Time

The lesson is expected to take about 15 minutes to complete. There are also reflection exercises, which are very good to do together with your fellow students.

About the lesson ‘Share your data with others – data must be used for maximum benefit’

In this lesson, you will be shown why it may be a good idea to share your data when you have completed a project or assignment. It goes without saying that confidential data must not be shared. This lesson is about assessing which data you are allowed to share and which you are not allowed to share, and you will also learn how you can share the data which you are allowed to share.

The lesson will mention several concepts that may be new to you. You can find definitions of these concepts in the glossary.

Learning outcomes

When you have completed the lesson, we expect that you can:

assess whether it is appropriate to share your data.
assess any rights issues associated with your data.
share your data in a data repository.

Source

The lesson was produced by the University of Copenhagen as part of its learning resources for digital literacy, 2023.

Course directors:

Lorna Wildgaard (PhD), Specialist Consultant, Copenhagen University Library (KUB), Research Support Services
Asger Væring Larsen, specialist consultant, Copenhagen University Library (KUB), Research Support Services

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Why share data?

Hear a student, a supervisor and a researcher talk about why it is a good idea to share data.

If you experience access denied, try reloading the page.
For English subtitles, please look for the CC icon in the lower right corner of the video and press English.

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Data sharing offers many benefits – for society and at an individual level

Data sharing can offer several benefits, both for the individual and for society as a whole:

Research collaboration: Data sharing can make it possible for researchers and others to collaborate and build on each other’s work. When data are shared, they can be used by others to verify research results, conduct further analyses and generate new insights. This can accelerate the pace of research and help answer complex scientific questions faster.

Transparency: Data sharing can promote transparency in research. Data that are made available to others can be scrutinised and evaluated for accuracy and bias. This can help to develop greater trust in research results, policies and decisions.

Efficiency: Data sharing can enhance efficiency in a number of ways. For example, it can reduce the need for double data collection, as researchers can use existing datasets instead. It can also help identify gaps in research or areas where further data collection is needed.

Innovation: Data sharing can stimulate innovation. Shared data can be combined to shed light on new research questions and support the development of new technologies and applications – for example during the corona pandemic, where researchers shared their data in order to quickly develop new drugs and new treatment programmes.

Benefits to society: Data sharing can benefit society, for example by improving public health outcomes, identifying environmental risks or promoting social justice. Data collected from portable devices, for example, can be used to track physical activity and help people make lifestyle changes that improve their health.

Acknowledgement and ownership: A researcher who shares their data can be acknowledged for the hard work involved in creating or collecting data and documenting the entire process. At the same time, the researcher can make sure that they are credited for the work and decide the terms and conditions on which the data can be reused by others.

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Data sharing strengthens the reproducibility of academic work

Data sharing can therefore help to improve the reproducibility of experiments, methods, the application of theory and results or findings, which is an important aspect of the academic process. Reproducibility is, of course, most important in academic disciplines that rely on empirical, quantitative data processing and, to a lesser extent, in the academic disciplines that are based to a greater extent on text interpretation. However, in academic disciplines where results are based on, for example, statistical analysis, it is important that you can show how you have arrived at your results.

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Which data can you share – and which data must not be shared?

Whether sharing your data is a good idea or not depends, of course, on a specific assessment of the data contained in your assignment or project:

In the case of personal data, it must not be shared openly with others – unless you anonymise them first.
Moreover, data covered by confidentiality agreements are not suitable for sharing.
Also, you must not share data to which others own the copyright.

Data covered by GDPR, the Danish Data Protection Act, agreements with companies or which are in any other way classified must be processed in accordance with the law or any relevant agreements.

Data derived from public sources, for example the Danish authorities or other sources, must be processed in accordance with their rules.

Assess whether you own the copyright to your data. If your dataset contains factual information, it is not protected by the Danish Copyright Act. If, for example, you collect information about the height and weight of 100 tobacco plants and enter it in a table, then no one owns the copyright to the information – not even you. If you choose to place the table in a data repository, others can use the data for what they want, even without referring to you. The data are not protected. This gives you the freedom to do what you want to do with the data, but it also gives other people the same freedom.

In other cases, the data may pass what is known as the threshold of originality in a copyright context. This means that the ‘work’ is protected by the Danish Copyright Act. In these cases, you must seek permission from the copyright owner to use the work, and you must reference the copyright owner if you use the work or parts of it for publication. Such works include images whose production has required a certain level of creativity. They will be protected by the Danish Copyright Act. In complex cases, it is a good idea to ask a copyright expert, for example the faculty’s lawyer, if you or your teacher are in doubt.

As a general rule, you can do what you want with the data you collect yourself and which are not covered by legislation or agreements.

