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This section covers sharing your data after the conclusion of your research project. For information about sharing during a collaborative research project, go to the “Information Security” section of the Ethics and Legal Issues page.
These benefits may also make research projects which plan to share their data more attractive to funding bodies, resulting in another potential benefit to researchers who opt to do this.
Researchers are sometimes concerned that their data will be misused, or that it will be used only to question the original analysis. However, in practice, this is rare. Researchers can reduce the risk of misinterpretation of their work by ensuring that data is well documented, and including clear methods information helps make it straightforward for other researchers to validate their conclusions. But shared data is also frequently used in ways not envisaged by the data creator: the focus may be on variables or aspects of the dataset deemed unimportant for the original project, for example, or the data may turn out to be valuable to researchers in another discipline, or may provide inspiration in terms of content or methodology.
Making research outputs more openly available allows anyone with an interest to make use of them, without the need for costly subscriptions. Where research is funded with public money, it is particularly appropriate that the fruits of that research should be publicly accessible.
Widening access makes it easier for researchers to build on each other’s work. This helps to make the process more efficient, and to increase the quantity of productive work that can be done.
Better information about how conclusions were arrived at helps researchers to retrace the steps of those who have gone before them. This helps to increase understanding, validate results, and make the research process more robust.
Open data is an important part of open scholarship. Data is open if it is available for anyone to access, use, modify, and share. There may be some minimal requirements (e.g. for the data creator to be credited), but potential reusers are given a large degree of freedom.
Open data should be made available under an open licence – see the Data licensing section below for more details.
Not all data is suitable for making fully open: if the content is confidential or sensitive, or if the researchers are intending to seek a patent, then there may be good reasons to restrict access. However, even when this is the case, it is worth considering whether some version of the dataset could be shared (for example, an anonymised, redacted, or aggregated version), or whether controlled access to the data could be permitted.
For more on open data, see the Open Data Handbook from the Open Knowledge Foundation.
As a general rule of thumb, it is good practice to make as much data as possible available for reuse. At a minimum, you should aim to make all data which supports research findings or conclusions available, unless there are specific reasons for keeping the data private. But your project may well produce other data which is well worth sharing.
Try not to limit your thinking to the confines of the original research: while data may certainly be valuable to those working in a similar field to your own, it could also have applications which are harder to predict. Data can sometimes turn out to be useful to researchers in other disciplines, to members of the general public, or for educational or training purposes. Remember that much historical data was originally collected for reasons that had nothing to do with academic scholarship, but has subsequently proved to be a treasure trove for researchers. Sailors who kept weather logs a century or so ago probably never envisaged that their observations would feed into climate change research; similarly, you never know who might find your data priceless in the future.
Rather than asking which data should be shared, it may be helpful to turn the question on its head, and ask instead which data cannot be shared, with a view to sharing everything else. Projects which produce very large quantities of data may need to make practical decisions about how much it is feasible to share. Other reasons for keeping data private may include confidentiality, intellectual property issues, or plans to seek a patent. However, even if data cannot be shared openly, it is worth considering whether it could be made available in a restricted manner: see the section on this below for more.
Choices made early on in a project may influence how easy it is to share data later, and thus it’s important to plan ahead, with preservation and reuse in mind right from the start. For example, it is essential that any research involving human participants secures appropriate consent, and it is much more straightforward to do this at the point when the data is collected, rather than trying to go back and do it retrospectively.
The sections below explore some specific aspects of preparing data for sharing.
If data contains sensitive or confidential information, this may be a barrier to sharing – especially if the wish is to share it without restrictions. For example, data which includes personal information about living identifiable individuals will need to be anonymised or pseudonymised, unless explicit consent has been obtained from the research subjects to share the non-anonymised version.
It should be noted, however, that deleting obvious identifiers (names, email addresses, and so on) may not be sufficient to fully anonymise a dataset: it may still be possible to deduce someone’s identity by combining other pieces of information (a postcode and a rare medical condition, for example). Additionally, some types of data, such as video recordings, are very difficult to anonymise adequately.
Data may sometimes need to be redacted for other reasons: for example, it may deal with the location of endangered species, or may contain third party material to which someone else controls the intellectual property rights. It is helpful to indicate what sort of information has been removed and why (insofar as doing so is compatible with the reasons for which the data has been redacted), so that data reusers will be able to make sense of any gaps in the dataset. Redacting data can sometimes make it less representative: explaining the steps taken can help guard against inadvertent misinterpretation.
There are many approaches or techniques that may be used to render data less sensitive in some way, and what is appropriate may vary considerably from case to case. A balance needs to be struck between protecting participants (and the original researcher) or removing other information that could be misused, and not unduly degrading the data. If it is not possible or practical to produce a dataset that can be shared openly without significantly reducing the value of the data, it may be necessary to consider other options – for example, deposit in a data archive which offers access restrictions.
