European Platform for Data Science: Incubation, Learning, Operations and Network

Objectives and target groups

The transnational project EPSILON with partners from Germany, Portugal, Cyprus and Lithuania addresses the needs of European Data for Good initiatives and higher education institutions with degrees in Data Science. As a first step, the project team will design tailored workflows and tools for European Data for Good initiatives. Based on this, EPSILON will set up a European Knowledge Platform and establish a new Data for Good initiative in Lithuania. The gained experience and knowledge will be transformed into target group specific learning material for students, university teachers and Data Science enthusiasts.

Project Results in Detail

PR1: European Data for Good Needs Analysis

This output aims to assess the needs of different types of stakeholders in the Data for Good field across Europe. A list of needs, drawbacks and suggestions will be determined, by taking into account what processes each Data for Good association is implementing, and the major challenges they have found along the way. The expected results of the output are a database of current European initiatives in Data for Good, a summary of interviews, a brief summary of the results of focus groups and a guide with workflows for best practices. It will fill a gap as up to now there are no European open resources containing a listing of best practices and processes for Data for Good associations.


PR2: Knowledge Platform and Business Intelligence Toolkit

This Project Result aims at producing a digital environment to support the objectives of the project and the work of Data science volunteers. In the context of this output, the project team will produce and test an interactive, open access, sustainable, online European Knowledge Platform providing comprehensive information and content for the target groups (text, video, audio, PDF), and a Business Intelligence (BI) Toolkit (e.g. interactive workflows, dashboards), for storing, synthesizing, and presenting data. The results from Project Result 1 will be initially considered, indicating the needs and suggestions from the target groups, and a software engineering process will be followed.


PR3: New Data for Good Initiative in Lithuania

Project EPSILON addresses the current lack of local a Data for Good initiative in Lithuania. This fact is especially relevant, as in Lithuania there is an increased need for digitalization in social organizations. Therefore, there is a need for initiating a pioneering regional Data for Good initiative in Lithuania. In doing so, we enable social organizations to get access to voluntary experts and relevant know how. The emergence of the new Data for Good squad would pave the way to get access to an international ecosystem of voluntary data enthusiasts to share best practices on solutions to data analytics problems. The output is highly innovative, because it involves the whole process of setting up, establishing and practically testing a new regional Data for Good initiative. This makes it a dynamic and practice-oriented pilot activity that involves numerous opportunities (and also risks)), from which great learning effects for the further establishment of regional teams can be expected.The newly established team could also serve as a role model for other regional teams in the Baltic States, Eastern Europe and beyond, sharing their expertise and providing step-by-step guidance for similar ventures.


PR4: Teaching & Training Material

The outcome of this Project Result is learning and training material which will be accessible via the Knowledge Platform established in PR2. Based on Project Results 1, 2 and 3 we create learning material for relevant use cases such as recruiting, project scoping, measuring impact of a project amongst others. The learning material and self-explanatory tutorials with tailored descriptions will be designed for two main target groups: First, our goal is to provide sufficient material for both needs information and teaching. Due to the fact, that the field of Data science describes a new field of multidisciplinary education, we provide learning material for higher education institutions on basic and advanced levels. The second target group are data enthusiasts working in Data science teams who need best practices in order to organize themselves efficiently as well as data enthusiasts who want to initiate a new team in a new region. Both target groups will then be addressed by self-learning tutorials. The created learning material will also be applicable to different higher education study programmes which are linked to the field of Data science (i.e. social science, mathematics, computer science, design amongst others).


Digital Transformation describes a staggering task for our society and access to relevant knowledge is limited. The increasing demand for technical and methodological expertise in combination with domain knowledge significantly impacts the European job market. In this vein, a new job description as well as a new string of education emerged: Data Science. As access to expertise in Data Science is sparse, all over Europe qualified volunteers are organized as regional or national initiatives to address the fact that social organizations in Europe do not have sufficient access to relevant knowledge and tools in order to adequately address Digital Transformation. Organized in regional or national teams, volunteers support social organizations to make use of their data by connecting them with volunteer data scientists and analysts. This idea goes back to the initiative “Data Science for Social Good”, founded in Chicago, and inspired regional experts in Europe to organize similar initiatives in their region. Leading initiatives in Europe are Data Science for Social Good (Germany, UK and Portugal), Data for Good (Denmark, France, Poland and Spain) and Correlaid (Germany and France).

