Transparency: As open as possible

In Week 1, you learned a bit about what transparency is, and why being transparent is important in research. Ensuring your study generates open data and materials is a good way to increase the transparency of your research.

This week, you will discover ways to increase the chances of people across the world finding your research, and how to explain the research in sufficient detail so that they know exactly how you carried out your study. You will also learn how to ensure your research is openly accessible while still protecting the anonymity of your participants.

Making the outputs of your study more open means the data and materials you have gathered during your research can be used, reused, and redistributed by anyone. Data refers to the information or facts collected, observed, or generated during the course of a study or investigation. For example, in the quantitative chocolate chip cookie example from Week 1, the chocolate chip ranking would be the data.

Materials refers to any materials used in a research study. These can include (but are not limited to) the code used to run any statistical analyses, protocols outlining exactly what was done in the study, auditory and visual stimulus files shown to participants, questionnaires, documents used to obtain consent from participants, and videos of the study being run.

There are many benefits to sharing data and materials. One benefit is financial – the more products that are shared from any individual study, the more efficient the use of the funding used to conduct the study. Sharing data also allows others to check the data for quality and accuracy, reproduce the analyses reported in a research paper, and expand on the analyses through running alternative analyses. In addition, most datasets have uses beyond what is reported in a paper, including secondary data analysis that addresses different questions altogether. Sharing data may also be required by the project funder or the journal in which the article is published (see Top Factor for a list of journal requirements). Sharing materials has similar benefits to sharing data – readers can check what was done in the study, re-run the same study, or change the materials to run a slightly different study.

Data can be shared even when it is not related to a paper. However, researchers tend to share data alongside their papers, so that readers can see the structure of their data more clearly, re-run analyses from the manuscript, run additional analyses, and use the data to answer new questions.

Data can look very different depending on the research field, for example:

• Biology: genomic data from projects like the Human Genome Project, providing sequences of human DNA and other organisms.
• Social Sciences: Survey data on demographics, attitudes, and behaviours collected by organisations like the United Nations or national statistical agencies.
• Medicine: Clinical trial data, including study protocols, patient demographics, treatment interventions, and health outcomes.
• History: Archives of historical documents, such as diaries, letters, manuscripts, and government records, providing insights into past events, societies, and cultures.
• Literature: Text datasets containing literary works, poetry, plays, and other written texts, facilitating analysis of language use, stylistic trends, and cultural themes.
• Musicology: Musical score datasets, containing compositions from different composers, genres, and historical periods, for analysis of musical structure and style.

Even within one study, there will often be multiple levels of data. For example, in a study using interviews there might be video recordings of the interviews themselves, the source data, the transcript of the interviews, the processed data, and then the text from the transcript may be coded quantitatively or qualitatively, resulting in the coding data.

It is possible for all of these to be shared, if participants have agreed to this and don’t mind that they will be identifiable, but usually, it is important to protect the anonymity of participants. While this is often possible to do with transcript data (after any identifying information about participants had been anonymised), this would be very difficult if sharing video data of them.

When we talk about open data, the phrase ‘as open as possible, as closed as necessary’ is often used – meaning that researchers should strive to make their data open, but not where this would be unethical or illegal. Researchers must work within the ethical codes of their country and type of data collection. For example, in Europe, the General Data Protection Regulation sets out guidelines for dealing with ‘personal data’, i.e., any information related to an identifiable individual. To ensure human participants are not identifiable in our datasets, we as researchers must ensure we have removed all identifiable data from our datasets.

In some cases, this is obvious, simple, and doesn’t affect the usefulness of the data shared, for example removing IP addresses from data collected online. However, there are other cases where this is much more complex, and may result in the data not being possible to share at all, for example where qualitative data on a very specific topic makes participants identifiable.

In the previous section, you considered different types of data, and how open you can be when sharing them. Given the subtleties, it is useful to have a clear set of guidelines. The FAIR principles provide this. They state that shared data should be FAIR – findable, accessible, interoperable, and reusable:

• Findable: Data should be easy to find for both humans and computers. This involves using unique identifiers and metadata (information about the data).
• Accessible: Once found, data should be easy to access, either openly or through an authentication or authorisation process. This ensures data is available in a standardised format.
• Interoperable: Data should be able to work with other data. This means using standardised formats and languages so that different systems can use the data together.
• Reusable: Data should be well-documented and organised so that it can be used again in future research, potentially by different people. This includes clear information about how the data were collected and any licenses or permissions needed for its use.

In this video, Isabel Chadwick, a research data specialist from the Open University, talks about the FAIR principles, and how they can help researchers look after their data. As you watch the video, think about how you could follow Isabel’s advice in your own research.

