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Minimum Viable Skills for Graduate Students: Master’s Students

Master’s level education is more specialised and focused than the undergraduate level. Subsequently, the MVS for Masters students considers the skills and competencies in Open Science (OS) as is needed for a more focused study, incorporating OS into their project workflows. This is a generic profile for students in postgraduate education.

Organisational context:

  • Research Performing Organisations (Universities)

Mission

It is recognised that Open Science skills should be immersed within formal education at its earliest stages. By engaging with Open Science practices and acquiring relevant knowledge of OS early in their careers, the benefits of these practices can be recognised, even if their careers are not in academia.

OS Activities

  • Supports OS practices.
  • Engages with OS outputs.
  • Contributes to data literacy activities that improve their understanding of the concepts and principles of Open Science, including:
    • Interpreting and critically evaluating data;
    • Finding, selecting, accessing, and creating data sets;
    • Ethically use and cite data;
    • Understand basic data types and formats;
    • Communicating data with visualizations;
    • Understand how the data was collected;
    • Manipulate data from different sources, formats, and structures;
    • Understand how to share data;
    • Understand how to store data;
    • Understand why it is important to manage data.
  • Plans, organises, and manages research activities as related to dissertations. Specific activities related to OS include:
    • Utilising a data management plan to describe efforts to make research reproducible, including providing descriptions of the data via metadata, research procedures and analyses;
    • Writing manuscripts and/or dissertations transparently, sharing hypotheses and justifying decisions, and sharing methodologies;
    • Sharing any data used for projects, making the data available for other researchers.

OS Outcomes

Non-research graduate students contribute to the following Open Science outcomes:

  • The adoption and practice of OS and FAIR principles and methods
  • Increasing transparency and knowledge sharing in research processes
  • Increasing knowledge and awareness of digital literacy

Essential Skills and Competences

Technical skills and competences

  • Ability to analyse OS and FAIR concepts and ideas in the respective field of study.
  • Understanding of the research life cycle, particularly the processes of conducting research including planning, design, analysis, publication, and dissemination.
  • Ability to organise project workflow throughout the research life cycle, including file folders, document naming conventions, version control, cloud storage, etc.
  • Intermediate digital research skills, data management, data communication, efficient literature searches.
  • Ability to understand, use, and analyse the appropriate data (type and format) for research projects.
  • Ability to obtain and apply open science knowledge and FAIR principles, including relevant discipline or domain-specific information.
  • Open publication literacy skills, including knowledge of how to navigate open access sources, knowledge of open access publication models.
  • Knowledge of ways to share and produce FAIR research data (including code and software), including knowledge of how to use institutional repositories.
  • Basic understanding of the relevant legal issues related to Open Science practices, including, but not limited to: Intellectual Property Rights (eg knowledge on copyright-related issues like Open Access, Open Licensing, using and citing third-parties works) and other Non-Personal Data (e.g., being aware of rules on the use of “research data”), Personal Data Protection and Governance (eg using Personal Data under the current legal framework, and following existing policies on Data Protection).
  • General understanding of ethical principles (e.g., transparency, diversity and accountability) and best practices (e.g., avoiding bias in data processing when using data-driven technologies) applicable to their field of expertise, including, but not limited to the general ethical principles, frameworks and codes of conduct applicable to research (e.g., the European Code of Conduct for Research Integrity);
  • Knowledge of data protection requirements with Open Science/FAIR principles.

Soft/ transversal skills

  • Collaboration skills, ability to engage in teamwork
  • Assignment and/or dissertation management
  • Communication and interpersonal skills
  • Written communication skills
  • Verbal communication skills
  • Time management skills
  • Problem solving skills
  • Critical thinking
  • Presentation skills
  • Organizational skills, including goal setting and prioritizing skills

Link to any other MVS that this MVS is based on (from those in Skills4EOSC D2.1)

Reference sources

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  1. Angus Whyte at al. Eoscpilot fair4s (skills and capability framework). 2018. URL: https://eoscpilot.eu/sites/default/files/eoscpilot-d7.3.pdf (visited on 2024-03-16). ↩

  2. A Whyte & K Ashley. Deliverable d7.1: skills landscape analysis and competence model. 2017. ↩

  3. Developing and Implementing hands-on training on Open Science and Open Innovation for Early Career Researchers \textbar DIOSI Project \textbar Fact Sheet \textbar H2020. URL: https://cordis.europa.eu/project/id/101006318 (visited on 2024-03-16). ↩

  4. European Commission, Directorate-General for Research, Innovation, C O'Carroll, B Hyllseth, R Berg, U Kohl, C Kamerlin, N Brennan, and G O’Neill. Providing researchers with the skills and competencies they need to practise Open Science. Publications Office, 2017. doi:doi/10.2777/121253. ↩

  5. European Commission, Directorate-General for Research, Innovation, N Manola, E Lazzeri, M Barker, I Kuchma, V Gaillard, and L Stoy. Digital skills for FAIR and Open Science – Report from the EOSC Executive Board Skills and Training Working Group. Publications Office, 2021. doi:doi/10.2777/59065. ↩

  6. Loek Brinkman, Elly Dijk, Hans de Jonge, Nicole Loorbach, and Daan Rutten. Open Science: A Practical Guide for Early-Career Researchers. May 2023. Version Number: 1.0. URL: https://doi.org/10.5281/zenodo.7716153, doi:10.5281/zenodo.7716153. ↩

  7. Ciara McCaffrey, Thorsten Meyer, Clara Riera Quintero, Cecile Swiatek, Nathalie Marcerou-Ramel, Camilla Gillén, Karin Clavel, Anna Wojciechowska, Helene Brinken, Mariëlle Prevoo, and Frank Egerton. Open Science Skills Visualisation - Visualisation des compétences en science ouverte. April 2021. Version Number: 2. URL: https://doi.org/10.5281/zenodo.4727592, doi:10.5281/zenodo.4727592. ↩

  8. Ummul-Kiram Kathawalla, Priya Silverstein, and Moin Syed. Easing Into Open Science: A Guide for Graduate Students and Their Advisors. Collabra: Psychology, 7(1):18684, January 2021. URL: https://doi.org/10.1525/collabra.18684 (visited on 2024-03-16), doi:10.1525/collabra.18684. ↩

  9. Universities Norway. Nor-cam–a toolbox for recognition and rewards in academic careers. 1er juin, 2021. ↩

  10. Yuri Demchenko, Adam Belloum, Wouter Los, Tomasz Wiktorski, Andrea Manieri, Holger Brocks, Jana Becker, Dominic Heutelbeck, Matthias Hemmje, and Steve Brewer. Edison data science framework: a foundation for building data science profession for research and industry. In 2016 IEEE international conference on cloud computing technology and science (CloudCom), 620–626. IEEE, 2016. ↩

  11. Juan J Cuadrado-Gallego and Yuri Demchenko. The data science framework: a view from the EDISON project. Springer Nature, 2020. ↩