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Minimum Viable Skills for Undergraduate Students

The Minimum Viable Skillset (MVS) for Undergraduates addresses the minimum competencies and skills needed by undergraduate students at the completion of their degree program in Open Science (OS). The MVS profile for undergraduates considers that undergraduate students do not typically undertake extensive research projects. However, there are opportunities for undergraduates to interact with data and software whilst working on assignments. Therefore, this MVS for undergraduate students is a general profile developed specifically in regard to data literacy knowledge, as it serves as basis for relevant OS activities and knowledge of the FAIR principles. For information related to basic skills for graduate students who are undertaking research activities, typically via dissertations and other assignments, see the MVS designed for Master’s Students. For the basic skills and competencies needed for early career researchers, including postgraduate students (PhDs), please see the MVS for early career researchers.

Organisational context:

  • Research Performing Organisations (Universities)

Mission

It is recognized that Open Science skills should be immersed within formal education at its earliest stages. Undergraduates are potential future researchers and open science practitioners. By taking part in relevant open science training early, undergraduates become concerned citizens and better equipped to support open science.

OS Activities

  • Contributes to data literacy activities that improves their OS knowledge and skills, including:
  • Interpreting and critically evaluating data
  • Finding, selecting, accessing, and creating data sets
  • Ethically use, collect and cite data
  • Understand basic data types and formats
  • Communicates data with appropriate visualizations
  • Understands how the data was collected

OS Outcomes

The undergraduate contributes to the following OS outcomes:

  • Developing data literacy abilities
  • Acquiring a baseline of generic foundational digital skills
  • Adopting awareness and understanding of OS and FAIR principles
  • Adopting responsible and ethical use of data

Essential Skills and Competences

Technical skills and competences

  • Organizing and documenting
  • Foundational digital research skills
  • Understanding the big why - why it is important for society at research and data is open and FAIR
  • Knowledge of the research life cycle
  • Ability to identify general knowledge and awareness of open science and FAIR principles, including identify relevant discipline or domain-specific information
  • Ability to apply basic open science principles in the relevant parts of the research life cycle , such as:
    • Recognise reliable and trustworthy sources of data
    • Evaluate the quality and reusability of the data
    • Recognizing the different open access model for scientific publications
    • Knowledge of how to share e FAIR research data (including code and software), including knowledge of how to use repositories

Soft/ transversal skills

  • Collaboration and interpersonal skills, being particularly able to engage in teamwork
  • Written communication skills
  • Verbal communication skills
  • Time management
  • Problem solving skills
  • Critical thinking

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

Reference sources

1 2 3 4 5 6

  1. 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. ↩

  2. 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. ↩

  3. Melissa K Kjelvik and Elizabeth H Schultheis. Getting messy with authentic data: exploring the potential of using data from scientific research to support student data literacy. CBE—Life Sciences Education, 18(2):es2, 2019. ↩

  4. 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. ↩

  5. OECD. Building digital workforce capacity and skills for data-intensive science. 2020. URL: https://www.oecd-ilibrary.org/content/paper/e08aa3bb-en, doi:https://doi.org/https://doi.org/10.1787/e08aa3bb-en. ↩

  6. Anne-Marie Badolato. Passport for Open Science – A Practical Guide for PhD Students. September 2021. URL: https://www.ouvrirlascience.fr/passport-for-open-science-a-practical-guide-for-phd-students (visited on 2024-03-16). ↩