blog

Academic publishers and funding agencies increasingly encourage researchers to share the data which underlies their research findings. There is evidence that when researchers use openly available datasets they can build upon existing research and make a greater impact across disciplines. 

In August 2022, we hosted a webinar on data reuse. In this session, Rebecca Grant (Head of Data and Software Publishing, F1000) and James Barker (Associate Publisher, F1000) discussed the benefits of sharing data openly. They also provided valuable insights on making research data more reusable and shared real success stories. 

Watch our on-demand webinar below, or keep reading for the webinar highlights. 

What’s the use in reuse? 

Making data openly available increases its visibility and potential reuse by other researchers, which benefits creators and users. Applying an open license to the data is very important as it allows reuse and explains what can be done with the published data. 

Benefits for the sharer 

Researchers most commonly share their data for reproducibility reasons allowing others to verify or build on results. Scholars might even openly share data they are not using themselves. When another researcher uses the data, this contributes to reducing research waste in the field. In addition, other researchers might take the datasets in innovative and creative directions or use them in ways that the data creator could not do due to a lack of equipment or relevant expertise. 

Furthermore, researchers often liaise with the original creators when reusing existing data, asking for additional information on data collection or the conducted study. This can lead to new collaborations and partnerships that may never have occurred had the data not been shared openly. 

Lastly, when their data is licensed and reused, the creators can get credit for producing the original datasets through citations.  

Benefits for the user  

Other researchers may use existing datasets either within their own studies or to conduct additional analyses on existing research questions. Such pre-made datasets enable scholars to start working on their research immediately. 

Furthermore, in most cases, researchers can access the methodology associated with a research project. For example, specific article types such as Data Notes allow the full reporting of methods alongside the associated research data. 

This way, a researcher knows the results and how they were produced beforehand. Using others’ data helps scholars start their analysis and generate results quickly.  

Plus, the user does not need to curate or upload data to a repository. When they reach the point of publication, all they need to do is cite the original source of the data. 

Reuse in action  

But, what does data reuse look like in practice? F1000Research is home to many research projects that have provided the basis for further scientific discoveries using open data. Take a look below at two data sharing success stories from our authors. 

From sugar production to bioethanol production  

In 2017, Riaño-Pachón and Mattiello published their Data Note ‘Draft genome sequencing of the sugarcane hybrid SP80-3280‘ on F1000Research.  

Sugarcane is a key waste product in sugar production and a complicated entity. A polyploid species, it has up to 130 chromosomes with a total genome size of 10 gigabase pairs. 

Up until the point of the publication of this article, only partial or transcriptome sequences were available. Riaño-Pachón and Mattiello were able to generate the full genome and made it openly available to others. In describing their sugarcane dataset using a Data Note, the authors enabled other researchers to identify and characterize new genes in this crop. 

One year later, Santiago et al. used the open data in the Data Note to identify 92 expansin genes in the sugarcane genome in their research article.  

The leaf of the sugarcane plant is one of the key waste products that are used in bioethanol production. Therefore, Santiago et al.’s research is particularly important as their identification of expansins can help improve the biomass and yield of the plant for the purpose of generating bioethanol. Their work might not have been available today had the original dataset not been shared by Riaño-Pachón and Mattiello. 

Creating a tool for ribosome profiling  

In 2020, Kimchi-Sarfaty et al. published their Data Note ‘Ribosome profiling of HEK293T cells overexpressing codon optimized coagulation factor IX’ on F1000Research.  

The authors conducted ribosome profiling of two versions of the F9 gene with identical protein amino acid sequences, but different nucleotide coding sequences. The profiles of these two variants were not previously available and Kimchi-Sarfaty et al. shared them openly in their dataset. 

François et al. used this dataset to test a new docker package for ribosome profiling called RiboDoc. The authors cited the set’s robust reporting and methods as factors that influenced their choice. “The human dataset was selected because quality controls had been rigorously performed, making it possible for us to compare RiboDoc with data analyzed with different scripts,” noted the authors. 

This case study is a great example of how data reuse can facilitate greater scientific discovery and advancement of the field. 

At F1000Research, we believe that access to original data is essential to replicate a given study, reproduce the original findings, or reuse the data to support new research. That’s why we advocate an Open Data Policy. Data reuse is a vital part of open data that benefits everyone and can lead to innovative and impactful research projects.  

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

You can also subscribe to our mailing list to receive our latest updates on upcoming webinars and new resources.