Data is only useful to potential reusers if they can make sense of it. Documentation provides contextual information that helps new users to orientate themselves within the dataset, and to interpret it properly.
Information may be given about the dataset as a whole (in a README file, for example), or about specific aspects of the data (for example, clear labelling of variables, or annotations of actual or apparent anomalies). Both of these have an important role to play.
Documentation should cover:
It can be helpful to ask a colleague who has not previously worked closely with the dataset to review the documentation: it is often easier for someone with an outside perspective to spot gaps, or to identify things that need additional clarification.
In addition to providing documentation which can be used alongside the dataset to aid comprehension, it’s also important to have good metadata, or data about the data. This often takes the form of a catalogue record which describes the dataset as a whole. Providing appropriate metadata helps to make a dataset more discoverable, thus increasing the chances of reuse. Rich metadata is a key aspect of the FAIR principles for data – see the section below for more details.
If data includes third party material (if, for example, it includes content from a number of pre-existing datasets), the rights holder(s) may have imposed restrictions on what can be done with it. It is important to abide by any terms of use when sharing data, and to ensure that any conditions that apply to subsequent reusers are made clear.
Some potential difficulties with using third party data can be alleviated by advance planning. For example, if you are using some third party data that cannot be shared, a combined dataset might be structured in a way that makes it easy to separate this from the rest of the material. This allows the rest of the content to be shared in an appropriate manner at the end of the project – along with a full citation for the non-shareable third party material.
The FAIR Data Principles are designed to promote:
Where it is lawful to do so, the University supports the broad global consensus that publicly funded research data should be made openly available as soon as possible with as few restrictions as necessary.
The FAIR principles emphasise machine-actionability: growth in volume and complexity mean that computational support is increasingly necessary when locating and dealing with data. Among other things, the principles promote the use of rich metadata, persistent identifiers, data licences, and shared vocabularies and community standards. The full FAIR principles are reproduced in the section below.
While a key goal of the principles is to promote reuse, FAIR data is not always open data. If discovery is facilitated by rich metadata, and clear details of the process for applying to access the data are provided, then even a sensitive dataset which cannot be made openly available can achieve a high degree of FAIRness.
When selecting a method of data sharing, there are a number of things to think about:
The ideal data sharing solution makes it straightforward for interested parties to find out about the data and to acquire a copy, and will ensure that the data remains available long into the future.
Whichever method is adopted, it is good practice for research publications which make use of the data to include a data availability statement. This is a brief note which either indicates where and how the relevant data can be accessed, or if some or all of the data is not available, explains why this is.
The pros and cons of some common methods of data sharing are discussed below. Further information can be found in the “Options for preserving your data” section on the Post-project Data Preservation page : a number of the options listed there can be used for sharing data.
One of the best methods of making data available for reuse is to deposit a copy in a specialist data archive or repository. Data archives exist for the specific purpose of preserving and sharing data, and as such, they are well equipped to make data discoverable, accessible, and sustainable.
Data archives are covered in more detail in the How to preserve your research data section
UWTSD policy requires researchers to deposit their research data in a suitable subject-based research data archive if available: search the Re3data registry of research data repositories, or the catalogue available at FAIRsharingto locate a data archive related to your discipline.
If a suitable data archive is not available, you can also archive your data to the University repository.
Many data archives are able to apply access controls to data deposits, and hence may be a good option for data which is not suitable for completely open sharing. This is covered in the next tab.
A number of services exist with the goal of making it easy to share research material (including but not limited to data) online. These may be publicly-funded services or commercial ones, and include Zenodo.
These platforms vary a good deal, so it’s important to check the terms and conditions carefully. They can provide a quick and convenient way of making data and other materials available, but data may not be as easily discoverable, and sustainability is not always guaranteed. They are also generally less likely to offer active curation of data or access controls than specialist repositories or institutional services.
If your research project has a website, it may be appropriate to host a copy the data there. This can be an effective way of sharing the data with a wider public, and may allow you to offer features that would not be available via a data archive, such as a custom search interface.
However, it is not advisable to rely on this as the sole method of making data available for the long term. Maintaining a website after a project concludes presents a number of challenges: funding bodies are generally reluctant to cover costs incurred after the end of the grant period, and project team members are likely to move on to other endeavours, and hence it is hard to predict how long a project website will remain viable for. A project website should therefore be seen as an additional method of sharing data alongside depositing a copy in a data archive, rather than as an alternative to it.
