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Minimum Viable Skills for Graduate Students: Master’s Students

Introduction: Open Science mission for this role

The Minimum Viable Skillset (MVS) for Master's students is one of two MVS for students  (the other for undergraduate students). As Master's level education is more specialised and focused than the undergraduate level this MVS considers the skills and competencies in Open Science (OS) needed for more focused study, incorporating OS into their project workflows. This is a generic profile for students in postgraduate education.

For information related to basic skills for researchers, see the MVS designed specifically for early career researchers, which addresses the minimum skills and competencies needed for PhD student and post-doctoral researchers.

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.

Master's student

Organisational context:

  • Governmental organisations

  • National agencies

  • Research Performing Organizations

Essential 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 find and reuse open scientific papers.

  • Efficient data searches: Ability to find, understand, (re) use, and analyse the appropriate data (type and format) for assignments.

  • 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 and  open access publication models including OA repositories

  • Knowledge of ways to share and produce FAIR research data (including code and software), including knowledge of how to use data repositories.

  • Basic understanding of the relevant legal issues related to Open Science practices, including, but not limited to: Intellectual Property Rights (e.g., 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 (e.g., 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

  • Organizational skills, including goal setting and prioritizing skills

  • Communication and interpersonal skills

  • Written communication skills

  • Verbal communication skills

  • Time management skills

  • Problem solving skills

  • Critical thinking

  • Presentation skills

Background assumptions

Main activities

  • 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 assignments 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 researchers.

Contributes to which Open Science 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

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: Demonstrate disciplinary expertise; Publish academic research; Disseminate results to the scientific community; Perform scientific research; Draft scientific or academic papers and technical documentation; Perform project management; Manage research data; Synthesize research publications; Manage open publications; Manage findable, accessible, interoperable, and reusable data; Operate open source software; Manage intellectual property rights; Apply research ethics and scientific integrity principles in research activities.

ESCO Transversal Skills:  Plan, conduct web searches; apply knowledge of science, technology, and engineering; Show commitment; Work in teams; Build team spirit; address an audience; Delegate responsibilities; Moderate a discussion; Address an audience; Report facts; Use communication and collaboration software; Cope with stress; Manage time; Show determination; Organise information, objects, and resources; Solve problems; Critically evaluate information and its sources; Think critically; Promote ideas, products and services; Show initiative; Assume responsibility; Show confidence.

ResearchComp: Manage research data; Apply research ethics and integrity principles; Problem solving; Creativity; Critical thinking.

Terms4FAIRskills: Assessment on FAIR data criteria; Knowledge to contextualise FAIR principles to domain; Data transformation; Data validation and cleaning; Open access publishing; Domain knowledge to contextualise data handling; Data access risk assessment and mitigation; Ethical application of patents, licenses; Research governance; Data policy.

CSCCE: Data analysis; Data visualization; Speaking and presenting; Time management.

Contributors

Dominique Green, Saba Sharma, Irakleitos Souyioultzoglou, Gabriela Torres-Ramos, Claire Sowinski, Karolina Dostatnia, Luca Schirru, Angus Whyte, Paula Martinez Lavanchy, Carolin Leister, Bernd Sarugger.

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

Reference sources

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  1. A. Whyte, J. Vries, R. Thorat, E. Kuehn, G. Sipos, V. Cavalli, V. Kalaitzi, and K. Ashley. Deliverable d7.3: skills and capability framework. EOSCPilot, 2018. Open Science stewardship competences and capabilities table, pp. 39-40. ↩

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

  3. Zoran Sušanj and Nikoleta Zubić. Formulation of “open science and open innovation & entrepreneurship training” joint strategic goals and relevant key performance criteria and perspectives. 2022. URL: https://diosi.eu/wp-content/uploads/2022/02/DIOSI_D5.2._Formulation-joint-strategic-goals_31.01.2022-2.pdf. ↩

  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. 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. 2021. URL: https://data.europa.eu/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 (1.0). 2023. URL: https://doi.org/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 (version 2). 2020. URL: https://doi.org/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, 2021. doi:https://doi.org/10.1525/collabra.18684. ↩

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

  10. Y. Demchenko. Edison data science framework: part 4. data science professional profiles (dspp), release 2. 2017. URL: https://edison-project.eu/sites/edison-project.eu/files/attached_files/node-486/edison-dspp-release2-v04.pdf. ↩

  11. T. Wiktorski Y. Demchenko, A. Belloum. Edison data science framework: part 1. data science competence framework (cf-ds) release 2. 2016. URL: https://edison-project.eu/sites/edison-project.eu/files/filefield_paths/edison_cf-ds-release1-v07.pdf. ↩