The FIRSTRUN project advances the theoretical and practical debates on the effective mechanisms of fiscal policy coordination. It analyzes the very reason why fiscal policy coordination may be needed in the first place, namely cross-country externalities (spillovers) related to national fiscal policies. Specifically, it identifies different types of spillover effects, investigates how they work in the EU and in the EMU, and analyses whether they work in the same fashion under different states of the economy and over the short and the long run. The project describes different forms that fiscal policy coordination can take in practice, e.g. ex-ante coordination and risk-sharing, and provides a critical assessment of the mechanisms already put in place. The FIRSTRUN project provides new tools for fiscal policy design by incorporating the new EU fiscal rules regarding e.g. government debt and deficit into applied models for fiscal policy evaluation. The tools can be used to support the decision makers in the implementation of the enhanced EU economic governance. FIRSTRUN also investigates the political economy of fiscal cooperation, for instance, the difficult inter-play between domestic political pressures and EU level priorities as well concerns about legitimation. By shedding light on the character of the governance framework for fiscal coordination, FIRSTRUN will highlight the features that work well or badly and provide insights that the EU level can exploit in its surveillance and advisory roles.

The web nowadays is overloaded with huge volumes of disparate information and linked data (i.e. Big Data) which requires the use of specific tools, Data Science methods (e.g. Machine Learning) and emerging technologies in big data in order to improve transparency and recognition of domain specific skills through the web. Job profiles contain general job description, expected competencies, skills and duties. Moreover, job centers and agencies typically aim at matching desired job profiles with suitable candidates. However, job seekers face difficulties in better understanding of the required job knowledge and competences through reviewing job profiles. Moreover, public and private sectors need IT-based tools to simplify transparent recognition of domain specific job knowledge while setting up their job profiles. It becomes more challenging when different countries provide different priorities and job descriptions due to various job market characteristics, vocational and educational trainings (VET) and demographic circumstances. This challenge prevents mobility of skilled workers, youth and workforce across Europe. It is shown that unemployment problem and risk of social exclusion hit more youth and young workforce in the European countries. Specially, the current refugee crisis caused by large amount of refugees and asylums in Europe enforces further difficulties to earlier stated challenges. Considering all stated challenges, the massive amount of information on the web such as job announcements, forums and wikis, is a gold mine for job knowledge discovery. The main issue in this regard is how to retrieve, cleanse, explore, visualize and interpret such huge volume of web data and put them in a sort of Job Knowledge Base (JKB). In addition, semantic web mining promotes exploitation of semantic structures in the JKB formed through web mining. Accordingly, enriched JBK using web data analytics (1) improves construction of job profile templates, (2) contributes to job analytics, labor market demand analysis, wage analysis, (3) facilitates skilled worker mobility, (4) supports identification of required skills and qualifications and (5) helps strengthening key competences in VET curricula.DISKOW will provide a neat Job Knowledge Base (JKB) as a prototype which collects job specific data from the web and provides recommendations through analytics. Job knowledge catalogue of a job definition in the JKB will be equipped with a template of the most typically required competences and skills for that job. Job seekers will be able to use the JKB in order to develop their domain specific skills and competences based on recommendations in specific job knowledge catalogue. In this regard, the mined information of jobs as well as their relevant competences and skills can be used to identify list of top demanding jobs, skills and competences and provide predictive analytics.The consortium consists of four partners, namely the L3S Research Center at the Leibniz University of Hanover in Germany, the Institute of Economic Research at the Slovak Academy of Sciences (IER SAS), Engineering as a large enterprise in software development and skill analysis modeling provider in Italy and Petanux GmbH as a private data science and research exploitation company in Germany. The consortium as a whole provides professional competences for fulfilling the objectives and promises of DISKOW. In addition, the project partners will disseminate the project results in cooperation with their networks through governmental as well as private employment agencies, VET providers and other related stakeholders to flourish the results and outcomes and sustain the project and the platform in long term.From the non-technical point of view, DISKOW aims at analyzing the labor market at the level of consortium partnership countries whereas the proof of concept can be used to the labor market analysis at the European level. As a result, DISKOW will be able to provide a streamline of the workforce development and provide predictions and road-maps for the future of specific required competences and jobs. Accordingly, data science has moved to the top of European labor markets’ list. Due to the importance of data science jobs in the European labor market, DISKOW will focus on the identification of skills and qualifications in the data science sector as a specific case study. The final solution will be ready to be adapted to a wide variety of sectors and workforces. This proposal has been once accepted for funding last year with great scores. The University of Koblenz had internal difficulties with EU to sign the contract, therefore we agreed to resubmit the proposal once through LUH this year. Furthermore, thanks to reviewers of last year, we even improved the quality of proposal in terms of review critics from last year, meaning that this year's submission targets even review comments after acceptance of last year for having higher quality project.