Once you have assessed the above and decided that you would like to share your data, the next step is to consider where and what would be the best way of doing so.

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Where can you share your data?

Data are typically shared via ‘data repositories’.

There is a place on the Internet where you can upload data files and describe them, providing the information that is necessary in order to be able to understand and reuse the data – otherwise known as metadata. There are many different repositories to choose from:

Institutional repositories are usually reserved for employees (and possibly students) at a particular university.
Subject-specific repositories only contain data pertaining to one or a few related academic disciplines – but can be open to everyone.
‘Catch-all repositories’ are repositories accessible to everyone, irrespective of institution, subject or data type.

To select the right repository, take a look at  www.re3data.org, which is a database of available data repositories. Here, you can browse or search for repositories according to the criteria which you think are important, for example subject, data type or country.

A very widely used repository is Zenodo. It is open to everyone and free of charge.

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Know your repository before you share your data

Before uploading your data to a repository, you should take a close look at the terms that might apply to the depositing of data. This can help you describe your data and assess the quality of the repository.

Essentially, find out :

whether it costs money to deposit your data.
whether the repository only receives data from your academic discipline.
in which formats you can deposit your data.
whether certain data documentation requirements apply.
whether the data can be deposited so they are freely accessible (Open Access).
whether you can stipulate special conditions for how your data may be used by others (a so-called end-user licence agreement (EULA)), which describes what users can do with data – typically Creative Commons).
whether your data are given a so-called digital object identifier (DOI) or similar identifier. A DOI is a kind of reference number for your dataset, and makes it possible for others to refer to your dataset in articles where they have used your data.

The above are useful guiding principles for you to follow. In addition, the repository should guarantee a lifespan of at least five years. It is a good idea to go for repositories that are used by others within the same academic discipline.

Please note! Please note that once you have deposited your data in a repository, it is not that easy to have the data deleted if you change your mind. Therefore, you need to be sure that the data are ready for publication, and that you have permission to do so.

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In which format can you share your data?

Ensure that your data are structured in such a way that others can navigate them. Your data must be described so others can understand why and how the data were collected and other contextual information. See more in the ‘Describe your data’ lesson.

Quantitative data

Organise quantitative data in an understandable way – for example in tables, and use easily understandable column headings. Consider whether it makes sense to use English column headings. Avoid using abbreviations in headings (for example, write ‘concentration’ instead of ‘conc.’). Remember to include units of measurement (e.g. cm, mol per litre, Hz etc.). If you need to provide a lot of background information, you can choose to create a so-called README file (see the lesson Save your data well – how to save data and find them again. The README file can be saved together with the dataset so that others can find the information they need to assess the nature of your data.

Qualitative data

Qualitative data can consist of sound, video or images. They can be analysed in several different ways, and it is important to write down what you have done. You can do this in a README file. Videos in particular take up a lot of space, but most repositories allow quite large files, so it will not necessarily be a problem. However, because it takes such a long time to upload/download the files, it may be a good idea to compress them. The same applies to audio files: Here, it may be a good idea to save the files in MP3 format, which does not take up so much space. If the audio quality is very important, you can choose .flac, which is a compressed format, but without data being lost, so that the sound quality is very close to the original.

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Reflection exercise on sharing your own data

As explained in the lesson, any sharing or reuse of data should be based on careful consideration. Think about a dataset you have worked with or collected data for as part of a previous assignment or your student job and reflect on the following questions.

What did the dataset consist of, and why were the data collected?
Would the data be suitable for sharing?
Are you allowed to share them?
What good and bad consequences could result from sharing your data – even if they were suitable and it was legal to do so?
What would be the consequences for you if someone misused the data shared by you, or used the data for something outside the context in which they were created?
What other advantages or disadvantages of sharing your data can you see?

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Reflection exercise on reusing other people’s data

Visit Zenodo, search or browse to find a dataset from your own academic discipline, and look at the information registered for it. In addition to the name of the dataset, a wide range of information is provided about the project from which the data originate, the actual files or those who created the dataset. This information is called metadata.

What information is there?

For example, see this dataset: https://zenodo.org/record/5638170#.Y5HSf4SZNaQ.

Can you access the data?
Try to download the Excel spreadsheet (XLS file).
What problems can you see with the dataset?
Is there enough information for you to understand the dataset?

Take a look at this dataset: https://zenodo.org/record/3368583#.Y5HTYISZNaQ.

Look at the metadata.
Is this dataset better described in Zenodo than the first?
In what ways?
Download and look at the Excel files.
Are they better or worse than the first dataset?
In what ways?

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Further information

There are several resources where you can find out more about data sharing:

Here, you can read more about the quality criteria for repositories:

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