Skip to content

Minimum Viable Skills for Early Career Researcher

A key principle of Open Science (OS) is that research is conducted openly and transparently leading to better science. Subsequently, researchers at every career stage have a key role in the promotion of Open Science and its practice. Early career researchers for the purposes of this Minimum Viable Skillset (MVS) include PhD students, postdoctoral researchers, and other researchers within 5 to 8 years of completion of their doctorate degree. The MVS addresses the competencies and skills needed by researchers in the early stages of their research career as is related to Open OS.

Organisational context: - Governmental organizations - National agencies - National funding organizations - Research Performing Organizations

Defined as: - PhD students - Post-doctoral researchers - Other researchers (working outside of academia and research councils)

Mission

Researchers have a key role in the promotion of Open Science (OS) and its practice. Not only do early career researchers contribute to the wider uptake of skills, building their own individual skills and organizational capacities, openness is at the core of the researchers role. They are an integral part of all production and dissemination of knowledge. OS can be facilitated via data sharing, exploring and reusing data and, data publication, and making codes available, promoting open research through peer review, and reproducible research. By adopting OS approaches, researchers work to ensure that the benefits which openness brings, such as the accessibility, reproducibility and transparency of research, are available to students, colleagues, and to society as a whole.

OS Activities

  • Promotes and supports Open Science (OS), which includes joining and supporting its initiatives.
  • Produces research data for management, analysis, use, reuse, and dissemination.
  • Collaborates with informal and formal research groups, including training students and other early career researchers on OS.
  • Interacts with the general public to enhance the impact of science and research.
  • Communicates research for scholarly and societal impact.
  • Publishes research openly, providing metadata, data, code publicly available.

Open Science activities are apparent in all aspects of the early career researchers work flow. In conducting research, the early career researcher participates in additional activities essential to Open Science, including the following:

  • Open access publishing, which can be applied to peer-reviewed journal articles, theses, book chapters, monographs, and conference papers.
  • Opens data, which involves making all data that are necessary to reproduce reported results publicly accessible and digitally shareable.
  • Opens materials, which includes having all components of the research methodology.

OS Outcomes

By adopting the Open Science (OS) approach in their work, the early career researcher ensures that their research is carried out with a high degree of transparency, collegiality, and research integrity. Mainly, by adopting OS approaches and practices, the early careers researcher contributes to:

  • The facilitation of sharing research inputs and outputs
  • The adoption and practice of OS and FAIR principles and methods
  • More efficient use of science information
  • Improvement of the dissemination of knowledge
  • Improvement of the reproducibility of scientific findings

Essential Skills and Competences

Technical skills and competences

  • Good understanding of OS and its practices, in a discipline-specific context, particularly knowledgeable of policies, opportunities and practices of OS (e.g., open access, data storage and archival)
  • Sound knowledge of the data life cycle and adequate capacity to implement discipline-specific FAIR principles
  • Ability to assess FAIRness of existing resources and make OS-compliant choices to capture, process, analyse, and preserve data
  • Upgradable digital skills: experience in using tools for conducting research, managing and sharing research output. Commitment to undertaking training on digital-oriented research practices
  • Research management skills
    • ability to design an open research strategy
    • implement an open research vision
    • coordinate research activities that embed open science principles
  • Entrepreneurial skills:
    • ability to identify and apply for OS funding opportunities
    • ability to acquire funding that furthers open science goals
    • writing grants and funding applications
  • Good understanding of the relevant legal aspects related to research and their field of expertise, and relevant Open Science practices, including, but not limited to:
    • Intellectual Property Rights (eg copyright, patents, and trade secrets)
    • Personal Data Protection and Governance (eg processing Personal Data under the current legal framework, and following existing policies on Data Protection)
    • other Non-Personal Data (eg use of IoT data and research data)
    • Privacy
    • (Open) Licensing rules and frameworks
  • Good 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 RRI Framework; the European Code of Conduct for Research Integrity);
  • Ability to balance (personal and non-personal) data protection requirements with Open Science/FAIR principles.

Soft/ transversal skills

  • Cooperation and collaboration skills
  • Ethical orientation towards open science principles and their broader social goals
  • Adaptable: ability to build openness into the research process, staying up to date with research processes, software, data management systems, etc.
  • Time management skills: incorporate Open Science/FAIR practices in a timely way during the research cycle
  • Communication and interpersonal skills: engage with a variety of stakeholders
  • Project management
  • Presentation 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 7 8 9 10 11

  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. 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). ↩

  4. 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. ↩

  5. 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. ↩

  6. Laurent Gatto. Lgatto/2017_09_25_openscience_epfl: EPFL Open Science talk slides. September 2017. URL: https://doi.org/10.5281/zenodo.995703, doi:10.5281/zenodo.995703. ↩

  7. Michal Janíčko, Zdeňka Šímová, and Denise McGrath. Skills of PhD Graduates for Open Science and Open Innovation. Lifelong Learning, 12(2):119–146, December 2022. Publisher: Mendel University Press. URL: https://lifelonglearning.mendelu.cz/12/2/0119/ (visited on 2024-03-16), doi:10.11118/lifele20221202139. ↩

  8. LERU. Open science and its role in universities: a roadmap for cultural change. 2018. URL: https://www.leru.org/files/LERU-AP24-Open-Science-full-paper.pdf. ↩

  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. ↩