What is it? Why is it important?

In order to facilitate the discovery, access, re-use, and citation of datasets, it is important to share research data.

 

The most adopted and efficient means for data sharing are the FAIR Data Principles. These principles provide guidelines on how data should be organized (e.g. described, saved, published) in order for the data to be:

  • Findable: Metadata and data should be easy to find for both humans and computers
  • Accessible: Once users find the required data, they need to know how it can be accessed.
  • Interoperable: Data should be readable, interpretable and usable when combined with other datasets (i.e.(semi-)automatic processing in a long-term approach)
  • Reusable: Metadata and data should be well described, so that they can be replicated and/or combined in different settings

More

Closed and open file formats:

  • A closed file format contains data that is stored according to a specific encoding-scheme. Thus the data can only be accessed and used by the company who created the closed file format (e.g. docx, xmlx, accdb)
  • An open file format is a format where data can be processed without the need to implement software, and can therefore be used by anyone (e.g. free/open-source software tool such as a JPEG file format which does not require the acquisition of s special licence to use, odt, ods, sql, csv)

 

Open licences have potential restrictions on sharing, which can include:

  • BY (attribution) give the author or licensor the credits as specified
  • SA (Share-Alike) the copy must be distributed under a licence identical to the original one
  • ND (Non-Derivative) no right to modify original work
  • NC (Non-Commercial) no right to redistribute or modify for commercial purposes

What do I need to do?

As SP-INV, optimise the reuse of data by sharing datasets that are:

  • Findable: Deposit data in a repository (e.g Zenodo, Dryad), and include an identifier (e.g. Digital Object Identifier). Provide descriptive metadata to allow the dataset to be easily findable by search engines.
  • Accessible: Specify conditions for accessing your data (e.g. public or restricted access). Note, accessible does not mean open and free. Store data and metadata on a server that allows for easy access, and for the data to be downloaded and processed. Include authentication and authorisation
  • Interoperable: Use open file format (i.e. in opposition to closed format) to store data. Preferably use controlled vocabularies (e.g. LOINC, CTCAE)
  • Reusable: Provide dataset documentation (e.g. type of variables, associated label, source-document), and include a data-licence (e.g. CC-BY). Describe the context and provenance of the data, and define how the data can be re-used in research projects. Provide instructions on how to cite/acknowledge the study team.

More

Many online tools help to improve data FAIRness:

  • The FAIR Cookbook provides recipes on how to make data FAIR
  • A FAIR data-self-assessment tool is a questionnaire that based on answers provide a respective FAIRness score
  • FAIR Aware is a first step questionnaire to help you towards a fair dataset
  • The “HealthyCloud FAIRness” assessment tool is similar to the FAIR self-assessment one but can be done offline

 

Data sharing procedures must be mentioned or described in the study protocol, DMP, ICF.

Where can I get help?

Your local CTU can support you with experienced staff regarding this topic

SNF – search for

  • Data Management Plan (DMP) - guidelines for researchers

 

Online supporting tools to make data sharing FAIR

Abbreviations
  • CC-BY – Creative Common Attribution
  • CTCAE – Common Terminology Criteria for Adverse Events
  • CTU – Clinical Trials Unit
  • DMP – Data Management Plan
  • FAIR – Findable, Accessible, Interoperable, Reusable
  • ICF – Informed Consent Form
  • LOINC – Logical Observation Identifiers Names and Codes
  • SP-INV – Sponsor-Investigator
Completion ↦ Data Management ↦ Data Export ↦ Data Sharing
Study
Basic

Provides some background knowledge and basic definitions

Basic Monitoring
Basic Drug or Device
Concept

Starts with a study idea

Ends after having assessed and evaluated study feasibility

Concept Statistic Methodology
Concept Drug or Device
Development

Starts with confidence that the study is feasible

Ends after having received ethics and regulatory approval

Development Drug or Device
Set-Up

Starts with ethics and regulatory approval

Ends after successful study initiation

Set-Up Ethics and Laws
Set-Up Statistic Methodology
Set-Up Quality and Risk
Set-Up Drug or Device
Conduct

Starts with participant recruitment

Ends after the last participant has completed the last study visit

Conduct Statistic Methodology
Conduct Drug or Device
Completion

Starts with last study visit completed

Ends after study publication and archiving

Completion Statistic Methodology
Completion Drug or Device
Current Path (click to copy): Completion ↦ Data Management ↦ Data Export ↦ Data Sharing

Please note: the Easy-GCS tool is currently under construction.