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Transcript: Looking after data

My name’s Isabel Chadwick. I've got a special interest in research data management. So my job involves helping researchers and research students to look after their data during their projects, thinking about all of the legal, ethical implications of looking after what is sometimes personal data, sometimes sensitive data, and sometimes just really, really big data.

The FAIR data principles are intended to give guidelines on the findability, the accessibility, the interoperability and the reusability of digital assets that are created during the course of research.

They go beyond merely saying that research data should be made open, and rather give more concrete guidance on how that data can be best exploited to enable reuse, replication, verification of results, any kind of scrutiny.

So they're really, really important because a huge amount of money and time is invested into generating research data globally.

And if that data isn't findable, if it can't be accessed, if it doesn't interoperate, then essentially it isn't reusable - and that sort of means that it's a huge financial loss for global research as well as a bit of a setback for research progress.

Sometimes there are legal, ethical or commercial reasons why research data cannot be made publicly accessible.

And FAIR data doesn't always mean open data. So even where data has to be restricted to only allow access to certain people, or maybe even no people, the really important thing is that the metadata that describes that data is made available.

Now, when we use the term metadata, what we mean is all of the information that builds up a picture about what that item or that data set might be. So a really good way of thinking about metadata is thinking about things that you use in your everyday life. So for example, you might have a record collection that you have organised according to different things.

So you might have put it in alphabetical order according to the name of the artist, or you might have put it into different sections for classical, pop, rock, for example. Or you might simply have just done it in a colour order to make it look like a pretty rainbow and all of those are ways of organising information using different types of metadata in order to be able to find things and understand things more easily.

And we do the same things with information that includes data sets, but also would be things like publications, so every published piece of research will have rich metadata assigned to it.

When it comes to research data, that information is really, really important, because in terms of transparency, allowing people to understand how you created your data, what the data is - and pretty key to this is - how they can reuse it, is really important.

To make your data FAIR, there are a few really key steps that come at different points in your research process. So right at the beginning of your research project, before you've even started collecting your data, we would always advise that you write a data management plan. And that plan should outline how you're going to look after your data during your project, and then what's going to happen to it after your project.

In terms of what happens after your project, the best piece of advice for making your data FAIR, would be to deposit it in a trusted digital repository. And you should do this whether your data is going to be openly available to the public, or whether it's going to have a restricted access placed upon it.

The reason why we say that you should put your data into a trusted digital repository is because it will provide you with a persistent identifier like a DOI, which will ensure findability and accessibility of your data.

In terms of reusability, it will also enable you to assign an open license to your data, so that people can understand what the terms of free use are, and they know what they are and aren't allowed to do with that data when they access it.

And finally, in terms of interoperability, what's really important is that you use those DOIs or those persistent identifiers that you are provided with by your repositories, or by your publishers, to link your different outputs. So we want to see you linking your data sets with your publications, with your other outputs, with your software, for example, and your materials.

That's a really important aspect of FAIR.

But, to start from the beginning, the data management plan is a really solid way to start.

End transcript: Looking after data

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Looking after data

There's a lot of effort going into building exactly these trusted data repositories to make your data FAIR. For example, in Europe, the European Open Science Cloud covers a very wide range of science and social sciences, while the European Cultural Heritage Cloud aims to do something similar for cultural heritage institutions and professionals.

In the video, Isabel Chadwick recommended that when researchers share their data, they should choose a license to apply to the data. A license is a set of rules and permissions that tells you how you can use someone else's data. It is like an agreement between the person who created the data (the data owner) and the person who wants to use it (the data user).

A license specifies what you can and cannot do with the data, whether or not you need to give attribution to the data owner, and whether or not you can further share the data. For example, a common open license used for research data is CC BY-NC 4.0 which allows the person using your data to share and adapt the data, but only if they give attribution to you, the data owner, and don’t use the data for commercial purposes.

There are other types of license, which allow you to specify different levels of openness. You can choose to give your work over to the public domain, so people can do whatever they like with it. Alternatively, you can choose a type of license which prevents users from adapting your work. You can find out more about licensing by referring to this helpful list on the Creative Commons website.

Activity 1:

Allow about 10 minutes

In this activity, you can test your understanding of the importance of considering anonymity when it comes to data sharing.

In the video, Isabel Chadwick explained that it is best practice to archive data and materials in an open access repository to make your research accessible. Whether you choose an institutional repository or a discipline’s repository, these trusted digital platforms provide a safe way to store research materials and data, and link to related content held elsewhere.

There are different repositories for different research fields and different types of data, but some examples are the Open Science Framework, the Qualitative Data Repository, and Zenodo. Here is an example of how to share data and materials on the Open Science Framework. The Open University also has its own repository (ORDO) where data and materials from researchers at The Open University can be shared.

