Complementing DILAN’s WP2 ethnographic study, Kampal Data solutions extracted several metrics from 11 EU scientists’ research publication productivity and impact. The purpose was to define and calculate these scientists’ influence and leadership using complex network properties.
One of the tasks of the DILAN project is to carry out a detailed study of the situation of researchers in terms of their scientific output and their impact on social networks related to scientific and academic issues.
The aim is to use AI tools to track the research productivity and impact of a representative sample population, 11 researchers, before and after the training that the DILAN partners are developing at the moment. In this way, we will be able to observe changes in their position within their scientific community, and assess improvements in the impact of their work.
To achieve this aim, DILAN’s partner company, Kampal Data Solutions, has used scientific output scraping systems based on Natural Language Modelling, which is one of the most advanced AI techniques. Tracking the selected researchers will involve two studies. The first one will focus on analysing their research community and their role within it and the second one will quantify the impact of their work using platforms such as Google Alerts, Google Scholar, YouTube and Twitter.
Complex Networks to analyse the ecosystem of Researchers and their community
In the DILAN’s network we show the 11 selected researchers as well as all the contributing co-authors who at some point have collaborated with them in a given publication (a journal article). This figure shows how we build their networks and visualise their situation within the ecosystem.
The Links represent the collaborations made between the researchers. A link is drawn between two researchers when they have collaborated in the publication of a paper. The colour of the nodes represent the different communities formed at the selection. These communities are formed by researchers who often collaborate with each other.
By studying these communities, we will be able to distinguish how different research ecosystems work and we will also identify the leaders of these communities.
The size of the node represents the importance of the researcher in relation to his or her collaborations with relevant people in the network. The relevance of the person defined as the quantity and quality of the relationships with important people in the graph. The larger the node, the more important its contributors are by measuring the number and impact of their publications.
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