Effective data sharing extends beyond simply depositing your dataset in a repository. At F1000Research, we believe that data should be shared in a discoverable, useable and reproducible way.
Enter: Data Notes.
Data Notes are short descriptions of a dataset indicating why and how the data was collected. Unlike a traditional Research Article, Data Notes do not include any analyses or conclusions. Rather, they cover the following:
- Dataset rationale, protocol, and validation details
- Information about any limitations of the dataset
- Information on where and how to access the dataset, as part of a Data Availability Statement
- Reference to the dataset using a formal data citation
Data Notes are suitable for all kinds of data no matter the subject or the size of the dataset. Furthermore, as with any F1000Research article, Data Notes are 'living', meaning you can come back and update your article with new findings at any time.
What are the benefits of publishing a Data Note on F1000Research?
Maximize the potential of your research data
Data Notes let you share contextual information about your scientific datasets in a highly discoverable, useable and reproducible way. This maximizes the potential reach and reuse of your data. Like all content published on F1000Research, Data Notes are fully open access. This means your article is easily available to all stakeholders, including fellow researchers, NGOs, and policymakers.
Credit where it’s due
Data Notes are a peer-reviewed, fully citable publication with a DOI, offering recognition and credit to data producers. Data Notes do not undermine the novelty or value of a Research Article which makes use of the published dataset. This has been confirmed by a list of other journals and publishers. In fact, there’s some evidence that linking your publications to your research data could increase the citation potential of your work.
Access expert guidance
F1000Research offer expert editorial guidance on preparing your data and choosing an appropriate repository for your work.
If you're confused about our Open Data Policy, why not browse our new hub of open data resources? The resources are designed to help researchers understand the what, why, and how of data sharing.
Increase the discoverability of your research
Once your Data Note has passed peer review, inclusion in Scopus, PubMed, Google Scholar and other major indexers will help it reach more readers. Furthermore, Data Notes include validated links to the dataset in your chosen repository, and your article is supported by machine-readable metadata to support greater discoverability.
All types of data welcome
We understand that data can come in many forms. Whether your dataset is computational, experimental, observational, curated (or something else entirely) we welcome your Data Note article on F1000Research.
Reusability in action: a Data Note case study
A key benefit to publishing a Data Note article is the potential for your dataset to be reused, supporting further research and making an impact across disciplines.
A great example of this in action can be seen in this PLOS One article from 2018: Santiago et al. identified 92 expansin genes in the sugarcane genome, facilitated by a Data Note published by Riaño-Pachón and Mattiello on F1000Research in 2017.
By publishing a Data Note, Riaño-Pachón and Mattiello enabled other researchers to identify and characterize new genes in this crop.
F1000Research is a fully open access publishing platform, offering rapid publication of articles and other research outputs without editorial bias. All articles benefit from transparent post-publication peer review and editorial guidance on making source data openly available.
F1000Research advocates for transparency and reproducibility in research and our unique publishing model supports this at every stage. Articles can be published in as few as 14 days, with post-publication peer review creating an open dialogue between authors and their research community.
Open data on F1000Research | Policies and principles
F1000Research advocates an Open Data Policy. Others need access to the original, raw data to be able to replicate, reproduce or reuse the data. Therefore, all articles on the F1000Research platform should include citations to repositories that host the data underlying the results, together with details of the software tools used to process these. Of course, there may be exceptional cases where openly sharing research data is not feasible. For example, due to ethical, data protection, or confidentiality reasons. You can find out more in our Data Guidelines.
Need help getting started with data sharing?
A dataset for the perceived vulnerability to disease scale in Japan before the spread of COVID-19 [version 1; peer review: 1 approved]
Yuki Yamada, Haoqin Xu, Kyoshiro Sasaki
This article from the Disease Outbreaks Gateway describes a dataset on the perceived vulnerability to disease scale for 1382 Japanese participants, obtained through an online survey conducted in 2018. This dataset provides a useful point of comparison with current or post-COVID-19 perceived vulnerability to disease data.
Curation of an intensive care research dataset from routinely collected patient data in an NHS trust [version 1; peer review: 2 approved]
Chris McWilliams et al.
This Data Note describes the details of a research database of 4831 adult intensive care patients, who were treated in the Bristol Royal Infirmary, UK between 2015 and 2019. This article is a great example of how Data Notes can still be used to describe datasets containing sensitive data in an ethical manner.