Skip to main content


The new RDA web platform is still being rolled out. Existing RDA members PLEASE REACTIVATE YOUR ACCOUNT using this link: Please report bugs, broken links and provide your feedback using the UserSnap tool on the bottom right corner of each page. Stay updated about the web site milestones at

Task Force on Defining data handling related competences and skills for different groups of professions – Working area

  • Creator
  • #137650

    Yuri Demchenko

    Task Force on Defining data handling related competences and skills for different groups of professions – Working area

    (This is a backup page for the Task Force activity 2015-2016)


    Task Force creation agreed at IG ETHRD meeting at RDA Plenary 5 (San Diego, March 2015) to draw together information on the skills and competences currently or soon likely to be required for handling research data in the following four professional areas:

    • 1. research librarians;
    • 2. research administrators;
    • 3. research infrastructure managers / operators;
    • 4. researchers.

    For each of these four professional areas, please contact the named person to contribute skills or competences that are considered essential or desireable in your own professional context.  Please also contribute citations to any relevant resources or reading material which helps us to identify current skills and competences for research data handling. 

    1. Research librarians: contact Laura Molloy,

    1.1 Essential skills:

    • Metadata (CE); Familiarity with various metadata standards;
    • Familiar with different data documentation practices, not just metadata schemas (AN)
    • Familiarity with various data standards (LW)
    • Data management planning (CE)
    • Data repositories (CE); knowledge of repositories and other digital initiatives (LW) Understanding the general requirments for preservable data or specific requirements for domain repositories (AN)
    • Data preparation (CE)
    • Marketing services (CE)
    • Building collaborations (CE); interaction and cooperation with business and IT personnel (LW)
    • Data backup and security (CE)
    • Understand and create organisational culture change (LW)
    • Experience of data and document management (LW)
    • Training, advocacy and awareness-raising activities (LW)
    • Communicating the importance of data stewardship, ability to make the case (AN)
    • Managing and influencing business change activities (LW)
    • Experience of working with an HE research establishment (LW)
    • Understanding of RDM policy issues and best practice (LW) Funder requirements for data planning, handling, deposition (AN)
    • Familiarity with RDM lifecycle (LW)
    • Ethical and legal requirements in handling data from human participants (VVdE) and other sensitive data types (AN)
    • Review and appraisal of research data (VVdE)
    • Data sharing/distribution/publication (AN)
    • Intellectual property and Data licenses (AN)
    • Defining the working space: What is research data? (AN)
    • Migration between working file formats and preservation file formats (AN)
    • File naming, organization, and versioning (AN)
    • Familiarity with existence of available tools for data handling (AN)
    • Identifying storage strategies for different data types, stages, quality. Selecting data for presearvation (AN)
    • Understanding of research reproducibility (AN)
    • From the Data Information Literacy (DIL) Program (AN)
      • Discovery and Acquisition 
      • Ethics and Attribution
      • Metadata and Data Description
      • Cultures of Practice
      • Data Management and Organization
      • Data Curation and Reuse
      • Data Quality and Documentation
      • Data Processing and Analysis
      • Data Visualization and Reuse
      • Databases and Data Formats
      • Data Conversion and Interoperability
      • Data Preservation

    1.2 Desirable skills:

    • Data citation (CE) (Would suggest this as essential – AN)
    • Data literacy (CE)
    • Data visualisation (CE)
    • Data mining (CE)
    • Databases (CE)
    • Data Rescue: Why it matters, how to do it, what to do so rescue is not required (AN, from Elizabeth Griffin)
      • Electronic data
      • Non-electronic data

    1.3 Background reading (currently in no particular order);

    a) (LM) DigCurV competence framework for digital curation in cultural heritage organisations (including libraries and universities):

    b) (LM) RDM Rose training resource v.3 (2015): 

    c) I (LM) did a keynote on this topic (RDM for research librarians including available training and guidance) to the association of French academic library directors in late 2013.

    c) (LM) Sheila Corrall’s work is very useful in this area

    d) (LM) Digital Curation Centre / University of Northampton training course, ‘RDM for librarians’ (2013): 

    e) (LM) Auckland, M, 2012, Re-skilling for research: an investigation into the role and skills of subject and liaison librarians required to effectively support the evolving information needs of researchers, Research Libraries UK, London, viewed 13 May 2015, <“>;.

