The Centre of Competence for Data Science and Simulation (DSS) is a cross-departmental initiative. Our ambition is to develop infrastructure, tools and competences in in machine learning, big data, microsimulation and geoexperimentation to conduct excellent research and advise policy-makers as well as stakeholders.

For LISER, this means developing best practices and fostering a data-driven culture for executing reliable research studies. The DSS has this in its scope while the wide range of seminars offered in this context has been a tangible proof of this. Discover some of the topics covered over the last months below.

The infrastructure helps build up skills in machine learning, Big Data methods, micro-simulation, etc., and to develop top standards capacity to work with data both for research and policy work. As a result, DSS is actively developing GitHub.

For LISER, GitHub proves useful as a tool for facilitating collaboration among researchers and optimising workflow. GitHub provides several advantages to researchers and policy makers: it serves as a repository for code, a library for research, and an organization tool for version control. It is also a place that can be used to centralise information (e.g. provide links) to relevant data.

Furthermore, GitHub serves as a Learning Management System (LMS), it encourages both intra-institutional and inter-institutional collaboration, creating a community of shared knowledge and progress. The platform is free and easily accessible with low entry barriers. Our experience is that setting up an account takes just a few clicks. Importantly, putting a project on GitHub does not mean making it available to anyone, unless one decides so.

The infrastructure helps build up skills in machine learning, Big Data methods, micro-simulation, etc., and to develop top standards capacity to work with data both for research and policy work. As a result, DSS is actively developing GitHub.

For LISER, GitHub proves useful as a tool for facilitating collaboration among researchers and optimising workflow. GitHub provides several advantages to researchers and policy makers: it serves as a repository for code, a library for research, and an organization tool for version control. It is also a place that can be used to centralise information (e.g. provide links) to relevant data.

SDI is a strategic project to facilitate efficient geographic research data management and sharing within and outside LISER, with complying with well-established Open Data and Research principles including FAIR (Fair, Accessible, Interoperable, Reusable) and TRUST (Transparency, Responsibility, User focus, Sustainability, Technology). The goal is to facilitate the discovery, access, management, distribution, reuse, and preservation of digital geospatial resources while centralising the management of spatial data and information related to multilateral projects for best sharing and exchange between researchers.

Patent data on advances in digital and green technologies

This task generates proxies for advances in digital and green technologies using NLP methods and big data. The data used come from the European Patent Office (EPO), the USPTO patent website following Kelly et al. (2021), and Google Patents. We transform the documents into text data, split the text into the relevant sections of the patents, and make sure that all patent texts are machine readable. We next compile a list of keywords describing the patents linked to digital and green technologies and use semantic matching to identify these technologies. For advanced digital technologies, e.g., we use Alekseeva et al. (2021). To match the patent information to the industries that potentially make use of digital and green technologies, we use probabilistic matching procedures.

We differentiate between automation and augmenting innovations and classify breakthrough patents, which serve as an instrumental variable to identify causal effects of new technologies.

Preparing big data from online platforms to analyse the supply and demand of skills in different EU countries

This task makes use of big data from online platforms such as LinkedIn, PayScale, Glassdoor, Indeed and OJV data to analyse the supply and demand of skills (using the keywords of Task 2.1) across European countries.

LinkedIn has over 100 million users in the EU and is therefore considered a particularly reliable data source for the analyses in this project. The platform provides data for both skills supply and skills demand. The skills supply can be derived from the user profiles on LinkedIn and the demand can be extracted from the job vacancies at LinkedIn. Using the job histories, we can also extract information on job transitions in different countries.

Exploiting the time dimension, we can track skills supply, skills demand and job mobility for EU countries and for different sectors over time. We will supplement this information with data from PayScale, Glassdoor, Indeed and OJV to have information on skills and salary at the occupation and company level in EU countries. Various data analysis techniques are used to extract and combine data from diverse sources. We use statistical models and NLP for the data analysis.

An illustration of this enhanced efficiency is the project Artificial intelligence, automation and job content: the implication for wages, job mobility and training, where the PI assesses the effect of Artificial Intelligence (AI) exposure at work on wages and employment. For this project, he relies on generative AI to meticulously identify keywords related to AI within a substantial list of 12,000 concepts frequently employed by developers. Research has shown that generative AI performs well in classifying objects. Furthermore, he uses Sentence Similarity to compare different texts between them in terms of meaning. Sentence Similarity, a prominent component of Natural Language Processing, provides a score measuring to what extent two texts have similar meanings. In total, 61,000 pairs of texts are compared.

David’s project with Andrea Albanese on cross-border workers from Belgium to Luxembourg is another example showing how data science techniques can be used. In this project, they assess the effect of becoming cross-border workers on wages after workers return to work in their country of living (i.e. Belgium). First, they use machine learning algorithms, namely Random Forest, to identify the most influential predictors of becoming a cross-border worker. Subsequently, they employ Double Machine Learning to assess the effect of becoming a cross-border worker on wages.

Problem description

This study focuses on the collaborative possibilities offered by the IoT to modern agents (including robots). Agents have no a priori information about the sensory capabilities of other existing nodes. An agent with a specific task could gain time, performance and precision by using the resources and capabilities present in the network. There are many challenges to achieving this opportunistic collaboration. These include a universal ontology for communicating available computational and algorithmic resources and sensory capabilities in a comprehensible language. For example, a security camera can be defined as having a processor, a hard disk, algorithms for detecting people and facial recognition, and producing images of a given resolution, etc. There is also the challenge of representing the world and objects in order to carry out tasks. For example how a robot looking for an object can describe it and how a safe camera with a wide view can help and guide.

We considered a specific case with two agents

A fixed camera at height and a mobile robot that has to carry out a task. The robot has to search for an object that is in the camera's wide field of view and this study seeks to explore and demonstrate the possibilities of IoT as opportunistic collaboration between 2 heterogeneous agents with no prior knowledge of each other's capabilities. This case study was implemented using the ROS (Robot Operating System) environment and produced promising results. The next step is to use real robots (such as Turtlebot, Cherokey, Xgo and Spotdog) to quantify environmental parameters, for example.

Team

Hichem Omrani (PI, research scientist, UDM dept, LISER - Luxembourg), Judy Akl (PhD student, University of Lorraine, France), Fahed Abdallah (Full Prof at the University of Lorraine, France), Amadou Gning (Robotics expert, Createc-UK)