Sharing and Archiving Data¶
Motivations For Sharing Data¶
The world is witnessing a significant global transformation, facilitated by technology and digital media, and fuelled by data and information. This transformation has enormous potential to foster more transparent, accountable, efficient, responsive, and effective research. Only a very small proportion of the original data is published in conventional scientific journals. Existing policies on data archiving notwithstanding, in today’s practice data are primarily stored in private files, not in secure institutional repositories, and are effectively lost to the public. This lack of access to scientific data should be removed for several reasons:
To allow the possibility to fully reproduce a scientific study.
To prevent duplicate efforts and speed up scientific progress. Large amounts of research funds and careers of researchers can be wasted by only sharing a small part of research in the form of publications.
To facilitate collaboration and increase the impact and quality of scientific research.
To make results of research openly available as a public good, since research is often publicly-funded.
Accordingly, there is an ongoing global data revolution that seeks to advance collaboration and the creation and expansion of effective, efficient research programs. Sharing data openly is crucial to meeting these objectives. Open data means that the data is freely available on the internet permitting any user to download, copy, analyse, re-process, and re-use it for any other purpose without financial, legal, or technical barriers, other than those inseparable from gaining access to the internet itself. This represents a real shift in how research works. At the moment, anyone who wishes to use scientific data from a researcher often has to contact that researcher and make a request. However, “open by default” presumes a publication for all. Researchers need to justify why data is kept closed. Such justification may be security or data protection reasons, for example. Free access to, and subsequent use of, data is of significant value to society and the economy, and that data should, therefore, be open by default. Research is often publicly-funded, so the results of this research should be openly available as a public good.
Barriers To Data Sharing¶
Some academics still find sharing data difficult.
Recent surveys [SBH+18] conducted amongst researchers show that they find sharing data difficult because:
Organising data in a presentable and useful way is challenging (mentioned by 46%)
They are unsure about copyright and licensing (mentioned by 37%)
They do not know which repository to use as (raised by 33%)
These are cultural challenges that might be addressed in changing practice going forward.
However, there are also legal, ethical or contractual reasons that sometimes prevent making data publicly available in its entirety or even in parts. These are addressed in the following paragraphs.
Privacy And Data Protection¶
Many fields of scientific disciplines involve working with sensitive personal data.
Their management is well regulated in data protection legislation (in Europe through national implementations of the General Data Protection Regulation) and ethics procedures as they are established in most research institutions {cite} EU2016protection.
Consent¶
In order for anonymised research data to be made available for future reuse, consent forms must cover sharing this data with other researchers. Research so far suggests that study participants are usually less concerned about the data being archived and shared than researchers think [Kuu10]. Participant information sheets and consent forms should include how research data will be stored, preserved and used in the long-term, and how confidentiality will be protected when needed.
Anonymisation¶
Individuals must be protected from (re)identification through their data Anonymisation of the data may be sufficient in some cases, but ensuring that re-identification is not possible is becoming increasingly difficult. It might even be impossible due to technical progress, growing computational power and – ironically – more open data.
For example, re-identification may be possible via data-mining of accessible data and so-called quasi-identifiers, a set of (common) properties that are – in their combination – so specific that they can be used to identify an individual. Preserving privacy may still be possible if partial or generalised datasets are provided, like age groups instead of birth date, or only the first two digits of postal codes. It may also be possible to provide the data in such a format that researchers can query whilst keeping the data itself closed. For example, a user may be able to send a query to retrieve the mean of a dataset, but not be able to access any of the individual data points. Another way to provide anonymised data is to provide synthetic data, data generated to reflect the conditions and properties of the raw data, without including any personal information.
National and Commercially Sensitive Data¶
In many cases, companies are understandably unwilling to publish much of their data. The reasoning goes that if commercially sensitive information of a company is disclosed, it will damage the company’s commercial interests and undermine competitiveness. This is based on the thinking that in competitive markets, innovation will only occur with some protection of information. If a company spends time and money developing something new, the details of which are then made public, then its competitors can easily copy it without having to invest the same resources. The result is that no-one would innovate in the first place. Similarly, for public safety concerns, governments are often unwilling to publish data that relates to issues such as national security. In such cases, it may not be possible to make data open, or it may only be possible to share partial/obscured datasets as outlined in the privacy section.