    f) (LM) Blogpost from University of the West of England, 2012.

    g) (LM) Digital Curation Centre: Core skills for research data management:

    Whilst we are looking at all research librarians, not just ‘data librarians’, the ‘data librarian’ is identified as one of the roles in this DCC resource. It may be useful to consider the following skills which are attached to this role:

    • value of data / economic issues
    • data preservation
    • facilitiation / communication
    • standards development
    • complaints and expectation management
    • advocacy, promotion, marketing, raising awareness
    • data appraisal and retention
    • coordination of practice across institutuion
    • negotiation skills

    h) (CE) Pryor, G. (2012). Managing Research Data. London: Facet Publishing

    i) (CE) Bailey, C. (2014) Research Data Curation Bibliography. Houston: Digital Scholarship. Retrieved 12 May 2015 from 

    j) (LW) Williamson, L. (2013) Roles, responsibilities and skills matrix for research data management (RDM) support; ADMIRe working document. Information Services, Nottingham.

    k) (AC) Cox AM, Verbaan E & Sen BA (2014) A Spider, an Octopus, or an Animal Just Coming into Existence? Designing a Curriculum for Librarians to Support Research Data Management. Journal of eScience Librarianship, 3(1).

    l) (AC) Ariadne article on RDM Rose project – location forthcoming.

    m) (WH) RDA Libraries for Research Data IG (2015). Briefing paper: How to maximise research data skills in libraries.

    n) (WH) Horstmann, W. (2014) Presentation: ‘A Data Science Preparatory Course (?)’.  IMCW2014.

    o) (AN) Data @ Libs: Data Librarianship Educational Resources

    p) (AN) Librarian RDM Learning Ojectives from Essentials for Data Support: Training the Front Office | Grootveld | International Journal of Digital Curation

    q) (AN) Hiring Data Librarians

    r) (AN) DataQ Research Data Knowledge Base – for librarians

    s) (AN) A spider, an octopus, or an animal just coming into existence? Designing a curriculum for librarians to support Research Data management

    t) (AN) DIY Research Data Management Training Kit for Librarians

    u) (AN) Tips for New Data Librarians: Working Around the Jargon | e-Science Community

    v) (AN) What is the role of a librarian in Research Data Management? | Practising Development

    w) (AN) Upskilling Liaison Librarians for Research Data Management | Ariadne (link is dead, I’ll try to attach it) (hmmmm – see if that works)

    x) (AN) 3TU.Dataintelligence for Librarians : About the course

    y) (AN) jeslib special issue:

    z) (AN) New England Collaborative Data Management Curriculum

    aa) (BS) Joint Task Force on Librarians’ Competencies in Support of E-Research and Scholarly Communication: Librarians’ Competencies Profile for Research Data Management, March 2014 (DRAFT),

    ab) (BS) Davis, H. M., & Cross, W. M. (2015).Using a Data Management Plan Review Service as a Training Ground for Librarians. Journal of Librarianship and Scholarly Communication, 3(2), eP1243.

    ac) (AN) Data Information Literacy, multiple sources: Program,, Slides (that competencies are taken from), Paper listing competencies:

    ad) (AN) Listings of curricular objectives:

    ae) Dooley, Jackie. 2015. The Archival Advantage: Integrating Archival Expertise into Management of Born-digital Library Materials. Dublin, Ohio: OCLC Research.…. – an overview of archival skills which are relevant to librarians approaching born digital material, including research data.

    af) Alex H. Poole, ‘How has your science data grown? Digital curation and the human factor: a critical literature review’, Archival Science 15 (2015) pp 113-119. – identifies key skills of records professionals in engaging with research data management – provenance, appraisal, authenticity, metadata, risk  management and trust.

    ag) Graham Pryor and Martin Donnelly, ‘Skilling Up to Do Data: Whose Role, Whose Responsibility, Whose Career?’, International Journal of Digital Curation 2, vol. 4 (2009) pp161-163) – and overview of roles and responsibilities in research data curation. It also identifies appropriate data-related education programmes internationally, which include library and information studies, information engineering, data curation and informatics degrees, as well as those in records management and archival studies.

    ah) Jenny Bunn and Sarah Higgins, ‘Mainstreaming Digital Curation. An overview of activity in the UK archives and records management profession’, Proceedings of the Framing the Digital Curation Curriculum conference, ed. Vittore Casarosa (2013) – explores the skills required for archivists and records managers to engage with digital curation, and whether these skills are taught by established educational programmes.

