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Open Science

Open Science is an approach to the scientific process that focusses on spreading knowledge as soon as it is available using digital and collaborative technology. Open Science is a policy priority for the European Commission as it improves the quality, efficiency and responsiveness of research


Open Access

Ensuring research publications are freely accessible to anyone, anywhere (detailed below). 

Open Data

Making research data openly available for analysis and reuse (use GitHub). 


Providing clear documentation of research methods and results


Encouraging interdisciplinary and international collaboration among researchers


Increased Visibility

by reaching a wider audience, consequently producing a higher research impact

Accelerated Innovation

by enabling researchers to build upon each other's work

Public Engagement

by promoting to a broader community, including policymakers, educators, and the general public


by improving transparency, quality and trustworthiness of scientific outputs

Our Approach 

Data Management Plan (DMP)

  • Data curation/Cost of data curation. DIGITAL will recommend DMP storage and backup best practices. Data will be permanently archived in OpenAIRE to support European research even after the project. A detailed DMP inline with GDPR will be written that follows open access principles to research data, being updated regularly according to new types and volumes of data used in each IRP. The DMP must contain documentation of data and metadata, data workflows, software licensing, privacy policies, and intellectual property rights.

  • A template for a DMP

Applying the FAIR Principles

  • Findability. DOIs and PIDs issued by platforms compliant with the European Open Science Cloud (EOSC) initiative (e.g. Zenodo, DataCite) 

  • Accessibility. Anonymized data sharing may be permitted online access to databases via NDA and VPN tools depending to the requirements of the research ethics committee and legal counsel service of the beneficiary.

  • Interoperability. The data used in each IRP will be described using Dublin Core and Simple Knowledge Organization System (SKOS) metadata to ensure interoperability.

  • Reusability. The research outcomes of DIGITAL will be published on GitHub/Gitlab as open-source libraries and tools. Proper documentation, licenses, user guides, examples, and evaluation results will be provided.

As Open as Possible, as Closed as Necessary

Open access may not be possible in certain case, for example:

  • Against the beneficiary's legitimate interests, including regarding commercial exploitation

  • Contrary to any other constrains, such as data protection rules, privacy, confidentiality, trade secrets,

Note: In Horizon 2020 both Green and Gold Open Access was allowed. This is no longer the case in Horizon Europe

Educating Open Science

MSCA DIGITAL takes part in multiple events​ advocating for Open Science:

  • The European Researchers' Night

  • Annually attend an Open Science Festival

  • DIGITAL provides a course on Open Science Principles (with mandatory attendees for PhD candidates)

  • Towards a European Financial Data Space:
    Collaborative learning across data silos Detecting anomalies and dependence structures in high dimensional, high frequency financial data Predicting financial trends using text mining and NLP Deep Generation of Financial Time Series
  • Artificial Intelligence for Financial Markets
    Strengthening European financial service providers through applicable reinforcement learning Developing industry-ready automated trading systems to conduct EcoFin analysis using deep learning algorithms Challenges and opportunities for the uptaking of technological development by industry
  • Towards explainable and fair AI-generated decisions
    Audience-dependent explanations Investigating the utility of classical XAI methods in financial time series Fair Algorithmic Design and Portfolio Optimization under Sustainability Concerns
  • Driving digital innovations with Blockchain applications
    Industry standard for blockchain Fraud detection in financial networks Risk index for cryptos
  • Sustainability of Digital Finance
    Modelling green credit scores for a network of retail and business clients A recommender system to re-orient investments towards more sustainable technologies and businesses Experimenting with Green AI to reduce processing time and contributes to creating a low-carbon economy Applications of Agent-based Models (ABM) to analyse finance growth in a sustainable manner over a long-term period
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