Research data is the information (whether it has been observed, collected or generated) needed for independent verification of research results.
In other words, research data is the underlying evidence upon which the claims made in your publication rely.
What research data looks like varies by discipline and subject area, and may include raw data or a manipulated or sub-set of data. It’s important to note that data doesn’t just mean data files or spreadsheets – it can take many forms such as video, transcripts, questionnaires or slides. Check out this useful page with examples of some of the different types of research data.
Some funders now make data sharing a requirement (you can check using this handy Sherpa-Juliet tool), and it’s become increasingly commonplace for some subject areas to make data available to everyone.
There are several benefits to sharing data:
Read more about the benefits of sharing data here.
One journal editor told us why he thinks data sharing is so important:
“In general, authors should consider data sharing as an opportunity to connect a reader of that single study to the larger research agenda. If data are published on a project that also directs readers to a main page where other study data sets are kept, the research can have even greater impact.”Jon E. Grahe, Editor of The Journal of Social Psychology
Read insights from across the research community on some of the opportunities and challenges that data sharing presents:
You can also hear insights on data sharing in Australia in this highlights post from the 2019 Melbourne Scholarly Summit. Keith Russell, Manager of Engagements at Australia Research Data Commons (ARDC) looks at the motivations for researchers to share data and shares updates from ARDC.
When submitting your article, check the Instructions for Authors for the journal you are submitting to, to find out what data policy applies (plus, check if your funder has a policy). If the data sharing policy for the funder and journal differ, you’ll need to follow the more progressive policy, i.e. the policy that encourages a greater level of data sharing.
Taylor & Francis offers a suite of standardized data sharing policies, with the ‘basic’ policy applying across many of our journals. Click on any of the policies below to read instructions for authors, along with step-by-step infographics.
Journal encourages authors to share and make data open where this does not violate protection of human subjects or other valid subject privacy concerns. Authors are further encouraged to cite data and provide a data availability statement.
Authors agree to make their data available upon reasonable request. It’s up to the author to determine whether a request is reasonable.
Authors make their data freely available, under a license of their choice.
Authors must make their data freely available, under a license allowing re-use by any third party for any lawful purpose. Data shall be findable and fully accessible.
Authors must make their data freely available, under a license allowing re-use by any third party for any lawful purpose. Additionally, data shall meet with FAIR (findable, accessible, interoperable and reusable) standards as established in the relevant subject area.
A number of our journals in earth, space and environmental sciences are introducing an open and FAIR data sharing policy as part of COPDESS (The Coalition for Publishing Data in the Earth and Space Sciences). Find out more about the initiative and which journals are included.
A data repository is a storage space for researchers to deposit data sets associated with their research. If you’re an author seeking to comply with a journal data sharing policy, you’ll need to identify a suitable repository for your data.
First we recommend speaking to your institutional librarian, funder or colleagues at your institution for guidance on choosing a repository that is relevant to your discipline. You can also use FAIRsharing and re3data.org to search for a suitable repository – both provide a list of certified data repositories.
For cases where there is no subject-specific repository, you may wish to consider some of the generalist repositories outlined on this page.
We encourage authors to select a data repository that issues a persistent identifier, preferably a Digital Object Identifier (DOI), and has established a robust preservation plan to ensure the data is preserved in perpetuity.
There are other factors you may also need to consider, such as:
Read our guidance on choosing a data repository
When you’re submitting your paper to a Taylor & Francis journal with a data sharing policy, you’ll be prompted to provide a data availability statement with your submission.
A data availability statement (also sometimes called a ‘data access statement’) tells the reader where the data associated with a paper is available, and under what conditions the data can be accessed. They also include links (where applicable) to the data set.
We’ve put together some template statements that can be used or adapted for writing a data availability statement – this is not an exhaustive list, and an individual data set might warrant a different type of statement.
Please note: if you are submitting to a journal where submissions are double-blind peer reviewed, the main text file should not include any information that might identify the authors. As a data availability statement could reveal your identity, we recommend that you remove this from the anonymized version of the manuscript.
Did you know that data availability statements are always openly available to view on all articles on Taylor & Francis Online? This helps ensure the transparency of research. At the same time it also helps you get credit for all the valuable outputs of your research.
“Data citation, like the citation of other evidence and sources, is good research practice and is part of the scholarly ecosystem supporting data reuse.”FORCE11 Joint Declaration of Data Citation Principles,
There is no universal referencing style for citing data – this varies across disciplines and journals. However, you need to cite in a clear and consistent way to make the citation useful, enabling the reader to identify and find the data set referenced in your article.
First, check the Instructions for Authors page of the journal you’re submitting to for guidance on citing data. For Taylor & Francis journals with data sharing policies, you will find instructions and examples of data citation, all of which adhere to the Force 11 Joint Declaration of Data Citation Principles.
In general, you should always include the following elements in data citations:
This will help the reader identify and find the data set, and ensures you give credit to the individual or group who created the data.
Don’t forget: even if you’re referring to your own data set within your article, it’s important that you cite it. Just like citing another person’s data set, you need to acknowledge yourself as the author and tell the reader where the data is located.
Alongside our data sharing policies, Taylor & Francis supports a number of open data initiatives:
The Center for Open Science TOP guidelines, created by journals, funders, and societies to align scientific ideals with practices. Read our interview with David Mellor from the Center of Open Science to find out more.
Enabling FAIR Data commitment statement – a FAIR data project designed to advance the Earth, space and environmental sciences towards open and FAIR sharing of research outputs. Discover our journals introducing a FAIR data policy.
FORCE11 Joint Declaration of Data Citation Principles, a set of guiding principles to help make data FAIR (findable, accessible, interoperable and reusable), maximizing the use of data.
Open sciences badges (from the Center of Open Science) are available on a number of our journals. These award authors contributing to scientific transparency and their efforts to make their research more open. Find out how to apply for a badge.
We’re part of I4OC (the initiative for open citations), a collaboration between publishers, researchers and organizations to promote access to data on citations that is structured, separable and open. Read more about the benefits.
We support Metadata 2020, a collaboration that advocates richer, connected, and reusable, open metadata for all research outputs, which will advance scholarly pursuits for the benefit of society.
We’re working with Code Ocean to help researchers use code within their research. Code Ocean is a cloud-based computational reproducibility platform that provides researchers and developers an easy way to share, discover, and run code. Find out more about Code Ocean.
Find answers to common questions on data sharing in our dedicated FAQs section. This is updated regularly based on the questions we get asked.
Listen to our podcast on making your research open, including how and why to share research data. You can also visit our dedicated open access hub to find out more about publishing your article open access.