1 of 18

Module 1:

Planning: Research Data Management, Data Management Plans, FAIR Principles

​

2 of 18

Learning Objectives

Name Surname | Event | Date

Describe the current policy landscape that shapes knowledge production in SSH

​

Identify Open Science practices in SSH

​

Recognise SSH specificities within the research workflow during knowledge production from an ethical, legal and methodological perspective

Describe what the research data management requirements and standards are

3 of 18

Content

Name Surname | Event | Date

  • Research Data Management
  • Data management plan
  • FAIR principles

​

​

Describe the current policy landscape that shapes knowledge production

4 of 18

Research Data Management

Research Data Management (RDM) involves the organization, storage, preservation, and sharing of data (or research materials) collected and used in a research project.

​

Crucial elements include:

  • Data Management Plans
  • Documentation
  • FAIR Principles
  • Metadata
  • Repositories

Name Surname | Event | Date

5 of 18

Research Data Management

Important steps in the RDM process:

​

  1. Assess and create an inventory of the data you expect to produce
  2. Establish a data management plan (‘DMP’) and document the data summary per dataset
  3. Think about data storage
  4. Provide contextual information about your research activities (Software/code/lab protocol)
  5. Deposit the data in a trusted repository
  6. Think about licencing for your resources
  7. Publish resources

​

Name Surname | Event | Date

6 of 18

Research Data Management - Why?

  • Increase chances for reusing the data by you and others
  • Ensures research integrity through proper documentation of how data were collected and analysed.
  • Anticipate & prepare actions for any challenges with personal or sensitive data
  • Assess capacity for data storage
  • Guards against loss of data
  • Commit to data sharing through appropriate licencing

7 of 18

Data Management Plan

8 of 18

Data Management Plans (DMPs)

Formal documents outlining:

    • How data will be used during a project
    • Archiving and preservation of data
    • Open Science in practice
    • FAIR Principles

9 of 18

Data Management Plan

What to consider:

Data Summary: describe your data (existing or new)

Metadata standards

Data Sensitivity: personal/sensitive data; ethical & legal concerns

Data Security: encryption/passwords

Data Storage: size and capacity for storage; back-up options

Licensing: open as possible, closed as necessary

Data preservation: repositories, and long-term storage, archiving

Data management: data owners, responsibilities & resources

Plan

Organise & Document

Process

Store

Protect

Publish

Discover

10 of 18

Horizon Europe DMP Template

Data Summary

FAIR Data

Other Research Outputs

Allocation of Resources

Data Security

Ethics

Other Issues

11 of 18

FAIR Principles

12 of 18

FAIR Principles

Findable

Accessible

Interoperable

Reusable

13 of 18

FAIR Data - Humanities

Key elements

​

    • Persistent Identifiers (PIDs)
    • Data Management Plan
    • Metadata
    • Licences
    • Repositories

Image: ALLEA Report, 2020 – Sustainable and FAIR data sharing in the humanities.

​

14 of 18

FAIR Principles - Findable

  • Persistent Identifiers
  • DOIs, or Handles or ARK
  • Search Engine Indexing

Image Citation: Deutz, D. B., Buss, M. C. H., Hansen, J. S., Hansen, K. K., Kjelmann, K. G., Larsen, A. V., Vlachos, E., & Holmstrand, K. F. (2020, June 30). How to FAIR: a website to guide researchers on making research data more FAIR. Zenodo. https://doi.org/10.5281/zenodo.3712065 – CC BY 4.0

​

Findability ensures that data are discoverable. Use rich descriptions, provide relevant keywords to increase chances of discovery.

15 of 18

FAIR Principles - Accessible

​

  • FAIR does not equal Open.
  • Accessibility is about providing a pathway towards Access.
  • Your data may still be restricted or embargoed, but reasons must be provided.
  • Trusted repositories�

Image Citation: Deutz, D. B., Buss, M. C. H., Hansen, J. S., Hansen, K. K., Kjelmann, K. G., Larsen, A. V., Vlachos, E., & Holmstrand, K. F. (2020, June 30). How to FAIR: a website to guide researchers on making research data more FAIR. Zenodo. https://doi.org/10.5281/zenodo.3712065 – CC BY 4.0

​

”one should provide the exact conditions under which the data are accessible.”

16 of 18

FAIR Principles – Interoperable

​

  • File Formats

​

  • Metadata standards

​

  • Ontologies

​

  • Vocabularies

�

Image Citation: Deutz, D. B., Buss, M. C. H., Hansen, J. S., Hansen, K. K., Kjelmann, K. G., Larsen, A. V., Vlachos, E., & Holmstrand, K. F. (2020, June 30). How to FAIR: a website to guide researchers on making research data more FAIR. Zenodo. https://doi.org/10.5281/zenodo.3712065 – CC BY 4.0

​

Common structures and terminologies.

17 of 18

FAIR Principles - Reusable

​

​

​

  • Data are well documented and curated, providing rich context information about data creation.
  • Data conform to community standards.
  • Include clear terms and conditions on access and reuse.
  • Preferably applying machine-readable standard licenses.

�

Image Citation: Deutz, D. B., Buss, M. C. H., Hansen, J. S., Hansen, K. K., Kjelmann, K. G., Larsen, A. V., Vlachos, E., & Holmstrand, K. F. (2020, June 30). How to FAIR: a website to guide researchers on making research data more FAIR. Zenodo. https://doi.org/10.5281/zenodo.3712065 – CC BY 4.0

​

Ensure widest reuse possible with the least obstacles and allow for integration with other data.

18 of 18

Thank you!�Questions?

[Trainer’s email address]

​