RDM, open and FAIR

Good data management is a necessary precursor for FAIR and open, and enables data to be created which is fit for sharing and reuse  | Higman, 2019

Research data management (RDM), data stewardship, open data, open science and FAIR. They are overlapping but different concepts, each of which emphasises different aspects of managing and sharing research data. In this section we explain the distinction in more detail and zoom in on ways to start the conversation with researchers about these topics.

A difference in definition

Open data, open science, FAIR data, data stewardship, RDM. These are terms that will not have escaped your attention as a future data supporter. But ... What do they mean and, more importantly, how do you find the right entrance to support researchers between all of the data jargon out there? In the presentation below, we will first show you a number of definitions for the concepts mentioned. You will see that open science, data management and data stewardship describe a process: a way in which you go through the phases of the research cycle. Open data and/or FAIR data can be the concrete and tangible research output that results from these processes and that can be built upon. 

From open to 'shades of FAIR'

The concept of open data (Open Knowledge Foundation, n.d.) followed almost automatically from the concept of open access (Openaccess.nl, n.d.), but also brought resistance (Higman, 2019). In particular, there is the misunderstanding that all data should necessarily be available in open access - in the public domain. The adage 'open if possible, closed if necessary' tries to find a way out of this misunderstanding (European Commission, 2017). The concept of FAIR also provides a starting point for softening black-and-white discussions about open access to research data.

Data resulting from publicly funded research must be made FAIR and citable, and be as open as possible, as closed as necessary | European Commission, 2017

The authors of the folder 'How open is it' (SPARC & PLOS, 2014) showed the 'Shades of Open' for open access to scientific publications. In a similar way, research data can be divided into 'Shades of Open' (LCRDM, 2019) and shades of FAIR (Mons, et al., 2017). There is no one-size-fits-all approach, and this awareness alone provides room for discussion with researchers. Especially after the introduction of the General Data Protection Regulation in 2018, which has made researchers more concerned about the risks of sharing research data, FAIR is a good starting point for discussions (Higman, 2019).   

 

FAIR at-a-glance


What does FAIR mean

In order to make full use of the potential of research data, it is necessary to include them in the research eco-system as Findable, Accessible, Interoperable and Reusable as possible (Force 11, 2014; Wilkinson et al., 2016; GoFAIR, n.d.). The FAIR principles consist of 15 facets (GoFAIR, n.d.). The main thing is that research data should not only be FAIR for people, but also for computers/machines. The FAIR principles are now an integral part of the data management landscape and form the basis of the construction plan for the European Open Science Cloud.

Does FAIR equal open?

No.

In an article by Mons et al. (2017) you can read the following explanation:  

The 'A' in FAIR stands for 'Accessible', accessible under well-defined circumstances. There may be legitimate reasons not to make research data available in open access, such as protecting the privacy of research participants. FAIR is about making research data findable by man and machine by:

  • enrichting the data in such a way that value of the data for reuse is clear;
  • explicitly - with a clear, preferably machine-readable licence - indicating the conditions under which data may be reused; 
  • giving clear instructions on how to quote in case of reuse.

None of these principles requires research data to be 'open' or 'free'. FAIR is about clarity and transparency about the conditions for access and reuse. 

Data can therefore be FAIR and not open.

The other way around, data can also be open but not FAIR.

What is FAIR data management?

FAIR data management is the set of decisions and measures taken during the life cycle of research data to deliver research data as FAIR as possible. What is important here is the choice of a data license, the data format, the recording of metadata and data documentation, naming conventions, etc. These aspects are all discussed in the following chapters of Essentials 4 Data Support.

FAIR is a brilliant acronym and this is one of the reasons why FAIR is an integral part of the data management landscape. By the way, they are not the only data principles in circulation. In addition to the FAIR acronym, there is also the FACT acronym - Fair, Accurate, Confidential, Transparent - as described on the Digital Society website (n.d.).

What is the role of data archives in making data FAIR?

The role of data archives in making data FAIR is very large. In this course we focus on researchers and what they can do to prepare data for FAIRness. But without a data infrastructure built on FAIR principles, researchers can't do very much. The recommendations for creating FAIR data often focus on both target groups: researchers and data archives. This can be confusing. 

The Parthenos Guidelines to FAIRify data management and make data reusable (Parthenos, 2018) make a distinction between the measures per target group. So that those who fit the shoe can also put it on.

