10 Sports Science Data Things: Thing 4

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Data may be shared in many ways. It can be shared within a research team who are collaborating on a research project or it can be made openly accessible. This page discusses the different ways that data can be shared and the issues that need to be considered.

Thing 4: Collaboration and sharing

Data sharing is becoming a key consideration for researchers at the end of projects or as a planned research output. There are a number of benefits to be gained by planning to share or publish data.

For example, an increasing number of publishers (including PLoS) require research data to be published and accessible before reviewing articles for submission and peer review.

Publicly-available data is a form of research promotion, as it encourages contacts from research collaborators, from government and from industry. Attaching a DOI (Digital Object Identifier) to your data allows it to be cited, its usage tracked in the same way as journal articles and your research can be more widely recognised. Providing a reference to the published results along with the data encourages further citation of the original published article.

Early research shows that articles with data attached are more likely to be cited and for longer:

'There is evidence that studies that make their data available do indeed receive more citations than similar studies that do not' (Pinowar & Vision 2013).

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What is FAIR data?

The FAIR Data Principles (Findable, Accessible, Interoperable, Reusable) were drafted at a Lorentz Center workshop in Leiden in the Netherlands in 2015. 

The principles have since received worldwide recognition by various organisations including FORCE11, NIH and the European Commission as a useful framework for thinking about sharing data in a way that will enable maximum use and reuse.

The principles are useful because they:

  • support knowledge discovery and innovation
  • support data and knowledge integration
  • promote sharing and reuse of data
  • are discipline independent and allow for differences in disciplines
  • move beyond high level guidance, containing detailed advice on activities that can be undertaken to make data more FAIR
  • help data and metadata to be ‘machine readable’, supporting new discoveries through the harvest and analysis of multiple datasets

Consider: Take one aspect of the F.A.I.R. data principles that could be applied to one of your datasets: e.g. a persistent identifier or rich metadata (data description) which can be added to an indexed repository such as the VU Repository.

Sensitive data can be shared!

Sensitive data can be Human data (e.g. health and personal data, secret or sacred practices)

1. Explore one of these examples of published sensitive data:

  • Have a look at this example of an open dataset. It shows how sensitive data can be safely de-identified and openly shared. Click on Pregnancy and Lifestyle study (PALS) and then “Go to Data Provider” to see the actual data.
  • This one page story tells how sensitive data from the Australian Longitudinal Study of Women’s Health data has been successfully published for almost 20 years. Note the data is available through conditional access.

Consider: Both the examples above are from health science researchers. Why aren't there many examples of sport science data being de-identified and shared?

2. Browse through the ANDS sensitive data webpage.

3. Click on this Sensitive Data Decision Tree image to get an overview of issues and solutions.

A sensitive data success story

Data sharing practices

Repositories are one means by which research data may be shared but in order to get data into repositories, research teams must be willing to publish their data: there are huge differences between data sharing practices by country and by discipline.

1. In 2014 Wiley conducted a survey into researcher views of data sharing. Take a look at this page and open the 2014 Infographic from Wiley titled Research Data Sharing Insights [PDF, 2.08MB]. It provides a succinct overview of current data sharing practice and perceptions.

2. Now look closely at the sections titled 'Global Data Sharing Trends' and 'Data Sharing By Discipline'.

Consider: Why do you think there are differences between disciplines and countries - what changes to these statistics would you expect between 2014 and now?

3. Do you think there is culture of sharing data amongst sports science researchers? Do you share your data with anyone outside your project team?

Introduction to 'open', 'shared' and 'closed' data

Repositories enable discovery of data by publishing data descriptions ("metadata") about the data they hold - like a library catalogue describes individual materials held in a library. Most repositories provide access to the data itself, but not always. Data portals or aggregators draw together research data records from a number of repositories, e.g. Research Data Australia (RDA) aggregates records from over 100 Australian research repositories. They currently hold 51,289 records of datasets - click here for a complete list of the data providers.

Research Data Australia (RDA) is a registry of data collections, which lists the metadata but does not store the data.

An example of mediated access to data through RDA:

1. Watch this 2.5 minute video from the Open Data Institute titled Open/Closed/Shared: the world of data.
2. Now open this ANDS open data webpage to see a more in-depth view of why data is sometimes open, shared or closed.
3. If you have time, go to Research Data Australia portal and try searching for data that is 'open'. Hint: Look for the option to limit your search to data that is Publicly accessible online.
4. How many data records does Victoria University have? How would it be of benefit for them to have any more?