The dos and don’ts of data sharing: How to pass our data checks

All Platforms powered by F1000 have a progressive open data policy to ensure all published research adheres to the highest standards of research integrity. Our Editorial team performs open data checks on every submitted article to maintain these standards. In this blog post, Lea Soler-Clavel (Senior Editorial Assistant, F1000) reveals the dos and don’ts of preparing your data so you can enjoy a smooth publishing journey.  

Open Data at F1000  

The F1000 open data policy and guidelines endorse the FAIR principles and are central to our publishing ethos. We aim to publish research that is transparent, trustworthy, and reproducible. We therefore ask our authors to make their data publicly available by: 

• Adding a Data Availability Statement. 
• Uploading data to a recognized, open online repository. 

• Assigning all data, a persistent digital object identifier (DOI) or accession number (or alternative persistent identifier), and an open license. 
• Describing the data with detailed metadata (e.g., rationale, methods of collection, and authors involved). 
• Making the data as “open as possible” and as closed as necessary”. In other words, if the authors have the appropriate permissions, and it is ethical and safe to do so, data should be shared openly. However, if necessary, authors can limit or restrict access to the data.   

We encourage you to review the ‘Data Guidelines’ page of your chosen publishing Platform to see our data policy in full. 

How does it work? 

Upon submission, editors check the manuscript, the data availability statement, and the data provided. Some imperfections will not prevent a manuscript from being processed. In fact, authors can make touch-ups during the revision stage before the article is published.  

Dos and don’ts  

Do upload your data to a recognized online repository  

F1000 will not host data files or supplementary materials alongside articles or allow data to be shared as part of the manuscript, as these methods don’t align with best practices in open data sharing.   

Publishing data in a repository with a citable DOI allows the data to stand on its own as a research product. As a result, your data can be cited in your research, and by other researchers that may reuse or refer to your data. Moreover, it ensures the data will be stored in the long term and makes it easier for other researchers to find via search engines. 

Do ensure that your article contains a Data Availability Statement – even for restricted data 

We recognize a few valid exemptions for not sharing data, including: 

• Confidentiality issues, such as the need to protect participants’ privacy 
• Ethical concerns with sharing the data 
• A third party owns the data. 

In these cases, we still require a detailed statement describing the reasons behind the restrictions and instructions for readers to apply for access.  

Check out our exemption policies for more details on what the statement should include for each exemption category.    

Do upload the correct file formats  

Data files should be uploaded in open file formats, allowing readers and reviewers to open them without needing to purchase specialist software. Raw quantitative data should be presented in spreadsheets in csv or xlsx format – not Word documents – as these formats make it more difficult for others to reuse and replicate the data. 

Do make sure to apply a license to your data  

Before publishing your data, make sure it is assigned an open license. This includes CC-based licenses for data and OSI-approved licenses for software and code. A license defines the terms under which your data can be reused and cited.   

Do remember to add as much metadata as required to your data project when you upload it to a repository 

Metadata is a set of data that describes and gives information about other data. Metadata helps others understand your data, how it was collected, and what hypotheses it set out to explore, among other information readers would need to reuse or replicate your data. For this reason, your data repository record should include as much detail as possible to provide sufficient context for your study. 

Don’t upload aggregated data 

We ask authors to share the raw data underlying their results in their repository project. Raw data includes the individual data points or the smallest unit of information the research is based upon. However, aggregated data such as averages and percentages can only be re-analyzed with limited methods. Moreover, outliers and missing data also can’t be seen from aggregated data, so the “whole story” can’t be accessed – like reading a cover summary instead of a whole novel.   

Don’t provide a “private” data link 

Data should be publicly available before the article is published. Sometimes authors include a link to a project still in “draft mode” or only visible using a privately shared link, but this means the data is neither accessible nor has a permanent record.   

Don’t submit an article without a Data Availability statement 

All articles must contain a data availability statement. Suppose your article presents new research and analyses. In that case, your data availability statement must include where the data can be accessed, including the persistent identifier and name of the repository. A statement identifying the restriction must be included if the data is restricted for data protection reasons. Articles must include data in the manuscript or submission notes to ensure the publication process is completed on time.   

Don’t submit an article with the wrong Data Availability Statement  

The statement “No data are associated with this article” can only apply to specific article types where no data was analyzed to draw conclusions. For example, Study Protocols or Opinion articles might include this statement where it would contradict the original research reported in Research Articles, Method Articles or Data Notes.  

Editorial support 

The F1000 Editorial team are here to help authors navigate making their research data openly available. We understand that data sharing can feel like a daunting or complicated process, especially for researchers unfamiliar with the practice.

This guidance will help you share your data confidently and simplify what our guidelines translate to in practice. If you have any questions or doubts, please don’t hesitate to contact the F1000 Editorial Team by emailing us at [email protected]; we’ll be happy to help.