One of the recommendations of the European Commission (2017) to make data FAIR is to publish it in a reliable data archive that adheres to the FAIR principles. How such a data archive exactly manages this is not something that a researcher needs to know exactly.  


How to make data FAIR?

Digital objects (such as research data, software, protocols or other research data) can be prepared for an FAIR future. The measures that researchers can take are broadly described below. These will be explained in more detail during the rest of the course.


Findable: Make sure data can be persistently found

Prepare data for FAIRness (During research) Publish data as FAIR as possible (After the research project is finished)
Document the data.   
  • Assign Persistent Identifiers (PIDs) (for example by publishing data in a data archive).
  • Assign metadata rich enough to be able to find, cite and reuse the digital objects.
  • Put the PID in the metadata.
 

Accessible: Ensure that man and machine can access the data

Prepare data for FAIRness Publish data as FAIR as possible
Determine any access conditions. In any case, make the metadata available in open access, even if this is not possible for the data itself.

 Interoperable: Make sure computers can recognise and combine data formats and metadata

Prepare data for FAIRness Publish data as FAIR as possible
Store data in a common and - if possible - open data format. Document the data using metadata standards.

Reusable: Ensure that the data can and may be used and interpreted

Prepare data for FAIRness Publish data as FAIR as possible
Document the data. Also store the software code used to edit and analyze the data. Assign a machine-readable licence which enables reuse. 

Practice

Would you like to get a better understanding of what FAIR means in practice? Go through the FAIR data assessment tool from the Australian Research Data Commons (ARDC, n.d.).

In the spotlight

FAIR is 'here to stay'. Below you will find a number of sources which will learn you more about 'FAIR in practice' as well as some possible tools and examples you could show to researchers you support.


Sources and information material to understand FAIR

Guides

Folders:

Fancy some magic? Have a look at 'A FAIRy tale' (Hansen, et al., 2018).

Examples of data support surrounding the topic of FAIR data

  • RDM Support at Utrecht University has written a guide with the title 'How to make your data FAIR' (Utrecht University, n.d.). On the one hand, this is a guide with which you can learn more about how to make data FAIR, but it is also an example of how you could give shape to data support surrounding this theme. By creating information material and making it available.
  • Employees of Wageningen University & Research write blogs about FAIR data and FAIR data stewardship (E.g. Wageningen University & Research, 2016 & 2019). 

Report: FAIR data in practice (UK)

The report 'FAIR data in practice' (Allen & Hartland, 2018) provides insight into the actual situation regarding FAIR data management in the UK. The following findings are central (Bruce & Cordewener, 2019): 

  • There is a lack of knowledge and understanding of what FAIR means and what the benefit of FAIR data management could be for research; 
  • This lack of knowledge is unevenly distributed across disciplines and stakeholders;
  • Change is needed and investment needs to be made in training researchers;
  • This requires coordination of activities and policy development at interdisciplinary, national and international levels.

UK researchers see the FAIR data principles as:

  • Going beyond open access;
  • A useful concept in bringing together different aspects of best practices for data management;  
  • Useful for supporting the reuse of research data via various types of data licenses. 

The report also offers a number of recommendations to best align existing tools and resources with FAIR. 


Sources

Click to open/close

Allen, Robert, & Hartland, David. (2018, May 21). FAIR in practice - Jisc report on the Findable Accessible Interoperable and Reuseable Data Principles (Version 1). Zenodo. http://doi.org/10.5281/zenodo.1245568

Allen, R., Hartland, D. (2018, May 21). FAIR in practice - Jisc report on the Findable Accessible Interoperable and Reuseable Data Principles (Version 1). Zenodo. http://doi.org/10.5281/zenodo.1245568

ARDC (n.d.). Retrieved from https://www.ands-nectar-rds.org.au/fair-tool

Bruce. R., Cordewener, B. Open science is all very well but how do you make it FAIR in practice? [Blog]. https://blogs.lse.ac.uk/impactofsocialsciences/2018/07/26/open-science-is-all-very-well-but-how-do-you-make-it-fair-in-practice/ 

CESSDA (2018). Data Management Expert Guide. Benefits of data management. https://www.cessda.eu/Training/Training-Resources/Library/Data-Management-Expert-Guide/1.-Plan/Benefits-of-data-management