    Here is who suggested each resource:

    LM=Laura Molloy, DCC and U Glasgow; CE=Christopher B Eaker, U Tennessee; LW=Laurian Williamson, U Nottingham (contributions from her citation above); AC=Andrew Cox, U Sheffield. WH=Wolfram Horstmann, U Goettingen. VVdE=Veerle Van den Eynden, UK Data Archive. AN=Amy Nurnberger, Columbia University. BS = Birgit Schmidt, U Göttingen, RG = Rebecca Grant, Digital Repository of Ireland.

    2. research administrators: contact Patrice Ajai-Ajagbe 

    TO DO


    3. research infrastructure managers/operators: contact Yuri Demchenko

    3.1. Background and approach

    This section adopts approach used in European e-Competence Framework 3.0 (eCFv3.0) defines 4-dimensional model for RI/ICT competences.

    Dimension 1: 5 e-Competence areas, derived from the ICT processes present in RI development, management and operation:

    A. PLAN and DESIGN

    Dimension 2: A set of reference competences for each area; currently identified 35 competences that are mapped from the general eCFv3.0.

    Dimension 3: Proficiency levels of each e-Competence, currently using eCF approach that provides European reference level specifications on e-Competence levels e-1 to e-5, which are related to the EQF levels 3 to 8.

    Dimension 4: Samples of knowledge and skills related to e-Competences in dimension 2. They will be provided to add value and context and are not intended to be exhaustive.

     Definitions (according to eCFv3.0):

    Competence is a demonstrated ability to apply knowledge, skills and attitudes for achieving observable results.

    Competence is not to be confused with process or technology concepts such as, ‘Cloud Computing’ or ‘Big Data’. These descriptions represent evolving technologies and in the context of the e-CF, they may be integrated as elements within knowledge and skill examples.

    Knowledge in the context of competence definition is treated as something to know, to be aware of, familiar with, and obtained as a part of education.

    Skills is treated as provable ability to do something and relies on the person’s experience.


    3.2. Competences by processes/groups split on essential and desirable


    Note: (1) Master Excell spreadshit for RI Management and Operation competences is available at this link (TODO).
    (2) Enumeration of competences left according to master spreadsheet for reference and compatibility

    A. PLAN and DESIGN       

     Essential competences

    A.2. Service Level Management

    A.3. Product / Service Planning

    A.5. Application Design

    A.4. Architecture Design

     Desirable (advanced) competences

    A.6. Sustainable Development

    A.7. Innovating and Technology Trend Monitoring

    A.8. Business/Research Plan Development and Grant application

    A.1. RI and Research Strategy Alignment


    Essential competences

    B.1. Application Development (Reqs Engineering, Function Specs, API, HCI)

    B.2. Component Integration

    B.3. Testing (RI services and Sci Apps)

    B.4. Solution/Apps Deployment

     Desirable (advanced) competences

    B.5. Documentation Production

    B.6. Systems Engineering (DevOps)

    C. OPERATE (RUN)          

    Essential competences

    C.1. User Support

    C.2. Service Delivery

    C.3. Problem Management

     Desirable (advanced) competences

    C.4. Change Support (Upgrade/Migration)

    D. USE: UTILISE (ENABLE)             

    Essential competences

    D.1. Scientific Applications Integration (on running RI)

    D.5. Data collection and preservation

    D.4. New reqs and change Identification

    D.6. Education and Training Provision

     Desirable (advanced) competences

    D.2. Information Security Strategy Development

    D.3. RI/ICT Quality Strategy Development

    D.7. Purchasing/Procurement

    D.8. Contract Management

    D.9. Personnel Development

    D.10. Dissemination and outreach

    E. MANAGE        

    Essential competences

    E.1. Overall RI management (by systems and components)

