F (Findable) - A (Accessible) - I (Interoperable) - R (Reusable)
The "FAIR Across Disciplines" activities create value by contributing to a common understanding of the FAIR principles and the challenges and opportunities related to FAIR in concrete research contexts, thus forming the basis for future initiatives, policies and requirements. The project runs until the end of 2018.
"FAIR Across Disciplines" Conference, November 20th 2018
The project hosted a final conference on November 20th. Below are the presentations from the day:
- Karsten Kryger Hansen - Welcome
- Karsten Kryger Hansen - Introduction to cases in FAIR Across
- Sarah Jones - FAIR play? Investigating the state of FAIR practice and what is needed to turn FAIR data into reality
- Costanza Navaretta and Lene Offersgaard - FAIRify debates from the Danish parliament (Folketinget)
- Anne Sofie Fink - Understanding and using the FAIR data principles across disciplines
- Troels Rasmussen - The political focus on FAIR in Denmark
- Nikola Vasiljevic - Case: DTU Wind Energy
- Anders Sparre Conrad - The FAIR Principles
- Anders Sparre Conrad - Summary of the day and outlook
- Anders Hede - FAIR from a funding perspective
- Ulf Jakobsson - FAIR data initiatives in social sciences and how it can inspire other disciplines
Participants are from Aalborg University, Copenhagen University, the Royal Library, Technical University of Denmark, Copenhagen Business School and the National Archives. The National Archives is project manager.
"FAIR across" contributes to a common understanding of the FAIR principles through a number of data pilots/cases (active research projects and research data services). The challenges of complying with the principles are covered and suggestions are provided for how pilots can work further towards FAIR data. Links to the descriptions of some of the data pilots/cases are found below.
- Case 1: FAIR in Biotechnology
- Case 2: Sensitive Health Data
- Case 3: TrygFonden
- Case 4: Analysis of Danish Parliament Data
- Case 5: The Writings of Ludvig Holberg
- Case 6: FAIR data for DTU Wind Energy
- Case 7: CBS I
- Case 8: CBS II
The project's deliveries of graphic nature
Some of the project's deliveries will be published/available here; posters and other material related to the specific use of the FAIR.
The PowerPoint presentation to "The path to a successful research project - how to get on the right track with FAIR data management" is available here.
"The path to a successful research project - how to get on the right track with FAIR data management":
- Scenario 1: Digital page 1 | Digital page 2 | Print
- Scenario 2: Digital page 1 | Digital page 2 | Print
- Scenario 3: Digital page 1 | Digital page 2 | Print
- Scenario 4: Digital page 1 | Digital page 2 | Print
- Scenario 5: Digital page 1 | Digital page 2 | Print
"Debunking FAIR myths":
- Myth 1: The myth | The answer | Print
- Myth 2: The myth | The answer | Print
- Myth 3: The myth | The answer | Print
- Myth 4: The myth | The answer | Print
- Myth 5: The myth | The answer | Print
- Myth 6: The myht | The answer | Print
- Myth 7: The myth | The answer | Print
- Myth 8: The myth | The answer | Print
- Myth 9: The myth | The answer | Print
The book "A FAIRy tale - A fake story in a trustworthy guide to the FAIR principles for research data" will be published soon. The front page can be found here.
The FAIR toolbox
This overview guides you on finding the right tools for making your research data (more) FAIR – depending on what data you have and what you want to do with these data. The presentation can be found here.
Create: Collect or generate new data from scratch, e.g. through measurements or surveys.
Process/analyze: Use data for any kind of research activities, including digitization, conversion and interpretation.
Document: Add context to the data, including provenance and metadata.
Recycle: Use previous output as input for new analysis or interpretation, also in collaborations.
Publish/disseminate: Make selected datasets available for other researchers or the general public.
Archive: Deposit selected datasets in systems suitable for long-term preservation.
Exploit: Perform research directly on previously published data.
Discover & re-use: Use previously published data for new research, e.g. from public databases and repositories.
Release: Provide access to raw data for others to use. preserve:
Retain: Raw data on long-term storage.
Discard: Destroy or delete any data – due to legal or contractual obligations, for example. This is not a FAIR process and is therefore not considered further.
Services/tools facilitating FAIR data, including including information resources with information about FAIR and sharing tools facilitating data sharing, can be found here.
List of FAIRification services: