Data Sharing : there is no One-Size Fits-All solution ...

Photo by PEXELS licensed under CC0 License

Although many researchers are already sharing their raw data and data sets, there are still researchers who question:

Why They Should Share Their Data?
What benefits are in it for them ? 
How will they be incentivized, when others use the outputs of their hard work?
* Where data can be stored in order to be right attributed and gain their add-value? ...

Upstream and downstream in Data Sharing ...

... can be seen, respectfully, either as:

(1.) compliance with data sharing policies and government regulations in place, in order to boost economy and to raise research visibility and re-usability (e.g., Guidelines to the Rules on Open Access to Scientific Publications and Open Access to Research Data in Horizon 2020or as  
(2.) the reluctance to embrace / contribute to data sharing movement due to a number of circumstances and fears, such as: 

* “Lack of resources to make the data available…”
* "Data will be misused or misinterpreted if …"

* "Possible loss of autonomy and control over information sources and organizational power, as well as sense of ownership", and so on and so forth…

During the Open Access Week 2017 #OAweek, a number of communities of practice and subject experts discussed open challenges in data sharing as well as proposed concrete solutions to incentivize data producers and facilitate processes in the context of data publishing and re-use.

For example, African Open Science Platform (AOSP) delivered a series of webinars on "Incentives for Sharing Research Data". Below you will find highlights of each one of the presentations united behind the common message : “Explain - Incentivize - Share - Benefit”. 

Incentivizing data sharing: a “bottom up” perspective 
(by Dr Louise Bezuidenhout, University of Oxford)


  • Not assume that globally-endorsed incentives have equal traction in local setting.
  • Examine all (social and physical) aspects of research environment and institutional challenges :

* WHAT data to share
* WHERE to share
* HOW to annotate and link data to processes of reasearch
* WHEN to share

  • Foster responsibility for:

* Producing accurate data
* Ensuring data are re-usable
* Surveilling data of others
* Affording credit for use of others’ data

Open Science and Data Sharing
(by Martin Wittenberg, DataFirst)


Data is not useful for research unless :

  • We know where it has come from
  • What sort of errors/biases are likely to be involved in the measurement process
  • People who are working on applied questions know that data exists and can be accessed and discoverable
  • There are transparent protocols for accessing , data sharing agreements

Proper science can only be done if:

  • Results can be replicated (to make sure what is the best practice from feed-back)
  • Errors in analysis/measurement is exposed

Open Science Incentives
(by Veerle Van den Eynden, UK Data Archive)


  • Data management advice, guidance, training (on data confidentiality, security, ethics) for data creators
  • Supporting researchers to make research data sharable (private management sharing; collaborative sharing; peer exchange; sharing to transparent governance; community sharing; public sharing).

Research data available for re-use to maximum extent are accessible via ReShare repository hosted by the UK Data Service.

Learn more about what motivates researchers to share their data :

Data sharing requires a holistic view (from a “bottom up” perspective) of how to plan data life cycle and document all processes on its support (e.g. Process Management Plans, PMPs), while considering all other related points and areas of overlap in the subject, such as : 

(1.) concerns of researchers, 
 explaining clear benefits to come along sharing fieldwork data, and incentives in place to push data sharing, 
(3.) concrete ways and means to develop and consolidate (institutional, inter/national) trusted digital repositories (e.g., “Secure Labs” in DataFirst) supporting ethics of data sharing throughout effective data lifecycle management.

P.S. “Equality is treating everyone the same. But Equity is taking differences into account, so everyone has a chance to succeed.” (Jodi Picoult) 

Why, How and Where to share ? A holistic approach to follow-up 

Share or not to share? That is the question of both hope and concern to find a balance for Data Sharing between:

(1.) data shared “by hook or by crook” (keeping in mind that relying on the good will of scientists to “just do it” is unfair) and 
(2.) data sharing incentivization (after all aspects of research environment have been discussed) to foster the spirit of Open Science and  Universal Access to Knowledge on inter/national and institutional levels.

In this context, there is an urgent need to find new ways to turn existing cross-border discussions on possibilities and pitfalls of data sharing into assurance that data producers can rely on good Data Governance policies and processes established to provide, track, enhance, maintain and push :

* (meta) data quality ,  
* stable (and where necessary - protected) access to data ,
* flexible data re-use conditions ,
data metrics to be used to incentivise data sharing ,
* data-driven value-added outcomes built upon sharing integrated /linked (big) data and (big) metadata ,  
* co-located documentation about all data curation processes and tools for monitoring troubleshooting in data-intensive research and analysis.

Learn more about Data Governance Capabilities :

Build and share in sustanaible Open Data Repositories. Start by Starting : 

In order to ensure data infrastructures remain relevant over time, a series of aspects should be considered and embedded in the design stages of any sustanaible Open Data portal. An easy-to-use practical guide is proposed of the European Data Portal.

To tackle the issues of Open Data, you might be interested to take a look at the World Bank Open Government Data Toolkit, and free eLearning modules available on European Data Portal.

To decide if and where to deposit your data, you might be interested to take a look at authoritative repository registries on the  OpenAIRE portal and Recommended Data Repositories with FAIRsharing entry.

To further deepen your knowledge of coordinated development and adoption of Research Data Shared Services, you might be interested to take a look at:

Research Data Alliance (RDA)

… that builds the social and technical bridges that enable - through focused Working Groups and Interest Groups (with about 5,500 members from 125 countries; 2017) - open sharing of data across technologies, disciplines, and countries.



…that is the Committee on Data of the International Council for Science (ICSU) to promote global collaboration to improve the availability and usability of data for all areas of research. CODATA works also to advance the interoperability and the usability of such data: research data should be intelligently open or FAIR.

EUDAT Collaborative Data Infrastructure (EUDAT CDI)

... that is essentially a European e-infrastructure of integrated data services and resources (from over 50 research communities spanning) to support research across many different scientific disciplines. The establishment of the EUDAT CDI is timely with the imminent realization of the European Open Science Cloud which aims to offer open and seamless services for storage, management, analysis and re-use of research data, across borders and scientific disciplines.

Research Data Network

… which is a site to exchange information on research data management and related topics within the UK (and beyond)  research information management sector. The site builds a body of useful resources (Knowledge Base) to help people share up to minute developments, practices and develop new ideas around data shared services.

Australian National Data Service (ANDS)

... A-Z guides and reports for assessing IT Infrastructure capability for Data Management and Research data management in practice. ANDS' flagship service is the Research Data Australia discovery portal where you can find, access and reuse data for research from Australian research organisations, government agencies and cultural institutions.

e-Infrastructures Austria

… a project promoting coordinated expansion and the further development of repository infrastructures throughout Austria. This is intended to ensure the secure archiving and provision of electronic publications, multimedia objects and other digital data from research and teaching.

Data Carpentry

... develops and teaches workshops on the fundamental data skills needed to conduct research. Its  mission is to provide researchers high-quality, domain-specific training covering the full lifecycle of data-driven research.

Fostering Capacity Building and Development ...

... up around the data on a global scale can bring a radical change of culture and mind-set in the research community and stakeholders. 

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