put links to the national institutions

that correspond to their field, even if they are “non-governmental”, because governmental

institutions are main actors.

– Propagating relevant information (event, publication: notorious actors are more read, so this is mainly bottom-up). Example: a researcher writes a post in his blog about a

publication he finds noticeable, and puts a link to it; and this publication is probably written by

more notorious actors (that is why our researcher watches their publications).

– Showing a partnership (by definition, this is half bottom-up and half top-down). When a consortium is created for a new project, the actors usually show it in their websites

and then put links to each others.

Most of these types of connectivity produce mainly bottom-up links. They apply well to the national networks of actors. But have to notice something for the network of countries. Obviously there is few institutional linking from one country to another, except to European institutions. We observe a massive connectivity to the International or European level, totally bottom-up. We cannot track it because it is too large, but we think that the links to the IoE level are mainly institutional links, because the website of the European Commission is by far the most cited website of our study. The study of the European Web by RTGI shows that this domain has a strongly connected core shaped around the website of the European Commission, thus confirming our analysis.

In the same perspective, we think that links from a country to another are mostly published to show a partnership or to propagate relevant information. These motivations produce more top-down links than the others.

We do not have enough elements to determine whether the situation is this one. But with it we can propose an important hypothesis. If the country to country links in the SiS field are due to strong interactions such as partnership, and this might be due to the importance of

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universities, then a lack of links means a lack of strong interactions. The point is that we observe a lack of links in lower countries (in the hierarchy) like Armenia and Montenegro. Of course the hierarchy is not complete. But at least it would mean that there is a noticeable lack of strong interactions between the upper and the lower countries that we have in our set. Maybe it is not a mystery that some small countries do not have many partnerships with their European neighbors. But we may bring a context to that, and propose some hypothesis about the way things happen. For now we propose the hypothesis that in the SiS field, the main interactions between the countries that we have are mainly due to scientific activity, and particularly through the activity of universities. Then the weak connections observed to certain countries would be the sign of the lack of partnerships with these countries. This point should then be put in perspective with the fact that the more attractive countries tend to gather more and more resources, and that partnership is an important mean to counterbalance this asymmetry. Thus promoting strong interactions with countries that lack the most could be a strategic action to prevent the SiS field in Europe from being dangerously concentrated.

This hypothesis is a proposal to read this work of mapping, and it will be one of our axes to analyze different aspects of the data we gathered. Actually this hypothesis simply reflects that besides the countries have hierarchical relations, we find some countries very isolated. That is the way we propose to interpret the hierarchy of connectivity between countries is the following:

• The network of national actors in each country is stronger than any transnational network. On one hand it protects the situation of local leading actors, by preventing stronger foreign actors from draining their resources. On the other hand a hierarchy appears between countries where few emerge as leaders.

• The access provided by inbound links is a limited resource, and it is shared on the basis of certain interactions. We think that countries compete for this resource (access) while cooperating to counterbalance the tendency of some countries to drain too much of this resource. It might reflect a network of interactions that is wider than the Web.

• We think that some countries might be excluded from this game, because nobody cooperate with them. We have not enough countries in this study to be more precise, but the network of interactions seems noticeably unbalanced to us, especially concerning weakly interacting countries.

• We suggest that the best way to counterbalance this might be an action on universities, because they play the main role between countries in the SiS field. We suggest too that institutions might interact essentially with their nation and the international or European level, but not directly with other countries.

This is not a conclusion but an hypothesis that should be tested in other (further) works and that will help us to put some results on perspective.

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Future steps

Propositions for a Web Information System dedicated to SIS (or SIS²)

EuroSiS task 3.2 (partner search) conclusions could be used to design an information system for a permanent and global process of description. This Web Information System (W.I.S) could involve the 27 countries of European Community operating through a large distributed network of observers. Such a system dedicated to SIS-mapping could be also useful for “science policy”, decision making and political survey. This knowledge-based system of Web resources extracted in European area could be a tool for the coordination with national activities and the European Commission.

Both at national and European level, this information system produces indications on thematic changes and emerging trends. Network mapping tools can display some temporal patterns of evolution in big sets of data as Web documents (time tracking). How information spreads in EURO-SIS network? How evolves distances and position between countries, organizations, actors through time and Web data? Such systems for tracking time series in data exist nowadays and include visualizations and statistical indicators.

Activated over 27 countries, observers could be involved in regular activities of knowledge sharing and Web data mapping. Such a distributed network of observers could help in identification of common resources, common trends, common interests and common challenges at the most appropriate level, be it national or European. To achieve this goal, the Baagz software has to be re-designed to become a fully personal workspace, including functionalities for online corpus monitoring, resources tagging and up-load/downloading space for NCP’s networking. The system must also be used for national reports.

