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Open data sharing policies are becoming increasingly common across academic publishing. The humanities and social sciences (HSS) are no exception to this trend, yet data sharing raises significant questions for HSS researchers.  

In July 2022, we hosted a webinar on open data in HSS. In this session, Rebecca Grant (Head of Data and Software Publishing, F1000) discussed how concepts of openness and FAIR-ness relate to HSS research data.  

Watch the recording below to catch up on the webinar or keep reading to uncover 3 key takeaways from this session. 

1. HSS researchers face significant challenges when sharing their data 

The concept of open data presents a variety of challenges for HSS researchers. Above all, there seems to be a general lack of experience in data sharing compared to the life sciences. 

In scientific disciplines, data is bedrock of research and discovery—it is gathered, stored, and analyzed as part of the scientific process. However, in the humanities, many researchers are unclear on what exactly constitutes “data”. Often, researchers have used or generated data, but don’t immediately recognize it as such. Field notes, photographs, maps, images, and survey results are examples of humanities data. 

Moreover, humanities scholars often use third-party sources in their research. This can include data generated by others, or provided by cultural heritage institutions, which could lead to copyright issues if the author attempted to re-share it under an open license. Lastly, researchers in the field might not be aware of resources that could facilitate data sharing, such as data repositories. 

In the social sciences, the primary challenge is not necessarily identifying the data, but understanding whether it can be shared. Most social sciences researchers work closely with human research participants. It is essential to protect their identity and privacy. Moreover, social science research often involves sensitive topics. In such cases, scholars in the field must get participant consent to share data openly. 

2. How HSS researchers can overcome open data challenges

F1000Research has a robust open data policy in place, which requires authors to share all datasets underpinning their research.  

We ask authors to do a few things with their research data before their paper gets accepted for publication. 

Identify all data from the outset 

An author might have generated their own data during the research process, or the data could have come from a third party, such as an existing survey. As a result, HSS data can be viewed as both the input and output of the research process. 

Plus, HSS researchers work with data that are physical, digital, or digitized (digital copy of physical data). HSS data can take different forms, including: 

• Archival documents 
• Museum objects 
• Audio files 
• Images 
• Survey results 
• Transcripts 

Obtain participant consent  

For all studies involving humans, authors must ensure that their research participants have given their informed consent to take part in the project before the study begins, and that their data can be shared openly when the study concludes. 

Usually such data should be anonymized, meaning that participants can no longer be identified when the data is shared.  

Deposit data in a data repository and apply an open license 

A repository is a location on the web for research data to be stored and accessed by others. To make research data accessible, authors should deposit it into a data repository, where they can add contextual information and receive a DOI. Placing data in a repository helps preserve it more securely over time than hosting it on a website.  

All shared data should also be reusable. To achieve this, we encourage authors to apply an open license to their data repository so others can reuse the hosted data, and to consider making their data FAIR (Findable, Accessible, Interoperable and Reusable).

Write a data availability statement  

We also ask authors to provide a data availability statement. This statement explains where the data can be found and is a required section of the manuscript.  

Data availability statements work for any data type, including data the researcher generated, data they reused, and material from sources like museums and archives. Even if a paper does not have any underlying data, authors should always include a data availability statement to explain that they did not generate or reuse data.   

Cite the data   

Finally, to ensure that data creators receive credit for their data, authors should cite any datasets they used in the body of their article and add it to their list of references.  

Data citation is equivalent to a standard bibliographic citation and allows proper accreditation when datasets are reused by other researchers.  

3. Benefits of data sharing for HSS researchers 

Increased credibility 

Open data enables validation of your research, boosting its credibility. By sharing data openly, entire research projects become more transparent. This way, others can follow the author’s thought process methodology, and potentially reproduce or replicate their study, if relevant. 

Enhanced visibility  

If authors publish their research as an article and deposit their datasets in a repository, their work can be found through both routes. The broader research community can access the author’s research whether they are looking in the repository or browsing the journal.  

Proper credit 

If data is cited, the author gets credit as the person who created it. Other stakeholders such as cultural heritage organizations also get credit for making resources (such as digitized materials) available.   

Greater citations 

There is evidence that open data sharing is associated with increased citations: sharing data openly in repositories is associated with up to 25% more citations to the research paper. 

Open data can be a challenge across all subject areas, but it can be especially daunting for humanities and social sciences researchers. At F1000Research, we know there is no one size fits all approach to sharing research data. This year, we are rolling out a set of data sharing guidelines that have been tailored specifically for HSS researchers. These new guidelines include terminology and best practice examples to make sure that our HSS authors understand how they can share data in ways that are appropriate to their research methods.  

For more information on open data sharing, visit our online hub of resources.   

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