Activity 2:

Allow around 15mins

In this activity, you will get the opportunity to explore an open access repository.

Here’s a real-life example of how someone used data from the Open Science Framework platform for a secondary data analysis.

Prinzing (2024) reused data from an experience sampling study (where participants are repeatedly asked about their daily experiences related to a particular topic) on pro-environmental, sustainable behaviour. Prinzing used these data to investigate whether engaging in sustainable behaviour increased a person’s wellbeing. They shared some of the original data that they used and their analysis code on a separate OSF project. There are a few other things they could have done: they did not share a data dictionary, and they didn’t apply a license to the materials on their OSF project. Nevertheless, the author of this course, Silverstein (2020), was still able to reproduce their analyses using their data and code. Interestingly, in the process of conducting this reproduction, Silverstein found a typo in one of the values in the paper! The authors have now updated this.

Open data is key to understanding one of the big concerns in quantitative research: reproducibility. Assessing reproducibility means assessing the value or accuracy of a scientific claim based on the original methods, data, and code. So, when you run the same analyses on the same data, do you get the same results?

Running the same analyses on the same data can mean different things depending on what materials the reproducer has access to. Investigating the reproducibility of a study can mean taking the original data and:

• Following the description of analyses in the paper.
• Following an analysis plan created by the original authors.
• Re-running the analysis code that has been shared with the data.

As you can imagine, it’s easier to get the same results as the original researchers if there is less uncertainty around what they did. So, re-running the analysis code will be more likely to produce the same results than following the description of analyses in the paper. Going back to our baking analogy, it would usually be easier to produce the same cake as a professional chef if they shared the recipe they used than if they just described what they did, and the more detail they provided in the recipe, the easier it would be. However, even if a professional chef shared both a detailed recipe and a description of what they did, your cake might end up with a soggy bottom! Similarly, in research, when we have both the code and the data, it can still be difficult to reproduce results.

Here are a few tips for making it more likely that others will be able to reproduce the results of a study:

• Share a data dictionary – list all the variables in your dataset, what they mean, how they were manipulated, and how they’re structured
• Annotate your code – make notes of what you did at each stage of the data pre-processing and analysis and why
• Make a note of software versions – analyses might stop working with future versions
• Make sure your data and code are suitably licensed – for example, a CC-BY-NC 4.0 license means that anyone can share or adapt the material as long as they give you appropriate credit and do not use the materials for commercial purposes.

Activity 3:

Allow about 10 minutes

This activity will allow you to test your understanding of reproducibility.

In the previous section we considered transparency in a quantitative study. To recap, in quantitative studies your data will usually be numerical. You might measure how quickly people respond to stimuli on a computer, or how much people would be willing to pay for a certain item.

Open data and materials can mean something quite different in qualitative research. This type of research focuses on patterns and themes in non-numerical data such as words, images, or observations. Imagine you are taking part in a qualitative study and are being interviewed about something close to your heart or your experiences. Try to imagine a topic that feels personal or emotive. Your data – instead of being a number – would be the actual words you said.

• How would you feel if you took part in an interview study about an emotive topic and your data was made open and accessible?
• Are there any situations where you would be happy for your data to be open?
• Are there any situations where you definitely wouldn’t want your data to be open?

Activity 4:

Allow about 20 minutes

Now read the vignette below, about a qualitative researcher considering sharing their data. Consider the benefits of making the data open, and the ethical issues that the researcher should consider. Make sure you work out your own responses before revealing our notes.

How can qualitative researchers overcome some of these challenges when planning their studies?

We hope the suggestions in this section have helped you think about this. Qualitative researchers should consider using a data management plan at research inception, carefully anonymising their data, licensing the data or only making the data available to other researchers on request, getting consent from participants for open data up-front.

Just as quantitative researchers aspire to make their research repeatable, for qualitative researchers, a bit of forward planning is important to make studies as transparent as they can be.

Throughout the course, we offer you self-test quizzes to help you test your understanding of the course concepts. These quizzes are there to help you consolidate your knowledge.

This week’s quiz covers concepts underlying the principle of transparency. The feedback we have given is important: you will learn more by engaging with this feedback, which explains why the answers are correct.

Answer the following questions about key terms:

This week you learned about transparency in research, particularly focusing on open data and materials. You learned about the benefits of sharing data and materials, and practical ways you can share your materials. You learned about some of the nuances of different data across disciplines, and the importance of protecting participant anonymity and complying with legal regulations.

In Week 3 you will learn about integrity. You will discover the ‘replication crisis’ which is gripping parts of the research community, explore some questionable research practices, and learn how to find out whether the results of your research can be applicable to a wider context.

Click here to move on to the next week