Finally, as the SINAPSE system (or in association with), this system could publicly display URL and tagged resources through an on-line portal. This king SIS-search engine could encourage public engagement ('downstream' and 'upstream') in scientific research, in particular, through involvement of civil society in debating and shaping the research agenda. An experiment must be carried out in this field to build a democratic knowledge based society and to address societal issues in European research.

Obviously, in a distributed network for SIS-mapping and tracking, personal workspaces must be extended to all kinds of documents: Web data but also dynamic information (such as “news”, RSS fluxes…) and “static” ones as scientific publications, national databases, even multi-formats documents (pdf, ppt, txt, odt…). This technical extension could permit to encompass all fields of SIS policies and civil society activities. This process of aggregation of Web Information, specific documents and dynamics news should be one the key-feature of an Information System for strategic watch and decision making in the SIS field.

The tagging of Web resources made by observers represents a high quality work and should be improved by continuous learning and common sharing. This regular task could lead to a general index of European actors in SIS field, from political ones to industrial, scientific and civil leaders involved in science and technology management. A continuous distributed work associated to centralized processes for data analysis and mapping could be considered as the key-feature of an Sustainable Information System in which technological development and evolution is governed by the needs of a community as a whole and not only by computational functionalities. In other words, Euro-SIS mapping and experience could lead to a future Sustainable Information System for Science In Society, a SIS².

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Bibliography

Actor-Network Theory B. LATOUR, Reassembling the Social: An Introduction to Actor-network-theory, Oxford University Press, 2005, ISBN 0199256047, 9780199256044 Theory, conceptual framework A.-L. BARABASI Linked - the new science of networks, new ed. 2005. S. JONHSON - Emergence: the connected lives of ants, brains, cities, and software, 2002. Graph Theory D. WATTS Six degrees - the science of a connected age, 2004. S. STROGATZ - Sync: the emerging science of spontaneous order, 2004. M. NEWMAN - The structure and dynamics of networks, 2003. Web-Mining S. CHAKRABARTI Mining the web, 2002. J. KLEINBERG - Algorithm design, 2006. Web Mapping N. ANDRIENKO, G. ANDRIENKO, Exploratory analysis of spatial and temporal data: a

systematic approach, Birkhäuser, 2006, ISBN 3540259945, 9783540259947 InfoViz B. SHNEIDERMAN - Readings in information visualization: using vision to think, 1999.

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Glossary

Actor: In our study, an organization involved in Science In Society. Because we use the web to extract information, an actor is always represented by a website. Connectivity: Amount of hypertext links. If a website has a high connectivity, it has many hypertext links. The internal connectivity of a set of websites represents the amount of links between these websites, while the external connectivity represents the amount of links between websites inside and outside the set. Data mining: The process of extracting hidden patterns from data. See Web Mining. Degree: the degree of a node is the count of its links. We distinguish indegree (how many links point the node) and outdegree (how many links “go out” of the node). Edge: Connects two nodes in a graph. In our study it represents hypertext links. Graph: Nodes connected by edges. A mathematical structure that we use to project data extracted from the web. InfoViz: Also called “Information graphics” or “infographics”. Visual representations of information, data or knowledge. We think that this discipline is better described as “spatialization of information”. In this study we stick to the “Exploratory Data Analysis” philosophy: the way of making statistics is hypothesis generation oriented, rather than hypothesis validation oriented. Link: Hypertext link that connects two web pages. By extension we consider most of the time links between websites (websites are linked if there exist links between their web pages). See “Edge”. Network Science: Network science is a new and emerging scientific discipline that examines the interconnections among diverse physical, informational, biological, cognitive, and social networks. This field of science seeks to discover common principles, algorithms and tools that govern network behavior. The US. National Research Council defines Network Science as “the study of network representations of physical, biological, and social phenomena leading to predictive models of these phenomena.” Node: The fundamental element of a graph. Nodes are connected by edges. In most of our graphs, nodes represent websites that represent actors (and edges represent hypertext links). Scale-free network: Network whose degree distribution follows a power law, at least asymptotically. The web is considered as a scale-free network, and it's the case of the subnetworks studied here. See “Small world network”. Site: Website. A collection of related web pages, images, videos or other digital assets that are addressed with a common domain name or IP address in an Internet Protocol-based network. There is no strict definition of a website. But Internet users intuitively know when they go out of a website and come to another. We relied here on the source page identified by NCPs. This means that a website isn't always defined by its domain. Small-world networking: In mathematics, physics and sociology a small-world network is a type of mathematical graph in which most nodes are not neighbors of one another, but most nodes can be reached from every other by a small number of hops or steps. A small world network, where nodes represent people and edges connect people that know each other, captures the small world phenomenon of strangers being linked by a mutual acquaintance. Such as the networks studied here. See “Scale-free Networks”.