Although a few initiatives in Europe do already exist, every initiative is individually organized and there are no European open resources comprising workflows and best practices for Data for Good initiatives. Groups are mostly organized in local or national heterogenous teams and are established by individual initiatives. Due to the fact that the demand for expert knowledge in the field of Data Science has been increasing, students and professionals need access to target group specific courses, and higher education institutions need access to customized learning material that takes into account different scientific backgrounds.


Universidade NOVA de Lisboa

Nova School of Business and Economics is a Public Higher Education Institution, a school focused on Management, Economics and Finance, and is an organic unit of the Nova University of Lisbon. Nova SBE was the first European institution organizing the Data Science for Social Good (DSSG) Summer Fellowship, back in 2017 - a partnership with the University of Chicago. This is a full-time summer program to train aspiring data scientists to work on machine learning, data science and AI projects, with social impact, in a fair and equitable manner. The program was hosted by the school again in 2018 and, in 2019, Nova SBE joined forces with the DSSG Foundation and supported the replication of the program at Imperial College London and at Warwick University, kicking-off a series of different DSSG chapters across Europe. Nova SBE implemented various data science related projects, partners, and events and, in 2019, Nova SBE formally established its Data Science Knowledge Center (DSKC), that aims to advance knowledge about data-driven decision-making and ist aimpact on society. As of today, DSKC supports a team of more than 70 researchers, faculty members, PhD and Masters students that, together, develop impactful data solutions for companies, Government and social impact organizations

University of Cyprus

The Software Engineering and Internet Technologies (SEIT) Laboratory is an integral part of the Department of Computer Science of the University of Cyprus. (SEIT) focuses its research activities on two important areas of Information Technology, namely Software Engineering and Internet Technologies. In the second area, the Laboratory concentrates its expertise on the development of ICT-enabled Creativity and Enhanced Learning Environments, platforms and tools. SEIT has extensive experience in the area of applying ICT to Technology Enhanced Learning (e-Learning, m-Learning, Blended Learning and Open and Distance Learning) in general, as well as Lifelong Learning in particular. This expertise is both at technical level and at developing policies and qualification frameworks for such activities.

Vilnius University

Vilnius University is the largest University in Lithuania involved in multi-disciplinary academic activity. This is one of the oldest higher education institutions in the Eastern and Central Europe (founded in 1579). During more than four centuries of its existence, Vilnius University (hereinafter – VU) grew into the leading University in Lithuania. The University retains its leading role in a broad spectrum of fundamental and applied research, education, training and retraining, consultancy, providing research and development services to a wide range of businesses. VU actively participates in international scientific and academic activities and embodies the concept of a traditional university – the integrity of research and education. VU takes responsibility to maintain the highest level of education and research sustained by University’s research teams of international acclaim and new teams led by talented young researchers. This way, University fulfils the needs of the state and society in the European research and education area. VU also seeks to ensure annually increasing involvement in European research and educational programs.

DSSG Portugal

Data Science for Social Good Portugal (DSSG PT) is a non-profit association based in Portugal, with the objective of building an open community of data scientists, data lovers and data enthusiasts that want to tackle problems that really matter. DSSG PT’s goal is to connect people who have data skills with institutions that work on projects with social impact, such as charities, public administration entities and non-profits. Together, the association helps them collect and transform data into actionable knowledge to ultimately increase their impact. DSSG PT’s approach is twofold: first, it engages the Portuguese data science community in data projects aimed at delivering high value products to relevant social good initiatives; second, it fosters the discussion of the social and ethical implications of data science in the society, through events (talks, roundtables, workshops, etc.) The volunteers’ community is over 400 members, mostly based in Portugal, working together in our initiatives, usually in our long-term projects (3-6 months). These projects are an initiative designed and managed by DSSG PT together with a beneficiary, with well-defined goals. DSSG PT is also a part of a network of European Data for Good organizations, in which monthly meetings are developed to share knowledge about each association’s goals, challenges and processes.

Project Data


01.02.2022 – 31.01.2025


Erasmus+, Cooperation Partnership (Project number: 2021-1-DE01-KA220-HED-000029711)


Universidade NOVA de Lisboa (Portugal)

University of Cyprus (Zypern)

Vilnius University (Litauen)

Associated Partner: DSSG Portugal (Portugal)


Prof. Dr. Philipp David Schaller

Project Leader

Tel +49 3943 659 297
Raum 2.308, Haus 2, Wernigerode
Sprechzeiten Mittwoch 10:00 - 11:00 Uhr

Grit Lehmann

Project Coordination

Tel +49 3943 659 872
Raum 9.212, Haus 9, Wernigerode