Digital Society (n.d.) Data principles. https://www.thedigitalsociety.info/about/data-principles/

Erdmann, C., Simons, N., Otsuji, R., Labou, S., Johnson, R., Castelao, G., ... Dennis, T. (2019). Top 10 FAIR Data & Software Things. Zenodo. http://doi.org/10.5281/zenodo.2555497

European Commission (2017). OSPP-REC. Open Science Policy Platform Recommendations. https://ec.europa.eu/research/openscience/pdf/integrated_advice_opspp_recommendations.pdf#view=fit&pagemode=none

European Commission (2019). Cost-benefit analysis for FAIR research data. Cost of not having FAIR research data - Study. https://publications.europa.eu/en/publication-detail/-/publication/d375368c-1a0a-11e9-8d04-01aa75ed71a1

F1000 (n.d). Your go-to guide to making your data Findable, Accessible, Interoperable, and Reusable (FAIR). https://f1000.com/resources/FAIR_Open_Guide.pdf

Force 11 (2014). The FAIR data principles. Retrieved from https://www.force11.org/group/fairgroup/fairprinciples

GO FAIR (n.d.) Fair principles. Retrieved from https://www.go-fair.org/fair-principles/

Hansen, K.K., Buss, M., Haahr, L.S. (2018). A FAIRy tale (p. 40). Zenodo. http://doi.org/10.5281/zenodo.2248200

Higman, R., Bangert, R., Jones. S. (2019). Three Camps, One Destination: The Intersections of Research Data Management, FAIR and Open.Insights 32 (1): 18. Retrieved from http://doi.org/10.1629/uksg.468

Imming, M. (2018). FAIR Data Advanced Use Cases: from principles to practice in the Netherlands. Zenodo. Retrieved from http://doi.org/10.5281/zenodo.126847

Jones, S., Grootveld, M. (2017, November). How FAIR are your data?. Zenodo. http://doi.org/10.5281/zenodo.3405141 

LCRDM (2019). Shades of Open. https://www.lcrdm.nl/files/lcrdm/2019-06/Shades%20of%20Open.pdf

Mons et al. (2017). Cloudy, increasingly FAIR; revisiting the FAIR Data guiding principles for the European Open Science Cloud. Information Services & Use, vol. 37, no. 1, pp. 49-56. https://doi.org/10.3233/ISU-170824​​​​​​

Openaccess.nl (n.d.). Wat is open access? https://www.openaccess.nl/nl/wat-is-open-access

Open Knowledge Foundation (n.d.) Open Data Handbook. What is open data? http://opendatahandbook.org/guide/en/what-is-open-data/

PARTHENOS, Hollander, H., Morselli, F., Uiterwaal, F., Admiraal, F.,  Trippel, T, Di Giorgio, S. (2018, December 1). PARTHENOS Guidelines to FAIRify data management and make data reusable. Zenodo. http://doi.org/10.5281/zenodo.2668479 

SPARC (Scholarly Publishing and Academic Resources Coalition), PLOS (Public Library of Science). (2014). How open is it? Open Access spectrum. [eprint]. Retrieved from https://www.plos.org/files/HowOpenIsIt_English.pdf

Utrecht University (n.d.). RDM Support. How to make your data FAIR [Guide]. ttps://www.uu.nl/en/research/research-data-management/guides/how-to-make-your-data-fair

Verheul, I., Imming, M., Ringersma, J., Mordant, A., Ploeg, J.L., Pronk, M. (2019, April 16). Datastewardship op de kaart: Een verkenning van taken en rollen in Nederlandse onderzoeksinstellingen. Zenodo. http://doi.org/10.5281/zenodo.2642066 (Zie ook https://www.lcrdm.nl/handreikingen-datastewardship voor meer LCRDM-informatie over data stewardship)

Wageningen University & Research (2016, 9 May). Can Wageningen be FAIR? [Open Science Blog]  https://weblog.wur.eu/openscience/can-wageningen-fair/ 

Wageningen University & Research (2019). Weblogs about FAIR data stewardship. https://weblog.wur.eu/openscience/data-stewardship-wur/

Wilkinson, M.D. et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship, Scientific Data 3. Retrieved from https://dx.doi.org/10.1038/sdata.2016.18