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Web: Abbreviation of World Wide Web. A system of interlinked hypertext documents accessed via the Internet (with a Browser). In this study we don't explore the Internet but only the web, meaning the part of Internet that is accessible to a Brower (not the emails, the voice over IP etc.) Web Mapping: The process of designing, implementing, generating and delivering maps on the World Wide Web. While web mapping primarily deals with technological issues, web cartography additionally studies theoretic aspects: the use of web maps, the evaluation and optimization of techniques and workflows, the usability of web maps, social aspects, and more Web Mining: The application of data mining techniques to discover patterns from the Web. According to analysis targets, web mining can be divided into three different types, which are Web usage mining, Web content mining and Web structure mining. In this project we used essentially Web structure mining.

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ANNEX 1

National Contact points’ CV

Armenia Tigran Arzumanyan

Mr. TIGRAN ARZUMANYAN: is the Head of International S&T Programmes Unit of the National Academy of Sciences of Armenia (NAS RA), and Vice-President of the Center of Ideas and Technologies NGO, and officially nominated FP7 INCO NCP in Armenia. Since 2003 he has been co-ordinating the activities of EU FP6 National Information Point established at the NAS RA to promote and facilitate participation of Armenian researchers in European research programmes, particularly, FP6/7. In this endeavour he participated in many trainings/information days organized in various EU/EECA countries dedicated for FP6/FP7 NCPs. He was Fellow of NATO Science Policy Fellowship Programme in 2001 and 2004, to carry out research and comparative studies on S&T and Innovation policy at the Science Department of Calouste Gulbenkian Foundation, Lisbon, Portugal, and Innovation Research Centre, IKU, Budapest Corvinus University, Hungary, respectively. During last years he has participated and contributed to several S&T and innovation policy related international events organized in Romania, Bulgaria, Portugal, South Africa, Russia, and USA.

Belgium Laurent Ghys

Laurent Ghys has a PhD degree in Chemistry awarded by the Free University of Brussels (ULB) in 2002. He is working at the Scientific and Technical Information Service (STIS) as a scientific collaborator since 2002. He is NCP for the Belgian Federal Authority for the specific Research Infrastructure programme of the 7th Framework Programme and occasionally supports the work of the other NCP.

Bulgaria Ivo Dimitrov

Dr. Ivaylo Dimitrov has a Ph.D. in Philosophy and Master degree in Public Relations. He is Assistant Professor of Epistemology at the Institute for Philosophical Research of the Bulgarian Academy of Sciences. Founder and chief editor of two websites for science popularization (Democrit.com and Green.Democrit.com). Due to his research interests and science communication activities Dr. Dimitrov is elected as SiS NCP in FP7.

Czech republic Michal Pacvon

Mr. Michal Pacvoň works for the National Information Centre for European Research, which is a part of the Technology Centre AS CR. He is graduated on Faculty of philosophy on Charles University in Prague. He was involved in research on Centre for Phenomenological Studies. He published some articles in the literary and film theory revues. He is also NCP for Socio-economic sciences and humanities and for INCO.

Estonia Terje Tuisk

Terje Tuisk works for Archimedes Foundation since 1997. She has been involved in NCP networks since 2000 - NCP for IHP program in FP 5, Science and Society NCP in FP6 and Science in Society NCP in FP7. She is a head of Science Popularization Unit in Foundation Archimedes - among other duties the unit works with Young People and Science topics as well as organizing the Science Communication Award contest in

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Estonia.

Finland Reetta Kettunen

Dr. Reetta Kettunen Ms., Secretary General for Committee for Public Information. The Committee for Public Information is an expert body attached to the Ministry of Education. It follows progress in research, arts and technology and the development of knowledge in Finland and abroad. Each year, the Committee awards grants, makes a shortlist of candidates for the State Award for Public Information, and makes a list of domestic quality non-fiction entitling libraries to acquisition support. In addition, it gives its opinion to the Ministry concerning matters within its mandate and makes proposals and takes initiative for different ways to promote the dissemination of knowledge. The Committee also has many responsibilities in the Science in Society work on national level in Finland (http://www.minedu.fi/export/sites/default/OPM/Julkaisut/2004/liitteet/opm_213_tr28.pdf?lang=fi). The committee coordinates the information website on R&D in Finland www.research.fi. The office of Committee is attached to the Federation of Finnish Learned Societies. (http://www.tjnk.fi/en/ ). Dr. Kettunen has an academic background with plant physiology and molecular biology as her area of scientific expertise. She has a strong experience in science communication and science policy. Her previous working experience includes: - Editor-in-chief in the field of popular science books, Art House Ltd - Scientific secretary, Viikki Research Group Organization in Molecular Biosciences, University of Helsinki - Programme manager, University of Helsinki (for research programmes funded by the Academy of Finland). In ERA-context, Dr. Kettunen has been working for two ERA-NETs (for ERA-SAGE as a task leader, for ERAPG as a ncp and as a science representative). She is also familiar with technology platforms. She is the FP7 SiS NCP (II) in Finland. Mrs. Sophie Tocreau has a master degree in scientific museology. After a first work experience at the “Palais de la découverte” (geology department), she joined the Ministry Delegate Research in 2000 where she was given the responsibility of coordinating the national science festival (2000-2005). Involved in the “Science festivals” European network, Sophie Tocreau she has followed up the Science in society European program, along with the official program committee representative. In this framework, she has started mobilizing « Science in society » potential actors. Now, she also carries out this task in the framework of the FP7 Science in society National contact point.

