Maybe I should share my research data

Jukka Rantasaari

The open sharing and reuse of digital research data collected and produced in research projects are goals in the University of Turku’s data policy, national and international guidelines, and a requirement or recommendation from several major funders (e.g., Horizon Europe, European Research Council, Wellcome Trust, National Science Found, National Institutes of Health, Research Council of Finland).

Practices vary

There can be significant differences in data sharing and reuse due to factors such as disciplinary cultural differences and the nature of the data.

To promote good, standardized data management practices and facilitate data sharing, incentives and skills in data management are needed, along with user-friendly processes and infrastructures linked to research processes (see, e.g., Key Perspectives, 2010).

Barriers and facilitators to sharing

Researchers may find data sharing problematic for various reasons. Below are some common perceived barriers and ways to overcome them.

Barrier 1: Uncertainty about the benefits of data sharing

”Many academics and scientists worry about getting published in “prestigious” journals. Instead of worrying about prestige, we should try to put our work out as quickly as possible like (Nobel laureate Katalin) Karikó did. Once you put your work out without caring about prestige, two good things happen: 1. Your work will lead to newer opportunities. 2. You will start getting feedback from the scholarly community, which you can use to iterate and improve.”

Mushtaq Bilal

  • Facilitator 3: By documenting the stages of data handling, you demonstrate how you have progressed from raw data collection to processing and analysis, leading to research publication. This ensures data consistency and serves as a tool for quality assurance of research and its results. By opening the raw data as much as possible, you demonstrate to reviewers and other readers that research results have been achieved following good scientific practices (e.g., Chiarelli et al., 2021; Ioannidis et al., 2014).

”The demise of the Great Library of Alexandria resulted in the catastrophic destruction of innumerable ancient works. Yet the gradual attrition of scientific data in the modern era is widely tolerated. (…) The findings reported here suggest that retrospectively retrieving data from highly cited studies is challenging. This means that even though these studies are some of the most influential in these fields and many other scientists have used them extensively, their claims have to be accepted based on trust and the data in question are no longer independently verifiable, or freely re-usable. (…) Nevertheless, growing awareness of the importance of data sharing and research transparency across the sciences is encouraging.”

 Hardwicke & Ioannidis, 2018

  • Facilitator 4: Shared data can reduce duplication of work and speed up the utilization of research findings, e.g., in addressing climate change, water conservation, and pandemic prevention (see, e.g., Nobel laureate Katalin Karikó’s story).
  • Facilitator 5: If your data cannot be shared, you can usually share metadata (= data description information and, if desired, information about research variables) in the University of Turku’s data catalog. Through the open data catalog, other researchers here and elsewhere can see the type of research you are doing/have done, which can open up collaboration opportunities either based on the described data or from the perspective of further research.

“In our project, we have truly unique data and excellent datasets that can be used for one-of-a-kind analyses. The data catalog can be utilized, for example, when considering new research setups, but also making high-quality datasets visible is important for us as an attractive factor, for instance, in recruiting international researchers and in developing broader collaboration.”

Simo Arhippainen, INVEST Flagship

 Barrier 2: Concerns about potential data misinterpretation

  •  Facilitator: Shared data is understandable and reusable for outsiders only if it is well-described and contextualized. You can promote this with good data documentation and metadata. If there is no commonly used metadata standard in your field, it’s advisable to create a clear guidance (e.g., a readme.txt file) alongside shared datasets.

Barrier 3: Lack of skills and/or time

  •  Facilitator: By documenting the different stages of data handling as the research process progresses, you can reduce the time required for this. Library data experts can guide, support, and educate on good documentation and description practices.

Barrier 4: Uncertainty about data protection and intellectual property, i.e., ethical and legal issues

  •  Facilitator: You can get initial guidance on data protection and intellectual property from the library’s data guide. Library, data protection, and legal services also provide guidance and advice gladly.

Barrier 5: Data scooping or misuse

  •  Facilitator: Consider at which stage and to what extent you can share data – before publishing the article, in conjunction with publication, or after publication. By choosing the right type of license for your data, you can prevent data misuse.

Barrier 6: Lack of user-friendly infrastructures

Barrier 7: Lack of incentives

  • Facilitator: It is true that the most effective incentives are currently directed towards producing research publications. However, you can also get merit by sharing well-documented, licensed research data with persistent identifier (e.g., DOI). Remember to include an appropriate citation in your reference list for your research publication, see the citation guidelines.

 

Jukka Rantasaari (ORCID 0000-0001-5927-3781)

The author is the UTU Library’s service manager, who is currently preparing a doctoral dissertation on the practices, competencies, and competency needs of young researchers in research data management.

 

Julkaisun tiedot: 4/2023, Open Up!-blogi, ISSN 2814-8967