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 an article type offered on F1000Research which allow researchers to describe their scientific dataset, including:
- 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 do not include any analyses or conclusions, but promote the discoverability and potential reuse of research data by providing a detailed description of the dataset itself. This gives credit to data producers with a citable, peer-reviewed publication, and supports new research collaborations across disciplines.
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.
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, and can even support inter-disciplinary research collaborations in the future. Like all content published on F1000Research, Data Notes are fully open access, meaning 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, which isn’t always possible within more traditional publishing formats. Data Notes do not undermine the novelty or value of a Research Article which makes use of the published dataset. In fact, Data Notes work best when they can be linked to a traditional Research Article reporting analysis of the published dataset, along with the results and conclusions; and there’s some evidence that linking your publications to your research data could increase the citation potential of your work. We've collated a list of journals and publishers which have confirmed that publishing a Data Note on F1000Research would not undermine the potential publication of a related Research Article. In fact, if you want to publish your Data Note on F1000Research and your Research Article elsewhere, we are happy to coordinate with the relevant journal to ensure simultaneous publication.
Get help with data management
F1000Research offer editorial support in making source data openly available, including expert guidance on preparing your data and choosing an appropriate repository for your work. Data Notes can even support you in meeting vital requirements from your funder or institution for sharing your data appropriately, so that others can access and reuse your datasets.
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.
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 a given study, reproduce the original findings, or reuse the data to support their own research. 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.
Alongside our Open Data policy, F1000Research endorses the FAIR Data Principles which aim to make research data Findable, Accessible, Interoperable, and Reusable; together these provide a framework to promote the broadest possible reuse of scientific data.
Need help getting started with data sharing?
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.
In describing their sugarcane dataset using a Data Note, Riaño-Pachón and Mattiello enabled other researchers to identify and characterize new genes in this crop.
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.