Competency framework for data professionals

We present a competency framework for research data professionals in the Netherlands. It specifies the competencies that data professionals can develop, and aims to:

  • Define the scope of the national curriculum, allowing to map trainings, find gaps, and build a training programme addressing the competencies in the framework; 
  • Help data professionals further professionalise; 
  • Help employers make explicit what competencies they expect from a data professional; 
  • Help training providers understand and improve the relevance of their training for data professionals. 
Full curriculum on Zenodo

Structure of the competency framework

The competency framework consists of seven competency areas, with underlying competencies, knowledge ('[K]') and skills ('[S]'), and topics that can be targeted in trainings for research data professionals. It was set up to cover a wide variety of data stewardship activities and competencies. The competency areas are: 

1

RDM, FAIR principles & open science

Providing tailored advice, curating datasets, providing hands-on data management, and developing and monitoring RDM services in line with good research data management (RDM) practices and the FAIR and open science principles.

2

Research Software Management

Providing tailored advice about research software management and computational reproducibility and applying these topics hands-on.

3

Data infrastructure

Providing tailored advice about adequate digital infrastructure and tools, and contributing to the digital infrastructure and tools available for researchers, such as for data storage, transfer, analysis and publication.

5

Legal and ethical responsibilities

Advising about compliance with relevant scientific, legal, and ethical standards, such as data protection, intellectual property, European data legislation, ethics, codes of conduct, etc.

7

Transversal skills (or 'soft' skills, 'non-cognitive skills')

Competencies that can be used in a wide variety of settings, relating most explicitly to the duties of a data professional, such as networking, effectively communicating, and managing RDM projects.

The competency framework is not prescriptive. Although we expect that data professionals need a basic understanding of all competency areas, some competencies within these areas will lend themselves better for further specialisation. Importantly, the competency framework does not explicitly cover discipline-specific competencies, whereas these can be important for a subset of data professionals.

Competencies and topics per competency area

RDM, FAIR principles & open science

1A. Provide tailored advice about RDM solutions and FAIR data across the research data lifecycle, in line with relevant policies and requirements 

  1. [K] Data definitions and data responsibilities in research
  2. [K] The research process and the research data lifecycle
  3. [K] The FAIR data principles
  4. [K] Open science principles and practices
  5. [K] Importance and benefits of RDM, FAIR data and open science
  6. [K] Role of research data in scholarly communication
  7. [K] Requirements, templates and tools for data management planning
  8. [K] The institute's organisational structure and processes
  9. [S] Assist researchers with and review Data Management Plans
  10. [S] Assist researchers with (systematic) discovery and reuse of existing datasets
1B. Curate and evaluate datasets for long-term preservation 

  1. [S] Apply the FAIR principles 
  2. [S] Share data effectively 
  3. [S] Evaluate and improve the FAIRness of datasets 
  4. [S] Formulate and apply data preservation strategies 
1C. Deliver / provide hands-on data management 

  1. [S] Apply and advise researchers on designing efficient and secure data (integration) workflows 
  2. [S] Implement data quality, validation, and cleaning mechanisms into the research process 
  3. [S] Create metadata and documentation using appropriate schemas and standards 
  4. [S] Support the development, mapping, and implementation of metadata standards 
  5. [S] Develop and implement relevant data models 
  6. [S] Work with data stored in databases (e.g. SQL, noSQL) and advise researchers on their application 
1D. Develop and monitor RDM support and services 

  1. [K] Relevant support and services available for RDM, both internally as well as externally 
  2. [S] Assess and analyse needs regarding support on RDM and define new requirements 
  3. [S] Initiate or supervise the set-up and update of suitable support facilities or services 
  4. [S] Communicate aboutand stimulate the use of available services 
  5. [S] Monitor and evaluate the uptake and effectiveness of RDM services 
Topics

  1. Research data
  2. Scientific disciplines
  3. Research methodologies
  4. Research data lifecycle
  5. Roles and responsibilities in RDM
  6. Open science
  7. Reproducibility and replicability
  8. Scholarly communication
  9. Costs for RDM
  10. Data management planning
  11. FAIR data principles
  12. Data discovery
  13. Data reuse
  14. Data collection
  15. Data documentation
  16. Data organisation
  17. File naming (conventions)
  18. Data versioning
  19. Data formats and types
  20. Data back-up
  21. Data selection
  22. Data destruction
  23. Data preservation and archiving
  24. Data publication
  25. Data curation
  26. Data visualisation
  27. Data provenance
  28. Metadata (standard)
  29. Controlled vocabulary, ontology, taxonomy, thesaurus
  30. Linked Open Data and SPARQL
  31. FAIR metrics
  32. 3-point FAIRification Framework (FAIR data point, FAIR Implementation Profile)
  33. Persistent identifier
  34. Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH)
  35. Spreadsheet tools
  36. Data modelling
  37. Data integration
  38. Data integrity, validation & quality
  39. Data cleaning & wrangling
  40. Database management
  41. Master data management
  42. Business intelligence
  43. RDM service models