In some fields, researchers may provide data files to be published alongside the journal article which presents the conclusions drawn from the data. This makes it very easy for readers to access the data, and has the advantage of presenting it in context. If the journal is a well-established one, it is also likely that the data will remain available for a considerable period (though it is worth checking the journal terms and conditions to see whether this is guaranteed).
However, there are once again reasons not to rely on this as the sole method of data sharing. While making the data available in this way is convenient for readers, it may be harder for other interested parties to discover the data. Additionally, the data relevant to a particular article will frequently only be a subset of the data produced by a research project. Where possible, it is therefore good practice also to deposit a fuller version of the dataset in a suitable data archive.
It has been common in the past for researchers to add a note to research publications saying that data is available on reasonable request from the authors. However, this is not an ideal method of sharing data: it relies on potential reusers being able to contact the original researchers, which may be difficult if some time has passed and contact details have changed.
Even if the data is very sensitive, and a custom data sharing agreement would be required for any reuse, it is better if possible to have this process mediated by a specialist data archive, rather than placing the responsibility on individual researchers. Some data archives have processes in place for relaying requests back to data creators when necessary, and it may be possible to reach an agreement in advance about what action (if any) should be taken if the data creators cannot be located.
If no other sharing solution is viable, making the data available on request is preferable to not making it available at all, but it should nevertheless be viewed as a last resort, and only be adopted once other possibilities have been exhausted.
There are a number of legitimate reasons for not making data publicly available. Some of these have to do with the nature of the data itself (e.g. the data is confidential or otherwise sensitive), whereas others result from the nature of the research process (e.g. researchers may still be working on their primary analysis, or may be intending to seek a patent). Note that if you intend to share confidential data, you are likely to need a data sharing agreement to be in place.
However, even if data cannot be shared openly, or shared immediately, this does not automatically mean it cannot be shared at all. The sections below explore some of the options for restricting access to data that has been deposited in a data archive.
It is generally acknowledged that researchers are entitled to a period of privileged access during which they can work on the data before making it available to others. However, if this period extends after the formal end of a project – because researchers are waiting for publications to appear, for example – the point at which the data becomes shareable may occur when researchers have already moved on to other endeavours. This is at best an annoyance, and at worst may make sharing significantly less likely to happen.
A convenient solution to this problem is to deposit a copy of the data with an archive which allows data to be placed under a fixed term embargo. This typically means that a metadata record will be available for the data, but the data itself will not be downloadable until the embargo has expired.
Another advantage of this approach is that the data is citable even before it is publicly available, and thus can be referenced in research publications and data availability statements.
The length of embargo that is deemed appropriate varies between disciplines and between funding bodies. Funders frequently stipulate that data should be made available as soon as possible; some specify a particular time frame (which can sometimes be shorter than researchers would like it to be), though there may be room for negotiation if there are good reasons to delay.
Some data archives can accommodate a range of different access restrictions. Common access controls include:
However, provision in this area varies considerably: not all data archives will be able to offer all the options listed above, and some may offer others. It is thus important to investigate what is available from archives for data in your discipline at an early stage, so that you can plan for ultimate sharing with this in mind.
For an example of a repository offering multiple access tiers, see the UK Data Service’s page on Access control.
Some data archives may ask you to nominate a data steward. This is a person who can take responsibility for answering questions (and where appropriate, making decisions) about your data in the event that you cannot be contacted. If possible, it is better to nominate the holder of a particular post rather than a named individual.
A License clarifies the terms of use for your data – disentangling what can otherwise be quite a complicated default legal position. If your research is funded, you will need to comply with any funder licensing requirements when depositing your research data..
A licence is a formal statement issued by the holder of the rights to a particular work (e.g. a database or other dataset), giving permission to use the work in certain specified ways. You might, for example, specify that if your data is reused, you must be cited as the creator, or that the data may be used for research and educational purposes, but not in commercial contexts.
Licences for data fall into two broad categories:
These grant rights to specific individuals or entities. They typically take the form of a relatively formal contract, which will need to be agreed by both parties.
These grant rights to anyone, often subject to certain minimal conditions such as attribution of the data’s creator. They usually take the form of a short statement which accompanies the dataset, sometimes with a link to more comprehensive information elsewhere.
If a potential reuser wishes to use your data for a purpose not covered by the open licence, they can still approach you directly to discuss the possibility – but the licence removes the need to do this in straightforward cases.
Commonly used open licences for data include Creative Commons and Open Data Commons. Alternatively, if you control all rights to your dataset, and wish to make it available for others to use without any restrictions at all, you might consider using a Creative Commons Open Data CC Zero public domain dedication and waiver.
The DCC’s guide How to License Research Data provides a useful overview.