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Minimum Viable Skills for Undergraduate Students

Introduction: Open Science mission for this role

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.

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.

Undergraduate students

Organisational context:

Research Performing Organisations (Universities)

Essential 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

  • Understand basic concepts of intellectual property and copyright and its importance in scientific work.

  • Ability to identify the principles of open licensing and the uses and implications of open licenses.

  • Know what is meant by research impact. Know the different types of impact indicators and how they work.

  • Understand the importance of research visibility and know different ways to increase the visibility of research.

  • 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 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

Background assumptions

Main 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

Contributes to which Open Science 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

Further information -- Open Science skills terms

OS skills terms match the essential skills in this MVS to competence definitions from relevant taxonomies. Terms are selected to add further information and to aid discovery of this MVS (an extended list is added at the foot of this document). Sources: European Skills, Competences and Occupations ontology (ESCO), ResearchComp, terms4FAIRskills, Center Scientific Collaboration and Community Engagement.

ESCO Research Skills: Manage research data; Perform scientific research; Synthesise research publications; Publish academic research; Disseminate results to the scientific community; Demonstrate disciplinary expertise; Manage open publications; Apply research ethics and scientific integrity principles in research activities; Manage findable, accessible, interoperable, and reusable data.

ESCO Transversal Skills:  Plan, Organise information, objects, and resources; Conduct web searches; Show commitment; Work in teams; Build team spirit; Address an audience; Report facts; Use communication and collaboration software; Moderate a discussion; Cope with stress; Manage time; Show determination; Solve problems; Critically evaluate information and its sources; Think critically; Apply knowledge of science, technology and engineering.

ResearchComp: Problem solving; Creativity; Critical thinking.

Terms4FAIRskills: Assessment on FAIR data criteria; Knowledge to contextualise FAIR principles to domain;  Data sharing and publication; Data quality assessment; Open access publishing.

CSCCE:  Speaking and presenting.

Contributors

Gabriela Torres-Ramos, Claire Sowinski, Saba Sharma, Irakleitos Souyioultzoglou, Karolina Dostatnia, Luca Schirru, Dominique Green, Angus Whyte

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 7

  1. EOSC Executive Board; Skills, Training Working Group, European Commission. Directorate General for Research, and Innovation. Digital skills for fair and open science: report from the eosc executive board skills and training working group. 2021. URL: https://data.europa.eu/doi/10.2777/59065, doi:10.2777/59065. ↩

  2. C. O'Carroll, B. Hyllseth, R. van den Berg, U. Kohl, C. L. Kamerlin, N. Brennan, and G. O'Neill. Providing researchers with the skills and competencies they need to practise open science. 2017. URL: https://data.europa.eu/doi/10.2777/121253. ↩

  3. Getting Messy with Authentic Data: Exploring the Potential of Using Data from Scientific Research to Support Student Data Literacy. Cbe—life sciences education. 2023. URL: https://www.lifescied.org/doi/full/10.1187/cbe.18-02-0023. ↩

  4. 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. ↩

  5. 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 (version 2). 2020. URL: https://doi.org/10.5281/zenodo.4727592. ↩

  6. 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. ↩

  7. Passport for open science – a practical guide for phd students. 2023. URL: https://www.ouvrirlascience.fr/passport-for-open-science-a-practical-guide-for-phd-students. ↩