France Martine Roussel Sophie Tocreau

Mrs. Martine Roussel has joined the ministry Delegate of Higher Education and Research since March 2007 where she assumes the task of NCP for the Science in society capacities program along with Sophie Tocreau. She worked previously for the French National Centre for Scientific Research (CNRS) as a communication officer. She was involved in FP5 IST projects while working for the Teleport Sachsen Anhalt (DE) Brussels office. She studied politics in Rennes (Fr) and at the university of Exeter (UK). She graduated from the college of Europe in 2001.

Hungary Agnes Hegyváriné Nagy

Mrs. Ágnes Hegyváriné Nagy, NCP for the FP7 programmes “Science in Society”, “Ideas” and the topics related to “Women in Science” has been working in the International Department of the National Office for Research and Technology since 1999. She worked as an NCP for the “INCO” programme, and as a contact point for legal and financial issues and an NCP coordinator

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during the 5th Framework Programme. She was responsible for several Hungarian and EU-funded training programmes for NCPs and Hungarian liaison offices. In 2002 she received an acknowledgement for her work from the Under-secretary of the Ministry of Education.

She graduated at the University of Debrecen in 1996 as a teacher of Mathematics and English, later she obtained a postgraduate degree in European Studies at the University of Economics (Corvinus) in Budapest.

Italy Mara Galandi Katia Insogna

Mrs. Mara Gualandi is a National Contact Point in Italy for the EU Programme "FP7-Science-in-Society". Mrs. Gualandi has been working for seven years, from 2000, in APRE with several tasks: -Responsible editor for the monthly electronic magazine "APRE news" - Responsible for the relations with CORDIS and IPR Helpdesk - Responsible for training courses organized by APRE with the presentation of various subjects as "Communicate the research" or "How to submit a successful proposal in the Framework Programme".

Montenegro Tamara Tovjanin

Mrs. Tamara Tovjanin is a senior advisor for scientific and technical cooperation in charge, among other duties, of cooperation with European Union. Her job is to prepare proposals for programs of international S&T cooperation and participate in their approval; coordinate and stimulate their implementation in our country through regular contacts with projects' holders; carry out coordination with the appropriate departments of the national and local bodies in the scope of her duties; propose measures for improvement of S&T cooperation and follows their implementation, provide expert opinions on draft programs, agreements, contracts and other documents in the field of S&T, etc. She was a project manager on the Montenegrin side of the ERA WESTBALKAN project and is in charge of the project management of the projects ERAWESTBALKAN+ and EU-Balkan-FABNET. She was FP6 NCP and is FP7 NCP for all of the above-mentioned priorities. Educational background: BA in Oriental Philology and MA in European Studies.

Poland Malgorzata Krotki

Mrs. Małgorzata Krótki holds a Master’s degree in Biotechnology. She also studied Managing EU Projects at the Warsaw Agricultural University, Faculty of Economics and Agriculture. After a first work experience at the Plant Breeding and Acclimatization Institute, Department of Foreign Cooperation (EU Centre of Excellence project „Crop Improvement Centre for Sustainable Agriculture”), she joined Instytut Podstawowych Problemow Techniki Polskiej Akademii Nauk (IPPT PAN) in 2005 where she was given the responsibility of supporting role for the FOOD NCP (2005-2007). Since January 2007 she has undertaken NCP positions for Socio-economic Sciences and the Humanities as well as Science in Society. She is a member of SSH and SiS Programme Committees. She is involved in several FP6 projects.

Portugal Mario Vilar

Mr. Mario Vilar has a Degree in European Studies from Universidade Moderna de Lisboa. In July 2004, he joined GRICES as Technical Staff for the Helpdesk of the Idealist34. He has undertaken several NCP positions, namely Energy and EURATOM NCP (since January 2005), IST and Infrastructure NCP (since January 2006), Infrastructure, SiS and SSH NCP (since January 2007).

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