Unlocking potential: navigating personal data sharing across multiple universities for enhanced collaboration

Are you or students you supervise involved in research collecting personal data across a number of different partners or institutions? Are you involved in collecting data at the post-graduate level? Before you encounter the myriad pitfalls and considerations, please read this blog… the insights will equip you with the confidence and expertise to navigate the challenges that lie ahead.

  1. Make sure you have a signed data sharing agreement (DSA)

When different universities share student data, it’s crucial to have an agreement in place. This protects student privacy and ensures compliance with laws and regulations. The importance of this agreement cannot be stressed enough. Without it, there’s a risk of misusing sensitive student information in unauthorised or unethical ways. A data sharing agreement helps minimise these risks by establishing clear rules for collecting, storing, and sharing data. It also determines who can access the data and under what conditions.

It’s vital for the different universities to sign up to the programme’s data sharing agreement. As the programme moves forward, new data may be collected, and priorities may change. That’s why the data sharing agreement needs to be periodically reviewed, updated if necessary, and re-signed to keep it relevant and accurate.

  1. Get permission from the data subjects

Each institution may have their own way of handling data permissions. It’s not a one-size-fits-all situation. Some institutions might include it in their applicant or student privacy notice, while others might ask each student for explicit consent to share their data. If you gain permissions through a privacy notice, ensure it states the data can be used for research purposes and covers the individuals whose data you are collecting.

It’s crucial for each institution to think about their own approach when sharing data with another partner institution. They need to make sure they’re following their own rules before any data gets sent externally. But the key point to remember is to obtain permission from the data subject.

  1. The challenges of extracting data

Data extraction can be a tricky business. First off, the data can be stored in different formats, spread across multiple datasets, and handled by different teams. And when it comes to post-graduate data, things can get even messier because there isn’t the mandatory requirement from HESA like there is with undergraduates. This means the data might be incomplete or missing, which messes up the accuracy of the analysis and makes it harder to get a complete picture.

On top of that, the data might need cleaning before it can be shared. This can take a lot of time but is necessary to make sure the data is accurate and can actually be used for research purposes.

It’s also important for the evaluators to try their best to match the requested data fields with the common HESA codes, so that where possible data can be extracted without the need to clean it. But sometimes, that’s just not possible, and partners may find it impossible to extract the data in a meaningful way. In such cases, the evaluators need to be flexible and willing to refine their data collection tools and requested variables.

Data extraction is no walk in the park. It requires careful consideration and planning to overcome the complexities and ensure you end up with meaningful insights.

  1. Watch out for data breaches: safeguarding your data

Data breaches are a big deal when it comes to sharing data outside of an institution. You’ve got to make sure the data is stored safely and take proper measures to protect it during transfer. That means using data encryption and secure transfer protocols to keep it secure.

But it’s not just about keeping the data safe. You also need to think about the consequences if a data breach does happen. It can be a real nightmare, with hefty fines, damage to your reputation, and a loss of trust from others. So, it’s crucial to take this seriously and do everything you can to prevent it from happening.

  1. Getting everyone on the same page: talking among teams

To make sure data sharing goes smoothly and without a hitch, it’s important for the researchers/evaluators, programme leads, data teams, and data protection teams to have some informal and open discussions. Here’s what those talks could cover:

  • Legal and ethical: make sure that the data can be used for research purposes, that you’ve got the green light from the individuals you will be collecting the data from, and that you’re following the data sharing agreement that you’ve put in place!
  • Talk about data extraction: dive into the details of data extraction. Figure out if the data you’re after is reliable and easy to get your hands on. You don’t want to end up with a load of messy data.
  • Watch out for data breaches: keep an eye out for any potential data breaches. Discuss the risks and how you can keep everything safe and secure.
  • Know your role: make sure everyone knows what they’re responsible for. Clear communication is the key to making data sharing run smoothly.

By having these discussions, you can make sure you’re all on the same page and working together to share data effectively and safely.

To sum up, sharing data across partnerships or between institutions isn’t easy! Comparing PGR data across the sector has been historically difficult, but I’m optimistic that our collaborative efforts will pave the way for better data collection processes in the future, benefitting the entire sector.

Lucy Clague is a senior research fellow in SIRKE.






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