Admission requirements

This course is aimed at PhD candidates from all scientific disciplines but is open to postdoc fellows if seats are available. If PhD candidates aim to include the credits from the course in their 30 mandatory PhD ECTS, they should discuss it with their supervisor.

PhD candidates from both UiT and other European universities may be admitted to the course. PhD candidates not registered at UiT must upload a document from their university, stating that they are registered PhD candidates.

All participants must have completed a Master’s degree (or equivalent).

Maximum 20 participants. If 10 applicants or less, the course responsible will evaluate whether the course will take place.

If more than 20 applicants, priority will be given as follows:

• Participants admitted to the PhD programme at UiT, with priority given to those candidates admitted most recently.

• PhD candidates from other universities

• Postdoc fellows

All PhD candidates at UiT apply for admission by registering for class in Studentweb by December 1 for the spring semester.

• Other participants apply for admission in Søknadsweb by December 1 for the spring semester. Application code 9301.

Course content

Data Stewardship offers a broad introduction to research data management. It covers all major steps in the research data management life cycle, from finding relevant data, via organising and documenting data, to publishing datasets in suitable data repositories.

The course content is tailored to the needs of PhD candidates and is relevant for all disciplines and all types of data. It combines theory with practical exercises that have direct relevance for PhD projects. It consists of three modules:

• Module 1 is a MOOC that goes through theory and best practice of research data management.

• Module 2 is run as a week-long workshop with in-person, multidisciplinary sessions where participants meet to work together on activities related to the theory in Module 1.

• Module 3 puts theory into practice where participants apply the knowledge acquired in Modules 1 and 2 during a placement with a local business or public sector employer.

The course offers PhD candidates the opportunity to develop skills that are sought-after within both the academic and private sector. It was developed through the DocEnhance project (2020-2022) specifically with transferable skills in mind.

Recommended prerequisites

Objectives of the course

Knowledge

• Articulate the rationale behind Research Data Management (RDM) in relation to transparency and reproducibility in science and the FAIR principles

• Understand the benefits of open archiving, while recognizing legitimate legal and ethical constraints

• Describe the role of Data Management Plans (DMPs) in ensuring effective management of a research project

• Recognize the value of RDM practices to workplaces outside the university and higher education sector

Skills

• Select and use suitable tools to write a DMP for a research project

• Organize, structure, document and store research data according to best practice

• Identify and use a suitable reputable repository for archiving research data

• Find published research data and reuse it in line with existing norms and conventions

General competence

• Carry out research with scholarly integrity

• Apply best practice in RDM to work tasks and assignments outside the university and higher education sector

Language of instruction and examination

Teaching methods

• Self-paced online course (Module 1), including voluntary reflection tasks and test-yourself quizzes.

• Teacher-led seminars with focus on group work. Pre-assignments given for each seminar (Module 2).

• Individual or group work carried out in collaboration with a non-academic partner (Module 3)