    E.5. Information/Data Security Management

     Desirable (advanced) competences

    E.6. Data Management (including planning and lifecycle mngnt, curation)

    E.4. RI Security and Risk/Dependabiity Management

    E.2. Project and Portfolio Management

    E.3. ICT Quality Management  and Compliance

    E.7. RI/IS Governance


    3.3. References to ETRD2 (in no special order)

    H.Harris, S. Murphy, M. Vaisman, “Analysing the Analysers: An Introspettive Survey of Data Scientists and Their work”, O’Reilly Media, Inc, 2013

    Skills and Human Resources for e-Infrastructures within Horizon 2020, The Report on the Consultation Workshop, May 2012

    e-Skills for the 21st Century Fostering Competitiveness, Growth and Jobs

    Big Data Analytics: Assessment of demand for Labour and Skills 2013-2020.  Tech Partnership publication, SAS UK & Ireland, November 2014 [online]

    European eCompetences Framework

    European e-Competence Framework 3.0. A common European Framework for ICT Professionals in all industry sectors. CWA 16234:2014 Part 1.

    User guide for the application of the European e-Competence Framework 3.0. CWA 16234:2014 Part 2.

    Building the e-CF – a combination of sound methodology and expert contribution. Methodology documentation. CWA 16234:2014 Part 3.

    Case Studies for the application of the e-CF. CWA 16234:2014 Part 4.

     Descriptors defining levels in the European Qualifications Framework (EQF) [online]

    Computational and Data Science Education Competencies. HPC University. [online]



    4. researchers: contact Christopher Jung,

    4.1 Essential skills:

    • Substantive Domain Expertise (DC)
    • Maths and Statistics Knowledge (DC)
      • Basic Statistics, Linear Algebra and Multivariable Calculus (DH)
    • Hacking Skills (DC)
      • Basic Tools for Data Management and Analysis: (statistical) programming language (e.g. R, Python), database querying language (e.g. SQL) (DH)
      • Machine Learning: knowing the algorithms (e.g. k-nearest neighbors) (DH) and when and how to apply them (CJ)
    • Data Wrangling/Munging: dealing with data imperfections (e.g. missing values, incosistent formats) (DH)
    • Data Visualization & Communication (DH)

    4.2 Desirable skills:

    • Software Engineering (e.g. development of data-driven products, data logging) (DH)
    • Big Data Frameworks (e.g. MapReduce) (CJ)
    • Data Privacy (CJ)
    • Data Preservation (CJ)
    • Data Curation (CJ)
    • Data management planning (VVdE)
    • Data documentation and metadata (VVdE)
    • Ethical handling of data from human participants (VVdE)
    • Rights and licencing of data (VVdE)
    • Publishing and citing data (VVdE)


    4.3 Background reading (currently in no particular order);

    a) (DC) Drew Conway: The Data Science Venn Diagram (

    b) (DH) Dave Holtz: 8 Skills You Need to Be a Data Scientist (

    c) (VVdE) Corti, L., Van den Eynden, V., Bishop, L. and Woollard, M. (2014) Managing and Sharing Research Data: A Guide to Good Practice, London: Sage. ISBN:  978-1-44626-726-4

    d) Corti, L. and Van den Eynden, V. (2015) Learning to manage and share data: jump-starting the research methods curriculum International Journal of Social Research Methodology:  10.1080/13645579.2015.1062627 (soon available as open access)

    e) Hailey Mooney W. Aaron Collie Shawn W. Nicholson Marya R. Sosulski (2014) Collaborative Approaches to Undergraduate Research Training: Information Literacy and Data Management Advances in Social Work Vol. 15 No. 2 (Fall 2014), 368-389. (… )

    f) Tenopir C, Allard S, Douglass K, Aydinoglu AU, Wu L, Read E, et al. (2011) Data Sharing by Scientists: Practices and Perceptions. PLoS ONE 6(6): e21101. doi:10.1371/journal.pone.0021101

    4.4. Additional thoughts by

    a) (CJ) Christopher Jung; VVdE=Veerle Van den Eynden, UK Data Archive

Log in to reply.