Research Software Management

2A. Provide tailored advice about Research Software Management

  1. [K] Different levels of research software (e.g. scripts, code, independent software)
  2. [S] Write and advise on creating a Software Management Plan
  3. [S] Advise on storing, documenting, publishing, licensing, and citing research software
  4. [S] Assist researchers with (systematic) discovery and reuse of existing research software
2B. Provide tailored advice on reproducibility

  1. [K] The FAIR principles for research software and the difference between FAIR, Open source, and good quality research software
  2. [K] How to embed research software in scientific workflows
  3. [S] Advise on good coding practices and tools to enhance reproducibility of research results
  4. [S] Evaluate and improve reproducibility of research projects that use research software
2C. Hands-on research software management and reproducibility

  1. [S] Support researchers in publishing their research software
  2. [S] Use and advise on version control systems
  3. [S] Write and assess software documentation
  4. [S] Understand, write, and evaluate basic programming code to automate (parts of) relevant stages of the research data lifecycle
Topics

  1. Research software 
  2. Software management planning 
  3. Software version control (e.g., git and GitHub) 
  4. Software documentation 
  5. Software packaging (R, Python, etc.) 
  6. Software citation 
  7. FAIR software 
  8. Reproducibility 
  9. Coding conventions 
  10. Literate programming 
  11. Scientific workflows and data pipelines 
  12. Computer programming 
  13. Virtual environments and Containerisation 
  14. Continuous integration 
  15. Use of generative Artificial Intelligence in writing research software

Data infrastructure

3A. Provide tailored advice to researchers about data infrastructure and tools in the research data lifecycle

  1. [K] Relevant facilities, data infrastructure, tools and emerging standards relevant to RDM (e.g. data collection tools, statistical programmes, etc.) 
  2. [K] RDM issues related to data infrastructure and tools in the data lifecycle 
  3. [S] Use data infrastructure and tools relevant to RDM 
  4. [S] Provide access, guidance and training on proper use of data infrastructure and tools relevant to RDM
3B. Assess and analyse needs regarding data infrastructure and tools for RDM for researchers and relevant stakeholders and translate these needs to requirements and advice

  1. [S] Monitor and evaluate effectiveness and use(ability) of data infrastructure 
  2. [S] Analyse data infrastructure and tool needs 
  3. [S] Translate needs to advice for local, national or international implementation 
  4. [S] Facilitate proper alignment of data infrastructure to relevant external data infrastructure and tools 
  5. [S] Facilitate proper alignment between researcher needs and IT-related departments within the organisation
Topics

  1. (Certified) Data Repositories 
  2. Repository quality standards (e.g. CoreTrustSeal, ISAD(G), OAIS reference model) 
  3. TRUST principles for digital repositories 
  4. Tool criticism 
  5. Data collection tools 
  6. Data management services/tools 
  7. Data storage (media) 
  8. Data transfer tools 
  9. Data analysis software/tools 
  10. Cloud computing and High-performance computing 
  11. Data security and Data classification 
  12. Available RDM infrastructure and organisations 
  13. European Open Science Cloud solutions 

Policy and governance

4A. Policy Development (stakeholders, (inter)national developments)

  1. [K] Importance of institutional policies such as data and software policy 
  2. [K] Elements of an RDM policy 
  3. [K] Policy development cycle and tools 
  4. [K] Interplay between data, software, AI policy and ethical/legal frameworks 
  5. [K] Relevant internal and external stakeholders and their requirements and needs 
  6. [S] Ensure alignment between institutional, national and international RDM-related policies and practices 
  7. [S] Contribute actively to national and international RDM policy development
4B. Policy implementation (communication, stakeholder engagement, translation into actionable strategies)

  1. [S] Communicate about RDM policies, explain implications and create awareness 
  2. [S] Turn national and international developments into RDM policy and implementation within the institute 
  3. [S] Translate institutional policies into actionable data management strategies
4C. Monitoring and evaluation of policy compliance and impact

  1. [S] Assess and monitor compliance to the FAIR data principles and the principles of open science 
  2. [S] Evaluate and monitor effectiveness of and compliance to policy
4D. Data governance and sovereignty

  1. [S] Advise relevant parties on opportunities for collaboration with internal and external organisations 
  2. [S] Advise management on data governance and data sovereignty issues, infrastructure and procedures to put in place 
  3. [S] Advise on conditions for sharing and collaborating on data with parties outside the own institution / Netherlands / EU
Topics

  1. European, national and institutional policies on RDM, RSM and open science 
  2. Funder RDM, RSM and open science requirements 
  3. Journal policies related to RDM 
  4. Policy development 
  5. Policy implementation 
  6. Policy monitoring 
  7. Translating policy to organisational strategy 
  8. Responsible metrics (bibliometrics, altmetrics) 
  9. Digital sovereignty 
  10. Data governance 
  11. Data ownership 
  12. Key Performance Indicators (KPI) for RDM 
5A. Advise on applicable legislation, regulations and codes of conduct

  1. [K] Legislation, ethics and codes of conduct with regards to research data 
  2. [K] Responsible use of AI and relevant AI legislation, policies and guidelines 
  3. [S] Translate legislation, regulations and codes of conducts to practical implications and guidelines 
  4. [S] Identify and manage ethical and legal risks in a research context and know when to refer to ethical/legal experts 
  5. [S] Facilitate proper alignment between researcher needs and legal and ethical departments within the organisation
Topics

  1. Privacy and data protection (GDPR, UAVG) 
  2. Sensitive data/Confidentiality 
  3. Intellectual property rights (copyright, patents, trademarks)
  4. Research in consortia 
  5. Data and software licenses  
  6. License compatibility 
  7. Information security 
  8. Knowledge security 
  9. European data legislation (AI Act, Data Governance Act, Data Act, European Health Data Space)
  10. (Cyber)security legislation, e.g. NIS2 Directive 
  11. Trade Secret Protection Act 
  12. Research ethics and integrity 
  13. CARE principles 
  14. Diversity, equity & inclusion 

Training and awareness raising

6A. Assess RDM knowledge and skills and identify gaps among researchers and relevant stakeholders

  1. [K] Required level of knowledge for researchers and relevant stakeholders 
  2. [K] Available RDM training opportunities and possibilities
6B. Ensure a sufficient level of awareness, knowledge and skills among researchers and research support staff

  1. [S] Translate RDM policies and DMPs to practical implications and guidelines that researchers can understand 
  2. [S] (Coordinate the) design (of) RDM-related training and tailor it to the target audience 
  3. [S] Plan, organise and execute training and awareness-related events 
  4. [S] Find, reuse and FAIRify training materials 
  5. [S] Provide training in an engaging, inclusive and accessible manner 
  6. [S] Advocate for integration of training and awareness activities in curricula, onboarding, or staff development programmes
6C. Evaluate the effectiveness and impact of training and awareness activities

  1. [S] Assess learning outcomes and student satisfaction for provided training and awareness activities 
  2. [S] Monitor and evaluate the impact and effectiveness of awareness activities
Topics

  1. Needs assessment 
  2. Instructional design 
  3. FAIR-by-Design methodology 
  4. Carpentries methodology 
  5. Training andragogy  
  6. Didactic methods 
  7. Presentation skills 
  8. Written communication skills 
  9. Open Educational Resources 
  10. Diagnostic, formative and summative assessment 
  11. Course evaluation  
  12. Student satisfaction 

Transversal skills

7A. Professional skills

  1. [K] Knowledge about the data steward's tasks and responsibilities 
  2. [S] Lead or mentor colleagues 
  3. [S] Work effectively in a(n interdisciplinary) team 
  4. [S] Navigate the complex structures, rules, power dynamics, and interpersonal relationships within research organisations
7B. Build and maintain networks

  1. [K] Where to find decision makers, RDM experts, researchers, and other stakeholders, including relevant (RDM) networks 
  2. [S] Identify and participate in local, national and international RDM networks 
  3. [K] The principles of community building and management 
  4. [S] Establish and maintain an active network in the field of RDM 
  5. [S] Connect data support staff in- and outside the institute (liaising)
7C. Advise and communicate effectively with stakeholders across disciplines and roles

  1. [S] Advocate for the importance of open science, research data management and data support with management and researchers 
  2. [S] Communicate effectively with a diverse range of stakeholders 
  3. [S] Think outside the box, generate new ideas, and find novel solutions to problems or opportunities
7D. Manage RDM-related projects effectively

  1. [S] Facilitate inclusive and purpose-driven meetings or discussions 
  2. [S] Effectively set-up and manage RDM-related projects (planning, coordination, monitoring, reporting) 
  3. [S] Accomplish sustainable change through strategies and personal influence 
  4. [S] Assess information, weigh options, consider potential outcomes, and choose the best course of action in a given situation
Topics

  1. Networking skills 
  2. Community management 
  3. Existing RDM networks/ communities 
  4. Consultancy 
  5. Advocacy 
  6. Conflict resolution 
  7. Negotiating 
  8. Active listening 
  9. Stakeholder analysis 
  10. Stakeholder engagement 
  11. Organisational development 
  12. Project management (methodologies) 
  13. Change management 
  14. Binding Leadership 
  15. Facilitation 
  16. Teamwork 

The contents of competency framework are heavily based on the work of Jetten et al. (2021), the activities described in the Dutch Data Steward UFO profile, and the Skills4EOSC data steward training curriculum and Minimum Viable Skills profile. For other sources used, please refer to the full Curriculum document.

The RDNL competency framework for research data professionals in the Netherlands © 2026 by Research Data Netherlands (4TU.ResearchData, Health-RI, DANS, SURF) is licensed under a CC BY 4.0 license.

Competency Framework version: March 2026.